ML with Python.ipynb

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   "source": [
    "<a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width = 400, align = \"center\"></a>\n",
    "\n",
    "<h1 align=center><font size = 5> Classification with Python</font></h1>"
   ]
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   "source": [
    "In this notebook we try to practice all the classification algorithms that we learned in this course.\n",
    "\n",
    "We load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n",
    "\n",
    "Lets first load required libraries:"
   ]
  },
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   "source": [
    "import itertools\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import NullFormatter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.ticker as ticker\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline"
   ]
  },
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   "source": [
    "### About dataset"
   ]
  },
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   "source": [
    "This dataset is about past loans. The __Loan_train.csv__ data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:\n",
    "\n",
    "| Field          | Description                                                                           |\n",
    "|----------------|---------------------------------------------------------------------------------------|\n",
    "| Loan_status    | Whether a loan is paid off on in collection                                           |\n",
    "| Principal      | Basic principal loan amount at the                                                    |\n",
    "| Terms          | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n",
    "| Effective_date | When the loan got originated and took effects                                         |\n",
    "| Due_date       | Since it’s one-time payoff schedule, each loan has one single due date                |\n",
    "| Age            | Age of applicant                                                                      |\n",
    "| Education      | Education of applicant                                                                |\n",
    "| Gender         | The gender of applicant                                                               |"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "Lets download the dataset"
   ]
  },
  {
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     "text": [
      "--2018-12-02 23:20:32--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 23101 (23K) [text/csv]\n",
      "Saving to: ‘loan_train.csv’\n",
      "\n",
      "100%[======================================>] 23,101      --.-K/s   in 0.002s  \n",
      "\n",
      "2018-12-02 23:20:33 (12.8 MB/s) - ‘loan_train.csv’ saved [23101/23101]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "### Load Data From CSV File  "
   ]
  },
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   "cell_type": "code",
   "execution_count": 3,
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>9/22/2016</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>10/8/2016</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>10/8/2016</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30       9/8/2016   \n",
       "1           2             2     PAIDOFF       1000     30       9/8/2016   \n",
       "2           3             3     PAIDOFF       1000     15       9/8/2016   \n",
       "3           4             4     PAIDOFF       1000     30       9/9/2016   \n",
       "4           6             6     PAIDOFF       1000     30       9/9/2016   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0  10/7/2016   45  High School or Below    male  \n",
       "1  10/7/2016   33              Bechalor  female  \n",
       "2  9/22/2016   27               college    male  \n",
       "3  10/8/2016   28               college  female  \n",
       "4  10/8/2016   29               college    male  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('loan_train.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346, 10)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.shape"
   ]
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   "metadata": {
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   },
   "source": [
    "### Convert to date time object "
   ]
  },
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   "outputs": [
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0 2016-10-07   45  High School or Below    male  \n",
       "1 2016-10-07   33              Bechalor  female  \n",
       "2 2016-09-22   27               college    male  \n",
       "3 2016-10-08   28               college  female  \n",
       "4 2016-10-08   29               college    male  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['due_date'] = pd.to_datetime(df['due_date'])\n",
    "df['effective_date'] = pd.to_datetime(df['effective_date'])\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
    "run_control": {
     "read_only": false
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   },
   "source": [
    "# Data visualization and pre-processing\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
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    }
   },
   "source": [
    "Let’s see how many of each class is in our data set "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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    "new_sheet": false,
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PAIDOFF       260\n",
       "COLLECTION     86\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['loan_status'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "260 people have paid off the loan on time while 86 have gone into collection \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets plot some columns to underestand data better:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fetching package metadata .............\n",
      "Solving package specifications: .\n",
      "\n",
      "Package plan for installation in environment /opt/conda/envs/DSX-Python35:\n",
      "\n",
      "The following packages will be UPDATED:\n",
      "\n",
      "    seaborn: 0.8.0-py35h15a2772_0 --> 0.9.0-py35_0 anaconda\n",
      "\n",
      "seaborn-0.9.0- 100% |################################| Time: 0:00:00  49.21 MB/s\n"
     ]
    }
   ],
   "source": [
    "# notice: installing seaborn might takes a few minutes\n",
    "!conda install -c anaconda seaborn -y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4529749160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44e4bf1748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.linspace(df.age.min(), df.age.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'age', bins=bins, ec=\"k\")\n",
    "\n",
    "g.axes[-1].legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Pre-processing:  Feature selection/extraction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Lets look at the day of the week people get the loan "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44e4b0c908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['dayofweek'] = df['effective_date'].dt.dayofweek\n",
    "bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\n",
    "g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
    "g.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\n",
    "g.axes[-1].legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "We see that people who get the loan at the end of the week dont pay it off, so lets use Feature binarization to set a threshold values less then day 4 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>female</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below    male          3        0  \n",
       "1 2016-10-07   33              Bechalor  female          3        0  \n",
       "2 2016-09-22   27               college    male          3        0  \n",
       "3 2016-10-08   28               college  female          4        1  \n",
       "4 2016-10-08   29               college    male          4        1  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3)  else 0)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Convert Categorical features to numerical values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets look at gender:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Gender  loan_status\n",
       "female  PAIDOFF        0.865385\n",
       "        COLLECTION     0.134615\n",
       "male    PAIDOFF        0.731293\n",
       "        COLLECTION     0.268707\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "86 % of female pay there loans while only 73 % of males pay there loan\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets convert male to 0 and female to 1:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below       0          3        0  \n",
       "1 2016-10-07   33              Bechalor       1          3        0  \n",
       "2 2016-09-22   27               college       0          3        0  \n",
       "3 2016-10-08   28               college       1          4        1  \n",
       "4 2016-10-08   29               college       0          4        1  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## One Hot Encoding  \n",
    "#### How about education?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "education             loan_status\n",
       "Bechalor              PAIDOFF        0.750000\n",
       "                      COLLECTION     0.250000\n",
       "High School or Below  PAIDOFF        0.741722\n",
       "                      COLLECTION     0.258278\n",
       "Master or Above       COLLECTION     0.500000\n",
       "                      PAIDOFF        0.500000\n",
       "college               PAIDOFF        0.765101\n",
       "                      COLLECTION     0.234899\n",
       "Name: loan_status, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Feature befor One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>education</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>0</td>\n",
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       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
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       "      <td>1</td>\n",
       "      <td>Bechalor</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
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       "      <td>27</td>\n",
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       "      <td>college</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
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       "      <td>1</td>\n",
       "      <td>college</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>college</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender             education\n",
       "0       1000     30   45       0  High School or Below\n",
       "1       1000     30   33       1              Bechalor\n",
       "2       1000     15   27       0               college\n",
       "3       1000     30   28       1               college\n",
       "4       1000     30   29       0               college"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['Principal','terms','age','Gender','education']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1000</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature = df[['Principal','terms','age','Gender','weekend']]\n",
    "Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n",
    "Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "Feature.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Feature selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Lets defind feature sets, X:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
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       "      <th>2</th>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Feature\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "What are our lables?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['loan_status'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Normalize Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Data Standardization give data zero mean and unit variance (technically should be done after train test split )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n",
       "        -0.38170062,  1.13639374, -0.86968108],\n",
       "       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n",
       "         2.61985426, -0.87997669, -0.86968108],\n",
       "       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "# Classification "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model\n",
    "You should use the following algorithm:\n",
    "- K Nearest Neighbor(KNN)\n",
    "- Decision Tree\n",
    "- Support Vector Machine\n",
    "- Logistic Regression\n",
    "\n",
    "\n",
    "\n",
    "__ Notice:__ \n",
    "- You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.\n",
    "- You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.\n",
    "- You should include the code of the algorithm in the following cells."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K Nearest Neighbor(KNN)\n",
    "Notice: You should find the best k to build the model with the best accuracy.  \n",
    "**warning:** You should not use the __loan_test.csv__ for finding the best k, however, you can split your train_loan.csv into train and test to find the best __k__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (276, 8) (276,)\n",
      "Test set: (70, 8) (70,)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neighbors import KNeighborsClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.894927536232\n",
      "Test set Accuracy:  0.671428571429\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.67142857,  0.65714286,  0.71428571,  0.68571429,  0.75714286,\n",
       "        0.71428571,  0.78571429,  0.75714286,  0.75714286])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "ConfustionMx = [];\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44e36e3390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Nabors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.785714285714 with k= 7\n"
     ]
    }
   ],
   "source": [
    "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.51578458 -0.95911111  0.67333883 ...,  2.61985426 -0.87997669\n",
      "  -0.86968108]\n",
      " [ 0.51578458  0.92071769 -0.15575453 ..., -0.38170062  1.13639374\n",
      "  -0.86968108]\n",
      " [-1.31458942 -0.95911111  1.33661351 ..., -0.38170062 -0.87997669\n",
      "   1.14984679]\n",
      " ..., \n",
      " [ 0.51578458  0.92071769  0.8391575  ..., -0.38170062  1.13639374\n",
      "  -0.86968108]\n",
      " [ 0.51578458  0.92071769  0.50752015 ...,  2.61985426 -0.87997669\n",
      "  -0.86968108]\n",
      " [ 0.51578458 -0.95911111  0.01006414 ..., -0.38170062  1.13639374\n",
      "  -0.86968108]]\n",
      "['PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'COLLECTION'\n",
      " 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'COLLECTION'\n",
      " 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'COLLECTION'\n",
      " 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'COLLECTION'\n",
      " 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'PAIDOFF' 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'COLLECTION' 'COLLECTION'\n",
      " 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
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      " 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF'\n",
      " 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'COLLECTION'\n",
      " 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF'\n",
      " 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION'\n",
      " 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'COLLECTION'\n",
      " 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'COLLECTION' 'COLLECTION' 'PAIDOFF' 'COLLECTION' 'PAIDOFF' 'PAIDOFF'\n",
      " 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF'\n",
      " 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION'\n",
      " 'COLLECTION' 'PAIDOFF' 'PAIDOFF' 'PAIDOFF' 'COLLECTION' 'PAIDOFF'\n",
      " 'PAIDOFF' 'PAIDOFF' 'COLLECTION']\n"
     ]
    }
   ],
   "source": [
    "print (X_trainset)\n",
    "print (y_trainset)\n",
    "print (X_testset)\n",
    "print (y_testset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loantree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
    "loantree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n",
       "            max_features=None, max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n",
       "            splitter='best')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loantree.fit(X_trainset,y_trainset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "predTree = loantree.predict(X_testset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  0.653846153846\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_testset, predTree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Support Vector Machine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set: (276, 8) (276,)\n",
      "Test set: (70, 8) (70,)\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "print ('Train set:', X_train.shape,  y_train.shape)\n",
    "print ('Test set:', X_test.shape,  y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import jaccard_similarity_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg F1-score: 0.7276\n",
      "Jaccard score: 0.7429\n"
     ]
    }
   ],
   "source": [
    "clf2 = svm.SVC(kernel='rbf')\n",
    "clf2.fit(X_train, y_train) \n",
    "yhat2 = clf2.predict(X_test)\n",
    "print(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))\n",
    "print(\"Jaccard score: %.4f\" % jaccard_similarity_score(y_test, yhat2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import confusion_matrix\n",
    "LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\n",
    "LR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF',\n",
       "       'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION',\n",
       "       'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'COLLECTION', 'PAIDOFF',\n",
       "       'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n",
       "       'PAIDOFF'], dtype=object)"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat = LR.predict(X_test)\n",
    "yhat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.5034238 ,  0.4965762 ],\n",
       "       [ 0.45206111,  0.54793889],\n",
       "       [ 0.30814132,  0.69185868],\n",
       "       [ 0.34259428,  0.65740572],\n",
       "       [ 0.32025894,  0.67974106],\n",
       "       [ 0.31680537,  0.68319463],\n",
       "       [ 0.48830185,  0.51169815],\n",
       "       [ 0.47823073,  0.52176927],\n",
       "       [ 0.34259428,  0.65740572],\n",
       "       [ 0.4934056 ,  0.5065944 ],\n",
       "       [ 0.33806706,  0.66193294],\n",
       "       [ 0.49662231,  0.50337769],\n",
       "       [ 0.24891907,  0.75108093],\n",
       "       [ 0.3419095 ,  0.6580905 ],\n",
       "       [ 0.43751789,  0.56248211],\n",
       "       [ 0.25760497,  0.74239503],\n",
       "       [ 0.52357188,  0.47642812],\n",
       "       [ 0.30450278,  0.69549722],\n",
       "       [ 0.50166363,  0.49833637],\n",
       "       [ 0.3195971 ,  0.6804029 ],\n",
       "       [ 0.44276988,  0.55723012],\n",
       "       [ 0.49410185,  0.50589815],\n",
       "       [ 0.51350333,  0.48649667],\n",
       "       [ 0.47203498,  0.52796502],\n",
       "       [ 0.40944694,  0.59055306],\n",
       "       [ 0.50846442,  0.49153558],\n",
       "       [ 0.51098415,  0.48901585],\n",
       "       [ 0.37457647,  0.62542353],\n",
       "       [ 0.50418423,  0.49581577],\n",
       "       [ 0.25299635,  0.74700365],\n",
       "       [ 0.46824113,  0.53175887],\n",
       "       [ 0.46024688,  0.53975312],\n",
       "       [ 0.46206917,  0.53793083],\n",
       "       [ 0.48402425,  0.51597575],\n",
       "       [ 0.38818191,  0.61181809],\n",
       "       [ 0.45821326,  0.54178674],\n",
       "       [ 0.50166363,  0.49833637],\n",
       "       [ 0.28973585,  0.71026415],\n",
       "       [ 0.4569882 ,  0.5430118 ],\n",
       "       [ 0.45494718,  0.54505282],\n",
       "       [ 0.50670462,  0.49329538],\n",
       "       [ 0.32179362,  0.67820638],\n",
       "       [ 0.45245776,  0.54754224],\n",
       "       [ 0.50846442,  0.49153558],\n",
       "       [ 0.30664231,  0.69335769],\n",
       "       [ 0.49515584,  0.50484416],\n",
       "       [ 0.47075244,  0.52924756],\n",
       "       [ 0.49662231,  0.50337769],\n",
       "       [ 0.45571125,  0.54428875],\n",
       "       [ 0.45567623,  0.54432377],\n",
       "       [ 0.27794059,  0.72205941],\n",
       "       [ 0.46744865,  0.53255135],\n",
       "       [ 0.30501081,  0.69498919],\n",
       "       [ 0.48906194,  0.51093806],\n",
       "       [ 0.28058426,  0.71941574],\n",
       "       [ 0.24921106,  0.75078894],\n",
       "       [ 0.31522806,  0.68477194],\n",
       "       [ 0.43036995,  0.56963005],\n",
       "       [ 0.46824113,  0.53175887],\n",
       "       [ 0.33513632,  0.66486368],\n",
       "       [ 0.41925226,  0.58074774],\n",
       "       [ 0.33133167,  0.66866833],\n",
       "       [ 0.45821326,  0.54178674],\n",
       "       [ 0.52608635,  0.47391365],\n",
       "       [ 0.32399805,  0.67600195],\n",
       "       [ 0.49410185,  0.50589815],\n",
       "       [ 0.33133167,  0.66866833],\n",
       "       [ 0.41737926,  0.58262074],\n",
       "       [ 0.44996108,  0.55003892],\n",
       "       [ 0.32399805,  0.67600195]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yhat_prob = LR.predict_proba(X_test)\n",
    "yhat_prob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.68571428571428572"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "jaccard_similarity_score(y_test, yhat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.57722876094796538"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import log_loss\n",
    "log_loss(y_test, yhat_prob)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation using Test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import jaccard_similarity_score\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.metrics import log_loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, download and load the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2018-12-03 00:58:33--  https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\n",
      "Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.193\n",
      "Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.193|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 3642 (3.6K) [text/csv]\n",
      "Saving to: ‘loan_test.csv’\n",
      "\n",
      "100%[======================================>] 3,642       --.-K/s   in 0s      \n",
      "\n",
      "2018-12-03 00:58:33 (539 MB/s) - ‘loan_test.csv’ saved [3642/3642]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Load Test set for evaluation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/8/2016</td>\n",
       "      <td>10/7/2016</td>\n",
       "      <td>50</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>300</td>\n",
       "      <td>7</td>\n",
       "      <td>9/9/2016</td>\n",
       "      <td>9/15/2016</td>\n",
       "      <td>35</td>\n",
       "      <td>Master or Above</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/10/2016</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>43</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>9/10/2016</td>\n",
       "      <td>10/9/2016</td>\n",
       "      <td>26</td>\n",
       "      <td>college</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>800</td>\n",
       "      <td>15</td>\n",
       "      <td>9/11/2016</td>\n",
       "      <td>9/25/2016</td>\n",
       "      <td>29</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>male</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           1             1     PAIDOFF       1000     30       9/8/2016   \n",
       "1           5             5     PAIDOFF        300      7       9/9/2016   \n",
       "2          21            21     PAIDOFF       1000     30      9/10/2016   \n",
       "3          24            24     PAIDOFF       1000     30      9/10/2016   \n",
       "4          35            35     PAIDOFF        800     15      9/11/2016   \n",
       "\n",
       "    due_date  age             education  Gender  \n",
       "0  10/7/2016   50              Bechalor  female  \n",
       "1  9/15/2016   35       Master or Above    male  \n",
       "2  10/9/2016   43  High School or Below  female  \n",
       "3  10/9/2016   26               college    male  \n",
       "4  9/25/2016   29              Bechalor    male  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_df = pd.read_csv('loan_test.csv')\n",
    "test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(346, 12)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "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>Unnamed: 0</th>\n",
       "      <th>Unnamed: 0.1</th>\n",
       "      <th>loan_status</th>\n",
       "      <th>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>effective_date</th>\n",
       "      <th>due_date</th>\n",
       "      <th>age</th>\n",
       "      <th>education</th>\n",
       "      <th>Gender</th>\n",
       "      <th>dayofweek</th>\n",
       "      <th>weekend</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>45</td>\n",
       "      <td>High School or Below</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-10-07</td>\n",
       "      <td>33</td>\n",
       "      <td>Bechalor</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>2016-09-08</td>\n",
       "      <td>2016-09-22</td>\n",
       "      <td>27</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>28</td>\n",
       "      <td>college</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>PAIDOFF</td>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>2016-09-09</td>\n",
       "      <td>2016-10-08</td>\n",
       "      <td>29</td>\n",
       "      <td>college</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  Unnamed: 0.1 loan_status  Principal  terms effective_date  \\\n",
       "0           0             0     PAIDOFF       1000     30     2016-09-08   \n",
       "1           2             2     PAIDOFF       1000     30     2016-09-08   \n",
       "2           3             3     PAIDOFF       1000     15     2016-09-08   \n",
       "3           4             4     PAIDOFF       1000     30     2016-09-09   \n",
       "4           6             6     PAIDOFF       1000     30     2016-09-09   \n",
       "\n",
       "    due_date  age             education  Gender  dayofweek  weekend  \n",
       "0 2016-10-07   45  High School or Below       0          3        0  \n",
       "1 2016-10-07   33              Bechalor       1          3        0  \n",
       "2 2016-09-22   27               college       0          3        0  \n",
       "3 2016-10-08   28               college       1          4        1  \n",
       "4 2016-10-08   29               college       0          4        1  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['due_date'] = pd.to_datetime(df['due_date'])\n",
    "df['effective_date'] = pd.to_datetime(df['effective_date'])\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "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",
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       "    }\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>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Feature = df[['Principal','terms','age','Gender','weekend']]\n",
    "Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n",
    "Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
    "Feature.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Principal</th>\n",
       "      <th>terms</th>\n",
       "      <th>age</th>\n",
       "      <th>Gender</th>\n",
       "      <th>weekend</th>\n",
       "      <th>Bechalor</th>\n",
       "      <th>High School or Below</th>\n",
       "      <th>college</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
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       "      <td>27</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>30</td>\n",
       "      <td>29</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Principal  terms  age  Gender  weekend  Bechalor  High School or Below  \\\n",
       "0       1000     30   45       0        0         0                     1   \n",
       "1       1000     30   33       1        0         1                     0   \n",
       "2       1000     15   27       0        0         0                     0   \n",
       "3       1000     30   28       1        1         0                     0   \n",
       "4       1000     30   29       0        1         0                     0   \n",
       "\n",
       "   college  \n",
       "0        0  \n",
       "1        0  \n",
       "2        1  \n",
       "3        1  \n",
       "4        1  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = Feature\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = df['loan_status'].values\n",
    "y[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.51578458,  0.92071769,  2.33152555, -0.42056004, -1.20577805,\n",
       "        -0.38170062,  1.13639374, -0.86968108],\n",
       "       [ 0.51578458,  0.92071769,  0.34170148,  2.37778177, -1.20577805,\n",
       "         2.61985426, -0.87997669, -0.86968108],\n",
       "       [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.48739188,  2.37778177,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679],\n",
       "       [ 0.51578458,  0.92071769, -0.3215732 , -0.42056004,  0.82934003,\n",
       "        -0.38170062, -0.87997669,  1.14984679]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X= preprocessing.StandardScaler().fit(X).transform(X)\n",
    "X[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.789855072464\n",
      "Test set Accuracy:  0.685714285714\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=4)\n",
    "from sklearn import metrics\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.67142857,  0.65714286,  0.71428571,  0.68571429,  0.75714286,\n",
       "        0.71428571,  0.78571429,  0.75714286,  0.75714286])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Ks = 10\n",
    "mean_acc = np.zeros((Ks-1))\n",
    "std_acc = np.zeros((Ks-1))\n",
    "ConfustionMx = [];\n",
    "for n in range(1,Ks):\n",
    "    \n",
    "    #Train Model and Predict  \n",
    "    neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
    "    yhat=neigh.predict(X_test)\n",
    "    mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
    "\n",
    "    \n",
    "    std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
    "\n",
    "mean_acc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f44e1651ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(1,Ks),mean_acc,'g')\n",
    "plt.fill_between(range(1,Ks),mean_acc - 1 * std_acc,mean_acc + 1 * std_acc, alpha=0.10)\n",
    "plt.legend(('Accuracy ', '+/- 3xstd'))\n",
    "plt.ylabel('Accuracy ')\n",
    "plt.xlabel('Number of Nabors (K)')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The best accuracy was with 0.785714285714 with k= 7\n"
     ]
    }
   ],
   "source": [
    "print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train set Accuracy:  0.807971014493\n",
      "Test set Accuracy:  0.785714285714\n"
     ]
    }
   ],
   "source": [
    "k = 7\n",
    "neigh = KNeighborsClassifier(n_neighbors = k).fit(X_train,y_train)\n",
    "yhat = neigh.predict(X_test)\n",
    "print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neigh.predict(X_train)))\n",
    "print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg F1-score: 0.7276\n",
      "Jaccard score: 0.7429\n"
     ]
    }
   ],
   "source": [
    "print(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))\n",
    "print(\"Jaccard score: %.4f\" % jaccard_similarity_score(y_test, yhat2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_trainset, X_testset, y_trainset, y_testset = train_test_split(X, y, test_size=0.3, random_state=3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "\n",
    "loantree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\n",
    "loantree.fit(X_trainset,y_trainset)\n",
    "predTree = loantree.predict(X_testset)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avg F1-score: 0.7276\n",
      "Jaccard score: 0.7429\n"
     ]
    }
   ],
   "source": [
    "clf2 = svm.SVC(kernel='rbf')\n",
    "clf2.fit(X_train, y_train) \n",
    "yhat2 = clf2.predict(X_test)\n",
    "print(\"Avg F1-score: %.4f\" % f1_score(y_test, yhat2, average='weighted'))\n",
    "print(\"Jaccard score: %.4f\" % jaccard_similarity_score(y_test, yhat2))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.685714285714\n",
      "0.727588201272\n",
      "0.577228760948\n"
     ]
    }
   ],
   "source": [
    "LR = LogisticRegression(C=0.01, solver='liblinear').fit(X_train,y_train)\n",
    "yhat = LR.predict(X_test)\n",
    "yhat_prob = LR.predict_proba(X_test)\n",
    "print (jaccard_similarity_score(y_test, yhat))\n",
    "print (f1_score(y_test, yhat2, average='weighted'))\n",
    "print (log_loss(y_test, yhat_prob))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Report\n",
    "You should be able to report the accuracy of the built model using different evaluation metrics:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm          | Jaccard | F1-score | LogLoss |\n",
    "|--------------------|---------|----------|---------|\n",
    "| KNN                | 0.7429       | 0.7276        | NA      |\n",
    "| Decision Tree      | 0.7213       | 0.7319        | NA      |\n",
    "| SVM                | 0.7429       | 0.7276        | NA      |\n",
    "| LogisticRegression | 0.6857       | 0.7275        | 0.5772       |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Algorithm          | Jaccard | F1-score | LogLoss |\n",
    "|--------------------|---------|----------|---------|\n",
    "| KNN                | 0.7429       | 0.7276        | NA      |\n",
    "| Decision Tree      | 0.7213       | 0.7319        | NA      |\n",
    "| SVM                | 0.7429       | 0.7276        | NA      |\n",
    "| LogisticRegression | 0.6857       | 0.7275        | 0.5772       |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "## Want to learn more?\n",
    "\n",
    "IBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems – by your enterprise as a whole. A free trial is available through this course, available here: [SPSS Modeler](http://cocl.us/ML0101EN-SPSSModeler).\n",
    "\n",
    "Also, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at [Watson Studio](https://cocl.us/ML0101EN_DSX)\n",
    "\n",
    "\n",
    "<hr>\n",
    "Copyright &copy; 2018 [Cognitive Class](https://cocl.us/DX0108EN_CC). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).​"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "### Thanks for completing this lesson!\n",
    "\n",
    "Notebook created by: <a href = \"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>"
   ]
  }
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