Prediction_of_heart_disease_smote.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f9ca1c66",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.neighbors import KNeighborsClassifier  \n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import confusion_matrix, accuracy_score, classification_report, precision_score, recall_score, f1_score\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from imblearn.over_sampling import SMOTE #generating synthetic samples of the minority class \n",
    "#from imblearn.under_sampling import RandomUnderSampler\n",
    "from imblearn.pipeline import Pipeline\n",
    "from collections import Counter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "fc3dadb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "heart=pd.read_csv(r\"/Users/souravghoshhansda/Library/CloudStorage/OneDrive-Personal/data exploration/Heart_Disease.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39133ebd",
   "metadata": {},
   "outputs": [
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       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
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       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
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      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  slope  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3      0   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5      0   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4      2   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8      2   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6      2   \n",
       "\n",
       "   ca  thal  target  \n",
       "0   0     1       1  \n",
       "1   0     2       1  \n",
       "2   0     2       1  \n",
       "3   0     2       1  \n",
       "4   0     2       1  "
      ]
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "heart.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0b36cd03",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
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       "      <th>count</th>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
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       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
       "      <td>303.000000</td>\n",
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       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>54.366337</td>\n",
       "      <td>0.683168</td>\n",
       "      <td>0.966997</td>\n",
       "      <td>131.623762</td>\n",
       "      <td>246.264026</td>\n",
       "      <td>0.148515</td>\n",
       "      <td>0.528053</td>\n",
       "      <td>149.646865</td>\n",
       "      <td>0.326733</td>\n",
       "      <td>1.039604</td>\n",
       "      <td>1.399340</td>\n",
       "      <td>0.729373</td>\n",
       "      <td>2.313531</td>\n",
       "      <td>0.544554</td>\n",
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       "      <th>std</th>\n",
       "      <td>9.082101</td>\n",
       "      <td>0.466011</td>\n",
       "      <td>1.032052</td>\n",
       "      <td>17.538143</td>\n",
       "      <td>51.830751</td>\n",
       "      <td>0.356198</td>\n",
       "      <td>0.525860</td>\n",
       "      <td>22.905161</td>\n",
       "      <td>0.469794</td>\n",
       "      <td>1.161075</td>\n",
       "      <td>0.616226</td>\n",
       "      <td>1.022606</td>\n",
       "      <td>0.612277</td>\n",
       "      <td>0.498835</td>\n",
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       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>94.000000</td>\n",
       "      <td>126.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>47.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>133.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>55.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>240.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>153.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>140.000000</td>\n",
       "      <td>274.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
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       "      <td>564.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>202.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
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      "text/plain": [
       "              age         sex          cp    trestbps        chol         fbs  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean    54.366337    0.683168    0.966997  131.623762  246.264026    0.148515   \n",
       "std      9.082101    0.466011    1.032052   17.538143   51.830751    0.356198   \n",
       "min     29.000000    0.000000    0.000000   94.000000  126.000000    0.000000   \n",
       "25%     47.500000    0.000000    0.000000  120.000000  211.000000    0.000000   \n",
       "50%     55.000000    1.000000    1.000000  130.000000  240.000000    0.000000   \n",
       "75%     61.000000    1.000000    2.000000  140.000000  274.500000    0.000000   \n",
       "max     77.000000    1.000000    3.000000  200.000000  564.000000    1.000000   \n",
       "\n",
       "          restecg     thalach       exang     oldpeak       slope          ca  \\\n",
       "count  303.000000  303.000000  303.000000  303.000000  303.000000  303.000000   \n",
       "mean     0.528053  149.646865    0.326733    1.039604    1.399340    0.729373   \n",
       "std      0.525860   22.905161    0.469794    1.161075    0.616226    1.022606   \n",
       "min      0.000000   71.000000    0.000000    0.000000    0.000000    0.000000   \n",
       "25%      0.000000  133.500000    0.000000    0.000000    1.000000    0.000000   \n",
       "50%      1.000000  153.000000    0.000000    0.800000    1.000000    0.000000   \n",
       "75%      1.000000  166.000000    1.000000    1.600000    2.000000    1.000000   \n",
       "max      2.000000  202.000000    1.000000    6.200000    2.000000    4.000000   \n",
       "\n",
       "             thal      target  \n",
       "count  303.000000  303.000000  \n",
       "mean     2.313531    0.544554  \n",
       "std      0.612277    0.498835  \n",
       "min      0.000000    0.000000  \n",
       "25%      2.000000    0.000000  \n",
       "50%      2.000000    1.000000  \n",
       "75%      3.000000    1.000000  \n",
       "max      3.000000    1.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6a671d2b",
   "metadata": {},
   "source": [
    "age - age in years\n",
    "\n",
    "sex - (1 = male; 0 = female)\n",
    "\n",
    "cp - chest pain type\n",
    "\n",
    "trestbps - resting blood pressure (in mm Hg on admission to the hospital)\n",
    "\n",
    "chol - serum cholestoral in mg/dl\n",
    "\n",
    "fbs - (fasting blood sugar > 120 mg/dl) (1 = true; 0 = false)\n",
    "\n",
    "restecg - resting electrocardiographic results\n",
    "\n",
    "thalach - maximum heart rate achieved\n",
    "\n",
    "exang - exercise induced angina (1 = yes; 0 = no)\n",
    "\n",
    "oldpeak - ST depression induced by exercise relative to rest\n",
    "\n",
    "slope - the slope of the peak exercise ST segment\n",
    "\n",
    "ca - number of major vessels (0-3) colored by flourosopy\n",
    "\n",
    "thal - 3 = normal; 6 = fixed defect; 7 = reversable defect\n",
    "\n",
    "target - have disease or not (1=yes, 0=no)Heart"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "35910fa1",
   "metadata": {},
   "outputs": [
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      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='target', ylabel='count'>"
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     "execution_count": 7,
     "metadata": {},
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Lauzs379fEydODO3n5eVJkubNm6fS0lItW7ZMzc3NWrhwoerr6zVu3DiVlZUpMTEx9Jp169YpNjZWs2fPVnNzsyZNmqTS0lLFxMT0+vsBAADOY9m2bUd7iGhraGiQ2+1WMBhUUlJSj32djKXP9Ni1gb6q6pEfRHuEiDixOj3aIwCOM2LVn3r0+l/132/HPrMDAAAQCcQOAAAwGrEDAACMRuwAAACjdSt2brrpJn366aed1hsaGnTTTTed70wAAAAR063Y2bVrl1pbWzutf/bZZ9qzZ895DwUAABAp/9TP2XnnnXdC//3uu++G/SqH9vZ2vfzyy/ra174WuekAAADO0z8VO2PGjJFlWbIs66wfVyUkJGj9+vURGw4AAOB8/VOxU1NTI9u2NWrUKL355psaOnRo6FhcXJyGDRvGTy4GAACO8k/FzqWXXipJ6ujo6JFhAAAAIq3bvxvryJEj2rVrl+rq6jrFz6pVq857MAAAgEjoVuw88cQT+vd//3cNGTJEXq9XlmWFjlmWRewAAADH6Fbs/Od//qceeughLV++PNLzAAAARFS3fs5OfX29br311kjPAgAAEHHdip1bb71VZWVlkZ4FAAAg4rr1MdbXv/51rVy5Un/84x+Vnp6u/v37hx2/7777IjIcAADA+epW7GzatEkXXXSRKioqVFFREXbMsixiBwAAOEa3YqempibScwAAAPSIbj2zAwAA0Fd0687OnXfeec7jTz31VLeGAQAAiLRuxU59fX3Yfltbmw4ePKhPP/30rL8gFAAAIFq6FTvbt2/vtNbR0aGFCxdq1KhR5z0UAABApETsmZ1+/frp/vvv17p16yJ1SQAAgPMW0QeUjx07ps8//zySlwQAADgv3foYKy8vL2zftm2dPHlSv/vd7zRv3ryIDAYAABAJ3Yqdt956K2y/X79+Gjp0qB599NEv/U4tAACA3tSt2HnttdciPQcAAECP6FbsnPHXv/5Vhw8flmVZuuyyyzR06NBIzQUAABAR3XpA+dSpU7rzzjs1fPhw3Xjjjbrhhhvk8/k0f/58nT59OtIzAgAAdFu3YicvL08VFRV66aWX9Omnn+rTTz/Vb37zG1VUVGjx4sWRnhEAAKDbuvUx1gsvvKD/+Z//UVZWVmjt5ptvVkJCgmbPnq0NGzZEaj4AAIDz0q07O6dPn5bH4+m0PmzYsIh+jPX555/rP/7jP5SSkqKEhASNGjVKq1evVkdHR+gc27ZVUFAgn8+nhIQEZWVl6dChQxGbAQAA9G3dip3x48frpz/9qT777LPQWnNzsx588EGNHz8+YsM9/PDD2rhxo0pKSvTee+9pzZo1euSRR7R+/frQOWvWrNHatWtVUlKiffv2yev1asqUKWpsbIzYHAAAoO/q1sdYxcXFysnJ0SWXXKLRo0fLsixVV1fL5XKprKwsYsP93//9n77zne/olltukSSNHDlSv/rVr7R//35Jf7+rU1xcrBUrVmjWrFmSpM2bN8vj8Wjr1q1asGBBxGYBAAB9U7fu7KSnp+vPf/6zioqKNGbMGF1zzTX62c9+pqNHj+rqq6+O2HDXX3+9Xn31VR05ckSS9Pbbb2vv3r26+eabJUk1NTUKBALKzs4OvcblcmnChAmqrKzs8rotLS1qaGgI2wAAgJm6dWenqKhIHo9Hd911V9j6U089pb/+9a9avnx5RIZbvny5gsGgrrjiCsXExKi9vV0PPfSQvv/970uSAoGAJHV6fsjj8ej48ePnnP/BBx+MyIwAAMDZunVn57//+791xRVXdFq/+uqrtXHjxvMe6oznnntOzz77rLZu3aoDBw5o8+bN+vnPf67NmzeHnWdZVti+bdud1r4oPz9fwWAwtNXW1kZsZgAA4CzdurMTCAQ0fPjwTutDhw7VyZMnz3uoM5YuXaoHHnhAt99+u6S/f3x2/PhxFRUVad68efJ6vWedp66u7qzfLXaGy+WSy+WK2JwAAMC5unVnx+/36/XXX++0/vrrr8vn8533UGecPn1a/fqFjxgTExP61vOUlBR5vV6Vl5eHjre2tqqiokKZmZkRmwMAAPRd3bqz86Mf/Ui5ublqa2vTTTfdJEl69dVXtWzZsoj+BOXp06froYce0ogRI3T11Vfrrbfe0tq1a0O/Wd2yLOXm5qqwsFCpqalKTU1VYWGhBgwYoDlz5kRsDgAA0Hd1K3aWLVumTz75RAsXLlRra6skKT4+XsuXL1d+fn7Ehlu/fr1WrlyphQsXqq6uTj6fTwsWLNCqVavCZmlubtbChQtVX1+vcePGqaysTImJiRGbAwAA9F2Wbdt2d1/c1NSk9957TwkJCUpNTe2zz8E0NDTI7XYrGAwqKSmpx75OxtJneuzaQF9V9cgPoj1CRJxYnR7tEQDHGbHqTz16/a/673e37uyccdFFF+m66647n0sAAAD0qG49oAwAANBXEDsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMJrjY+fDDz/Uv/7rv2rw4MEaMGCAxowZo6qqqtBx27ZVUFAgn8+nhIQEZWVl6dChQ1GcGAAAOImjY6e+vl7f+ta31L9/f/3v//6v3n33XT366KO6+OKLQ+esWbNGa9euVUlJifbt2yev16spU6aosbExeoMDAADHiI32AOfy8MMPy+/36+mnnw6tjRw5MvTftm2ruLhYK1as0KxZsyRJmzdvlsfj0datW7VgwYLeHhkAADiMo+/svPjiixo7dqxuvfVWDRs2TN/4xjf0xBNPhI7X1NQoEAgoOzs7tOZyuTRhwgRVVlZ2ed2WlhY1NDSEbQAAwEyOjp2//OUv2rBhg1JTU7Vz507dfffduu+++/TMM89IkgKBgCTJ4/GEvc7j8YSOnU1RUZHcbndo8/v9PfcmAABAVDk6djo6OnTttdeqsLBQ3/jGN7RgwQLddddd2rBhQ9h5lmWF7du23Wnti/Lz8xUMBkNbbW1tj8wPAACiz9GxM3z4cF111VVha1deeaVOnDghSfJ6vZLU6S5OXV1dp7s9X+RyuZSUlBS2AQAAMzk6dr71rW/p8OHDYWtHjhzRpZdeKklKSUmR1+tVeXl56Hhra6sqKiqUmZnZq7MCAABncvR3Y91///3KzMxUYWGhZs+erTfffFObNm3Spk2bJP3946vc3FwVFhYqNTVVqampKiws1IABAzRnzpwoTw8AAJzA0bFz3XXXafv27crPz9fq1auVkpKi4uJi3XHHHaFzli1bpubmZi1cuFD19fUaN26cysrKlJiYGMXJAQCAUzg6diRp2rRpmjZtWpfHLctSQUGBCgoKem8oAADQZzj6mR0AAIDzRewAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMFqfip2ioiJZlqXc3NzQmm3bKigokM/nU0JCgrKysnTo0KHoDQkAABylz8TOvn37tGnTJl1zzTVh62vWrNHatWtVUlKiffv2yev1asqUKWpsbIzSpAAAwEn6ROw0NTXpjjvu0BNPPKFBgwaF1m3bVnFxsVasWKFZs2YpLS1Nmzdv1unTp7V169Yur9fS0qKGhoawDQAAmKlPxM4999yjW265RZMnTw5br6mpUSAQUHZ2dmjN5XJpwoQJqqys7PJ6RUVFcrvdoc3v9/fY7AAAILocHzvbtm3TgQMHVFRU1OlYIBCQJHk8nrB1j8cTOnY2+fn5CgaDoa22tjayQwMAAMeIjfYA51JbW6uf/OQnKisrU3x8fJfnWZYVtm/bdqe1L3K5XHK5XBGbEwAAOJej7+xUVVWprq5OGRkZio2NVWxsrCoqKvRf//Vfio2NDd3R+ce7OHV1dZ3u9gAAgAuTo2Nn0qRJ+tOf/qTq6urQNnbsWN1xxx2qrq7WqFGj5PV6VV5eHnpNa2urKioqlJmZGcXJAQCAUzj6Y6zExESlpaWFrQ0cOFCDBw8Orefm5qqwsFCpqalKTU1VYWGhBgwYoDlz5kRjZAAA4DCOjp2vYtmyZWpubtbChQtVX1+vcePGqaysTImJidEeDQAAOECfi51du3aF7VuWpYKCAhUUFERlHgAA4GyOfmYHAADgfBE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADCao2OnqKhI1113nRITEzVs2DDNnDlThw8fDjvHtm0VFBTI5/MpISFBWVlZOnToUJQmBgAATuPo2KmoqNA999yjP/7xjyovL9fnn3+u7OxsnTp1KnTOmjVrtHbtWpWUlGjfvn3yer2aMmWKGhsbozg5AABwithoD3AuL7/8ctj+008/rWHDhqmqqko33nijbNtWcXGxVqxYoVmzZkmSNm/eLI/Ho61bt2rBggXRGBsAADiIo+/s/KNgMChJSk5OliTV1NQoEAgoOzs7dI7L5dKECRNUWVnZ5XVaWlrU0NAQtgEAADP1mdixbVt5eXm6/vrrlZaWJkkKBAKSJI/HE3aux+MJHTuboqIiud3u0Ob3+3tucAAAEFV9JnbuvfdevfPOO/rVr37V6ZhlWWH7tm13Wvui/Px8BYPB0FZbWxvxeQEAgDM4+pmdMxYtWqQXX3xRu3fv1iWXXBJa93q9kv5+h2f48OGh9bq6uk53e77I5XLJ5XL13MAAAMAxHH1nx7Zt3Xvvvfr1r3+tP/zhD0pJSQk7npKSIq/Xq/Ly8tBaa2urKioqlJmZ2dvjAgAAB3L0nZ177rlHW7du1W9+8xslJiaGnsNxu91KSEiQZVnKzc1VYWGhUlNTlZqaqsLCQg0YMEBz5syJ8vQAAMAJHB07GzZskCRlZWWFrT/99NP64Q9/KElatmyZmpubtXDhQtXX12vcuHEqKytTYmJiL08LAACcyNGxY9v2l55jWZYKCgpUUFDQ8wMBAIA+x9HP7AAAAJwvYgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YyJnccff1wpKSmKj49XRkaG9uzZE+2RAACAAxgRO88995xyc3O1YsUKvfXWW7rhhhuUk5OjEydORHs0AAAQZUbEztq1azV//nz96Ec/0pVXXqni4mL5/X5t2LAh2qMBAIAoi432AOertbVVVVVVeuCBB8LWs7OzVVlZedbXtLS0qKWlJbQfDAYlSQ0NDT03qKT2luYevT7QF/X037ve0vhZe7RHABynp/9+n7m+bdvnPK/Px87HH3+s9vZ2eTyesHWPx6NAIHDW1xQVFenBBx/stO73+3tkRgBdc6+/O9ojAOgpRe5e+TKNjY1yu7v+Wn0+ds6wLCts37btTmtn5OfnKy8vL7Tf0dGhTz75RIMHD+7yNTBHQ0OD/H6/amtrlZSUFO1xAEQQf78vLLZtq7GxUT6f75zn9fnYGTJkiGJiYjrdxamrq+t0t+cMl8sll8sVtnbxxRf31IhwqKSkJP5nCBiKv98XjnPd0Tmjzz+gHBcXp4yMDJWXl4etl5eXKzMzM0pTAQAAp+jzd3YkKS8vT3PnztXYsWM1fvx4bdq0SSdOnNDdd/MsAAAAFzojYue2227T3/72N61evVonT55UWlqafv/73+vSSy+N9mhwIJfLpZ/+9KedPsoE0Pfx9xtnY9lf9v1aAAAAfViff2YHAADgXIgdAABgNGIHAAAYjdgBAABGI3ZwQXn88ceVkpKi+Ph4ZWRkaM+ePdEeCUAE7N69W9OnT5fP55NlWdqxY0e0R4KDEDu4YDz33HPKzc3VihUr9NZbb+mGG25QTk6OTpw4Ee3RAJynU6dOafTo0SopKYn2KHAgvvUcF4xx48bp2muv1YYNG0JrV155pWbOnKmioqIoTgYgkizL0vbt2zVz5sxojwKH4M4OLgitra2qqqpSdnZ22Hp2drYqKyujNBUAoDcQO7ggfPzxx2pvb+/0y2E9Hk+nXyILADALsYMLimVZYfu2bXdaAwCYhdjBBWHIkCGKiYnpdBenrq6u090eAIBZiB1cEOLi4pSRkaHy8vKw9fLycmVmZkZpKgBAbzDit54DX0VeXp7mzp2rsWPHavz48dq0aZNOnDihu+++O9qjAThPTU1NOnr0aGi/pqZG1dXVSk5O1ogRI6I4GZyAbz3HBeXxxx/XmjVrdPLkSaWlpWndunW68cYboz0WgPO0a9cuTZw4sdP6vHnzVFpa2vsDwVGIHQAAYDSe2QEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAOE5WVpZyc3OjPUaI0+YB8M8hdgAYqbW1NdojAHAIYgeAo/zwhz9URUWFHnvsMVmWJcuydOzYMc2fP18pKSlKSEjQ5Zdfrscee6zT62bOnKmioiL5fD5ddtllkqTKykqNGTNG8fHxGjt2rHbs2CHLslRdXR167bvvvqubb75ZF110kTwej+bOnauPP/64y3nef//93vrjABABsdEeAAC+6LHHHtORI0eUlpam1atXS5IGDRqkSy65RM8//7yGDBmiyspK/fjHP9bw4cM1e/bs0GtfffVVJSUlqby8XLZtq7GxUdOnT9fNN9+srVu36vjx450+jjp58qQmTJigu+66S2vXrlVzc7OWL1+u2bNn6w9/+MNZ5xk6dGiv/XkAOH/EDgBHcbvdiouL04ABA+T1ekPrDz74YOi/U1JSVFlZqeeffz4sdgYOHKhf/OIXiouLkyRt3LhRlmXpiSeeUHx8vK666ip9+OGHuuuuu0Kv2bBhg6699loVFhaG1p566in5/X4dOXJEl1122VnnAdB3EDsA+oSNGzfqF7/4hY4fP67m5ma1trZqzJgxYeekp6eHQkeSDh8+rGuuuUbx8fGhtW9+85thr6mqqtJrr72miy66qNPXPHbsWOjjMAB9F7EDwPGef/553X///Xr00Uc1fvx4JSYm6pFHHtEbb7wRdt7AgQPD9m3blmVZnda+qKOjQ9OnT9fDDz/c6esOHz48Qu8AQDQROwAcJy4uTu3t7aH9PXv2KDMzUwsXLgytHTt27Euvc8UVV+iXv/ylWlpa5HK5JEn79+8PO+faa6/VCy+8oJEjRyo29uz/S/zHeQD0LXw3FgDHGTlypN544w29//77+vjjj/X1r39d+/fv186dO3XkyBGtXLlS+/bt+9LrzJkzRx0dHfrxj3+s9957Tzt37tTPf/5zSQrd8bnnnnv0ySef6Pvf/77efPNN/eUvf1FZWZnuvPPOUOD84zwdHR099+YBRByxA8BxlixZopiYGF111VUaOnSovv3tb2vWrFm67bbbNG7cOP3tb38Lu8vTlaSkJL300kuqrq7WmDFjtGLFCq1atUqSQs/x+Hw+vf7662pvb9fUqVOVlpamn/zkJ3K73erXr99Z5zlx4kTPvXkAEWfZ//gBNgAY7Je//KX+7d/+TcFgUAkJCdEeB0Av4JkdAEZ75plnNGrUKH3ta1/T22+/HfoZOoQOcOEgdgAYLRAIaNWqVQoEAho+fLhuvfVWPfTQQ9EeC0Av4mMsAABgNB5QBgAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABjt/wEA/FSZ6b2XBAAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x='target',data=heart)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92e62147",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3a46da4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "138"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_no_disease=len(heart[heart.target==0])\n",
    "count_no_disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "66cfe6cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "165"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_have_disease=len(heart[heart.target==1])\n",
    "count_have_disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "df83c06d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "no Heart Disease %: 45.54%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of inviduals having heart disease and not having heart disease\n",
    "print(\"no Heart Disease %: {:.2f}%\".format((count_no_disease / (len(heart.target))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "81186e26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Having Heart Disease %: 54.46%\n"
     ]
    }
   ],
   "source": [
    "print(\"Having Heart Disease %: {:.2f}%\".format((count_have_disease / (len(heart.target))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "83190650",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male Patients  %: 68.32%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of male patients\n",
    "print(\"Male Patients  %: {:.2f}%\".format((male_patients / (len(heart.target))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dbcf498f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female Patients %: 31.68%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of female patients\n",
    "print(\"Female Patients %: {:.2f}%\".format((female_patients / (len(heart.target))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "99146b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "207"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "male_patients=len(heart[heart.sex==1])\n",
    "male_patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1bf08c84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "96"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "female_patients=len(heart[heart.sex==0])\n",
    "female_patients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "df8985a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female Patients %: 31.68%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of female patients\n",
    "print(\"Female Patients %: {:.2f}%\".format((len(heart[heart.sex==0]) / (len(heart.target))*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e3e8c1cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "93"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# male patients who are suffering from heart disease\n",
    "countmaleTarget1 = len(heart[(heart['sex'] == 1) & (heart['target'] == 1)])\n",
    "countmaleTarget1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "72e7e342",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Male Patients  %: 44.93%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of male patients who are suffering from heart disease\n",
    "print(\"Male Patients  %: {:.2f}%\".format((countmaleTarget1 / male_patients*100)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "88a4de94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# female patients who are suffering from heart disease\n",
    "countfemaleTarget1 = len(heart[(heart['sex'] == 0) & (heart['target'] == 1)])\n",
    "countfemaleTarget1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "2ff057e3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Female Patients  %: 75.00%\n"
     ]
    }
   ],
   "source": [
    "#find the percentage of female patients who are suffering from heart disease\n",
    "print(\"Female Patients  %: {:.2f}%\".format((countfemaleTarget1 / female_patients*100)))\n",
    "#females are grossly risky to the occurence of heart disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "810511e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56.601449</td>\n",
       "      <td>0.826087</td>\n",
       "      <td>0.478261</td>\n",
       "      <td>134.398551</td>\n",
       "      <td>251.086957</td>\n",
       "      <td>0.159420</td>\n",
       "      <td>0.449275</td>\n",
       "      <td>139.101449</td>\n",
       "      <td>0.550725</td>\n",
       "      <td>1.585507</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>1.166667</td>\n",
       "      <td>2.543478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.496970</td>\n",
       "      <td>0.563636</td>\n",
       "      <td>1.375758</td>\n",
       "      <td>129.303030</td>\n",
       "      <td>242.230303</td>\n",
       "      <td>0.139394</td>\n",
       "      <td>0.593939</td>\n",
       "      <td>158.466667</td>\n",
       "      <td>0.139394</td>\n",
       "      <td>0.583030</td>\n",
       "      <td>1.593939</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>2.121212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              age       sex        cp    trestbps        chol       fbs  \\\n",
       "target                                                                    \n",
       "0       56.601449  0.826087  0.478261  134.398551  251.086957  0.159420   \n",
       "1       52.496970  0.563636  1.375758  129.303030  242.230303  0.139394   \n",
       "\n",
       "         restecg     thalach     exang   oldpeak     slope        ca      thal  \n",
       "target                                                                          \n",
       "0       0.449275  139.101449  0.550725  1.585507  1.166667  1.166667  2.543478  \n",
       "1       0.593939  158.466667  0.139394  0.583030  1.593939  0.363636  2.121212  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.groupby('target').mean()\n",
    "#data patterns of target=1 vs target=0 with respect to other features in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5ad19d7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>slope</th>\n",
       "      <th>ca</th>\n",
       "      <th>thal</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>target</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>58.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>52.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>234.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         age  sex   cp  trestbps   chol  fbs  restecg  thalach  exang  \\\n",
       "target                                                                  \n",
       "0       58.0  1.0  0.0     130.0  249.0  0.0      0.0    142.0    1.0   \n",
       "1       52.0  1.0  2.0     130.0  234.0  0.0      1.0    161.0    0.0   \n",
       "\n",
       "        oldpeak  slope   ca  thal  \n",
       "target                             \n",
       "0           1.4    1.0  1.0   3.0  \n",
       "1           0.2    2.0  0.0   2.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.groupby('target').median()\n",
    "#data patterns of target=1 vs target=0 with respect to other features in the data\n",
    "#This is suitable when data has outliers and additionally the categorical nature of the columns are retained in the output helping us to conclude all data patterns with respect to target easily."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "ad8dff42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(303, 14)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b7d651f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['age', 'sex', 'cp', 'trestbps', 'chol', 'fbs', 'restecg', 'thalach',\n",
       "       'exang', 'oldpeak', 'slope', 'ca', 'thal', 'target'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4422dbaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age           int64\n",
       "sex           int64\n",
       "cp            int64\n",
       "trestbps      int64\n",
       "chol          int64\n",
       "fbs           int64\n",
       "restecg       int64\n",
       "thalach       int64\n",
       "exang         int64\n",
       "oldpeak     float64\n",
       "slope         int64\n",
       "ca            int64\n",
       "thal          int64\n",
       "target        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0eae5de2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "age         0\n",
       "sex         0\n",
       "cp          0\n",
       "trestbps    0\n",
       "chol        0\n",
       "fbs         0\n",
       "restecg     0\n",
       "thalach     0\n",
       "exang       0\n",
       "oldpeak     0\n",
       "slope       0\n",
       "ca          0\n",
       "thal        0\n",
       "target      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.isnull().sum()\n",
    "#no missing values in data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "66181e9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the inter relationship of all the features with the target variable to understand the importance of the feature columns\n",
    "features = ['sex', 'cp', 'fbs', 'restecg', \n",
    "             'exang', 'slope', 'ca', 'thal']\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "for i, feature in enumerate(features, 1):\n",
    "    plt.subplot(4, 2, i)#4 rows, 2 columns\n",
    "    sns.countplot(x=feature, hue='target', data=heart.astype(str))\n",
    "    plt.title(f'Count plot of {feature} with Target')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Count')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "#hw:write the insights from this output to explain which of the 8 features are impacted by target and which are not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dbe059ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a4c0f7e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x1000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "features = ['age', 'chol', 'oldpeak', 'thalach', \n",
    "             'trestbps']\n",
    "\n",
    "plt.figure(figsize=(15, 10))\n",
    "\n",
    "for i, feature in enumerate(features, 1):\n",
    "    plt.subplot(3, 2, i)#3 rows, 2 columns\n",
    "    sns.boxplot(y=feature, x='target', data=heart)\n",
    "    plt.title(f'Box plot of {feature} with Target')\n",
    "    plt.xlabel(feature)\n",
    "    plt.ylabel('Target')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "#hw:write the insights from this output to explain which of the 5 features are impacted by target and which are not"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c898e01b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "cae4f373",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find the cross tab for age vs target and showcase through a visualization what is the frequency of heart disease occurence with respect to different ages\n",
    "pd.crosstab(heart.age,heart.target).plot(kind=\"bar\",figsize=(20,6))\n",
    "plt.title('Heart disease frequency for Ages')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('Heart disease frequency for Ages.png')#how to save an image in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3b818ca8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1a69ee9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target   0   1\n",
       "age           \n",
       "29       0   1\n",
       "34       0   2\n",
       "35       2   2\n",
       "37       0   2\n",
       "38       1   2\n",
       "39       1   3\n",
       "40       2   1\n",
       "41       1   9\n",
       "42       1   7\n",
       "43       3   5\n",
       "44       3   8\n",
       "45       2   6\n",
       "46       3   4\n",
       "47       2   3\n",
       "48       3   4\n",
       "49       2   3\n",
       "50       3   4\n",
       "51       3   9\n",
       "52       4   9\n",
       "53       2   6\n",
       "54       6  10\n",
       "55       5   3\n",
       "56       6   5\n",
       "57      10   7\n",
       "58      12   7\n",
       "59       9   5\n",
       "60       8   3\n",
       "61       7   1\n",
       "62       7   4\n",
       "63       6   3\n",
       "64       4   6\n",
       "65       4   4\n",
       "66       3   4\n",
       "67       6   3\n",
       "68       2   2\n",
       "69       1   2\n",
       "70       3   1\n",
       "71       0   3\n",
       "74       0   1\n",
       "76       0   1\n",
       "77       1   0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(heart.age,heart.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a9354f9",
   "metadata": {},
   "source": [
    "Individuals around 50-55 years are more likely to get affected with heart disease"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "002eb61d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=heart.age[heart.target==1],  color =\"red\")\n",
    "plt.hist(x=heart.age[heart.target==0],  color =\"lightblue\")\n",
    "plt.legend([\"Disease\", \"Not Disease\"])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"target\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8f6ba42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "8b5b47ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>target</th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cp</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>104</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "target    0   1\n",
       "cp             \n",
       "0       104  39\n",
       "1         9  41\n",
       "2        18  69\n",
       "3         7  16"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#cross tab of cp vs target\n",
    "pd.crosstab(heart.cp,heart.target)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "502fd06e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'numpy.int64' object has no attribute 'startswith'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[36], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43msns\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mbarplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mcp\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43my\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtarget\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mhue\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msex\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43mdata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheart\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/categorical.py:2763\u001b[0m, in \u001b[0;36mbarplot\u001b[0;34m(data, x, y, hue, order, hue_order, estimator, errorbar, n_boot, units, seed, orient, color, palette, saturation, width, errcolor, errwidth, capsize, dodge, ci, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2760\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   2761\u001b[0m     ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n\u001b[0;32m-> 2763\u001b[0m \u001b[43mplotter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2764\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ax\n",
      "File \u001b[0;32m~/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/categorical.py:1587\u001b[0m, in \u001b[0;36m_BarPlotter.plot\u001b[0;34m(self, ax, bar_kws)\u001b[0m\n\u001b[1;32m   1585\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Make the plot.\"\"\"\u001b[39;00m\n\u001b[1;32m   1586\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdraw_bars(ax, bar_kws)\n\u001b[0;32m-> 1587\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mannotate_axes\u001b[49m\u001b[43m(\u001b[49m\u001b[43max\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1588\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39morient \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mh\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n\u001b[1;32m   1589\u001b[0m     ax\u001b[38;5;241m.\u001b[39minvert_yaxis()\n",
      "File \u001b[0;32m~/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/categorical.py:767\u001b[0m, in \u001b[0;36m_CategoricalPlotter.annotate_axes\u001b[0;34m(self, ax)\u001b[0m\n\u001b[1;32m    764\u001b[0m     ax\u001b[38;5;241m.\u001b[39mset_ylim(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m.5\u001b[39m, \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mplot_data) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m.5\u001b[39m, auto\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    766\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhue_names \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 767\u001b[0m     \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlegend\u001b[49m\u001b[43m(\u001b[49m\u001b[43mloc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mbest\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtitle\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhue_title\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/anaconda3/envs/notebook/lib/python3.10/site-packages/matplotlib/axes/_axes.py:322\u001b[0m, in \u001b[0;36mAxes.legend\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    204\u001b[0m \u001b[38;5;129m@_docstring\u001b[39m\u001b[38;5;241m.\u001b[39mdedent_interpd\n\u001b[1;32m    205\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mlegend\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m    206\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m    207\u001b[0m \u001b[38;5;124;03m    Place a legend on the Axes.\u001b[39;00m\n\u001b[1;32m    208\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    320\u001b[0m \u001b[38;5;124;03m    .. plot:: gallery/text_labels_and_annotations/legend.py\u001b[39;00m\n\u001b[1;32m    321\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 322\u001b[0m     handles, labels, kwargs \u001b[38;5;241m=\u001b[39m \u001b[43mmlegend\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_parse_legend_args\u001b[49m\u001b[43m(\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    323\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend_ \u001b[38;5;241m=\u001b[39m mlegend\u001b[38;5;241m.\u001b[39mLegend(\u001b[38;5;28mself\u001b[39m, handles, labels, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m    324\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlegend_\u001b[38;5;241m.\u001b[39m_remove_method \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_remove_legend\n",
      "File \u001b[0;32m~/anaconda3/envs/notebook/lib/python3.10/site-packages/matplotlib/legend.py:1361\u001b[0m, in \u001b[0;36m_parse_legend_args\u001b[0;34m(axs, handles, labels, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1357\u001b[0m     handles \u001b[38;5;241m=\u001b[39m [handle \u001b[38;5;28;01mfor\u001b[39;00m handle, label\n\u001b[1;32m   1358\u001b[0m                \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(_get_legend_handles(axs, handlers), labels)]\n\u001b[1;32m   1360\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:  \u001b[38;5;66;03m# 0 args: automatically detect labels and handles.\u001b[39;00m\n\u001b[0;32m-> 1361\u001b[0m     handles, labels \u001b[38;5;241m=\u001b[39m \u001b[43m_get_legend_handles_labels\u001b[49m\u001b[43m(\u001b[49m\u001b[43maxs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mhandlers\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1362\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m handles:\n\u001b[1;32m   1363\u001b[0m         log\u001b[38;5;241m.\u001b[39mwarning(\n\u001b[1;32m   1364\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mNo artists with labels found to put in legend.  Note that \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1365\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124martists whose label start with an underscore are ignored \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   1366\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mwhen legend() is called with no argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n",
      "File \u001b[0;32m~/anaconda3/envs/notebook/lib/python3.10/site-packages/matplotlib/legend.py:1291\u001b[0m, in \u001b[0;36m_get_legend_handles_labels\u001b[0;34m(axs, legend_handler_map)\u001b[0m\n\u001b[1;32m   1289\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m handle \u001b[38;5;129;01min\u001b[39;00m _get_legend_handles(axs, legend_handler_map):\n\u001b[1;32m   1290\u001b[0m     label \u001b[38;5;241m=\u001b[39m handle\u001b[38;5;241m.\u001b[39mget_label()\n\u001b[0;32m-> 1291\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m label \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[43mlabel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstartswith\u001b[49m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_\u001b[39m\u001b[38;5;124m'\u001b[39m):\n\u001b[1;32m   1292\u001b[0m         handles\u001b[38;5;241m.\u001b[39mappend(handle)\n\u001b[1;32m   1293\u001b[0m         labels\u001b[38;5;241m.\u001b[39mappend(label)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'numpy.int64' object has no attribute 'startswith'"
     ]
    },
    {
     "data": {
      "image/png": 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cZrMpPDxcQ4cO5cRiADBIH3tTq38DwcrvuBkzZoz3quBXf5X4/18lvFu3bkpLS9OGDRu4SjgAGGDxPd9bPQLgF7/Pudm1a5eGDx+ujRs36q9//atKS0u1ceNGjRgxQtu3b9emTZv04Ycf6re//W0g5gUAALguv/fcvPjii8rPz9fDDz/sXTZq1CgNGTJES5cu1V/+8hf16NFD8+fP18svv9yuwwIAAPwQv/fcnDhxQtHR0S2WR0dH68SJE5KuHLq6es0pAACAjuR33IwcOVIrV65UQ0ODd9nly5e1cuVKjRw5UpL07bffKjIysv2mBAAAaCO/D0utW7dOjz76qIYMGaJRo0bJZrPp+PHjampq0vvvvy9JOnPmjLKystp9WAAAgB/id9wkJSXpq6++0h//+Ed98cUX8ng8+s1vfqMpU6aoZ8+ekqSpU6e2+6AAAABt4VfcXL58WSNGjND777+vzMzMQM0EAADwo/l1zk23bt1UX1/v87s2AAAAwcTvE4pnzZqlVatWqbGxMRDzAAAA3BC/z7n59NNP9cEHH2jfvn26++671aNHD5/Hd+7c2W7DAQAA+MvvuOndu7cef/zxQMwCAABww/yOmy1btgRiDgAAgHbh9zk3AAAAwczvPTeS9NZbb+lPf/qTXC6Xzy8VS9Lnn3/eLoMBAAD8GH7vuXn11Vc1Y8YMDRgwQE6nU2PHjlXfvn115swZpaamBmJGAACANvM7bgoKCrRx40atXbtWYWFhWrRokRwOh2bPnq3q6upAzAgAANBmfseNy+VSUlKSJKl79+6qra2VdOWSC6+//nr7TgcAAOAnv+Nm4MCBqqqqkiRFR0fr6NGjkqSzZ8/K4/G073QAAAB+8jtuxo8fr/fee0+SlJ6ernnz5mnChAlKS0vTr371q3YfEAAAwB9+f1tqyZIluvXWWyVJmZmZ6tOnjw4fPqxJkyZxQjEAALCc33EzbNgwud1uDRgwQJI0efJkTZ48WVVVVRowYICamprafUgAAIC28vuw1LXOq6mrq1N4ePgNDwQAAHAj2rznJjs7W5Jks9m0bNky3Xzzzd7Hmpqa9Omnn2rMmDHtPiAAAIA/2hw3TqdT0pU9NydOnFBYWJj3sbCwMI0ePVoLFixo/wkBAAD80Oa4+eijjyRJM2bMUH5+viIiIgI2VDCKX7g1YM8d8V2d9/ig+7u6gL1WyeppAXleAACCCVcFBwAARuGq4AAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMEqo1QMAAIDg0cfe1OrfnQlxAwAAvBbf873VI9wwyw9LFRQUKCYmRuHh4YqPj9ehQ4fatN3HH3+s0NBQjRkzJrADAgCATsXSuNmxY4fmzp2rJUuWyOl0Kjk5WampqXK5XNfdrrq6WtOmTdNDDz3UQZMCAIDOwtK4WbNmjdLT05WRkaHY2Fjl5eUpKipKhYWF193uqaee0pQpU5SYmNhBkwIAgM7CsrhpaGhQSUmJUlJSfJanpKToyJEj19xuy5Yt+sc//qHnnnuuTa9TX1+vmpoanxsAADCXZXFTWVmppqYmRUZG+iyPjIxUWVlZq9v8/e9/17PPPqtt27YpNLRt50Ln5uaqV69e3ltUVNQNzw4AAIKX5ScU22w2n/sej6fFMklqamrSlClTtHz5ct15551tfv6cnBxVV1d7b+fOnbvhmQEAQPCy7Kvg/fr1U0hISIu9NOXl5S325khSbW2tjh07JqfTqaefflqS1NzcLI/Ho9DQUO3bt0/jx49vsZ3dbpfdbg/MmwAAAEHHsj03YWFhio+Pl8Ph8FnucDiUlJTUYv2IiAidOHFCpaWl3ltmZqZGjBih0tJS3XvvvR01OgAACGKW/ohfdna2pk6dqoSEBCUmJmrjxo1yuVzKzMyUdOWQ0rfffqutW7fqpptuUlxcnM/2AwYMUHh4eIvlAACg67I0btLS0lRVVaUVK1bI7XYrLi5OxcXFio6OliS53e4f/M0bAACA/8/yyy9kZWUpKyur1ceKioquu+3zzz+v559/vv2HAgAAnZbl35YCAABoT8QNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwSqjVA0Bq7taj1b8BAID/iJsgUDci1eoRAAAwBoelAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBR+LYU2sWcOXNUUVEhSerfv7/y8/MtnggA0FURN2gXFRUVunDhgtVjAADAYSkAAGAW4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAU4gYAABiFuAEAAEYhbgAAgFGIGwAAYBTiBgAAGIW4AQAARiFuAACAUYgbAABgFOIGAAAYhbgBAABGIW4AAIBRiBsAAGAUy+OmoKBAMTExCg8PV3x8vA4dOnTNdXfu3KkJEyaof//+ioiIUGJiovbu3duB0wIAgGBnadzs2LFDc+fO1ZIlS+R0OpWcnKzU1FS5XK5W1z948KAmTJig4uJilZSUaNy4cZo0aZKcTmcHTw4AAIJVqJUvvmbNGqWnpysjI0OSlJeXp71796qwsFC5ubkt1s/Ly/O5/7vf/U67d+/We++9p3vuuafV16ivr1d9fb33fk1NTfu9AQAAEHQsi5uGhgaVlJTo2Wef9VmekpKiI0eOtOk5mpubVVtbqz59+lxzndzcXC1fvvyGZjWFa8XdAXvuxu/7Sgr5z9/nA/paQ5edCNhzAwA6P8sOS1VWVqqpqUmRkZE+yyMjI1VWVtam5/j973+vS5cuafLkyddcJycnR9XV1d7buXPnbmhuAAAQ3Cw9LCVJNpvN577H42mxrDWvv/66nn/+ee3evVsDBgy45np2u112u/2G5wQAAJ2DZXHTr18/hYSEtNhLU15e3mJvzn/bsWOH0tPT9eabb+oXv/hFIMcEAACdjGWHpcLCwhQfHy+Hw+Gz3OFwKCkp6Zrbvf7663riiSe0fft2PfLII4EeEwAAdDKWHpbKzs7W1KlTlZCQoMTERG3cuFEul0uZmZmSrpwv8+2332rr1q2SroTNtGnTlJ+fr5///OfevT7du3dXr169LHsfAAAgeFgaN2lpaaqqqtKKFSvkdrsVFxen4uJiRUdHS5LcbrfPb95s2LBBjY2NmjlzpmbOnOldPn36dBUVFXX0+AAAIAhZfkJxVlaWsrKyWn3sv4Nl//79gR8IAAB0apZffgEAAKA9ETcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjBJq9QAwQx97U6t/AwDQ0YgbtIvF93xv9QgAAEjisBQAADAMcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADCK5XFTUFCgmJgYhYeHKz4+XocOHbru+gcOHFB8fLzCw8N1++23a/369R00KQAA6AwsjZsdO3Zo7ty5WrJkiZxOp5KTk5WamiqXy9Xq+mfPntXEiROVnJwsp9OpxYsXa/bs2Xr77bc7eHIAABCsLI2bNWvWKD09XRkZGYqNjVVeXp6ioqJUWFjY6vrr16/X0KFDlZeXp9jYWGVkZOjJJ5/Uyy+/3MGTAwCAYBVq1Qs3NDSopKREzz77rM/ylJQUHTlypNVtPvnkE6WkpPgse/jhh7Vp0yZdvnxZ3bp1a7FNfX296uvrvferq6slSTU1NX7N21T/L7/WD0a13ZqsHqFd+PvZ/Rh83sGDz7tt+Lzbjs87ePjzeV9d1+Px/OC6lsVNZWWlmpqaFBkZ6bM8MjJSZWVlrW5TVlbW6vqNjY2qrKzUoEGDWmyTm5ur5cuXt1geFRV1A9N3TnFWD9BecntZPUGnwOfdtfB5dy1d+fOura1Vr17X386yuLnKZrP53Pd4PC2W/dD6rS2/KicnR9nZ2d77zc3Nunjxovr27Xvd1zFNTU2NoqKidO7cOUVERFg9DgKMz7tr4fPuWrrq5+3xeFRbW6vBgwf/4LqWxU2/fv0UEhLSYi9NeXl5i70zVw0cOLDV9UNDQ9W3b99Wt7Hb7bLb7T7Levfu/eMH7+QiIiK61L8MXR2fd9fC5921dMXP+4f22Fxl2QnFYWFhio+Pl8Ph8FnucDiUlJTU6jaJiYkt1t+3b58SEhJaPd8GAAB0PZZ+Wyo7O1uvvfaaNm/erNOnT2vevHlyuVzKzMyUdOWQ0rRp07zrZ2Zm6uuvv1Z2drZOnz6tzZs3a9OmTVqwYIFVbwEAAAQZS8+5SUtLU1VVlVasWCG32624uDgVFxcrOjpakuR2u31+8yYmJkbFxcWaN2+e1q1bp8GDB+vVV1/V448/btVb6DTsdruee+65FofoYCY+766Fz7tr4fP+YTZPW75TBQAA0ElYfvkFAACA9kTcAAAAoxA3AADAKMQNAAAwCnHTRRQUFCgmJkbh4eGKj4/XoUOHrB4JAXLw4EFNmjRJgwcPls1m0zvvvGP1SAiQ3Nxc/exnP1PPnj01YMAAPfbYY/rb3/5m9VgIkMLCQo0aNcr7432JiYn685//bPVYQYm46QJ27NihuXPnasmSJXI6nUpOTlZqaqrP1+xhjkuXLmn06NFau3at1aMgwA4cOKCZM2fq6NGjcjgcamxsVEpKii5dumT1aAiAIUOGaOXKlTp27JiOHTum8ePH65e//KVOnjxp9WhBh6+CdwH33nuvfvrTn6qwsNC7LDY2Vo899phyc3MtnAyBZrPZtGvXLj322GNWj4IOUFFRoQEDBujAgQN64IEHrB4HHaBPnz5avXq10tPTrR4lqLDnxnANDQ0qKSlRSkqKz/KUlBQdOXLEoqkABEJ1dbWkK//Bg9mampr0xhtv6NKlS0pMTLR6nKBj+VXBEViVlZVqampqcTHSyMjIFhchBdB5eTweZWdn6/7771dcXJzV4yBATpw4ocTERP373//WLbfcol27dumuu+6yeqygQ9x0ETabzee+x+NpsQxA5/X000/r+PHjOnz4sNWjIIBGjBih0tJSff/993r77bc1ffp0HThwgMD5L8SN4fr166eQkJAWe2nKy8tb7M0B0DnNmjVL7777rg4ePKghQ4ZYPQ4CKCwsTMOGDZMkJSQk6LPPPlN+fr42bNhg8WTBhXNuDBcWFqb4+Hg5HA6f5Q6HQ0lJSRZNBaA9eDwePf3009q5c6c+/PBDxcTEWD0SOpjH41F9fb3VYwQd9tx0AdnZ2Zo6daoSEhKUmJiojRs3yuVyKTMz0+rREAB1dXX68ssvvffPnj2r0tJS9enTR0OHDrVwMrS3mTNnavv27dq9e7d69uzp3UPbq1cvde/e3eLp0N4WL16s1NRURUVFqba2Vm+88Yb279+vPXv2WD1a0OGr4F1EQUGBXnrpJbndbsXFxemVV17hq6KG2r9/v8aNG9di+fTp01VUVNTxAyFgrnXe3JYtW/TEE0907DAIuPT0dH3wwQdyu93q1auXRo0apWeeeUYTJkywerSgQ9wAAACjcM4NAAAwCnEDAACMQtwAAACjEDcAAMAoxA0AADAKcQMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3ADql5uZmrVq1SsOGDZPdbtfQoUP14osv6quvvpLNZtMbb7yhpKQkhYeH6yc/+Yn2799v9cgAOghxA6BTysnJ0apVq7R06VKdOnVK27dvV2RkpPfxhQsXav78+XI6nUpKStKjjz6qqqoqCycG0FG4KjiATqe2tlb9+/fX2rVrlZGR4fPYV199pZiYGK1cuVLPPPOMJKmxsVExMTGaNWuWFi1aZMXIADoQe24AdDqnT59WfX29HnrooWuuk5iY6P07NDRUCQkJOn36dEeMB8BixA2ATqd79+4/ajubzdbOkwAIRsQNgE5n+PDh6t69uz744INrrnP06FHv342NjSopKdHIkSM7YjwAFgu1egAA8Fd4eLieeeYZLVq0SGFhYbrvvvtUUVGhkydPeg9VrVu3TsOHD1dsbKxeeeUVfffdd3ryySctnhxARyBuAHRKS5cuVWhoqJYtW6bz589r0KBByszM9D6+cuVKrVq1Sk6nU3fccYd2796tfv36WTgxgI7Ct6UAGOXqt6WcTqfGjBlj9TgALMA5NwAAwCjEDQAAMAqHpQAAgFHYcwMAAIxC3AAAAKMQNwAAwCjEDQAAMApxAwAAjELcAAAAoxA3AADAKMQNAAAwyv8CTWy10QDac5oAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot(x='cp',y='target',hue='sex',data=heart)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89391b98",
   "metadata": {},
   "source": [
    "The error 'AttributeError: 'numpy.int64' object has no attribute 'startswith'\n",
    "'needs to be resolved by treating the columns as data=heart.astype(str). However that resolution will add an issue to convert the variables into string. BarPlot won't support that unlike CountPlot because BarPlot needs at least 1 numeric variable for it to be represented."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "09aceb27",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.countplot(x='cp',hue='target',data=heart.astype(str))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec88c47c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5073f3a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "#cross tab sex vs target\n",
    "pd.crosstab(heart.sex,heart.target).plot(kind=\"bar\",figsize=(10,6))\n",
    "plt.title('Heart disease frequency according to Sex')\n",
    "plt.xlabel('Sex')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('Heart disease frequency according to Sex.png')#how to save an image in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "371ef163",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "53a7b29a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='age', ylabel='thalach'>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#find the scatterplot of age vs target with respect to thalach\n",
    "sns.scatterplot(x='age',y='thalach',data=heart,hue='target')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5cd29cc5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "a2ec10a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thalach\n",
       "162    11\n",
       "160     9\n",
       "163     9\n",
       "152     8\n",
       "173     8\n",
       "       ..\n",
       "202     1\n",
       "184     1\n",
       "121     1\n",
       "192     1\n",
       "90      1\n",
       "Name: count, Length: 91, dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.thalach.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4175e76a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x=heart.age[heart.target==1], y=heart.thalach[(heart.target==1)], c=\"red\")\n",
    "plt.scatter(x=heart.age[heart.target==0], y=heart.thalach[(heart.target==0)])\n",
    "plt.legend([\"Disease\", \"Not Disease\"])\n",
    "plt.xlabel(\"Age\")\n",
    "plt.ylabel(\"Maximum Heart Rate\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6a3c63d0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "794baa5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cross tab slope vs target\n",
    "pd.crosstab(heart.slope,heart.target).plot(kind=\"bar\",figsize=(10,6))\n",
    "plt.title('Heart disease frequency for Slope')\n",
    "plt.xlabel('Slope')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('Heart disease frequency for Slope.png')#how to save an image in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4269158",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5d49b0e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cross tab cp vs target\n",
    "pd.crosstab(heart.cp,heart.target).plot(kind=\"bar\",figsize=(10,6))\n",
    "plt.title('Heart disease frequency for Chest Pain')\n",
    "plt.xlabel('ChestPain')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('Heart disease frequency for ChestPain.png')#how to save an image in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "540f1063",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "77ff8eb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cross tab sex vs target\n",
    "pd.crosstab(heart.sex,heart.target).plot(kind=\"bar\",figsize=(10,6))\n",
    "plt.title('Heart disease frequency for Sex')\n",
    "plt.xlabel('Sex')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('Heart disease frequency for Sex.png')#how to save an image in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8fc5044a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "58ae6929",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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du3Yt07a3u2QvJzIuwfP398+0Lqu2W3388ceaMmWK3n//fb322msqXry4unXrphkzZsjf319//vmnJGX6w+pmFy9elIeHh0aNGqUFCxbolVdeUYsWLVSyZEkVK1ZMgwcPtjn38ePHy8PDQ6tXr9bixYvl4OCg5s2ba/r06WrYsKEuXLig1NRUzZs3T/PmzcvymLfec2bGrc95xjmOGTNGY8aMue3xLly4kOvnOjt//vmnNm/enO1lbbeeq6+vr81yxmV1Gc/xX3/9JenOl6O5uLhoyJAhmjVrlmbOnKmUlBStX79eo0aNytXkG5cuXZJhGFm+pgMDAyXJ5pJRyfzrv1y5clm+Zzds2KCePXvqiSee0Msvvyx/f385Ojpq0aJFNvcqNW/eXJs2bdK7776rvn37KikpSTVq1NCECROy/MM7p279mUg3nt+bX/cXLlzI8jMku8+VrDg6OmY6VsZr79bn9mYXLlzI8n6jW38uGa/vW+9BKlOmjBwdHW363a6WO7nTZ4AZGbXcOjNnVs9rTj+jMtzt5zNQ2BGkANxRqVKlZLFYtGvXriz/wMxoO3r0qA4fPqzly5erX79+1vW3u0H5br+TJuMPmdjY2EzrYmNj7/h9WKVKldKcOXM0Z84cnT17Vp999pnGjRunuLg4ff3119b/PZ83b162s6Vl/MGScS/V1KlTbdb//fffKlGihHXZ0dFRo0aN0qhRo/TPP/9o27Zt+t///V+1a9dO586dU8mSJa2jGM8//3yWxwwJCbnted3Orc95xjmOHz9e3bt3z3KbjHsnfH19s32ub+Xi4pLl5Am3/sFbqlQp1a5dW2+++WaWx874YzenSpcuLUn6/fff79j3ueee01tvvaUPP/xQ169fV2pqqoYOHWrqeBky/iiNiYnJtO78+fOSZDMaI9396z/D6tWrFRISoo8//thmn1k9/48//rgef/xxJSUl6fvvv9e0adPUu3dvBQcHq0mTJnlST1Z8fX2tof1mWb12spOamqoLFy7YBJiM7bMKczcfOyc/F19fX/3www8yDMPmeYyLi1NqaqpNv9vVcid3+gxwd3eXq6trlj+/rP5jITU1VRcvXrQJU1nVktPPqAz2+s4w4H7BpX0A7qhz584yDEN//PGHGjZsmOlRq1YtSf/3S/fWsPXee++ZOt6tIwy389BDD8nV1dU6qUKGyMhIm8uKcqJ8+fJ64YUX1KZNGx08eFCS1LRpU5UoUULHjh3L8twbNmwoZ2dnSTfO/9Zz/+KLL257+VSJEiXUo0cPPf/887p48aLOnDkjd3d3tWrVSocOHVLt2rWzPObt/mg0q0qVKgoNDdXhw4ezPUdPT09JUqtWrfTNN9/Y/EGclpamjz/+ONN+g4ODdeTIEZu27du368qVKzZtnTt3tk7XntWxzQapypUrq1KlSvrwww/vOAteQECAnnjiCS1cuFCLFy9Wly5dbC5RM8PDw0ONGzfWhg0bbF676enpWr16tcqVK6fKlSvnat93YrFY5OzsbPOHb2xsbJaz9mVwcXFRixYtNH36dEnSoUOHrO1Szt5/ZrRo0ULbt2+3CQLp6en65JNPTO3n1vd6xkQnN3/59q1at26tY8eOWd/XGTJmaGzVqpW135UrVzJ9ufLKlSut6yVZ+2dXixlZfQZIN94/cXFxNu+15ORkbdmyxWb7Fi1aSFKm9+C6desyHSs3n1EAsseIFIA7atq0qZ599lkNGDBA+/fvV/PmzeXh4aGYmBjt3r1btWrV0nPPPaeqVauqUqVKGjdunAzDkI+PjzZv3qyIiAhTx8sIZtOnT1eHDh3k4OCg2rVrWwPLzUqWLKkxY8ZoypQpGjx4sJ544gmdO3dO4eHhd7zMJj4+Xq1atVLv3r1VtWpVeXp6at++ffr666+tIzPFixfXvHnz1K9fP128eFE9evRQmTJl9Ndff+nw4cP666+/tGjRIkk3AsHy5ctVtWpV1a5dWwcOHNDMmTMzXWLWpUsX1axZUw0bNlTp0qUVHR2tOXPmqEKFCgoNDZUkzZ07V82aNdPDDz+s5557TsHBwbp8+bJOnjypzZs3W+/pyCvvvfeeOnTooHbt2ql///4qW7asLl68qJ9//lkHDx60/rH76quv6rPPPtMjjzyiiRMnyt3dXQsWLLDeJ3azPn366LXXXtPEiRPVokULHTt2TPPnz7fOrJdh8uTJioiIUFhYmIYPH64qVaro+vXrOnPmjL788kstXrzY9HcXLViwQF26dNFDDz2kl156SeXLl9fZs2e1ZcuWTH/8jhgxwnqf0a0zBJo1bdo0tWnTRq1atdKYMWPk7OyshQsX6ujRo1q7dm2+/Q9/586dtWHDBg0bNsw6c+Ubb7yhgIAA/frrr9Z+EydO1O+//67WrVurXLly+ueffzR37lw5OTlZ/yCvVKmS3Nzc9NFHH6latWoqXry4AgMDTQfaW02YMEGbN29W69atNWHCBLm5uWnx4sXW106xYnf+v11nZ2fNmjVLV65cUaNGjayz9nXo0CHTvZ03e+mll7Ry5Up16tRJkydPVoUKFfTFF19o4cKFeu6556wBt2/fvlqwYIH69eunM2fOqFatWtq9e7emTp2qjh076tFHH5UktW3bVs2bN9fYsWOVmJiohg0bas+ePVq1alWOnoucfAb06tVLEydO1JNPPqmXX35Z169f17vvvqu0tDSbfbVv315NmzbV6NGjlZCQoAYNGmjv3r3W8Hfz85rTzygAOWTPmS4A2EfGjGH79u3Lcn2nTp2ynC3qww8/NBo3bmx4eHgYbm5uRqVKlYy+ffsa+/fvt/Y5duyY0aZNG8PT09MoWbKk8cQTTxhnz57NNHtUxsxTN8/glSEpKckYPHiwUbp0acNisdxxRq/09HRj2rRpRlBQkOHs7GzUrl3b2Lx5s9GiRYvbztp3/fp1Y+jQoUbt2rUNLy8vw83NzahSpYrx+uuvG4mJiTbH2Llzp9GpUyfDx8fHcHJyMsqWLWt06tTJ+OSTT6x9Ll26ZAwaNMgoU6aM4e7ubjRr1szYtWtXpjpmzZplhIWFGaVKlTKcnZ2N8uXLG4MGDTLOnDljc8zTp08bAwcONMqWLWs4OTkZpUuXNsLCwowpU6Zk+1xkuN2sfbfONJfh8OHDRs+ePY0yZcoYTk5Ohr+/v/HII48Yixcvtum3Z88e46GHHjJcXFwMf39/4+WXXzaWLFmS6eeUlJRkjB071ggKCjLc3NyMFi1aGFFRUZlmVDMMw/jrr7+M4cOHGyEhIYaTk5Ph4+NjNGjQwJgwYYJx5cqVO9Z/6+vLMG7MNtmhQwfD29vbcHFxMSpVqmQzM9nNgoODjWrVqmW5Liu3q2XXrl3GI488Yn2fPPTQQ8bmzZtt+tzpPWjmeBneeustIzg42HBxcTGqVatmLF26NNMMb59//rnRoUMHo2zZsoazs7NRpkwZo2PHjsauXbts9rV27VqjatWqhpOTk81zm92sfZ06dcpUz62ve8O48dw0btzY5rWTMSPe7WYSNIwbs9h5eHgYR44cMVq2bGm4ubkZPj4+xnPPPWd9jdxc062vsejoaKN3796Gr6+v4eTkZFSpUsWYOXOmdUbKDBcuXDCGDh1qBAQEGI6OjkaFChWM8ePHG9evX7fp988//xgDBw40SpQoYbi7uxtt2rQxfvnllxzN2pfTz4Avv/zSqFu3ruHm5mZUrFjRmD9/fpY/g4sXLxoDBgywqeX777/PNLNgTj+jMmbtu/nzDUBmFsPI4gtjAAAwYfny5RowYIBOnz59x/vSCpojR46oTp06WrBggYYNG2bvcoqctm3b6syZMzn+MlvkzJo1a/TUU09pz549CgsLs3c5QKHEpX0AgCLp1KlTio6O1v/+7/8qICDAOsU18s+oUaNUr149BQUF6eLFi/roo48UERGhDz74wN6l3dfWrl2rP/74Q7Vq1VKxYsX0/fffa+bMmWrevDkhCshHBCkAQJH0xhtvaNWqVapWrZo++eQTubu727ukQi8tLU0TJ05UbGysLBaLqlevrlWrVunpp5+2d2n3NU9PT61bt05TpkxRYmKi9T8GpkyZYu/SgEKNS/sAAAAAwCSmPwcAAAAAkwhSAAAAAGASQQoAAAAATGKyCd34ZvXz58/L09Mz374sEQAAAEDBZxiGLl++rMDAwNt+WThBStL58+cVFBRk7zIAAAAAFBDnzp1TuXLlsl1PkNKNaUOlG0+Wl5eXnasBAAAAYC8JCQkKCgqyZoTsEKQk6+V8Xl5eBCkAAAAAd7zlh8kmAAAAAMAkghQAAAAAmESQAgAAAACTuEcKAAAAgI20tDSlpKTYu4x84eTkJAcHh7veD0EKAAAAgKQb36EUGxurf/75x96l5KsSJUrI39//rr5DliAFAAAAQJKsIapMmTJyd3e/q6BREBmGoatXryouLk6SFBAQkOt9EaQAAAAAKC0tzRqifH197V1OvnFzc5MkxcXFqUyZMrm+zI/JJgAAAABY74lyd3e3cyX5L+Mc7+Y+MIIUAAAAAKvCdjlfVvLiHAlSAAAAAGASQQoAAAAATCJIAQAAADClZcuWGjlypL3LsLJHPQQpAAAAAPdccnKyvUu4KwQpAAAAADnWv39/7dy5U3PnzpXFYpHFYtGpU6c0aNAghYSEyM3NTVWqVNHcuXMzbde1a1dNmzZNgYGBqly5siQpMjJSdevWlaurqxo2bKhNmzbJYrEoKirKuu2xY8fUsWNHFS9eXH5+furTp4/+/vvvbOs5c+ZMvj8PfI8UAAAAgBybO3euTpw4oZo1a2ry5MmSpJIlS6pcuXJav369SpUqpcjISD377LMKCAhQz549rdt+88038vLyUkREhAzD0OXLl9WlSxd17NhRa9asUXR0dKZL9GJiYtSiRQs988wzmj17tq5du6ZXXnlFPXv21Pbt27Osp3Tp0vn+PBCkAAAAAOSYt7e3nJ2d5e7uLn9/f2v7pEmTrP8OCQlRZGSk1q9fbxOkPDw89P7778vZ2VmStHjxYlksFi1dulSurq6qXr26/vjjDz3zzDPWbRYtWqT69etr6tSp1rYPP/xQQUFBOnHihCpXrpxlPfmNIAUAAADgri1evFjvv/++oqOjde3aNSUnJ6tu3bo2fWrVqmUNUZJ0/Phx1a5dW66urta2Bx980GabAwcOaMeOHSpevHimY546dcp6ieC9RpACAAAAcFfWr1+vl156SbNmzVKTJk3k6empmTNn6ocffrDp5+HhYbNsGEamL8c1DMNmOT09XV26dNH06dMzHTcgICCPzsA8ghQAAAAAU5ydnZWWlmZd3rVrl8LCwjRs2DBr26lTp+64n6pVq+qjjz5SUlKSXFxcJEn79++36VO/fn19+umnCg4OlqNj1vHl1nruBYIUgP8T7m3vChAeb+8KAAC4o+DgYP3www86c+aMihcvrgceeEArV67Uli1bFBISolWrVmnfvn0KCQm57X569+6tCRMm6Nlnn9W4ceN09uxZvf3225JkHal6/vnntXTpUv3rX//Syy+/rFKlSunkyZNat26dli5dKgcHh0z1+Pj4qFix/J2gnOnPAQAAAJgyZswYOTg4qHr16ipdurTat2+v7t27q1evXmrcuLEuXLhgMzqVHS8vL23evFlRUVGqW7euJkyYoIkTJ0qS9b6pwMBA7dmzR2lpaWrXrp1q1qypESNGyNvb2xqWbq3n7Nmz+Xfy/5/FuPUixHvou+++08yZM3XgwAHFxMRo48aN6tq1a5Z9hwwZoiVLluidd96xmRIxKSlJY8aM0dq1a3Xt2jW1bt1aCxcuVLly5XJcR0JCgry9vRUfHy8vL6+7PCvgPsaIlP0xIgUAsJPr16/r9OnTCgkJsZn84V776KOPNGDAAMXHx8vNzS1fjnG7c81pNrDriFRiYqLq1Kmj+fPn37bfpk2b9MMPPygwMDDTupEjR2rjxo1at26ddu/erStXrqhz5873/BpJAAAAAOatXLlSu3fv1unTp7Vp0ybrd0TlV4jKK3a9R6pDhw7q0KHDbfv88ccfeuGFF7RlyxZ16tTJZl18fLw++OADrVq1So8++qgkafXq1QoKCtK2bdvUrl27fKsdAAAAwN2LjY3VxIkTFRsbq4CAAD3xxBN688037V3WHRXoySbS09PVp08fvfzyy6pRo0am9QcOHFBKSoratm1rbQsMDFTNmjUVGRmZbZBKSkpSUlKSdTkhISHviwcAAABwR2PHjtXYsWPtXYZpBXqyienTp8vR0VHDhw/Pcn1sbKycnZ1VsmRJm3Y/Pz/FxsZmu99p06bJ29vb+ggKCsrTugEAAAAUbgU2SB04cEBz587V8uXLM31J151k9cVeNxs/frzi4+Otj3Pnzt1tuQAAAACKkAIbpHbt2qW4uDiVL19ejo6OcnR0VHR0tEaPHq3g4GBJkr+/v5KTk3Xp0iWbbePi4uTn55ftvl1cXOTl5WXzAAAAAICcKrBBqk+fPjpy5IiioqKsj8DAQL388svasmWLJKlBgwZycnJSRESEdbuYmBgdPXpUYWFh9iodAAAAQCFn18kmrly5opMnT1qXT58+raioKPn4+Kh8+fLy9fW16e/k5CR/f39VqVJFkuTt7a1BgwZp9OjR8vX1lY+Pj8aMGaNatWpZZ/EDAAAAgLxm1yC1f/9+tWrVyro8atQoSVK/fv20fPnyHO3jnXfekaOjo3r27Gn9Qt7ly5fLwcEhP0oGAAAAAPsGqZYtW8owjBz3P3PmTKY2V1dXzZs3T/PmzcvDygAAAAAgewX6e6QAAAAA2F/wuC/u2bHOvNUpV9stXLhQM2fOVExMjGrUqKE5c+bo4YcfzuPq/k+BnWwCAAAAAHLi448/1siRIzVhwgQdOnRIDz/8sDp06KCzZ8/m2zEJUgAAAADua7Nnz9agQYM0ePBgVatWTXPmzFFQUJAWLVqUb8ckSAEAAAC4byUnJ+vAgQNq27atTXvbtm0VGRmZb8clSAEAAAC4b/39999KS0uTn5+fTbufn59iY2Pz7bgEKQAAAAD3PYvFYrNsGEamtrxEkAIAAABw3ypVqpQcHBwyjT7FxcVlGqXKSwQpAAAAAPctZ2dnNWjQQBERETbtERERCgsLy7fj8j1SAAAAAO5ro0aNUp8+fdSwYUM1adJES5Ys0dmzZzV06NB8OyZBCgAAAMBt5fZLcu+VXr166cKFC5o8ebJiYmJUs2ZNffnll6pQoUK+HZMgBQAAAOC+N2zYMA0bNuyeHY97pAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACY5GjvAgAAAAAUcOHe9/BY8aY3+e677zRz5kwdOHBAMTEx2rhxo7p27Zr3td2EESkAAAAA97XExETVqVNH8+fPv2fHZEQKAAAAwH2tQ4cO6tChwz09JiNSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmMWsfAAAAgPvalStXdPLkSevy6dOnFRUVJR8fH5UvXz5fjkmQAgAAAHB7ufiS3Htp//79atWqlXV51KhRkqR+/fpp+fLl+XJMghQAAACA+1rLli1lGMY9PSb3SAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAq3s9aYM95MU5EqQAAAAAyMnJSZJ09epVO1eS/zLOMeOcc4PpzwEAAADIwcFBJUqUUFxcnCTJ3d1dFovFzlXlLcMwdPXqVcXFxalEiRJycHDI9b4IUgAAAAAkSf7+/pJkDVOFVYkSJaznmlsEKQAAAACSJIvFooCAAJUpU0YpKSn2LidfODk53dVIVAaCFAAAAAAbDg4OeRI2CjMmmwAAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJPsGqS+++47denSRYGBgbJYLNq0aZN1XUpKil555RXVqlVLHh4eCgwMVN++fXX+/HmbfSQlJenFF19UqVKl5OHhoccee0y///77PT4TAAAAAEWJXYNUYmKi6tSpo/nz52dad/XqVR08eFCvvfaaDh48qA0bNujEiRN67LHHbPqNHDlSGzdu1Lp167R7925duXJFnTt3Vlpa2r06DQAAAABFjMUwDMPeRUiSxWLRxo0b1bVr12z77Nu3Tw8++KCio6NVvnx5xcfHq3Tp0lq1apV69eolSTp//ryCgoL05Zdfql27djk6dkJCgry9vRUfHy8vL6+8OB3g/hTube8KEB5v7woAACjScpoN7qt7pOLj42WxWFSiRAlJ0oEDB5SSkqK2bdta+wQGBqpmzZqKjIzMdj9JSUlKSEiweQAAAABATt03Qer69esaN26cevfubU2GsbGxcnZ2VsmSJW36+vn5KTY2Ntt9TZs2Td7e3tZHUFBQvtYOAAAAoHC5L4JUSkqKnnzySaWnp2vhwoV37G8YhiwWS7brx48fr/j4eOvj3LlzeVkuAAAAgEKuwAeplJQU9ezZU6dPn1ZERITNdYr+/v5KTk7WpUuXbLaJi4uTn59ftvt0cXGRl5eXzQMAAAAAcqpAB6mMEPXrr79q27Zt8vX1tVnfoEEDOTk5KSIiwtoWExOjo0ePKiws7F6XCwAAAKCIcLTnwa9cuaKTJ09al0+fPq2oqCj5+PgoMDBQPXr00MGDB/X5558rLS3Net+Tj4+PnJ2d5e3trUGDBmn06NHy9fWVj4+PxowZo1q1aunRRx+112kBAAAAKOTsGqT279+vVq1aWZdHjRolSerXr5/Cw8P12WefSZLq1q1rs92OHTvUsmVLSdI777wjR0dH9ezZU9euXVPr1q21fPlyOTg43JNzAAAAAFD0FJjvkbInvkcK+P/4Hin743ukAACwq0L5PVIAAAAAUBAQpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGCSXac/B24WPO4Le5dQ5J1xtXcFAAAA9wdGpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMsmuQ+u6779SlSxcFBgbKYrFo06ZNNusNw1B4eLgCAwPl5uamli1b6qeffrLpk5SUpBdffFGlSpWSh4eHHnvsMf3+++/38CwAAAAAFDV2DVKJiYmqU6eO5s+fn+X6GTNmaPbs2Zo/f7727dsnf39/tWnTRpcvX7b2GTlypDZu3Kh169Zp9+7dunLlijp37qy0tLR7dRoAAAAAihhHex68Q4cO6tChQ5brDMPQnDlzNGHCBHXv3l2StGLFCvn5+WnNmjUaMmSI4uPj9cEHH2jVqlV69NFHJUmrV69WUFCQtm3bpnbt2t2zcwEAAABQdBTYe6ROnz6t2NhYtW3b1trm4uKiFi1aKDIyUpJ04MABpaSk2PQJDAxUzZo1rX2ykpSUpISEBJsHAAAAAORUgQ1SsbGxkiQ/Pz+bdj8/P+u62NhYOTs7q2TJktn2ycq0adPk7e1tfQQFBeVx9QAAAAAKswIbpDJYLBabZcMwMrXd6k59xo8fr/j4eOvj3LlzeVIrAAAAgKKhwAYpf39/Sco0shQXF2cdpfL391dycrIuXbqUbZ+suLi4yMvLy+YBAAAAADlVYINUSEiI/P39FRERYW1LTk7Wzp07FRYWJklq0KCBnJycbPrExMTo6NGj1j4AAAAAkNfsOmvflStXdPLkSevy6dOnFRUVJR8fH5UvX14jR47U1KlTFRoaqtDQUE2dOlXu7u7q3bu3JMnb21uDBg3S6NGj5evrKx8fH40ZM0a1atWyzuIHAAAAAHnNrkFq//79atWqlXV51KhRkqR+/fpp+fLlGjt2rK5du6Zhw4bp0qVLaty4sbZu3SpPT0/rNu+8844cHR3Vs2dPXbt2Ta1bt9by5cvl4OBwz88HAAAAQNFgMQzDsHcR9paQkCBvb2/Fx8dzv5QdBY/7wt4lFHlnXHvbuwSEx9u7AgAAirScZoMCe48UAAAAABRUBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYFKugtTp06fzug4AAAAAuG/kKkg98MADatWqlVavXq3r16/ndU0AAAAAUKDlKkgdPnxY9erV0+jRo+Xv768hQ4bov//9b17XBgAAAAAFUq6CVM2aNTV79mz98ccfWrZsmWJjY9WsWTPVqFFDs2fP1l9//ZXXdQIAAABAgXFXk004OjqqW7duWr9+vaZPn65Tp05pzJgxKleunPr27auYmJi8qhMAAAAACoy7ClL79+/XsGHDFBAQoNmzZ2vMmDE6deqUtm/frj/++EOPP/54XtUJAAAAAAWGY242mj17tpYtW6bjx4+rY8eOWrlypTp27KhixW7kspCQEL333nuqWrVqnhYLAAAAAAVBroLUokWLNHDgQA0YMED+/v5Z9ilfvrw++OCDuyoOAAAAAAqiXAWpX3/99Y59nJ2d1a9fv9zsHgAAAAAKtFzdI7Vs2TJ98sknmdo/+eQTrVix4q6LAgAAAICCLFdB6q233lKpUqUytZcpU0ZTp06966IAAAAAoCDLVZCKjo5WSEhIpvYKFSro7Nmzd10UAAAAABRkuQpSZcqU0ZEjRzK1Hz58WL6+vnddVIbU1FS9+uqrCgkJkZubmypWrKjJkycrPT3d2scwDIWHhyswMFBubm5q2bKlfvrppzyrAQAAAABulasg9eSTT2r48OHasWOH0tLSlJaWpu3bt2vEiBF68skn86y46dOna/HixZo/f75+/vlnzZgxQzNnztS8efOsfWbMmKHZs2dr/vz52rdvn/z9/dWmTRtdvnw5z+oAAAAAgJvlata+KVOmKDo6Wq1bt5aj441dpKenq2/fvnl6j9TevXv1+OOPq1OnTpKk4OBgrV27Vvv375d0YzRqzpw5mjBhgrp37y5JWrFihfz8/LRmzRoNGTIkz2oBAAAAgAy5GpFydnbWxx9/rF9++UUfffSRNmzYoFOnTunDDz+Us7NznhXXrFkzffPNNzpx4oSkG5cO7t69Wx07dpQknT59WrGxsWrbtq11GxcXF7Vo0UKRkZHZ7jcpKUkJCQk2DwAAAADIqVyNSGWoXLmyKleunFe1ZPLKK68oPj5eVatWlYODg9LS0vTmm2/qX//6lyQpNjZWkuTn52eznZ+fn6Kjo7Pd77Rp0zRp0qR8qxsAAABA4ZarIJWWlqbly5frm2++UVxcnM3kD5K0ffv2PCnu448/1urVq7VmzRrVqFFDUVFRGjlypAIDA22+7NdisdhsZxhGprabjR8/XqNGjbIuJyQkKCgoKE9qBgAAAFD45SpIjRgxQsuXL1enTp1Us2bN24aWu/Hyyy9r3Lhx1gksatWqpejoaE2bNk39+vWTv7+/pBsjUwEBAdbt4uLiMo1S3czFxUUuLi75UjMAAACAwi9XQWrdunVav3699V6l/HL16lUVK2Z7G5eDg4N1BCwkJET+/v6KiIhQvXr1JEnJycnauXOnpk+fnq+1AQAAACi6chWknJ2d9cADD+R1LZl06dJFb775psqXL68aNWro0KFDmj17tgYOHCjpxiV9I0eO1NSpUxUaGqrQ0FBNnTpV7u7u6t27d77XBwAAAKBoylWQGj16tObOnav58+fn22V9kjRv3jy99tprGjZsmOLi4hQYGKghQ4Zo4sSJ1j5jx47VtWvXNGzYMF26dEmNGzfW1q1b5enpmW91AQAAACjaLIZhGGY36tatm3bs2CEfHx/VqFFDTk5ONus3bNiQZwXeCwkJCfL29lZ8fLy8vLzsXU6RFTzuC3uXUOSdcWUk1+7C4+1dAQAARVpOs0GuRqRKlCihbt265bo4AAAAALif5SpILVu2LK/rAAAAAID7RrE7d8laamqqtm3bpvfee0+XL1+WJJ0/f15XrlzJs+IAAAAAoCDK1YhUdHS02rdvr7NnzyopKUlt2rSRp6enZsyYoevXr2vx4sV5XScAAAAAFBi5GpEaMWKEGjZsqEuXLsnNzc3a3q1bN33zzTd5VhwAAAAAFES5GpHavXu39uzZI2dnZ5v2ChUq6I8//siTwgAAAACgoMrViFR6errS0tIytf/+++98fxMAAACAQi9XQapNmzaaM2eOddlisejKlSt6/fXX1bFjx7yqDQAAAAAKpFxd2vfOO++oVatWql69uq5fv67evXvr119/ValSpbR27dq8rhEAAAAACpRcBanAwEBFRUVp7dq1OnjwoNLT0zVo0CA99dRTNpNPAAAAAEBhlKsgJUlubm4aOHCgBg4cmJf1AAAAAECBl6sgtXLlytuu79u3b66KAQAAAID7Qa6C1IgRI2yWU1JSdPXqVTk7O8vd3Z0gBQAAAKBQy9WsfZcuXbJ5XLlyRcePH1ezZs2YbAIAAABAoZerIJWV0NBQvfXWW5lGqwAAAACgsMmzICVJDg4OOn/+fF7uEgAAAAAKnFzdI/XZZ5/ZLBuGoZiYGM2fP19NmzbNk8IAAAAAoKDKVZDq2rWrzbLFYlHp0qX1yCOPaNasWXlRFwAAAAAUWLkKUunp6XldBwAAAADcN/L0HikAAAAAKApyNSI1atSoHPedPXt2bg4BAAAAAAVWroLUoUOHdPDgQaWmpqpKlSqSpBMnTsjBwUH169e39rNYLHlTJQAAAAAUILkKUl26dJGnp6dWrFihkiVLSrrxJb0DBgzQww8/rNGjR+dpkQAAAABQkOTqHqlZs2Zp2rRp1hAlSSVLltSUKVOYtQ8AAABAoZerIJWQkKA///wzU3tcXJwuX75810UBAAAAQEGWqyDVrVs3DRgwQP/+97/1+++/6/fff9e///1vDRo0SN27d8/rGgEAAACgQMnVPVKLFy/WmDFj9PTTTyslJeXGjhwdNWjQIM2cOTNPCwQAAACAgiZXQcrd3V0LFy7UzJkzderUKRmGoQceeEAeHh55XR8AAAAAFDh39YW8MTExiomJUeXKleXh4SHDMPKqLgAAAAAosHIVpC5cuKDWrVurcuXK6tixo2JiYiRJgwcPZupzAAAAAIVeroLUSy+9JCcnJ509e1bu7u7W9l69eunrr7/Os+IAAAAAoCDK1T1SW7du1ZYtW1SuXDmb9tDQUEVHR+dJYQAAAABQUOVqRCoxMdFmJCrD33//LRcXl7suCgAAAAAKslwFqebNm2vlypXWZYvFovT0dM2cOVOtWrXKs+IAAAAAoCDK1aV9M2fOVMuWLbV//34lJydr7Nix+umnn3Tx4kXt2bMnr2sEAAAAgAIlVyNS1atX15EjR/Tggw+qTZs2SkxMVPfu3XXo0CFVqlQpr2sEAAAAgALF9IhUSkqK2rZtq/fee0+TJk3Kj5oAAAAAoEAzPSLl5OSko0ePymKx5Ec9AAAAAFDg5erSvr59++qDDz7I61oAAAAA4L6Qq8kmkpOT9f777ysiIkINGzaUh4eHzfrZs2fnSXEAAAAAUBCZClK//fabgoODdfToUdWvX1+SdOLECZs+XPIHAAAAoLAzFaRCQ0MVExOjHTt2SJJ69eqld999V35+fvlSHAAAAAAURKbukTIMw2b5q6++UmJiYp4WBAAAAAAFXa4mm8hwa7ACAAAAgKLAVJCyWCyZ7oHinigAAAAARY2pe6QMw1D//v3l4uIiSbp+/bqGDh2aada+DRs25F2FAAAAAFDAmApS/fr1s1l++umn87QYAAAAALgfmApSy5Yty686AAAAAOC+cVeTTQAAAABAUUSQAgAAAACTCFIAAAAAYFKBD1J//PGHnn76afn6+srd3V1169bVgQMHrOsNw1B4eLgCAwPl5uamli1b6qeffrJjxQAAAAAKuwIdpC5duqSmTZvKyclJX331lY4dO6ZZs2apRIkS1j4zZszQ7NmzNX/+fO3bt0/+/v5q06aNLl++bL/CAQAAABRqpmbtu9emT5+uoKAgm9kCg4ODrf82DENz5szRhAkT1L17d0nSihUr5OfnpzVr1mjIkCH3umQAAAAARUCBHpH67LPP1LBhQz3xxBMqU6aM6tWrp6VLl1rXnz59WrGxsWrbtq21zcXFRS1atFBkZGS2+01KSlJCQoLNAwAAAAByqkAHqd9++02LFi1SaGiotmzZoqFDh2r48OFauXKlJCk2NlaS5OfnZ7Odn5+fdV1Wpk2bJm9vb+sjKCgo/04CAAAAQKFToINUenq66tevr6lTp6pevXoaMmSInnnmGS1atMimn8VisVk2DCNT283Gjx+v+Ph46+PcuXP5Uj8AAACAwqlAB6mAgABVr17dpq1atWo6e/asJMnf31+SMo0+xcXFZRqlupmLi4u8vLxsHgAAAACQUwU6SDVt2lTHjx+3aTtx4oQqVKggSQoJCZG/v78iIiKs65OTk7Vz506FhYXd01oBAAAAFB0Feta+l156SWFhYZo6dap69uyp//73v1qyZImWLFki6cYlfSNHjtTUqVMVGhqq0NBQTZ06Ve7u7urdu7edqwcAAABQWBXoINWoUSNt3LhR48eP1+TJkxUSEqI5c+boqaeesvYZO3asrl27pmHDhunSpUtq3Lixtm7dKk9PTztWDgAAAKAwsxiGYdi7CHtLSEiQt7e34uPjuV/KjoLHfWHvEoq8M66M5NpdeLy9KwAAoEjLaTYo0PdIAQAAAEBBRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABg0n0VpKZNmyaLxaKRI0da2wzDUHh4uAIDA+Xm5qaWLVvqp59+sl+RAAAAAAq9+yZI7du3T0uWLFHt2rVt2mfMmKHZs2dr/vz52rdvn/z9/dWmTRtdvnzZTpUCAAAAKOzuiyB15coVPfXUU1q6dKlKlixpbTcMQ3PmzNGECRPUvXt31axZUytWrNDVq1e1Zs0aO1YMAAAAoDC7L4LU888/r06dOunRRx+1aT99+rRiY2PVtm1ba5uLi4tatGihyMjIbPeXlJSkhIQEmwcAAAAA5JSjvQu4k3Xr1ungwYPat29fpnWxsbGSJD8/P5t2Pz8/RUdHZ7vPadOmadKkSXlbKAAAAIAio0CPSJ07d04jRozQ6tWr5erqmm0/i8Vis2wYRqa2m40fP17x8fHWx7lz5/KsZgAAAACFX4EekTpw4IDi4uLUoEEDa1taWpq+++47zZ8/X8ePH5d0Y2QqICDA2icuLi7TKNXNXFxc5OLikn+FAwAAACjUCvSIVOvWrfXjjz8qKirK+mjYsKGeeuopRUVFqWLFivL391dERIR1m+TkZO3cuVNhYWF2rBwAAABAYVagR6Q8PT1Vs2ZNmzYPDw/5+vpa20eOHKmpU6cqNDRUoaGhmjp1qtzd3dW7d297lAwAAACgCCjQQSonxo4dq2vXrmnYsGG6dOmSGjdurK1bt8rT09PepQEAAAAopCyGYRj2LsLeEhIS5O3trfj4eHl5edm7nCIreNwX9i6hyDvjykiu3YXH27sCAACKtJxmgwJ9jxQAAAAAFEQEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTHO1dAAAAAAqQcG97V4DweHtXgBxgRAoAAAAATCJIAQAAAIBJXNoHAAAKjOBxX9i7hCLvjKu9KwDuD4xIAQAAAIBJBTpITZs2TY0aNZKnp6fKlCmjrl276vjx4zZ9DMNQeHi4AgMD5ebmppYtW+qnn36yU8UAAAAAioICHaR27typ559/Xt9//70iIiKUmpqqtm3bKjEx0dpnxowZmj17tubPn699+/bJ399fbdq00eXLl+1YOQAAAIDCrEDfI/X111/bLC9btkxlypTRgQMH1Lx5cxmGoTlz5mjChAnq3r27JGnFihXy8/PTmjVrNGTIEHuUDQAAAKCQK9AjUreKj78xp76Pj48k6fTp04qNjVXbtm2tfVxcXNSiRQtFRkZmu5+kpCQlJCTYPAAAAAAgp+6bIGUYhkaNGqVmzZqpZs2akqTY2FhJkp+fn01fPz8/67qsTJs2Td7e3tZHUFBQ/hUOAAAAoNC5b4LUCy+8oCNHjmjt2rWZ1lksFptlwzAytd1s/Pjxio+Ptz7OnTuX5/UCAAAAKLwK9D1SGV588UV99tln+u6771SuXDlru7+/v6QbI1MBAQHW9ri4uEyjVDdzcXGRi4tL/hUMAAAAoFAr0CNShmHohRde0IYNG7R9+3aFhITYrA8JCZG/v78iIiKsbcnJydq5c6fCwsLudbkAAAAAiogCPSL1/PPPa82aNfrPf/4jT09P631P3t7ecnNzk8Vi0ciRIzV16lSFhoYqNDRUU6dOlbu7u3r37m3n6gEAAAAUVgU6SC1atEiS1LJlS5v2ZcuWqX///pKksWPH6tq1axo2bJguXbqkxo0ba+vWrfL09LzH1QIAAAAoKgp0kDIM4459LBaLwsPDFR4env8FAQAAAIAK+D1SAAAAAFAQEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYRJACAAAAAJMIUgAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYBJBCgAAAABMIkgBAAAAgEkEKQAAAAAwiSAFAAAAACYRpAAAAADAJIIUAAAAAJhEkAIAAAAAkwhSAAAAAGASQQoAAAAATCJIAQAAAIBJBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYVGiC1MKFCxUSEiJXV1c1aNBAu3btsndJAAAAAAqpQhGkPv74Y40cOVITJkzQoUOH9PDDD6tDhw46e/asvUsDAAAAUAgViiA1e/ZsDRo0SIMHD1a1atU0Z84cBQUFadGiRfYuDQAAAEAh5GjvAu5WcnKyDhw4oHHjxtm0t23bVpGRkVluk5SUpKSkJOtyfHy8JCkhISH/CsUdpSddtXcJRV6CxbB3CeBzCEUcvwvsj98FBQC/C+wqIxMYxu3fC/d9kPr777+VlpYmPz8/m3Y/Pz/FxsZmuc20adM0adKkTO1BQUH5UiNwv/C2dwGQ3uKnAMC++BQqAPhdUCBcvnxZ3t7Z/yzu+yCVwWKx2CwbhpGpLcP48eM1atQo63J6erouXrwoX1/fbLcBCruEhAQFBQXp3Llz8vLysnc5AAA74HcBcCNHXL58WYGBgbftd98HqVKlSsnBwSHT6FNcXFymUaoMLi4ucnFxsWkrUaJEfpUI3Fe8vLz45QkARRy/C1DU3W4kKsN9P9mEs7OzGjRooIiICJv2iIgIhYWF2akqAAAAAIXZfT8iJUmjRo1Snz591LBhQzVp0kRLlizR2bNnNXToUHuXBgAAAKAQKhRBqlevXrpw4YImT56smJgY1axZU19++aUqVKhg79KA+4aLi4tef/31TJe9AgCKDn4XADlnMe40rx8AAAAAwMZ9f48UAAAAANxrBCkAAAAAMIkgBQAAAAAmEaQAAAAAwCSCFAAAAACYVCimPwdg3u+//65FixYpMjJSsbGxslgs8vPzU1hYmIYOHaqgoCB7lwgAAFBgMf05UATt3r1bHTp0UFBQkNq2bSs/Pz8ZhqG4uDhFRETo3Llz+uqrr9S0aVN7lwoAsKNz587p9ddf14cffmjvUoAChyAFFEGNGjVSs2bN9M4772S5/qWXXtLu3bu1b9++e1wZAKAgOXz4sOrXr6+0tDR7lwIUOAQpoAhyc3NTVFSUqlSpkuX6X375RfXq1dO1a9fucWUAgHvps88+u+363377TaNHjyZIAVngHimgCAoICFBkZGS2QWrv3r0KCAi4x1UBAO61rl27ymKx6Hb/r26xWO5hRcD9gyAFFEFjxozR0KFDdeDAAbVp00Z+fn6yWCyKjY1VRESE3n//fc2ZM8feZQIA8llAQIAWLFigrl27Zrk+KipKDRo0uLdFAfcJghRQBA0bNky+vr5655139N5771kv2XBwcFCDBg20cuVK9ezZ085VAgDyW4MGDXTw4MFsg9SdRquAoox7pIAiLiUlRX///bckqVSpUnJycrJzRQCAe2XXrl1KTExU+/bts1yfmJio/fv3q0WLFve4MqDgI0gBAAAAgEnF7F0AAAAAANxvCFIAAAAAYBJBCgAAAABMIkgBAO6pli1bauTIkfl+nODg4DtO42+xWLRp06Z8rePMmTOyWCyKiorK1+MAAO4tghQAFHH9+/eXxWLJ9Dh58uRd7ffbb7+VxWLRP//8Y9O+YcMGvfHGG3e1bwAA7I3vkQIAqH379lq2bJlNW+nSpfPlWD4+PvmyX+StlJQUvg4BAG6DESkAgFxcXOTv72/zcHBw0OzZs1WrVi15eHgoKChIw4YN05UrV6zbRUdHq0uXLipZsqQ8PDxUo0YNffnllzpz5oxatWolSSpZsqQsFov69+8vKfOlfcHBwZo6daoGDhwoT09PlS9fXkuWLLGpLzIyUnXr1pWrq6saNmyoTZs25ehyucuXL6t3794qXry4AgMDNW/evNv2//HHH/XII4/Izc1Nvr6+evbZZ23ONz09XZMnT1a5cuXk4uKiunXr6uuvv7bZx3//+1/Vq1fPWuuhQ4due0xJWrhwoUJDQ+Xq6io/Pz/16NHD5vm59RLFunXrKjw83Lr8yy+/qFmzZnJ1dVX16tW1bdu2TJctvvLKK6pcubLc3d1VsWJFvfbaa0pJSbGuDw8PV926dfXhhx+qYsWKcnFx4YtYAeA2CFIAgGwVK1ZM7777ro4ePaoVK1Zo+/btGjt2rHX9888/r6SkJH333Xf68ccfNX36dBUvXlxBQUH69NNPJUnHjx9XTEyM5s6dm+1xZs2aZQ0dw4YN03PPPadffvlF0o0w1KVLF9WqVUsHDx7UG2+8oVdeeSVH9c+cOVO1a9fWwYMHNX78eL300kuKiIjIsu/Vq1fVvn17lSxZUvv27dMnn3yibdu26YUXXrD2mTt3rmbNmqW3335bR44cUbt27fTYY4/p119/lXTjy0s7d+6sKlWq6MCBAwoPD9eYMWNuW+P+/fs1fPhwTZ48WcePH9fXX3+t5s2b5+j8pBvhrmvXrnJ3d9cPP/ygJUuWaMKECZn6eXp6avny5Tp27Jjmzp2rpUuX6p133rHpc/LkSa1fv16ffvop93QBwJ0YAIAirV+/foaDg4Ph4eFhffTo0SPLvuvXrzd8fX2ty7Vq1TLCw8Oz7Ltjxw5DknHp0iWb9hYtWhgjRoywLleoUMF4+umnrcvp6elGmTJljEWLFhmGYRiLFi0yfH19jWvXrln7LF261JBkHDp0KNvzqlChgtG+fXubtl69ehkdOnSwLksyNm7caBiGYSxZssQoWbKkceXKFev6L774wihWrJgRGxtrGIZhBAYGGm+++abNPhs1amQMGzbMMAzDeO+99wwfHx8jMTHRun7RokW3rfXTTz81vLy8jISEhGzP45133rFpq1OnjvH6668bhmEYX331leHo6GjExMRY10dERNicW1ZmzJhhNGjQwLr8+uuvG05OTkZcXFy22wAA/g/3SAEA1KpVKy1atMi67OHhIUnasWOHpk6dqmPHjikhIUGpqam6fv26EhMT5eHhoeHDh+u5557T1q1b9eijj+p//ud/VLt2bdPHv3kbi8Uif39/xcXFSboxolW7dm25urpa+zz44IM52m+TJk0yLWc3k9/PP/+sOnXqWM9dkpo2bar09HQdP35cbm5uOn/+vJo2bWqzXdOmTXX48GGbfbi7u2dbw63atGmjChUqqGLFimrfvr3at2+vbt262ezjdo4fP66goCD5+/tb27J6fv79739rzpw5OnnypK5cuaLU1FR5eXnZ9KlQoUK+3RsHAIUNl/YBAOTh4aEHHnjA+ggICFB0dLQ6duyomjVr6tNPP9WBAwe0YMECSbLeWzN48GD99ttv6tOnj3788Uc1bNjwjvchZeXWSQ0sFovS09MlSYZhyGKx2Kw37uLenVv3dfM+s1t3c3tWtWS05aYuT09PHTx4UGvXrlVAQIAmTpyoOnXqWGc7LFasWKb93nxv0+3qzvD999/rySefVIcOHfT555/r0KFDmjBhgpKTk2363RwiAQC3R5ACAGRp//79Sk1N1axZs/TQQw+pcuXKOn/+fKZ+QUFBGjp0qDZs2KDRo0dr6dKlkiRnZ2dJUlpa2l3VUbVqVR05ckRJSUk2teXE999/n2m5atWqWfatXr26oqKilJiYaG3bs2ePihUrpsqVK8vLy0uBgYHavXu3zXaRkZGqVq2adR+HDx/WtWvXsq0hK46Ojnr00Uc1Y8YMHTlyRGfOnNH27dsl3Zg9MSYmxto3ISFBp0+fti5XrVpVZ8+e1Z9//mlt27dvn83+9+zZowoVKmjChAlq2LChQkNDFR0dfce6AADZI0gBALJUqVIlpaamat68efrtt9+0atUqLV682KbPyJEjtWXLFp0+fVoHDx7U9u3braGiQoUKslgs+vzzz/XXX3/ZzH5nRu/evZWenq5nn31WP//8s7Zs2aK3335bUvajSxn27NmjGTNm6MSJE1qwYIE++eQTjRgxIsu+Tz31lFxdXdWvXz8dPXpUO3bs0Isvvqg+ffrIz89PkvTyyy9r+vTp+vjjj3X8+HGNGzdOUVFR1n327t1bxYoV06BBg3Ts2DF9+eWX1lqz8/nnn+vdd99VVFSUoqOjtXLlSqWnp6tKlSqSpEceeUSrVq3Srl27dPToUfXr108ODg7W7du0aaNKlSqpX79+OnLkiPbs2WOdbCLj+XnggQd09uxZrVu3TqdOndK7776rjRs33umpBwDcBkEKAJClunXravbs2Zo+fbpq1qypjz76SNOmTbPpk5aWpueff17VqlVT+/btVaVKFS1cuFCSVLZsWU2aNEnjxo2Tn5+fzex3Znh5eWnz5s2KiopS3bp1NWHCBE2cOFGSbO6bysro0aN14MAB1atXT2+88YZmzZqldu3aZdnX3d1dW7Zs0cWLF9WoUSP16NFDrVu31vz58619hg8frtGjR2v06NGqVauWvv76a3322WcKDQ2VJBUvXlybN2/WsWPHVK9ePU2YMEHTp0+/bY0lSpTQhg0b9Mgjj6hatWpavHix1q5dqxo1akiSxo8fr+bNm6tz587q2LGjunbtqkqVKlm3d3Bw0KZNm3TlyhU1atRIgwcP1quvvmrz/Dz++ON66aWX9MILL6hu3bqKjIzUa6+9dtu6AAC3ZzHu5kJzAADs4KOPPtKAAQMUHx8vNzc3e5dT4OzZs0fNmjXTyZMnbUIXACDvMGsfAKDAW7lypSpWrKiyZcvq8OHDeuWVV9SzZ09C1P+3ceNGFS9eXKGhoTp58qRGjBihpk2bEqIAIB8RpAAABV5sbKwmTpyo2NhYBQQE6IknntCbb75p77IKjMuXL2vs2LE6d+6cSpUqpUcffVSzZs2yd1kAUKhxaR8AAAAAmMRkEwAAAABgEkEKAAAAAEwiSAEAAACASQQpAAAAADCJIAUAAAAAJhGkAAAAAMAkghQAAAAAmESQAgAAAACTCFIAAAAAYNL/A63KkSM5g2Q5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#cross tab fbs vs target\n",
    "pd.crosstab(heart.fbs,heart.target).plot(kind=\"bar\",figsize=(10,6))\n",
    "plt.title('Heart disease frequency for Fasting blood sugar')\n",
    "plt.xlabel('Fasting blood sugar')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('Heart disease frequency for Fasting blood sugar.png')#how to save an image in python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a4f26204",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fbs\n",
       "0    258\n",
       "1     45\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.fbs.value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "499ba48d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cp\n",
       "0    143\n",
       "2     87\n",
       "1     50\n",
       "3     23\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.cp.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "2fb8021f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "slope\n",
       "2    142\n",
       "1    140\n",
       "0     21\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.slope.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "d29d68a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "thal\n",
       "2    166\n",
       "3    117\n",
       "1     18\n",
       "0      2\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "heart.thal.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9d08a35",
   "metadata": {},
   "source": [
    "applying one-hot encoding to slope,thal,cp to remove the inherent ordering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "307994ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = pd.get_dummies(heart['cp'], prefix = \"cp\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "0f858658",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = pd.get_dummies(heart['slope'], prefix = \"slope\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "290b6135",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = pd.get_dummies(heart['thal'], prefix = \"thal\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "40d19706",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>cp</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>...</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>cp_2</th>\n",
       "      <th>cp_3</th>\n",
       "      <th>slope_0</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>thal_0</th>\n",
       "      <th>thal_1</th>\n",
       "      <th>thal_2</th>\n",
       "      <th>thal_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>63</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>145</td>\n",
       "      <td>233</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>0</td>\n",
       "      <td>2.3</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>...</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>...</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   age  sex  cp  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  ...  \\\n",
       "0   63    1   3       145   233    1        0      150      0      2.3  ...   \n",
       "1   37    1   2       130   250    0        1      187      0      3.5  ...   \n",
       "2   41    0   1       130   204    0        0      172      0      1.4  ...   \n",
       "3   56    1   1       120   236    0        1      178      0      0.8  ...   \n",
       "4   57    0   0       120   354    0        1      163      1      0.6  ...   \n",
       "\n",
       "    cp_1   cp_2   cp_3  slope_0  slope_1  slope_2  thal_0  thal_1  thal_2  \\\n",
       "0  False  False   True     True    False    False   False    True   False   \n",
       "1  False   True  False     True    False    False   False   False    True   \n",
       "2   True  False  False    False    False     True   False   False    True   \n",
       "3   True  False  False    False    False     True   False   False    True   \n",
       "4  False  False  False    False    False     True   False   False    True   \n",
       "\n",
       "   thal_3  \n",
       "0   False  \n",
       "1   False  \n",
       "2   False  \n",
       "3   False  \n",
       "4   False  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = [heart, a, b, c]\n",
    "df = pd.concat(frames, axis = 1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cb812d6c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  ca  \\\n",
       "0     63    1       145   233    1        0      150      0      2.3   0   \n",
       "1     37    1       130   250    0        1      187      0      3.5   0   \n",
       "2     41    0       130   204    0        0      172      0      1.4   0   \n",
       "3     56    1       120   236    0        1      178      0      0.8   0   \n",
       "4     57    0       120   354    0        1      163      1      0.6   0   \n",
       "..   ...  ...       ...   ...  ...      ...      ...    ...      ...  ..   \n",
       "298   57    0       140   241    0        1      123      1      0.2   0   \n",
       "299   45    1       110   264    0        1      132      0      1.2   0   \n",
       "300   68    1       144   193    1        1      141      0      3.4   2   \n",
       "301   57    1       130   131    0        1      115      1      1.2   1   \n",
       "302   57    0       130   236    0        0      174      0      0.0   1   \n",
       "\n",
       "     target   cp_0   cp_1   cp_2  slope_1  slope_2  thal_1  thal_2  thal_3  \n",
       "0         1  False  False  False    False    False    True   False   False  \n",
       "1         1  False  False   True    False    False   False    True   False  \n",
       "2         1  False   True  False    False     True   False    True   False  \n",
       "3         1  False   True  False    False     True   False    True   False  \n",
       "4         1   True  False  False    False     True   False    True   False  \n",
       "..      ...    ...    ...    ...      ...      ...     ...     ...     ...  \n",
       "298       0   True  False  False     True    False   False   False    True  \n",
       "299       0  False  False  False     True    False   False   False    True  \n",
       "300       0   True  False  False     True    False   False   False    True  \n",
       "301       0   True  False  False     True    False   False   False    True  \n",
       "302       0  False   True  False     True    False   False    True   False  \n",
       "\n",
       "[303 rows x 19 columns]"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dropping columns cp,thal,slope and one from each of them(slope_0,,thal_0,cp_3)\n",
    "df.drop(['slope_0','thal_0','cp_3','slope','thal','cp'],axis=1,inplace=True)\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "47a0d886",
   "metadata": {},
   "outputs": [
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  ca  \\\n",
       "0     63    1       145   233    1        0      150      0        2   0   \n",
       "1     37    1       130   250    0        1      187      0        3   0   \n",
       "2     41    0       130   204    0        0      172      0        1   0   \n",
       "3     56    1       120   236    0        1      178      0        0   0   \n",
       "4     57    0       120   354    0        1      163      1        0   0   \n",
       "..   ...  ...       ...   ...  ...      ...      ...    ...      ...  ..   \n",
       "298   57    0       140   241    0        1      123      1        0   0   \n",
       "299   45    1       110   264    0        1      132      0        1   0   \n",
       "300   68    1       144   193    1        1      141      0        3   2   \n",
       "301   57    1       130   131    0        1      115      1        1   1   \n",
       "302   57    0       130   236    0        0      174      0        0   1   \n",
       "\n",
       "     target  cp_0  cp_1  cp_2  slope_1  slope_2  thal_1  thal_2  thal_3  \n",
       "0         1     0     0     0        0        0       1       0       0  \n",
       "1         1     0     0     1        0        0       0       1       0  \n",
       "2         1     0     1     0        0        1       0       1       0  \n",
       "3         1     0     1     0        0        1       0       1       0  \n",
       "4         1     1     0     0        0        1       0       1       0  \n",
       "..      ...   ...   ...   ...      ...      ...     ...     ...     ...  \n",
       "298       0     1     0     0        1        0       0       0       1  \n",
       "299       0     0     0     0        1        0       0       0       1  \n",
       "300       0     1     0     0        1        0       0       0       1  \n",
       "301       0     1     0     0        1        0       0       0       1  \n",
       "302       0     0     1     0        1        0       0       1       0  \n",
       "\n",
       "[303 rows x 19 columns]"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "43d0b6ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "#dividing the data set into x and y.\n",
    "x_data=df.drop('target',axis=1)\n",
    "y=df['target'].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "780c2e40",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>trestbps</th>\n",
       "      <th>chol</th>\n",
       "      <th>fbs</th>\n",
       "      <th>restecg</th>\n",
       "      <th>thalach</th>\n",
       "      <th>exang</th>\n",
       "      <th>oldpeak</th>\n",
       "      <th>ca</th>\n",
       "      <th>cp_0</th>\n",
       "      <th>cp_1</th>\n",
       "      <th>cp_2</th>\n",
       "      <th>slope_1</th>\n",
       "      <th>slope_2</th>\n",
       "      <th>thal_1</th>\n",
       "      <th>thal_2</th>\n",
       "      <th>thal_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>37</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>250</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>187</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>41</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>204</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>172</td>\n",
       "      <td>0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>178</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>354</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>241</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>123</td>\n",
       "      <td>1</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>264</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>132</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>68</td>\n",
       "      <td>1</td>\n",
       "      <td>144</td>\n",
       "      <td>193</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>141</td>\n",
       "      <td>0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>2</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>301</th>\n",
       "      <td>57</td>\n",
       "      <td>1</td>\n",
       "      <td>130</td>\n",
       "      <td>131</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>1</td>\n",
       "      <td>1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>130</td>\n",
       "      <td>236</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>174</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>303 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     age  sex  trestbps  chol  fbs  restecg  thalach  exang  oldpeak  ca  \\\n",
       "0     63    1       145   233    1        0      150      0      2.3   0   \n",
       "1     37    1       130   250    0        1      187      0      3.5   0   \n",
       "2     41    0       130   204    0        0      172      0      1.4   0   \n",
       "3     56    1       120   236    0        1      178      0      0.8   0   \n",
       "4     57    0       120   354    0        1      163      1      0.6   0   \n",
       "..   ...  ...       ...   ...  ...      ...      ...    ...      ...  ..   \n",
       "298   57    0       140   241    0        1      123      1      0.2   0   \n",
       "299   45    1       110   264    0        1      132      0      1.2   0   \n",
       "300   68    1       144   193    1        1      141      0      3.4   2   \n",
       "301   57    1       130   131    0        1      115      1      1.2   1   \n",
       "302   57    0       130   236    0        0      174      0      0.0   1   \n",
       "\n",
       "      cp_0   cp_1   cp_2  slope_1  slope_2  thal_1  thal_2  thal_3  \n",
       "0    False  False  False    False    False    True   False   False  \n",
       "1    False  False   True    False    False   False    True   False  \n",
       "2    False   True  False    False     True   False    True   False  \n",
       "3    False   True  False    False     True   False    True   False  \n",
       "4     True  False  False    False     True   False    True   False  \n",
       "..     ...    ...    ...      ...      ...     ...     ...     ...  \n",
       "298   True  False  False     True    False   False   False    True  \n",
       "299  False  False  False     True    False   False   False    True  \n",
       "300   True  False  False     True    False   False   False    True  \n",
       "301   True  False  False     True    False   False   False    True  \n",
       "302  False   True  False     True    False   False    True   False  \n",
       "\n",
       "[303 rows x 18 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "cdf69669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
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       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d806884",
   "metadata": {},
   "source": [
    "scaling=MinMaxScaler()\n",
    "\n",
    "scaled_data=scaling.fit_transform(df)\n",
    "\n",
    "scaled_data=pd.DataFrame(scaled_data,columns=df.columns)\n",
    "\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "899fc618",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
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      ],
      "text/plain": [
       "          age       sex  trestbps      chol       fbs   restecg   thalach  \\\n",
       "0    0.111702  0.001773  0.257092  0.413121  0.001773  0.000000  0.265957   \n",
       "1    0.065603  0.001773  0.230496  0.443262  0.000000  0.001773  0.331560   \n",
       "2    0.072695  0.000000  0.230496  0.361702  0.000000  0.000000  0.304965   \n",
       "3    0.099291  0.001773  0.212766  0.418440  0.000000  0.001773  0.315603   \n",
       "4    0.101064  0.000000  0.212766  0.627660  0.000000  0.001773  0.289007   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "298  0.101064  0.000000  0.248227  0.427305  0.000000  0.001773  0.218085   \n",
       "299  0.079787  0.001773  0.195035  0.468085  0.000000  0.001773  0.234043   \n",
       "300  0.120567  0.001773  0.255319  0.342199  0.001773  0.001773  0.250000   \n",
       "301  0.101064  0.001773  0.230496  0.232270  0.000000  0.001773  0.203901   \n",
       "302  0.101064  0.000000  0.230496  0.418440  0.000000  0.000000  0.308511   \n",
       "\n",
       "        exang   oldpeak        ca      cp_0      cp_1      cp_2   slope_1  \\\n",
       "0    0.000000  0.004078  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "1    0.000000  0.006206  0.000000  0.000000  0.000000  0.001773  0.000000   \n",
       "2    0.000000  0.002482  0.000000  0.000000  0.001773  0.000000  0.000000   \n",
       "3    0.000000  0.001418  0.000000  0.000000  0.001773  0.000000  0.000000   \n",
       "4    0.001773  0.001064  0.000000  0.001773  0.000000  0.000000  0.000000   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "298  0.001773  0.000355  0.000000  0.001773  0.000000  0.000000  0.001773   \n",
       "299  0.000000  0.002128  0.000000  0.000000  0.000000  0.000000  0.001773   \n",
       "300  0.000000  0.006028  0.003546  0.001773  0.000000  0.000000  0.001773   \n",
       "301  0.001773  0.002128  0.001773  0.001773  0.000000  0.000000  0.001773   \n",
       "302  0.000000  0.000000  0.001773  0.000000  0.001773  0.000000  0.001773   \n",
       "\n",
       "      slope_2    thal_1    thal_2    thal_3  \n",
       "0    0.000000  0.001773  0.000000  0.000000  \n",
       "1    0.000000  0.000000  0.001773  0.000000  \n",
       "2    0.001773  0.000000  0.001773  0.000000  \n",
       "3    0.001773  0.000000  0.001773  0.000000  \n",
       "4    0.001773  0.000000  0.001773  0.000000  \n",
       "..        ...       ...       ...       ...  \n",
       "298  0.000000  0.000000  0.000000  0.001773  \n",
       "299  0.000000  0.000000  0.000000  0.001773  \n",
       "300  0.000000  0.000000  0.000000  0.001773  \n",
       "301  0.000000  0.000000  0.000000  0.001773  \n",
       "302  0.000000  0.000000  0.001773  0.000000  \n",
       "\n",
       "[303 rows x 18 columns]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalize\n",
    "x = (x_data - np.min(x_data)) / (np.max(x_data) - np.min(x_data))\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "6df9d220",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 165, 0: 138}"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_before=dict(Counter(y))\n",
    "num_before"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "d0262506",
   "metadata": {},
   "outputs": [],
   "source": [
    "smote=SMOTE()#saving the instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "23f94cb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_smote,y_smote=smote.fit_resample(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "a5d90ec7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{1: 165, 0: 165}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_after=dict(Counter(y_smote))\n",
    "num_after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "ffa9ed06",
   "metadata": {},
   "outputs": [],
   "source": [
    "after_smote=list(num_after.values())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "06ac072f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1765: FutureWarning: unique with argument that is not not a Series, Index, ExtensionArray, or np.ndarray is deprecated and will raise in a future version.\n",
      "  order = pd.unique(vector)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "labels = [\"1\",\"0\"]\n",
    "plt.figure(figsize=(15,6))\n",
    "plt.subplot(1,2,1)\n",
    "sns.barplot(x=labels, y=list(num_before.values()))\n",
    "plt.title(\"Target - Numbers Before Balancing\")\n",
    "plt.subplot(1,2,2)\n",
    "sns.barplot(x=labels, y=after_smote)\n",
    "plt.title(\"Target - Numbers After Balancing\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "7fb143dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "#train-test split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x_smote,y_smote,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fadf31c",
   "metadata": {},
   "source": [
    "#train-test split\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2,random_state=0)\n",
    "x_train,x_test,y_train,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "68c7f9cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "models = []\n",
    "models.append(('Logistic Regression' ,LogisticRegression(random_state=42)))\n",
    "models.append(('Naive bayes', GaussianNB()))\n",
    "#models.append(('KNN', KNeighborsClassifier()))\n",
    "models.append(('CART', DecisionTreeClassifier(random_state = 42)))\n",
    "models.append(('Random Forest', RandomForestClassifier(random_state = 42)))\n",
    "models.append(('SVM', SVC(gamma='auto', random_state = 42)))\n",
    "models.append(('XGBoost', XGBClassifier(random_state = 42)))\n",
    "models.append(('GradientBoosting', GradientBoostingClassifier(random_state = 42)))\n",
    "results = []\n",
    "names = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "ad342bdb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression : 0.6666666666666666\n",
      "Naive bayes : 0.8939393939393939\n",
      "CART : 0.7727272727272727\n",
      "Random Forest : 0.8484848484848485\n",
      "SVM : 0.45454545454545453\n",
      "XGBoost : 0.803030303030303\n",
      "GradientBoosting : 0.8484848484848485\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "        accuracy = accuracy_score(y_test, y_pred)\n",
    "        print(\"{} : {}\".format(name,accuracy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "46c96162",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Precision_score\n",
      "Logistic Regression : 76.92%\n",
      "Precision_score\n",
      "Naive bayes : 89.19%\n",
      "Precision_score\n",
      "CART : 80.00%\n",
      "Precision_score\n",
      "Random Forest : 84.21%\n",
      "Precision_score\n",
      "SVM : 0.00%\n",
      "Precision_score\n",
      "XGBoost : 81.08%\n",
      "Precision_score\n",
      "GradientBoosting : 86.11%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/sklearn/metrics/_classification.py:1469: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "        precision = precision_score(y_test, y_pred)*100\n",
    "        print('Precision_score')\n",
    "        print(\"{} : {:.2f}%\".format(name,precision))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f7b8caf3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Recall_score\n",
      "Logistic Regression : 55.56%\n",
      "Recall_score\n",
      "Naive bayes : 91.67%\n",
      "Recall_score\n",
      "CART : 77.78%\n",
      "Recall_score\n",
      "Random Forest : 88.89%\n",
      "Recall_score\n",
      "SVM : 0.00%\n",
      "Recall_score\n",
      "XGBoost : 83.33%\n",
      "Recall_score\n",
      "GradientBoosting : 86.11%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "        recall = recall_score(y_test, y_pred)*100\n",
    "        print('Recall_score')\n",
    "        print(\"{} : {:.2f}%\".format(name,recall))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "79fd32a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score\n",
      "Logistic Regression : 64.52%\n",
      "f1_score\n",
      "Naive bayes : 90.41%\n",
      "f1_score\n",
      "CART : 78.87%\n",
      "f1_score\n",
      "Random Forest : 86.49%\n",
      "f1_score\n",
      "SVM : 0.00%\n",
      "f1_score\n",
      "XGBoost : 82.19%\n",
      "f1_score\n",
      "GradientBoosting : 86.11%\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "        f1 = f1_score(y_test, y_pred)*100\n",
    "        print('f1_score')\n",
    "        print(\"{} : {:.2f}%\".format(name,f1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "fbce1850",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Logistic Regression : [[24  6]\n",
      " [16 20]]\n",
      "----------------------------------\n",
      "Naive bayes : [[26  4]\n",
      " [ 3 33]]\n",
      "----------------------------------\n",
      "CART : [[23  7]\n",
      " [ 8 28]]\n",
      "----------------------------------\n",
      "Random Forest : [[24  6]\n",
      " [ 4 32]]\n",
      "----------------------------------\n",
      "SVM : [[30  0]\n",
      " [36  0]]\n",
      "----------------------------------\n",
      "XGBoost : [[23  7]\n",
      " [ 6 30]]\n",
      "----------------------------------\n",
      "GradientBoosting : [[25  5]\n",
      " [ 5 31]]\n",
      "----------------------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:299: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:301: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  elif is_categorical_dtype(dtype) and enable_categorical:\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:332: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype)\n",
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/xgboost/data.py:323: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n"
     ]
    }
   ],
   "source": [
    "for name, model in models:\n",
    "        model.fit(x_train, y_train)\n",
    "        y_pred = model.predict(x_test)\n",
    "        confusion = confusion_matrix(y_test, y_pred)\n",
    "        print(\"{} : {}\".format(name,confusion))\n",
    "        print('----------------------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "101a2118",
   "metadata": {},
   "source": [
    "Random Forest:- TN=23,FP=4,FN=5,TP=29\n",
    "\n",
    "Precision=TP/(TP+FP)=29/(29+4)=0.87878788\n",
    "\n",
    "Recall=TP/(TP+FN)=29/(29+5)=0.85294118"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "6b619384",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#applying default random forest\n",
    "rf=RandomForestClassifier(random_state=0)\n",
    "rf.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "85cf4b49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf=rf.predict(x_test)\n",
    "y_pred_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "c6c13f6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_rf= accuracy_score(y_test, y_pred_rf)\n",
    "accuracy_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "5812e50b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_rf=rf.score(x_test,y_test)\n",
    "test_score_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "f63aa018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_rf=rf.score(x_train,y_train)\n",
    "train_score_rf\n",
    "#train score is 1 and test score is 0.85 hence it is an overfitting scenario for which let us try to apply hyper parameter tuning on random forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "d49ce4d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_tuned=RandomForestClassifier(random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "cdcaa330",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [2,3,5],            # Maximum depth of the tree\n",
    "    'min_samples_split': [2, 3, 6]           # Minimum number of samples required to split an internal node\n",
    "    #'min_samples_leaf': [1, 2, 4],             # Minimum number of samples required to be at a leaf node\n",
    "   # 'max_features': [None, 'sqrt', 'log2'],   # Number of features to consider at each split\n",
    "    #'criterion': ['gini', 'entropy'],         # Split criterion: Gini impurity or entropy\n",
    "    #'max_leaf_nodes': [None, 10, 20, 30]       # Maximum number of leaf nodes in the tree\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "3b778a38",
   "metadata": {},
   "outputs": [],
   "source": [
    "rf_gridsearch=GridSearchCV(rf_tuned, param_grid,cv=5, scoring='accuracy')\n",
    "#['bootstrap', 'ccp_alpha', 'class_weight', 'criterion', 'max_depth', 'max_features', 'max_leaf_nodes', 'max_samples', 'min_impurity_decrease', 'min_samples_leaf', 'min_samples_split', 'min_weight_fraction_leaf', 'n_estimators', 'n_jobs', 'oob_score', 'random_state', 'verbose', 'warm_start']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "a073dca1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=0),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3, 5],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 6]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=0),\n",
       "             param_grid={&#x27;max_depth&#x27;: [2, 3, 5],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 3, 6]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=0),\n",
       "             param_grid={'max_depth': [2, 3, 5],\n",
       "                         'min_samples_split': [2, 3, 6]},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_gridsearch.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "cbc1ba5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'max_depth': 3, 'min_samples_split': 6}"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params_rf = rf_gridsearch.best_params_\n",
    "best_params_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "1eafccce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(max_depth=3, min_samples_split=6)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" checked><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(max_depth=3, min_samples_split=6)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(max_depth=3, min_samples_split=6)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we are creating a new decision tree classifier with the best hyper parameters\n",
    "best_rf_classifier = RandomForestClassifier(**best_params_rf)\n",
    "best_rf_classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "0de3ea8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 0, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_rf_tune=best_rf_classifier.predict(x_test)\n",
    "y_pred_rf_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5adab900",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8484848484848485"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_rf_tune= accuracy_score(y_test, y_pred_rf_tune)\n",
    "accuracy_rf_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "da74f972",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8863636363636364"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_rf_tune=best_rf_classifier.score(x_train,y_train)\n",
    "train_score_rf_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "24c6a68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8484848484848485"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_rf_tune=best_rf_classifier.score(x_test,y_test)\n",
    "test_score_rf_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "633ca122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.66666666666666"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#decision tree with hyper parameter tuning model gives an accuracy score of 78.68% with a good fit hence finding out the evaluation metrics for this model.\n",
    "recall_rf = recall_score(y_test, y_pred_rf_tune)*100\n",
    "recall_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "016051f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.5"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_rf = precision_score(y_test, y_pred_rf_tune)*100\n",
    "precision_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "a149c814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "86.84210526315789"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_rf = f1_score(y_test, y_pred_rf_tune)*100\n",
    "f1_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "5efc0e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23,  7],\n",
       "       [ 3, 33]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_rf = confusion_matrix(y_test, y_pred_rf_tune)\n",
    "confusion_rf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "3ff6e503",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.88      0.77      0.82        30\n",
      "           1       0.82      0.92      0.87        36\n",
      "\n",
      "    accuracy                           0.85        66\n",
      "   macro avg       0.85      0.84      0.84        66\n",
      "weighted avg       0.85      0.85      0.85        66\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_report__random=classification_report(y_test, y_pred_rf_tune)\n",
    "print(classification_report__random)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "c0be62db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" checked><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression(solver=&#x27;liblinear&#x27;)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(solver='liblinear')"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr=LogisticRegression(solver='liblinear')\n",
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "c934322d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1,\n",
       "       1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1, 0])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_lr=lr.predict(x_test)\n",
    "y_pred_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "f013aec5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8484848484848485"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_lr= accuracy_score(y_test, y_pred)\n",
    "accuracy_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "2d679f50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6666666666666666"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_lr=lr.score(x_train,y_train)\n",
    "train_score_lr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "e644cfce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6666666666666666"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_lr=lr.score(x_test,y_test)\n",
    "test_score_lr"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8497ebc",
   "metadata": {},
   "source": [
    "#This model is  giving 62.2950 % accuracy score with fairly good fit.However the accuracy score is towards the low side.Hence trying out other models to cross compare accuracy scores along with overfitting scenario"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "9ff4b04f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {color: black;}#sk-container-id-5 pre{padding: 0;}#sk-container-id-5 div.sk-toggleable {background-color: white;}#sk-container-id-5 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-5 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-5 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-5 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-5 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-5 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-5 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-5 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-5 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-5 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-5 div.sk-item {position: relative;z-index: 1;}#sk-container-id-5 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-5 div.sk-item::before, #sk-container-id-5 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-5 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-5 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-5 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-5 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-5 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-5 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-5 div.sk-label-container {text-align: center;}#sk-container-id-5 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-5 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" checked><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Applying Decision Tree or Cart Model\n",
    "decision=DecisionTreeClassifier()\n",
    "decision.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "382b7f4d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_decision=decision.predict(x_test)\n",
    "y_pred_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "8a00d04b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7727272727272727"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_decision= accuracy_score(y_test, y_pred_decision)\n",
    "accuracy_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "a75cd838",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_decision=decision.score(x_train,y_train)\n",
    "train_score_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "9b2d1c1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7727272727272727"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_decision=decision.score(x_test,y_test)\n",
    "test_score_decision"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd3b1c7e",
   "metadata": {},
   "source": [
    "Decison tree with parameters has overfitting and has accuracy score of 78%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "2f8af97d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#applying decision tree with hyper parameter tuning\n",
    "dt_classifier = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "dca71a23",
   "metadata": {},
   "outputs": [],
   "source": [
    "param_grid = {\n",
    "    'max_depth': [None, 5, 10, 15],            # Maximum depth of the tree\n",
    "    'min_samples_split': [2, 5, 10],           # Minimum number of samples required to split an internal node\n",
    "    'min_samples_leaf': [1, 2, 4],             # Minimum number of samples required to be at a leaf node\n",
    "    'max_features': [None, 'sqrt', 'log2'],   # Number of features to consider at each split\n",
    "    'criterion': ['gini', 'entropy'],         # Split criterion: Gini impurity or entropy\n",
    "    'splitter': ['best', 'random'],            # Strategy to choose the split at each node\n",
    "    'max_leaf_nodes': [None, 10, 20, 30]       # Maximum number of leaf nodes in the tree\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "f5b78a7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "grid_search = GridSearchCV(dt_classifier, param_grid, cv=5, scoring='accuracy')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "83b38c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {color: black;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [None, 5, 10, 15],\n",
       "                         &#x27;max_features&#x27;: [None, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [None, 10, 20, 30],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GridSearchCV</label><div class=\"sk-toggleable__content\"><pre>GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={&#x27;criterion&#x27;: [&#x27;gini&#x27;, &#x27;entropy&#x27;],\n",
       "                         &#x27;max_depth&#x27;: [None, 5, 10, 15],\n",
       "                         &#x27;max_features&#x27;: [None, &#x27;sqrt&#x27;, &#x27;log2&#x27;],\n",
       "                         &#x27;max_leaf_nodes&#x27;: [None, 10, 20, 30],\n",
       "                         &#x27;min_samples_leaf&#x27;: [1, 2, 4],\n",
       "                         &#x27;min_samples_split&#x27;: [2, 5, 10],\n",
       "                         &#x27;splitter&#x27;: [&#x27;best&#x27;, &#x27;random&#x27;]},\n",
       "             scoring=&#x27;accuracy&#x27;)</pre></div></div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">estimator: DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier()</pre></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=5, estimator=DecisionTreeClassifier(),\n",
       "             param_grid={'criterion': ['gini', 'entropy'],\n",
       "                         'max_depth': [None, 5, 10, 15],\n",
       "                         'max_features': [None, 'sqrt', 'log2'],\n",
       "                         'max_leaf_nodes': [None, 10, 20, 30],\n",
       "                         'min_samples_leaf': [1, 2, 4],\n",
       "                         'min_samples_split': [2, 5, 10],\n",
       "                         'splitter': ['best', 'random']},\n",
       "             scoring='accuracy')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(x_train, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "654aefdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'criterion': 'gini',\n",
       " 'max_depth': 5,\n",
       " 'max_features': 'log2',\n",
       " 'max_leaf_nodes': 10,\n",
       " 'min_samples_leaf': 4,\n",
       " 'min_samples_split': 5,\n",
       " 'splitter': 'best'}"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_params = grid_search.best_params_\n",
    "best_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "16612ef1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {color: black;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=5, max_features=&#x27;log2&#x27;, max_leaf_nodes=10,\n",
       "                       min_samples_leaf=4, min_samples_split=5)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" checked><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=5, max_features=&#x27;log2&#x27;, max_leaf_nodes=10,\n",
       "                       min_samples_leaf=4, min_samples_split=5)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=5, max_features='log2', max_leaf_nodes=10,\n",
       "                       min_samples_leaf=4, min_samples_split=5)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we are creating a new decision tree classifier with the best hyper parameters\n",
    "best_dt_classifier = DecisionTreeClassifier(**best_params)\n",
    "best_dt_classifier.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "38c7adbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 0,\n",
       "       0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 0])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_tune=best_dt_classifier.predict(x_test)\n",
    "y_pred_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6ece36b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_decision_tune= accuracy_score(y_test, y_pred_tune)\n",
    "accuracy_decision_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "973d1785",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8446969696969697"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score_decision_tune=best_dt_classifier.score(x_train,y_train)\n",
    "train_score_decision_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "9df2bfaf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8333333333333334"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score_decision_tune=best_dt_classifier.score(x_test,y_test)\n",
    "test_score_decision_tune"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "65e4d7ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88.88888888888889"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#decision tree with hyper parameter tuning model gives an accuracy score of 78.68% with a good fit hence finding out the evaluation metrics for this model.\n",
    "recall_decision = recall_score(y_test, y_pred_tune)*100\n",
    "recall_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "64003814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "82.05128205128204"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_decision = precision_score(y_test, y_pred_tune)*100\n",
    "precision_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3124d8c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85.33333333333333"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f1_decision = f1_score(y_test, y_pred_tune)*100\n",
    "f1_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "4d94e304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[23,  7],\n",
       "       [ 4, 32]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_decision = confusion_matrix(y_test, y_pred_tune)\n",
    "confusion_decision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "e229ff92",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.77      0.81        30\n",
      "           1       0.82      0.89      0.85        36\n",
      "\n",
      "    accuracy                           0.83        66\n",
      "   macro avg       0.84      0.83      0.83        66\n",
      "weighted avg       0.83      0.83      0.83        66\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_report__decision=classification_report(y_test, y_pred_tune)\n",
    "print(classification_report__decision)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ac4c3d10",
   "metadata": {},
   "source": [
    "can be done with smote"
   ]
  }
 ],
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   "language": "python",
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