Diamonds.ipynb

{
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  {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd# It is used for data manipulation for data cleaning purposes,even it is helpful to treat missing values\n",
    "import seaborn as sns# visualisation library based on matplotlib\n",
    "from scipy.stats import skew \n",
    "from scipy.stats import kurtosis\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import accuracy_score,precision_score,recall_score,f1_score,confusion_matrix,classification_report\n",
    "import numpy as np\n",
    "# Load the Diamonds dataset\n",
    "diamonds_data = sns.load_dataset(\"diamonds\")\n",
    "\n",
    "# Display the first few rows of the dataset\n",
    "diamonds_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a991ce05",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.797940</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.474011</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.705699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.250000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              carat         depth         table         price             x  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean       0.797940     61.749405     57.457184   3932.799722      5.731157   \n",
       "std        0.474011      1.432621      2.234491   3989.439738      1.121761   \n",
       "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
       "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
       "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
       "75%        1.040000     62.500000     59.000000   5324.250000      6.540000   \n",
       "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
       "\n",
       "                  y             z  \n",
       "count  53940.000000  53940.000000  \n",
       "mean       5.734526      3.538734  \n",
       "std        1.142135      0.705699  \n",
       "min        0.000000      0.000000  \n",
       "25%        4.720000      2.910000  \n",
       "50%        5.710000      3.530000  \n",
       "75%        6.540000      4.040000  \n",
       "max       58.900000     31.800000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "db900ae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['carat', 'cut', 'color', 'clarity', 'depth', 'table', 'price', 'x', 'y',\n",
       "       'z'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e61a46f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 10)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d77dd974",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat\n",
       "0.30    2604\n",
       "0.31    2249\n",
       "1.01    2242\n",
       "0.70    1981\n",
       "0.32    1840\n",
       "        ... \n",
       "3.02       1\n",
       "3.65       1\n",
       "3.50       1\n",
       "3.22       1\n",
       "3.11       1\n",
       "Name: count, Length: 273, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.carat.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a46cc85c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cut\n",
       "Ideal        21551\n",
       "Premium      13791\n",
       "Very Good    12082\n",
       "Good          4906\n",
       "Fair          1610\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.cut.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "5b054a57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       "G    11292\n",
       "E     9797\n",
       "F     9542\n",
       "H     8304\n",
       "D     6775\n",
       "I     5422\n",
       "J     2808\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.color.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "f9b3b107",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "clarity\n",
       "SI1     13065\n",
       "VS2     12258\n",
       "SI2      9194\n",
       "VS1      8171\n",
       "VVS2     5066\n",
       "VVS1     3655\n",
       "IF       1790\n",
       "I1        741\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.clarity.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e027b783",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "depth\n",
       "62.0    2239\n",
       "61.9    2163\n",
       "61.8    2077\n",
       "62.2    2039\n",
       "62.1    2020\n",
       "        ... \n",
       "71.3       1\n",
       "44.0       1\n",
       "53.0       1\n",
       "53.1       1\n",
       "54.7       1\n",
       "Name: count, Length: 184, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.depth.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c4cdc30f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "table\n",
       "56.0    9881\n",
       "57.0    9724\n",
       "58.0    8369\n",
       "59.0    6572\n",
       "55.0    6268\n",
       "        ... \n",
       "51.6       1\n",
       "63.5       1\n",
       "43.0       1\n",
       "62.4       1\n",
       "61.6       1\n",
       "Name: count, Length: 127, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.table.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "340d07a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price\n",
       "605      132\n",
       "802      127\n",
       "625      126\n",
       "828      125\n",
       "776      124\n",
       "        ... \n",
       "8816       1\n",
       "14704      1\n",
       "14699      1\n",
       "14698      1\n",
       "9793       1\n",
       "Name: count, Length: 11602, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.price.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "97bdbf18",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x\n",
       "4.37     448\n",
       "4.34     437\n",
       "4.33     429\n",
       "4.38     428\n",
       "4.32     425\n",
       "        ... \n",
       "10.74      1\n",
       "9.36       1\n",
       "8.89       1\n",
       "10.23      1\n",
       "10.00      1\n",
       "Name: count, Length: 554, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.x.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "c18c42a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "y\n",
       "4.34     437\n",
       "4.37     435\n",
       "4.35     425\n",
       "4.33     421\n",
       "4.32     414\n",
       "        ... \n",
       "8.89       1\n",
       "10.16      1\n",
       "9.46       1\n",
       "9.63       1\n",
       "31.80      1\n",
       "Name: count, Length: 552, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.y.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "defe286e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "z\n",
       "2.70     767\n",
       "2.69     748\n",
       "2.71     738\n",
       "2.68     730\n",
       "2.72     697\n",
       "        ... \n",
       "5.79       1\n",
       "5.72       1\n",
       "5.91       1\n",
       "5.61       1\n",
       "31.80      1\n",
       "Name: count, Length: 375, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.z.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0bdcf1be",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 13,
     "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": [
    "sns.boxplot(diamonds_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df4c089f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_94557/346902044.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(diamonds_data[\"price\"])\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:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='price', ylabel='Density'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "sns.distplot(diamonds_data[\"price\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d1d007d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x_/bydwqxmj22b17_nc_zdgjlhc0000gn/T/ipykernel_94557/964490455.py:1: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(diamonds_data[\"table\"])\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:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='table', ylabel='Density'>"
      ]
     },
     "execution_count": 15,
     "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": [
    "sns.distplot(diamonds_data[\"table\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "544ed0a0",
   "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"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='price', ylabel='carat'>"
      ]
     },
     "execution_count": 33,
     "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": [
    "sns.scatterplot(x='price',y='carat',data=diamonds_data)#carat has central tendency from 1 to 3 and price is distributed majorly accross 5000 to 17500, with the increse of carat price is increasing as well and vice versa hence they are positively correlated   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "47c4203d",
   "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/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cut', ylabel='price'>"
      ]
     },
     "execution_count": 34,
     "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": [
    "sns.boxplot(x=\"cut\",y=\"price\",data=diamonds_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3165139b",
   "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/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='cut', ylabel='price'>"
      ]
     },
     "execution_count": 35,
     "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": [
    "sns.barplot(x=\"cut\",y=\"price\",data=diamonds_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "067f9cfb",
   "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/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='clarity', ylabel='price'>"
      ]
     },
     "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": [
    "sns.barplot(x=\"clarity\",y=\"price\",data=diamonds_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "daff786f",
   "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/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
      "  grouped_vals = vals.groupby(grouper)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='color', ylabel='price'>"
      ]
     },
     "execution_count": 38,
     "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": [
    "sns.barplot(x=\"color\",y=\"price\",data=diamonds_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fdc794a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "le=LabelEncoder()#saving the instance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "167db0dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds_data[\"cut_new\"]=le.fit_transform(diamonds_data[\"cut\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "20ddd412",
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds_data[\"color_new\"]=le.fit_transform(diamonds_data[\"color\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6858e74a",
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds_data[\"clarity_new\"]=le.fit_transform(diamonds_data[\"clarity\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6e7879fd",
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th>cut_new</th>\n",
       "      <th>color_new</th>\n",
       "      <th>clarity_new</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
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       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "      <th>53935</th>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
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       "      <td>2</td>\n",
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       "      <td>0.72</td>\n",
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       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
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       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z  \\\n",
       "0       0.23      Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43   \n",
       "1       0.21    Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31   \n",
       "2       0.23       Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31   \n",
       "3       0.29    Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63   \n",
       "4       0.31       Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75   \n",
       "...      ...        ...   ...     ...    ...    ...    ...   ...   ...   ...   \n",
       "53935   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75  5.76  3.50   \n",
       "53936   0.72       Good     D     SI1   63.1   55.0   2757  5.69  5.75  3.61   \n",
       "53937   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66  5.68  3.56   \n",
       "53938   0.86    Premium     H     SI2   61.0   58.0   2757  6.15  6.12  3.74   \n",
       "53939   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83  5.87  3.64   \n",
       "\n",
       "       cut_new  color_new  clarity_new  \n",
       "0            2          1            3  \n",
       "1            3          1            2  \n",
       "2            1          1            4  \n",
       "3            3          5            5  \n",
       "4            1          6            3  \n",
       "...        ...        ...          ...  \n",
       "53935        2          0            2  \n",
       "53936        1          0            2  \n",
       "53937        4          0            2  \n",
       "53938        3          4            3  \n",
       "53939        2          0            3  \n",
       "\n",
       "[53940 rows x 13 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "725d8c2c",
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds_data.drop(\"cut\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c54e3ece",
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds_data.drop(\"color\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "5e45f8f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "diamonds_data.drop(\"clarity\",axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "af56d691",
   "metadata": {},
   "outputs": [
    {
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       "       carat  depth  table  price     x     y     z  cut_new  color_new  \\\n",
       "0       0.23   61.5   55.0    326  3.95  3.98  2.43        2          1   \n",
       "1       0.21   59.8   61.0    326  3.89  3.84  2.31        3          1   \n",
       "2       0.23   56.9   65.0    327  4.05  4.07  2.31        1          1   \n",
       "3       0.29   62.4   58.0    334  4.20  4.23  2.63        3          5   \n",
       "4       0.31   63.3   58.0    335  4.34  4.35  2.75        1          6   \n",
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       "53939   0.75   62.2   55.0   2757  5.83  5.87  3.64        2          0   \n",
       "\n",
       "       clarity_new  \n",
       "0                3  \n",
       "1                2  \n",
       "2                4  \n",
       "3                5  \n",
       "4                3  \n",
       "...            ...  \n",
       "53935            2  \n",
       "53936            2  \n",
       "53937            2  \n",
       "53938            3  \n",
       "53939            3  \n",
       "\n",
       "[53940 rows x 10 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "diamonds_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "759450f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['carat', 'depth', 'table', 'price', 'x', 'y', 'z', 'cut_new',\n",
       "       'color_new', 'clarity_new'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "5b7f12c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=diamonds_data.drop('cut_new',axis=1)\n",
    "y=diamonds_data['cut_new']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9f4eaff4",
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.535382</td>\n",
       "      <td>0.097793</td>\n",
       "      <td>0.110063</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>0.108108</td>\n",
       "      <td>0.558333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.529795</td>\n",
       "      <td>0.097623</td>\n",
       "      <td>0.113522</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>0.103950</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.326923</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.527002</td>\n",
       "      <td>0.096435</td>\n",
       "      <td>0.111950</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.285714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>0.137214</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.288462</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.572626</td>\n",
       "      <td>0.103905</td>\n",
       "      <td>0.117610</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>0.114345</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.230769</td>\n",
       "      <td>0.131427</td>\n",
       "      <td>0.542831</td>\n",
       "      <td>0.099660</td>\n",
       "      <td>0.114465</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.428571</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>53940 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          carat     depth     table     price         x         y         z  \\\n",
       "0      0.006237  0.513889  0.230769  0.000000  0.367784  0.067572  0.076415   \n",
       "1      0.002079  0.466667  0.346154  0.000000  0.362197  0.065195  0.072642   \n",
       "2      0.006237  0.386111  0.423077  0.000054  0.377095  0.069100  0.072642   \n",
       "3      0.018711  0.538889  0.288462  0.000433  0.391061  0.071817  0.082704   \n",
       "4      0.022869  0.563889  0.288462  0.000487  0.404097  0.073854  0.086478   \n",
       "...         ...       ...       ...       ...       ...       ...       ...   \n",
       "53935  0.108108  0.494444  0.269231  0.131427  0.535382  0.097793  0.110063   \n",
       "53936  0.108108  0.558333  0.230769  0.131427  0.529795  0.097623  0.113522   \n",
       "53937  0.103950  0.550000  0.326923  0.131427  0.527002  0.096435  0.111950   \n",
       "53938  0.137214  0.500000  0.288462  0.131427  0.572626  0.103905  0.117610   \n",
       "53939  0.114345  0.533333  0.230769  0.131427  0.542831  0.099660  0.114465   \n",
       "\n",
       "       color_new  clarity_new  \n",
       "0       0.166667     0.428571  \n",
       "1       0.166667     0.285714  \n",
       "2       0.166667     0.571429  \n",
       "3       0.833333     0.714286  \n",
       "4       1.000000     0.428571  \n",
       "...          ...          ...  \n",
       "53935   0.000000     0.285714  \n",
       "53936   0.000000     0.285714  \n",
       "53937   0.000000     0.285714  \n",
       "53938   0.666667     0.428571  \n",
       "53939   0.000000     0.428571  \n",
       "\n",
       "[53940 rows x 9 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler=MinMaxScaler()#saving the instance\n",
    "scaled_data=scaler.fit_transform(x)\n",
    "scaled_data=pd.DataFrame(scaled_data,columns=x.columns)\n",
    "scaled_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "7c814be4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train,x_test,y_train,y_test=train_test_split(scaled_data,y,test_size=0.2,random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "9a8e022c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(          carat     depth     table     price         x         y         z  \\\n",
       " 26250  0.297297  0.519444  0.230769  0.831000  0.703911  0.129032  0.147170   \n",
       " 31510  0.029106  0.533333  0.269231  0.023734  0.416201  0.075382  0.087107   \n",
       " 40698  0.041580  0.519444  0.250000  0.044980  0.440410  0.080985  0.092138   \n",
       " 42634  0.079002  0.530556  0.230769  0.054387  0.500931  0.090832  0.104717   \n",
       " 47714  0.089397  0.550000  0.269231  0.084284  0.502793  0.092699  0.107233   \n",
       " ...         ...       ...       ...       ...       ...       ...       ...   \n",
       " 45891  0.066528  0.491667  0.307692  0.075364  0.482309  0.087267  0.098428   \n",
       " 52416  0.103950  0.572222  0.326923  0.118181  0.520484  0.093548  0.110377   \n",
       " 42613  0.024948  0.508333  0.288462  0.009677  0.405028  0.074533  0.084277   \n",
       " 43567  0.043659  0.500000  0.269231  0.059739  0.447858  0.081324  0.092138   \n",
       " 2732   0.147609  0.502778  0.230769  0.157863  0.581006  0.105093  0.119497   \n",
       " \n",
       "        color_new  clarity_new  \n",
       " 26250   0.500000     0.571429  \n",
       " 31510   0.500000     0.714286  \n",
       " 40698   0.166667     1.000000  \n",
       " 42634   0.666667     0.285714  \n",
       " 47714   0.000000     0.285714  \n",
       " ...          ...          ...  \n",
       " 45891   0.333333     0.714286  \n",
       " 52416   0.000000     0.285714  \n",
       " 42613   0.833333     0.571429  \n",
       " 43567   0.500000     0.142857  \n",
       " 2732    0.333333     0.428571  \n",
       " \n",
       " [43152 rows x 9 columns],\n",
       "           carat     depth     table     price         x         y         z  \\\n",
       " 10176  0.187110  0.527778  0.230769  0.238255  0.615456  0.112903  0.129245   \n",
       " 16083  0.226611  0.544444  0.250000  0.329675  0.648045  0.117657  0.136792   \n",
       " 13420  0.207900  0.502778  0.288462  0.280262  0.640596  0.115450  0.131447   \n",
       " 20407  0.270270  0.497222  0.250000  0.456506  0.691806  0.124958  0.141509   \n",
       " 8909   0.145530  0.519444  0.269231  0.225280  0.574488  0.105433  0.120126   \n",
       " ...         ...       ...       ...       ...       ...       ...       ...   \n",
       " 42208  0.066528  0.572222  0.269231  0.052062  0.470205  0.086587  0.101572   \n",
       " 3638   0.147609  0.483333  0.346154  0.168081  0.578212  0.106621  0.118553   \n",
       " 5508   0.182952  0.566667  0.230769  0.190355  0.608007  0.110357  0.129874   \n",
       " 19535  0.170478  0.513889  0.269231  0.423961  0.599628  0.109847  0.124843   \n",
       " 47950  0.062370  0.605556  0.288462  0.086014  0.462756  0.084890  0.101572   \n",
       " \n",
       "        color_new  clarity_new  \n",
       " 10176   0.666667     0.428571  \n",
       " 16083   0.666667     0.285714  \n",
       " 13420   0.833333     0.285714  \n",
       " 20407   0.333333     0.285714  \n",
       " 8909    0.333333     0.714286  \n",
       " ...          ...          ...  \n",
       " 42208   0.666667     0.714286  \n",
       " 3638    0.500000     0.428571  \n",
       " 5508    0.333333     0.428571  \n",
       " 19535   0.500000     0.857143  \n",
       " 47950   0.166667     1.000000  \n",
       " \n",
       " [10788 rows x 9 columns],\n",
       " 26250    2\n",
       " 31510    2\n",
       " 40698    2\n",
       " 42634    3\n",
       " 47714    4\n",
       "         ..\n",
       " 45891    3\n",
       " 52416    1\n",
       " 42613    3\n",
       " 43567    2\n",
       " 2732     2\n",
       " Name: cut_new, Length: 43152, dtype: int64,\n",
       " 10176    2\n",
       " 16083    2\n",
       " 13420    3\n",
       " 20407    2\n",
       " 8909     4\n",
       "         ..\n",
       " 42208    1\n",
       " 3638     4\n",
       " 5508     4\n",
       " 19535    2\n",
       " 47950    1\n",
       " Name: cut_new, Length: 10788, dtype: int64)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train,x_test,y_train,y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "16e95802",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/souravghoshhansda/anaconda3/envs/notebook/lib/python3.10/site-packages/sklearn/linear_model/_logistic.py:460: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "data": {
      "text/html": [
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      ],
      "text/plain": [
       "LogisticRegression()"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lr=LogisticRegression()\n",
    "lr.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "73374911",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 3, ..., 2, 2, 4])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred=lr.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "03d29485",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10788,)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ba993cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10176    2\n",
       "16083    2\n",
       "13420    3\n",
       "20407    2\n",
       "8909     4\n",
       "        ..\n",
       "42208    1\n",
       "3638     4\n",
       "5508     4\n",
       "19535    2\n",
       "47950    1\n",
       "Name: cut_new, Length: 10788, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "141a64a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5995550611790879"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy=accuracy_score(y_pred,y_test)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "e594d8e3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4070403979191576"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision=precision_score(y_pred,y_test,average='macro')\n",
    "precision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "eb7b08b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5995550611790879"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall1=recall_score(y_pred,y_test,average='micro')\n",
    "recall1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "c0024a9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  49,  100,   40,   60,   71],\n",
       "       [   3,   77,  272,  352,  279],\n",
       "       [   0,    1, 3996,  187,   75],\n",
       "       [   2,    1,  549, 1873,  365],\n",
       "       [   1,   16,  993,  953,  473]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix=confusion_matrix(y_test,y_pred)\n",
    "confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "262ecdb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.15      0.26       320\n",
      "           1       0.39      0.08      0.13       983\n",
      "           2       0.68      0.94      0.79      4259\n",
      "           3       0.55      0.67      0.60      2790\n",
      "           4       0.37      0.19      0.26      2436\n",
      "\n",
      "    accuracy                           0.60     10788\n",
      "   macro avg       0.58      0.41      0.41     10788\n",
      "weighted avg       0.56      0.60      0.55     10788\n",
      "\n"
     ]
    }
   ],
   "source": [
    "classification_report_logistic=classification_report(y_test,y_pred)\n",
    "print(classification_report_logistic)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "552f0cca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5986744530960326"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_score=lr.score(x_train,y_train)\n",
    "train_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "74b1aed0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5995550611790879"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_score=lr.score(x_test,y_test)\n",
    "test_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0154202a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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