SLC_Lab-3_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"center\" width=100%>\n",
    "    <tr>\n",
    "        <td width=\"15%\">\n",
    "            <img src=\"homework.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"center\">\n",
    "                <font color=\"#21618C\" size=8px>\n",
    "                    <b> Take-Home <br>(Day 3)\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import the required libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import 'Pandas' \n",
    "import pandas as pd \n",
    "\n",
    "# import 'Numpy' \n",
    "import numpy as np\n",
    "\n",
    "# import subpackage of Matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "\n",
    "# import 'Seaborn' \n",
    "import seaborn as sns\n",
    "\n",
    "# to suppress warnings \n",
    "from warnings import filterwarnings\n",
    "filterwarnings('ignore')\n",
    "\n",
    "# display all columns of the dataframe\n",
    "pd.options.display.max_columns = None\n",
    "\n",
    "# display all rows of the dataframe\n",
    "pd.options.display.max_rows = None\n",
    " \n",
    "# to display the float values upto 6 decimal places     \n",
    "pd.options.display.float_format = '{:.6f}'.format\n",
    "\n",
    "# import train-test split \n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# import functions to perform scaling and normalization\n",
    "from sklearn.preprocessing import StandardScaler \n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# import various functions from sklearn \n",
    "from sklearn import metrics\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import roc_curve\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Read the data\n",
    "\n",
    "Load the csv file and print the first five observations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Obs_Id</th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Wind_Speed</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.225000</td>\n",
       "      <td>12.333333</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>1028.100000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>4.900000</td>\n",
       "      <td>23.075000</td>\n",
       "      <td>44.333333</td>\n",
       "      <td>69.500000</td>\n",
       "      <td>1015.650000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>1.500000</td>\n",
       "      <td>10.175000</td>\n",
       "      <td>16.666667</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>1026.450000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>42.666667</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>1010.050000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>40.400000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>18.725000</td>\n",
       "      <td>35.333333</td>\n",
       "      <td>81.500000</td>\n",
       "      <td>1005.600000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Obs_Id  Rainfall  Evaporation  Sunshine      Temp  Wind_Speed  Humidity  \\\n",
       "0       1  3.600000     1.000000  3.000000 14.225000   12.333333 82.000000   \n",
       "1       2  0.000000     7.200000  4.900000 23.075000   44.333333 69.500000   \n",
       "2       3  0.000000     0.600000  1.500000 10.175000   16.666667 87.000000   \n",
       "3       4  0.800000     2.400000  0.500000 19.000000   42.666667 89.000000   \n",
       "4       5 40.400000     3.000000  3.600000 18.725000   35.333333 81.500000   \n",
       "\n",
       "     Pressure  RainTomorrow  \n",
       "0 1028.100000             0  \n",
       "1 1015.650000             1  \n",
       "2 1026.450000             0  \n",
       "3 1010.050000             1  \n",
       "4 1005.600000             0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('rainfall_prediction.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Obs_Id            int64\n",
       "Rainfall        float64\n",
       "Evaporation     float64\n",
       "Sunshine        float64\n",
       "Temp            float64\n",
       "Wind_Speed      float64\n",
       "Humidity        float64\n",
       "Pressure        float64\n",
       "RainTomorrow      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**The data definition is as follows:** <br>\n",
    "\n",
    "**Obs_Id:** Observation Id\n",
    "\n",
    "**Rainfall:** Rainfall recorded in a day (in mm) \n",
    "\n",
    "**Evaporation:** Number of field goals made \n",
    "\n",
    "**Sunshine:** Number of hours of bright sunshine per day\n",
    "\n",
    "**Temp:** Temperature in degree C\n",
    "\n",
    "**Wind_Speed:** Average speed of the wind in km/hour\n",
    "\n",
    "**Humidity:** Humidity in percentage (%)\n",
    "\n",
    "**Pressure:** Atmospheric pressure in hpa (hecto Pascals)\n",
    "\n",
    "**Rain Tomorrow:** Whether it will rain or not(0 = No, it won't rain tomorrow, 1 = Yes, it will rain tomorrow) (target/dependent variable)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Our objective is to predict whether it will rain tomorrow or not."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's begin with some hands-on practice exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>1. Identify and remove the variables in the data which are insignificant for the classification analysis</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'Obs_Id' is insignificant for the classification analysis\n",
    "df.drop('Obs_Id',axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>2. Is there any record that is recorded more than once? If yes, do the needful</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1520, 8)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Wind_Speed</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>RainTomorrow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>142</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>8.725000</td>\n",
       "      <td>18.666667</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1019.200000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>143</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.600000</td>\n",
       "      <td>2.700000</td>\n",
       "      <td>13.450000</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>64.500000</td>\n",
       "      <td>1024.700000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>144</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>3.100000</td>\n",
       "      <td>13.100000</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>57.500000</td>\n",
       "      <td>1022.000000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <td>145</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.800000</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>11.725000</td>\n",
       "      <td>12.666667</td>\n",
       "      <td>78.500000</td>\n",
       "      <td>1029.900000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>146</td>\n",
       "      <td>5.600000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>11.500000</td>\n",
       "      <td>14.950000</td>\n",
       "      <td>21.333333</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>1023.600000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>147</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>12.800000</td>\n",
       "      <td>30.525000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>36.500000</td>\n",
       "      <td>1008.950000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>148</td>\n",
       "      <td>9.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>10.700000</td>\n",
       "      <td>29.775000</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>1010.150000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>149</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>4.200000</td>\n",
       "      <td>16.225000</td>\n",
       "      <td>25.333333</td>\n",
       "      <td>75.500000</td>\n",
       "      <td>1023.400000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>150</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>10.400000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>22.475000</td>\n",
       "      <td>28.333333</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>1009.550000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>151</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.400000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>10.950000</td>\n",
       "      <td>24.333333</td>\n",
       "      <td>66.500000</td>\n",
       "      <td>1020.800000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1510</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>18.725000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>86.500000</td>\n",
       "      <td>1004.550000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1511</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.600000</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>15.750000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>71.000000</td>\n",
       "      <td>1021.600000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1512</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>5.200000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>19.950000</td>\n",
       "      <td>23.666667</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>1015.250000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1513</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>19.050000</td>\n",
       "      <td>21.333333</td>\n",
       "      <td>39.000000</td>\n",
       "      <td>1014.350000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1514</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.200000</td>\n",
       "      <td>9.500000</td>\n",
       "      <td>15.375000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>1031.750000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1515</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.600000</td>\n",
       "      <td>11.200000</td>\n",
       "      <td>29.650000</td>\n",
       "      <td>22.666667</td>\n",
       "      <td>46.500000</td>\n",
       "      <td>1012.350000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1516</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>10.900000</td>\n",
       "      <td>29.175000</td>\n",
       "      <td>17.333333</td>\n",
       "      <td>67.500000</td>\n",
       "      <td>1011.050000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1517</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>9.300000</td>\n",
       "      <td>14.250000</td>\n",
       "      <td>18.333333</td>\n",
       "      <td>61.500000</td>\n",
       "      <td>1027.150000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1518</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.400000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>18.925000</td>\n",
       "      <td>24.666667</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>1017.000000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1519</td>\n",
       "      <td>3.400000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>9.400000</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>41.333333</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>1004.750000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Rainfall  Evaporation  Sunshine      Temp  Wind_Speed  Humidity  \\\n",
       "142   0.000000     1.600000  6.900000  8.725000   18.666667 76.000000   \n",
       "143   0.000000     2.600000  2.700000 13.450000   22.666667 64.500000   \n",
       "144   0.000000     3.200000  3.100000 13.100000   28.333333 57.500000   \n",
       "145   0.000000     0.800000  5.600000 11.725000   12.666667 78.500000   \n",
       "146   5.600000     1.600000 11.500000 14.950000   21.333333 54.500000   \n",
       "147   0.000000    14.600000 12.800000 30.525000   26.000000 36.500000   \n",
       "148   9.800000     3.000000 10.700000 29.775000   22.666667 61.500000   \n",
       "149   0.000000     2.400000  4.200000 16.225000   25.333333 75.500000   \n",
       "150   0.200000    10.400000  5.800000 22.475000   28.333333 73.500000   \n",
       "151   0.000000     2.400000  4.400000 10.950000   24.333333 66.500000   \n",
       "1510  5.200000     0.400000  1.100000 18.725000   18.333333 86.500000   \n",
       "1511  0.000000     3.600000  7.600000 15.750000   14.000000 71.000000   \n",
       "1512  0.400000     5.200000 13.000000 19.950000   23.666667 52.000000   \n",
       "1513  0.000000     6.000000 10.900000 19.050000   21.333333 39.000000   \n",
       "1514  0.000000     3.200000  9.500000 15.375000   24.000000 53.000000   \n",
       "1515  0.000000     8.600000 11.200000 29.650000   22.666667 46.500000   \n",
       "1516  0.000000     4.000000 10.900000 29.175000   17.333333 67.500000   \n",
       "1517  0.000000     3.400000  9.300000 14.250000   18.333333 61.500000   \n",
       "1518  0.000000     5.400000 11.000000 18.925000   24.666667 69.000000   \n",
       "1519  3.400000     1.400000  9.400000 15.300000   41.333333 57.000000   \n",
       "\n",
       "        Pressure  RainTomorrow  \n",
       "142  1019.200000             0  \n",
       "143  1024.700000             0  \n",
       "144  1022.000000             0  \n",
       "145  1029.900000             0  \n",
       "146  1023.600000             0  \n",
       "147  1008.950000             0  \n",
       "148  1010.150000             0  \n",
       "149  1023.400000             1  \n",
       "150  1009.550000             1  \n",
       "151  1020.800000             0  \n",
       "1510 1004.550000             0  \n",
       "1511 1021.600000             0  \n",
       "1512 1015.250000             0  \n",
       "1513 1014.350000             0  \n",
       "1514 1031.750000             0  \n",
       "1515 1012.350000             0  \n",
       "1516 1011.050000             0  \n",
       "1517 1027.150000             0  \n",
       "1518 1017.000000             0  \n",
       "1519 1004.750000             0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.duplicated()==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.drop_duplicates(keep=False,subset=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1480, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>3. Are there any outliers present in the data? If yes, remove such observations using the quartiles of the variables\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(963, 8)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculate the first quartile\n",
    "Q1 = df.quantile(0.25)\n",
    "\n",
    "#calculate the third quartile\n",
    "Q3 = df.quantile(0.75)\n",
    "\n",
    "# The Interquartile Range (IQR) is defined as the difference between the third and first quartile\n",
    "# calculate IQR\n",
    "IQR = Q3 - Q1\n",
    "\n",
    "# remove the outliers from the dataframe\n",
    "df = df[~((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).any(axis=1)]\n",
    "\n",
    "# reset the row index\n",
    "df = df.reset_index(drop=True)\n",
    "\n",
    "# check the shape of the data\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>4. Is scaling required for the KNN algorithm? If yes, scale the data such that the range of each variable will be between 0 and 1\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df.drop('RainTomorrow',axis=1)\n",
    "y=df['RainTomorrow']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>Rainfall</th>\n",
       "      <th>Evaporation</th>\n",
       "      <th>Sunshine</th>\n",
       "      <th>Temp</th>\n",
       "      <th>Wind_Speed</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Pressure</th>\n",
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       "      <td>0</td>\n",
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       "    </tr>\n",
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       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
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       "      <td>0.607143</td>\n",
       "      <td>0.626010</td>\n",
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       "      <td>0.483037</td>\n",
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       "      <td>0.232955</td>\n",
       "      <td>0.382609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
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       "      <td>0.490307</td>\n",
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       "      <td>0.261364</td>\n",
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       "      <td>4</td>\n",
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       "      <td>0.107955</td>\n",
       "      <td>0.924638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rainfall  Evaporation  Sunshine     Temp  Wind_Speed  Humidity  Pressure\n",
       "0  0.000000     0.028571  0.107143 0.168821    0.272727  0.857955  0.794203\n",
       "1  0.000000     0.485714  0.607143 0.626010    0.206612  0.517045  0.346377\n",
       "2  0.000000     0.771429  0.828571 0.483037    0.776860  0.232955  0.382609\n",
       "3  0.000000     0.471429  0.442857 0.490307    0.710744  0.261364  0.265217\n",
       "4  0.000000     0.342857  0.821429 0.575929    0.256198  0.107955  0.924638"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# initialize the Min Max scalar\n",
    "X_scaler = MinMaxScaler()\n",
    "\n",
    "# scale all the numerical columns\n",
    "# standardize all the columns of the dataframe 'df_feature'\n",
    "num_scaled = X_scaler.fit_transform(X)\n",
    "\n",
    "# create a dataframe of scaled numerical variables\n",
    "# pass the required column names to the parameter 'columns'\n",
    "X_norm = pd.DataFrame(num_scaled, columns = X.columns)\n",
    "\n",
    "# display first five observations\n",
    "X_norm.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>5. Build a 7-NN model on 70% of the data using the 'Chebyshev' distance and find its accuracy \n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_norm, X_test_norm, y_train, y_test = train_test_split(X_norm, y, random_state = 10, test_size = 0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "knn_classification = KNeighborsClassifier(n_neighbors = 3,metric='chebyshev')\n",
    "knn_model = knn_classification.fit(X_train_norm, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test accuracy is  1.0\n"
     ]
    }
   ],
   "source": [
    "print('Test accuracy is ',knn_model.score(X_train_norm, y_train))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>6. Find the best value of 'K' for the KNN model from the given list of values (use 5-fold cross validation)\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Use the given list:**\n",
    "\n",
    "K = [3, 5, 7, 9, 11, 13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for KNN Classifier:  {'n_neighbors': 3} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "tuned_paramaters = {'n_neighbors': [3, 5, 7, 9, 11, 13]}\n",
    "knn=KNeighborsClassifier()\n",
    "knn_grid = GridSearchCV(estimator = knn, \n",
    "                        param_grid = tuned_paramaters, \n",
    "                        cv = 5, \n",
    "                        scoring = 'accuracy')\n",
    "knn_grid.fit(X_train_norm, y_train)\n",
    "print('Best parameters for KNN Classifier: ', knn_grid.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>7. Build a naive bayes model on 70% of the original data and plot the ROC curve along with the AUC score\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.560870</td>\n",
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       "     Rainfall  Evaporation  Sunshine     Temp  Wind_Speed  Humidity  Pressure\n",
       "456  0.000000     0.028571  0.471429 0.071890    0.223140  0.647727  0.866667\n",
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       "753  0.285714     0.342857  0.321429 0.335218    0.471074  0.494318  0.372464\n",
       "943  1.000000     0.114286  0.400000 0.174475    0.214876  0.653409  0.550725\n",
       "27   0.000000     0.371429  0.771429 0.432149    0.504132  0.073864  0.560870"
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     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test_norm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gnb = GaussianNB()\n",
    "gnb_model = gnb.fit(X_train_norm, y_train)\n",
    "\n",
    "probas_ = gnb_model.predict_proba(X_test_norm)\n",
    "probas_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_roc(model, test_data):\n",
    "    y_pred_prob = model.predict_proba(test_data)[:,1]\n",
    "    fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "    plt.plot(fpr, tpr)\n",
    "    plt.xlim([0.0, 1.0])\n",
    "    plt.ylim([0.0, 1.0])\n",
    "    plt.plot([0, 1], [0, 1],'r--')\n",
    "    plt.title('ROC curve for Cancer Prediction Classifier', fontsize = 15)\n",
    "    plt.xlabel('False positive rate (1-Specificity)', fontsize = 15)\n",
    "    plt.ylabel('True positive rate (Sensitivity)', fontsize = 15)\n",
    "    plt.text(x = 0.02, y = 0.9, s = ('AUC Score:',round(roc_auc_score(y_test, y_pred_prob),4)))\n",
    "    plt.grid(True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#plot_roc(gnb_model, test_data=X_test_norm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "fpr, tpr, thresholds = roc_curve(y_test, probas_[:, 1])\n",
    "roc_auc = auc(fpr, tpr)\n",
    "print(\"Area under the ROC curve : %f\" % roc_auc)\n",
    "\n",
    "#plot the ROC curve\n",
    "plt.plot(fpr, tpr)\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.0])\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.title('ROC curve for Closed account (RFE Model)', fontsize = 15)\n",
    "plt.xlabel('False positive rate (1-Specificity)', fontsize = 15)\n",
    "plt.ylabel('True positive rate (Sensitivity)', fontsize = 15)\n",
    "plt.text(x = 0.02, y = 0.9, s = ('AUC Score:', round(roc_auc,4)))\n",
    "\n",
    "plt.grid(True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>8. Which distance metric among 'manhattan', 'euclidean' and 'chebyshev' is suitable for the given dataset? (consider K = 19)\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters for KNN Classifier:  {'metric': 'euclidean', 'n_neighbors': 19} \n",
      "\n"
     ]
    }
   ],
   "source": [
    "tuned_paramaters = {'n_neighbors':  [19],\n",
    "                   'metric': ['euclidean','manhattan','Chebyshev']}\n",
    "knn_classification = KNeighborsClassifier()\n",
    "knn_grid = GridSearchCV(estimator = knn_classification, \n",
    "                        param_grid = tuned_paramaters, \n",
    "                        cv = 5, \n",
    "                        scoring = 'accuracy')\n",
    "knn_grid.fit(X_train_norm, y_train)\n",
    "print('Best parameters for KNN Classifier: ', knn_grid.best_params_, '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>9. Find the euclidean distance between the first observation of the dataframe 'X_test_scaled' and its five neighboring points in the train set (use for loop)</b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# type your code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<table align=\"left\">\n",
    "    <tr>\n",
    "        <td width=\"6%\">\n",
    "            <img src=\"question_icon.png\">\n",
    "        </td>\n",
    "        <td>\n",
    "            <div align=\"left\", style=\"font-size:120%\">\n",
    "                <font color=\"#21618C\">\n",
    "                    <b>10. Use the parameters obtained in Q8 to build the KNN model, and find the number of false predictions using the test set\n",
    "                    </b>\n",
    "                </font>\n",
    "            </div>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "knn=KNeighborsClassifier(n_neighbors=19,metric='euclidean')\n",
    "knn_model_tuned=knn.fit(X_train_norm,y_train)\n",
    "y_pred=knn_model_tuned.predict(X_test_norm)\n",
    "cm=confusion_matrix(y_test,y_pred)\n",
    "false_predict=cm[0][1]+cm[1][0]\n",
    "print(\"False predictions = \",false_predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "confusion_matrix(y_test,y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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