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diff --git a/Classification/Caravan_Insurance_Claim_Prediction/Caravan Insurance Company Benchmark (COIL 2000) (1).ipynb b/Classification/Caravan_Insurance_Claim_Prediction/Caravan Insurance Company Benchmark (COIL 2000) (1).ipynb
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@@ -0,0 +1,2462 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Caravan Insurance Prediction"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Data Set Information\n",
+    "\n",
+    "Information about customers consists of 86 variables and includes product usage data and socio-demographic data derived from zip area codes. The data was supplied by the Dutch data mining company Sentient Machine Research and is based on a real world business problem. The training set contains over 5000 descriptions of customers, including the information of whether or not they have a caravan insurance policy. A test set contains 4000 customers of whom only the organisers know if they have a caravan insurance policy. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Importing packages\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from sklearn import preprocessing\n",
+    "from sklearn.preprocessing import MinMaxScaler\n",
+    "from sklearn.cross_validation import train_test_split\n",
+    "from imblearn.over_sampling import SMOTE\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "from sklearn.metrics import accuracy_score\n",
+    "from sklearn.tree import DecisionTreeClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import cohen_kappa_score as kappa\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn import metrics\n",
+    "import matplotlib.pyplot as plt\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\")"
+   ]
+  },
+  {
+   "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",
+       "    }\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>Customer Subtype</th>\n",
+       "      <th>Number of houses</th>\n",
+       "      <th>vg size household</th>\n",
+       "      <th>Avg age</th>\n",
+       "      <th>Customer main type</th>\n",
+       "      <th>Roman catholic</th>\n",
+       "      <th>Protestant</th>\n",
+       "      <th>Other religion</th>\n",
+       "      <th>No religion</th>\n",
+       "      <th>Married</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Number of private accident insurance policies</th>\n",
+       "      <th>Number of family accidents insurance policies</th>\n",
+       "      <th>Number of disability insurance policies</th>\n",
+       "      <th>Number of fire policies</th>\n",
+       "      <th>Number of surfboard policies</th>\n",
+       "      <th>Number of boat policies</th>\n",
+       "      <th>Number of bicycle policies</th>\n",
+       "      <th>Number of property insurance policies</th>\n",
+       "      <th>Number of social security insurance policies</th>\n",
+       "      <th>Number of mobile home policies</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "      <td>5822.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>24.253349</td>\n",
+       "      <td>1.110615</td>\n",
+       "      <td>2.678805</td>\n",
+       "      <td>2.991240</td>\n",
+       "      <td>5.773617</td>\n",
+       "      <td>0.696496</td>\n",
+       "      <td>4.626932</td>\n",
+       "      <td>1.069907</td>\n",
+       "      <td>3.258502</td>\n",
+       "      <td>6.183442</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.005325</td>\n",
+       "      <td>0.006527</td>\n",
+       "      <td>0.004638</td>\n",
+       "      <td>0.570079</td>\n",
+       "      <td>0.000515</td>\n",
+       "      <td>0.006012</td>\n",
+       "      <td>0.031776</td>\n",
+       "      <td>0.007901</td>\n",
+       "      <td>0.014256</td>\n",
+       "      <td>0.059773</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>12.846706</td>\n",
+       "      <td>0.405842</td>\n",
+       "      <td>0.789835</td>\n",
+       "      <td>0.814589</td>\n",
+       "      <td>2.856760</td>\n",
+       "      <td>1.003234</td>\n",
+       "      <td>1.715843</td>\n",
+       "      <td>1.017503</td>\n",
+       "      <td>1.597647</td>\n",
+       "      <td>1.909482</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.072782</td>\n",
+       "      <td>0.080532</td>\n",
+       "      <td>0.077403</td>\n",
+       "      <td>0.562058</td>\n",
+       "      <td>0.022696</td>\n",
+       "      <td>0.081632</td>\n",
+       "      <td>0.210986</td>\n",
+       "      <td>0.090463</td>\n",
+       "      <td>0.119996</td>\n",
+       "      <td>0.237087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>30.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>35.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>4.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>41.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>10.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>5.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>9.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>7.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>8 rows × 86 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Customer Subtype  Number of houses  vg size household      Avg age  \\\n",
+       "count       5822.000000       5822.000000        5822.000000  5822.000000   \n",
+       "mean          24.253349          1.110615           2.678805     2.991240   \n",
+       "std           12.846706          0.405842           0.789835     0.814589   \n",
+       "min            1.000000          1.000000           1.000000     1.000000   \n",
+       "25%           10.000000          1.000000           2.000000     2.000000   \n",
+       "50%           30.000000          1.000000           3.000000     3.000000   \n",
+       "75%           35.000000          1.000000           3.000000     3.000000   \n",
+       "max           41.000000         10.000000           5.000000     6.000000   \n",
+       "\n",
+       "       Customer main type  Roman catholic   Protestant  Other religion  \\\n",
+       "count         5822.000000     5822.000000  5822.000000     5822.000000   \n",
+       "mean             5.773617        0.696496     4.626932        1.069907   \n",
+       "std              2.856760        1.003234     1.715843        1.017503   \n",
+       "min              1.000000        0.000000     0.000000        0.000000   \n",
+       "25%              3.000000        0.000000     4.000000        0.000000   \n",
+       "50%              7.000000        0.000000     5.000000        1.000000   \n",
+       "75%              8.000000        1.000000     6.000000        2.000000   \n",
+       "max             10.000000        9.000000     9.000000        5.000000   \n",
+       "\n",
+       "       No religion      Married               ...                \\\n",
+       "count  5822.000000  5822.000000               ...                 \n",
+       "mean      3.258502     6.183442               ...                 \n",
+       "std       1.597647     1.909482               ...                 \n",
+       "min       0.000000     0.000000               ...                 \n",
+       "25%       2.000000     5.000000               ...                 \n",
+       "50%       3.000000     6.000000               ...                 \n",
+       "75%       4.000000     7.000000               ...                 \n",
+       "max       9.000000     9.000000               ...                 \n",
+       "\n",
+       "       Number of private accident insurance policies  \\\n",
+       "count                                    5822.000000   \n",
+       "mean                                        0.005325   \n",
+       "std                                         0.072782   \n",
+       "min                                         0.000000   \n",
+       "25%                                         0.000000   \n",
+       "50%                                         0.000000   \n",
+       "75%                                         0.000000   \n",
+       "max                                         1.000000   \n",
+       "\n",
+       "       Number of family accidents insurance policies  \\\n",
+       "count                                    5822.000000   \n",
+       "mean                                        0.006527   \n",
+       "std                                         0.080532   \n",
+       "min                                         0.000000   \n",
+       "25%                                         0.000000   \n",
+       "50%                                         0.000000   \n",
+       "75%                                         0.000000   \n",
+       "max                                         1.000000   \n",
+       "\n",
+       "       Number of disability insurance policies  Number of fire policies  \\\n",
+       "count                              5822.000000              5822.000000   \n",
+       "mean                                  0.004638                 0.570079   \n",
+       "std                                   0.077403                 0.562058   \n",
+       "min                                   0.000000                 0.000000   \n",
+       "25%                                   0.000000                 0.000000   \n",
+       "50%                                   0.000000                 1.000000   \n",
+       "75%                                   0.000000                 1.000000   \n",
+       "max                                   2.000000                 7.000000   \n",
+       "\n",
+       "       Number of surfboard policies  Number of boat policies  \\\n",
+       "count                   5822.000000              5822.000000   \n",
+       "mean                       0.000515                 0.006012   \n",
+       "std                        0.022696                 0.081632   \n",
+       "min                        0.000000                 0.000000   \n",
+       "25%                        0.000000                 0.000000   \n",
+       "50%                        0.000000                 0.000000   \n",
+       "75%                        0.000000                 0.000000   \n",
+       "max                        1.000000                 2.000000   \n",
+       "\n",
+       "       Number of bicycle policies  Number of property insurance policies  \\\n",
+       "count                 5822.000000                            5822.000000   \n",
+       "mean                     0.031776                               0.007901   \n",
+       "std                      0.210986                               0.090463   \n",
+       "min                      0.000000                               0.000000   \n",
+       "25%                      0.000000                               0.000000   \n",
+       "50%                      0.000000                               0.000000   \n",
+       "75%                      0.000000                               0.000000   \n",
+       "max                      3.000000                               2.000000   \n",
+       "\n",
+       "       Number of social security insurance policies  \\\n",
+       "count                                   5822.000000   \n",
+       "mean                                       0.014256   \n",
+       "std                                        0.119996   \n",
+       "min                                        0.000000   \n",
+       "25%                                        0.000000   \n",
+       "50%                                        0.000000   \n",
+       "75%                                        0.000000   \n",
+       "max                                        2.000000   \n",
+       "\n",
+       "       Number of mobile home policies  \n",
+       "count                     5822.000000  \n",
+       "mean                         0.059773  \n",
+       "std                          0.237087  \n",
+       "min                          0.000000  \n",
+       "25%                          0.000000  \n",
+       "50%                          0.000000  \n",
+       "75%                          0.000000  \n",
+       "max                          1.000000  \n",
+       "\n",
+       "[8 rows x 86 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Reading the caravan Insurance data \n",
+    "data = pd.read_csv('D:\\\\WorkPlace_R\\\\caravan_insurance.csv')\n",
+    "# Analysing the data \n",
+    "data.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Extracting only Independent Variables\n",
+    "features = ['Customer Subtype', 'Number of houses', 'vg size household', 'Avg age',\n",
+    "       'Customer main type', 'Roman catholic', 'Protestant', 'Other religion',\n",
+    "       'No religion', 'Married', 'Living together', 'Other relation',\n",
+    "       'Singles', 'Household without children', 'Household with children',\n",
+    "       'High level education', 'Medium level education',\n",
+    "       'Lower level education', 'High status', 'Entrepreneur', 'Farmer',\n",
+    "       'Middle management', 'Skilled labourers', 'Unskilled labourers',\n",
+    "       'Social class A', 'Social class B1', 'Social class B2',\n",
+    "       'Social class C', 'Social class D', 'Rented house', 'Home owners',\n",
+    "       'car', 'cars', 'No car', 'National Health Service',\n",
+    "       'Private Health Service', 'Income < 30.000', 'Income 30-45.000',\n",
+    "       'Income 45-75.000', 'Income 75-122.000', 'Income >123.000',\n",
+    "       'Average income', 'Purchasing power class',\n",
+    "       'Contribution private third party insurance',\n",
+    "       'Contribution third party insurance (firms)',\n",
+    "       'Contribution third party insurane (agriculture)',\n",
+    "       'Contribution car policies', 'Contribution delivery van policies',\n",
+    "       'Contribution motorcycle/scooter policies',\n",
+    "       'Contribution lorry policies', 'Contribution trailer policies',\n",
+    "       'Contribution tractor policies',\n",
+    "       'Contribution agricultural machines policies ',\n",
+    "       'Contribution moped policies', 'Contribution life insurances',\n",
+    "       'Contribution private accident insurance policies',\n",
+    "       'Contribution family accidents insurance policies',\n",
+    "       'Contribution disability insurance policies',\n",
+    "       'Contribution fire policies', 'Contribution surfboard policies',\n",
+    "       'Contribution boat policies', 'Contribution bicycle policies',\n",
+    "       'Contribution property insurance policies',\n",
+    "       'Contribution social security insurance policies',\n",
+    "       'Number of private third party insurance',\n",
+    "       'Number of third party insurance (firms) ',\n",
+    "       'Number of third party insurane (agriculture)',\n",
+    "       'Number of car policies', 'Number of delivery van policies',\n",
+    "       'Number of motorcycle/scooter policies', 'Number of lorry policies',\n",
+    "       'Number of trailer policies', 'Number of tractor policies',\n",
+    "       'Number of agricultural machines policies', 'Number of moped policies',\n",
+    "       'Number of life insurances',\n",
+    "       'Number of private accident insurance policies',\n",
+    "       'Number of family accidents insurance policies',\n",
+    "       'Number of disability insurance policies', 'Number of fire policies',\n",
+    "       'Number of surfboard policies', 'Number of boat policies',\n",
+    "       'Number of bicycle policies', 'Number of property insurance policies',\n",
+    "       'Number of social security insurance policies']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "X and Y Input Data:    (5822, 85) (5822, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Normalization - Using MinMax Scaler\n",
+    "min_max_scaler = preprocessing.MinMaxScaler()\n",
+    "X = min_max_scaler.fit_transform(data[features])\n",
+    "\n",
+    "y = np.vstack(data['Number of mobile home policies'].values)\n",
+    "print('X and Y Input Data:   ', X.shape, y.shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Shape:    (4075, 85) (4075, 1)\n",
+      "Training Set Shape:    (4075, 85) (4075, 1)\n",
+      "Validation Set Shape:  (1170, 85) (1170, 1)\n",
+      "Test Set Shape:        (577, 85) (577, 1)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Spliting test and train data \n",
+    "X_train_original, X_test2, y_train_original, y_test2 = train_test_split(X, y, test_size=0.3,\n",
+    "                                                                        random_state=42)\n",
+    "\n",
+    "print('Training Set Shape:   ', X_train_original.shape, y_train_original.shape)\n",
+    "X_train_original, X_test2, y_train_original, y_test2 = train_test_split(X, y, test_size=0.3,\n",
+    "                                                                        random_state=42)\n",
+    "\n",
+    "print('Training Set Shape:   ', X_train_original.shape, y_train_original.shape)\n",
+    "\n",
+    "X_val, X_test, y_val, y_test = train_test_split(X_test2, y_test2, test_size=0.33,random_state=42)\n",
+    "# Used Seed in Partitioning so that Test Set remains same for every Run\n",
+    "\n",
+    "print('Validation Set Shape: ', X_val.shape,y_val.shape)\n",
+    "print('Test Set Shape:       ', X_test.shape, y_test.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Training Set Shape after oversampling:  (7692, 85) (7692,)\n",
+      "col_0     0     1\n",
+      "row_0            \n",
+      "0      3846     0\n",
+      "1         0  3846\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Oversampling of underrepresented class\n",
+    "# Class to perform over-sampling using SMOTE\n",
+    "# conda install -c glemaitre imbalanced-learn\n",
+    "doOversampling = True\n",
+    "\n",
+    "if doOversampling:\n",
+    "# Apply regular SMOTE\n",
+    "    sm = SMOTE(kind='regular')\n",
+    "    X_train, y_train = sm.fit_sample(X_train_original, y_train_original)\n",
+    "    print('Training Set Shape after oversampling: ', X_train.shape, y_train.shape)\n",
+    "    print(pd.crosstab(y_train,y_train))\n",
+    "else:\n",
+    "    X_train = X_train_original\n",
+    "    y_train = y_train_original "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 1. Logistic Regression\n",
+    "Logistic Regression (aka logit, MaxEnt) classifier.\n",
+    "1. solver : {‘newton-cg’, ‘lbfgs’, ‘liblinear’, ‘sag’, ‘saga’} - Solver refers to the optimization method to use to find the optimum of the objective function.\n",
+    " 1. newton-cg - the nonlinear conjugate gradient method generalizes the conjugate gradient method to nonlinear optimization. The minimum of f(x) is obtained when the gradient is 0.\n",
+    " 2. lbfgs - Limited-memory Broyden–Fletcher–Goldfarb–Shanno algorithm. The algorithm's target problem is to minimise f(x) over unconstrained values of the real-vector X  where f is a differentiable scalar function.\n",
+    " 3. LIBLINEAR is a linear classifier for data with millions of instances and features. It supports\n",
+    "\n",
+    "    * L2-regularized classifiers \n",
+    "    * L2-loss linear SVM, L1-loss linear SVM, and logistic regression (LR)\n",
+    "    * L1-regularized classifiers (after version 1.4) \n",
+    "    * L2-loss linear SVM and logistic regression (LR)\n",
+    "    * L2-regularized support vector regression (after version 1.9) \n",
+    "    * L2-loss linear SVR and L1-loss linear SVR.\n",
+    "   \n",
+    "      i) Main features of LIBLINEAR include\n",
+    "\n",
+    "        * Multi-class classification: 1) one-vs-the rest, 2) Crammer & Singer\n",
+    "        * Cross validation for model evaulation\n",
+    "        * Automatic parameter selection\n",
+    "        * Probability estimates (logistic regression only)\n",
+    "        * Weights for unbalanced data\n",
+    " 4. Stochastic Average Gradient (SAG) method, as well as several related methods, for the problem of L2-regularized logistic regression with a finite training set\n",
+    " 5. SAGA - Which handle both multinomial loss and L1 penalty\n",
+    "\n",
+    "2. fit_intercept : bool, default: True, fit_intercept=False sets the y-intercept to 0. If fit_intercept=True, the y-intercept will be determined by the line of best fit."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 121,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'liblinear' Solver</td>\n",
+       "      <td>0.673318</td>\n",
+       "      <td>0.128852</td>\n",
+       "      <td>0.630137</td>\n",
+       "      <td>0.711111</td>\n",
+       "      <td>0.123106</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                          Model  ROC Score  Precision Score  \\\n",
+       "0   Logistic Regression with 'liblinear' Solver   0.673318         0.128852   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.630137        0.711111     0.123106  "
+      ]
+     },
+     "execution_count": 121,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a logistic Regression model using 'liblinear' solver\n",
+    "clf_Log1 = LogisticRegression(solver='liblinear', max_iter=1000, random_state=123)\n",
+    "# Fitting the model\n",
+    "clf_Log1.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_Log1 = clf_Log1.predict(X_val)\n",
+    "\n",
+    "cols = ['Model', 'ROC Score', 'Precision Score', 'Recall Score','Accuracy Score','Kappa Score']\n",
+    "models_report = pd.DataFrame(columns = cols)\n",
+    "\n",
+    "tmp1 = pd.Series({'Model': \" Logistic Regression with 'liblinear' Solver\",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_Log1),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_Log1),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_Log1),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_Log1),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_Log1)})\n",
+    "\n",
+    "model1_report = models_report.append(tmp1, ignore_index = True)\n",
+    "model1_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 122,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'newton-cg' Solver</td>\n",
+       "      <td>0.670115</td>\n",
+       "      <td>0.12931</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.717094</td>\n",
+       "      <td>0.123351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           Model  ROC Score  Precision Score  \\\n",
+       "0   Logistic Regression with 'newton-cg' Solver    0.670115          0.12931   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.616438        0.717094     0.123351  "
+      ]
+     },
+     "execution_count": 122,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a logistic Regression model using 'newton-cg' solver\n",
+    "clf_Log2 = LogisticRegression(solver='newton-cg', max_iter=50, random_state=123)\n",
+    "# Fitting the model\n",
+    "clf_Log2.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_Log2 = clf_Log2.predict(X_val)\n",
+    "\n",
+    "tmp2 = pd.Series({'Model': \" Logistic Regression with 'newton-cg' Solver \",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_Log2),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_Log2),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_Log2),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_Log2),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_Log2)})\n",
+    "\n",
+    "model2_report = models_report.append(tmp2, ignore_index = True)\n",
+    "model2_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 123,
+   "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",
+       "        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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'lbfgs' Solver</td>\n",
+       "      <td>0.669659</td>\n",
+       "      <td>0.12894</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.716239</td>\n",
+       "      <td>0.122736</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      Model  ROC Score  Precision Score  \\\n",
+       "0   Logistic Regression with 'lbfgs' Solver   0.669659          0.12894   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.616438        0.716239     0.122736  "
+      ]
+     },
+     "execution_count": 123,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a logistic Regression model using 'lbfgs' solver\n",
+    "clf_Log3 = LogisticRegression(solver='lbfgs', max_iter=100, random_state=123)\n",
+    "# Fitting the model\n",
+    "clf_Log3.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_Log3 = clf_Log3.predict(X_val)\n",
+    "\n",
+    "tmp3 = pd.Series({'Model': \" Logistic Regression with 'lbfgs' Solver\",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_Log3),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_Log3),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_Log3),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_Log3),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_Log3)})\n",
+    "\n",
+    "model3_report = models_report.append(tmp3, ignore_index = True)\n",
+    "model3_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 124,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'sag' Solver</td>\n",
+       "      <td>0.670115</td>\n",
+       "      <td>0.12931</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.717094</td>\n",
+       "      <td>0.123351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    Model  ROC Score  Precision Score  \\\n",
+       "0   Logistic Regression with 'sag' Solver   0.670115          0.12931   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.616438        0.717094     0.123351  "
+      ]
+     },
+     "execution_count": 124,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a logistic Regression model using 'sag' solver\n",
+    "clf_Log4 = LogisticRegression(solver='sag', max_iter=500, random_state=123)\n",
+    "# Fitting the model\n",
+    "clf_Log4.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_Log4 = clf_Log4.predict(X_val)\n",
+    "\n",
+    "\n",
+    "tmp4 = pd.Series({'Model': \" Logistic Regression with 'sag' Solver\",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_Log4),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_Log4),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_Log4),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_Log4),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_Log4)})\n",
+    "\n",
+    "model4_report = models_report.append(tmp4, ignore_index = True)\n",
+    "model4_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 125,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "convergence after 143 epochs took 4 seconds\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.0s remaining:    0.0s\n",
+      "[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    4.0s finished\n"
+     ]
+    },
+    {
+     "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'saga' Solver</td>\n",
+       "      <td>0.670115</td>\n",
+       "      <td>0.12931</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.717094</td>\n",
+       "      <td>0.123351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     Model  ROC Score  Precision Score  \\\n",
+       "0   Logistic Regression with 'saga' Solver   0.670115          0.12931   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.616438        0.717094     0.123351  "
+      ]
+     },
+     "execution_count": 125,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a logistic Regression model using 'saga' solver\n",
+    "clf_Log5 = LogisticRegression(solver='saga', max_iter=1000, random_state=123,verbose=2,class_weight='balanced')\n",
+    "# Fitting the model\n",
+    "clf_Log5.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_Log5 = clf_Log5.predict(X_val)\n",
+    "\n",
+    "tmp5 = pd.Series({'Model': \" Logistic Regression with 'saga' Solver\",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_Log5),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_Log5),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_Log5),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_Log5),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_Log5)})\n",
+    "\n",
+    "model5_report = models_report.append(tmp5, ignore_index = True)\n",
+    "model5_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 126,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'liblinear' Solver</td>\n",
+       "      <td>0.673318</td>\n",
+       "      <td>0.128852</td>\n",
+       "      <td>0.630137</td>\n",
+       "      <td>0.711111</td>\n",
+       "      <td>0.123106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'newton-cg' Solver</td>\n",
+       "      <td>0.670115</td>\n",
+       "      <td>0.129310</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.717094</td>\n",
+       "      <td>0.123351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'lbfgs' Solver</td>\n",
+       "      <td>0.669659</td>\n",
+       "      <td>0.128940</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.716239</td>\n",
+       "      <td>0.122736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'sag' Solver</td>\n",
+       "      <td>0.670115</td>\n",
+       "      <td>0.129310</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.717094</td>\n",
+       "      <td>0.123351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'saga' Solver</td>\n",
+       "      <td>0.670115</td>\n",
+       "      <td>0.129310</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.717094</td>\n",
+       "      <td>0.123351</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           Model  ROC Score  Precision Score  \\\n",
+       "0    Logistic Regression with 'liblinear' Solver   0.673318         0.128852   \n",
+       "0   Logistic Regression with 'newton-cg' Solver    0.670115         0.129310   \n",
+       "0        Logistic Regression with 'lbfgs' Solver   0.669659         0.128940   \n",
+       "0          Logistic Regression with 'sag' Solver   0.670115         0.129310   \n",
+       "0         Logistic Regression with 'saga' Solver   0.670115         0.129310   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.630137        0.711111     0.123106  \n",
+       "0      0.616438        0.717094     0.123351  \n",
+       "0      0.616438        0.716239     0.122736  \n",
+       "0      0.616438        0.717094     0.123351  \n",
+       "0      0.616438        0.717094     0.123351  "
+      ]
+     },
+     "execution_count": 126,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Comparison of Logistic Regression models interms of various solvers\n",
+    "cols = ['Model', 'ROC Score', 'Precision Score', 'Recall Score','Accuracy Score','Kappa Score']\n",
+    "model_log = pd.DataFrame(columns = cols)\n",
+    "model_log = model_log.append([model1_report,model2_report,model3_report,model4_report,model5_report])\n",
+    "model_log"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 2. Decision Tree Classifier\n",
+    "\n",
+    "1. criterion : string, optional (default=”gini”). The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain.\n",
+    "\n",
+    "2. splitter : string, optional (default=”best”). The strategy used to choose the split at each node. Supported strategies are “best” to choose the best split and “random” to choose the best random split."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 172,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (GINI &amp; BEST)</td>\n",
+       "      <td>0.539816</td>\n",
+       "      <td>0.107438</td>\n",
+       "      <td>0.178082</td>\n",
+       "      <td>0.85641</td>\n",
+       "      <td>0.060932</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                Model  ROC Score  Precision Score  \\\n",
+       "0   Decision Tree with (GINI & BEST)    0.539816         0.107438   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.178082         0.85641     0.060932  "
+      ]
+     },
+     "execution_count": 172,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a Decision Tree model using 'gini' solver and splitter 'best'\n",
+    "clf_DT1 = DecisionTreeClassifier(criterion='gini', splitter='best', max_depth=10, \n",
+    "                                min_samples_split=2, min_samples_leaf=1, \n",
+    "                                min_weight_fraction_leaf=0.0,\n",
+    "                                min_impurity_split=1e-07)\n",
+    "# Fitting the model\n",
+    "clf_DT1.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_DT1 = clf_DT1.predict(X_val)\n",
+    "\n",
+    "tmp1 = pd.Series({'Model': \" Decision Tree with (GINI & BEST) \",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_DT1),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_DT1),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_DT1),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_DT1),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_DT1)})\n",
+    "\n",
+    "model_dt1_report = models_report.append(tmp1, ignore_index = True)\n",
+    "model_dt1_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 173,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (GINI &amp; RANDOM)</td>\n",
+       "      <td>0.575923</td>\n",
+       "      <td>0.123529</td>\n",
+       "      <td>0.287671</td>\n",
+       "      <td>0.828205</td>\n",
+       "      <td>0.093722</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                  Model  ROC Score  Precision Score  \\\n",
+       "0   Decision Tree with (GINI & RANDOM)    0.575923         0.123529   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.287671        0.828205     0.093722  "
+      ]
+     },
+     "execution_count": 173,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a Decision Tree model using 'gini' solver and splitter 'random'\n",
+    "clf_DT2 = DecisionTreeClassifier(criterion='gini', splitter='random', max_depth=10, \n",
+    "                                min_samples_split=2, min_samples_leaf=1, \n",
+    "                                min_weight_fraction_leaf=0.0, min_impurity_split=1e-07)\n",
+    "# Fitting the model\n",
+    "clf_DT2.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_DT2 = clf_DT2.predict(X_val)\n",
+    "\n",
+    "tmp2 = pd.Series({'Model': \" Decision Tree with (GINI & RANDOM) \",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_DT2),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_DT2),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_DT2),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_DT2),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_DT2)})\n",
+    "\n",
+    "model_dt2_report = models_report.append(tmp2, ignore_index = True)\n",
+    "model_dt2_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 174,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (ENTROPY &amp; BEST)</td>\n",
+       "      <td>0.522459</td>\n",
+       "      <td>0.09009</td>\n",
+       "      <td>0.136986</td>\n",
+       "      <td>0.859829</td>\n",
+       "      <td>0.036137</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   Model  ROC Score  Precision Score  \\\n",
+       "0   Decision Tree with (ENTROPY & BEST)    0.522459          0.09009   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.136986        0.859829     0.036137  "
+      ]
+     },
+     "execution_count": 174,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a Decision Tree model using 'entropy' solver and splitter 'best'\n",
+    "clf_DT3 = DecisionTreeClassifier(criterion='entropy', splitter='best', max_depth=10, \n",
+    "                                min_samples_split=2, min_samples_leaf=1, \n",
+    "                                min_weight_fraction_leaf=0.0,  \n",
+    "                                min_impurity_split=1e-07)\n",
+    "# Fitting the model\n",
+    "clf_DT3.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_DT3 = clf_DT3.predict(X_val)\n",
+    "\n",
+    "tmp3 = pd.Series({'Model': \" Decision Tree with (ENTROPY & BEST) \",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_DT3),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_DT3),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_DT3),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_DT3),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_DT3)})\n",
+    "\n",
+    "model_dt3_report = models_report.append(tmp3, ignore_index = True)\n",
+    "model_dt3_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 175,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (ENTROPY &amp; RANDOM)</td>\n",
+       "      <td>0.61065</td>\n",
+       "      <td>0.135922</td>\n",
+       "      <td>0.383562</td>\n",
+       "      <td>0.809402</td>\n",
+       "      <td>0.1196</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     Model  ROC Score  Precision Score  \\\n",
+       "0   Decision Tree with (ENTROPY & RANDOM)     0.61065         0.135922   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.383562        0.809402       0.1196  "
+      ]
+     },
+     "execution_count": 175,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a Decision Tree model using 'entropy' solver and splitter 'random'\n",
+    "clf_DT4 = DecisionTreeClassifier(criterion='entropy', splitter='random', max_depth=10, \n",
+    "                                min_samples_split=2, min_samples_leaf=1, \n",
+    "                                min_weight_fraction_leaf=0.0,  \n",
+    "                                min_impurity_split=1e-07)\n",
+    "# Fitting the model\n",
+    "clf_DT4.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_DT4 = clf_DT4.predict(X_val)\n",
+    "\n",
+    "tmp4 = pd.Series({'Model': \" Decision Tree with (ENTROPY & RANDOM) \",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_DT4),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_DT4),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_DT4),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_DT4),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_DT4)})\n",
+    "\n",
+    "model_dt4_report = models_report.append(tmp4, ignore_index = True)\n",
+    "model_dt4_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 176,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (GINI &amp; BEST)</td>\n",
+       "      <td>0.539816</td>\n",
+       "      <td>0.107438</td>\n",
+       "      <td>0.178082</td>\n",
+       "      <td>0.856410</td>\n",
+       "      <td>0.060932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (GINI &amp; RANDOM)</td>\n",
+       "      <td>0.575923</td>\n",
+       "      <td>0.123529</td>\n",
+       "      <td>0.287671</td>\n",
+       "      <td>0.828205</td>\n",
+       "      <td>0.093722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (ENTROPY &amp; BEST)</td>\n",
+       "      <td>0.522459</td>\n",
+       "      <td>0.090090</td>\n",
+       "      <td>0.136986</td>\n",
+       "      <td>0.859829</td>\n",
+       "      <td>0.036137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (ENTROPY &amp; RANDOM)</td>\n",
+       "      <td>0.610650</td>\n",
+       "      <td>0.135922</td>\n",
+       "      <td>0.383562</td>\n",
+       "      <td>0.809402</td>\n",
+       "      <td>0.119600</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                     Model  ROC Score  Precision Score  \\\n",
+       "0        Decision Tree with (GINI & BEST)    0.539816         0.107438   \n",
+       "0      Decision Tree with (GINI & RANDOM)    0.575923         0.123529   \n",
+       "0     Decision Tree with (ENTROPY & BEST)    0.522459         0.090090   \n",
+       "0   Decision Tree with (ENTROPY & RANDOM)    0.610650         0.135922   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.178082        0.856410     0.060932  \n",
+       "0      0.287671        0.828205     0.093722  \n",
+       "0      0.136986        0.859829     0.036137  \n",
+       "0      0.383562        0.809402     0.119600  "
+      ]
+     },
+     "execution_count": 176,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Comparison of Decision Tree models based on criterion and splitter\n",
+    "cols = ['Model', 'ROC Score', 'Precision Score', 'Recall Score','Accuracy Score','Kappa Score']\n",
+    "model_DT = pd.DataFrame(columns = cols)\n",
+    "model_DT = model_DT.append([model_dt1_report,model_dt2_report,model_dt3_report,model_dt4_report])\n",
+    "model_DT"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 3. Random Forest\n",
+    "\n",
+    "1. criterion : string, optional (default=”gini”)\n",
+    "    The function to measure the quality of a split. Supported criteria are “gini” for the Gini impurity and “entropy” for the information gain. Note: this parameter is tree-specific.\n",
+    "\n",
+    "2. max_features : int, float, string or None, optional (default=”auto”)\n",
+    "\n",
+    "    The number of features to consider when looking for the best split:\n",
+    "\n",
+    "    1. If “auto”, then max_features=sqrt(n_features)\n",
+    "    2. If “sqrt”, then max_features=sqrt(n_features) (same as “auto”).\n",
+    "    3. If “log2”, then max_features=log2(n_features)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 164,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with GINI</td>\n",
+       "      <td>0.535183</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.09589</td>\n",
+       "      <td>0.919658</td>\n",
+       "      <td>0.092948</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Model  ROC Score  Precision Score  Recall Score  \\\n",
+       "0   Random Forest with GINI    0.535183              0.2       0.09589   \n",
+       "\n",
+       "   Accuracy Score  Kappa Score  \n",
+       "0        0.919658     0.092948  "
+      ]
+     },
+     "execution_count": 164,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a Decision Tree model using 'gini' solver and 'auto' max_features\n",
+    "clf_RF1 = RandomForestClassifier(n_estimators=500, criterion='gini', max_depth=15,\n",
+    "                                min_samples_split=2, min_samples_leaf=1,\n",
+    "                                max_features='auto', \n",
+    "                                n_jobs=1,random_state=42)\n",
+    "# Fitting the model\n",
+    "clf_RF1.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_RF1 = clf_RF1.predict(X_val)\n",
+    "\n",
+    "tmp1 = pd.Series({'Model': \" Random Forest with GINI \",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_RF1),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_RF1),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_RF1),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_RF1),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_RF1)})\n",
+    "\n",
+    "model_rt1_report = models_report.append(tmp1, ignore_index = True)\n",
+    "model_rt1_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 165,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy</td>\n",
+       "      <td>0.529701</td>\n",
+       "      <td>0.193548</td>\n",
+       "      <td>0.082192</td>\n",
+       "      <td>0.921368</td>\n",
+       "      <td>0.081209</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                          Model  ROC Score  Precision Score  Recall Score  \\\n",
+       "0   Random Forest with Entropy    0.529701         0.193548      0.082192   \n",
+       "\n",
+       "   Accuracy Score  Kappa Score  \n",
+       "0        0.921368     0.081209  "
+      ]
+     },
+     "execution_count": 165,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a Decision Tree model using 'entropy' solver and 'auto' max_features\n",
+    "clf_RF2 = RandomForestClassifier(n_estimators=500, criterion='entropy', max_depth=15,\n",
+    "                                min_samples_split=2, min_samples_leaf=1,\n",
+    "                                max_features='auto', random_state=42)\n",
+    "# Fitting the model\n",
+    "clf_RF2.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_RF2 = clf_RF2.predict(X_val)\n",
+    "\n",
+    "tmp2 = pd.Series({'Model': \" Random Forest with Entropy \",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_RF2),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_RF2),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_RF2),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_RF2),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_RF2)})\n",
+    "\n",
+    "model_rt2_report = models_report.append(tmp2, ignore_index = True)\n",
+    "model_rt2_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 166,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with GINI (sqrt)</td>\n",
+       "      <td>0.518281</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.054795</td>\n",
+       "      <td>0.923932</td>\n",
+       "      <td>0.053243</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             Model  ROC Score  Precision Score  Recall Score  \\\n",
+       "0   Random Forest with GINI (sqrt)   0.518281         0.166667      0.054795   \n",
+       "\n",
+       "   Accuracy Score  Kappa Score  \n",
+       "0        0.923932     0.053243  "
+      ]
+     },
+     "execution_count": 166,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a Decision Tree model using 'gini' solver and 'sqrt' max_features\n",
+    "clf_RF3 = RandomForestClassifier(n_estimators=100, criterion='gini', max_depth=15,\n",
+    "                                min_samples_split=5, min_samples_leaf=1,\n",
+    "                                max_features='sqrt', random_state=42)\n",
+    "# Fitting the model\n",
+    "clf_RF3.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_RF3 = clf_RF3.predict(X_val)\n",
+    "\n",
+    "tmp3 = pd.Series({'Model': \" Random Forest with GINI (sqrt)\",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_RF3),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_RF3),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_RF3),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_RF3),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_RF3)})\n",
+    "\n",
+    "model_rt3_report = models_report.append(tmp3, ignore_index = True)\n",
+    "model_rt3_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 178,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy (sqrt)</td>\n",
+       "      <td>0.519649</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.054795</td>\n",
+       "      <td>0.926496</td>\n",
+       "      <td>0.05887</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Model  ROC Score  Precision Score  \\\n",
+       "0   Random Forest with Entropy (sqrt)    0.519649         0.190476   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.054795        0.926496      0.05887  "
+      ]
+     },
+     "execution_count": 178,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a Decision Tree model using 'entropy' solver and 'sqrt' max_features\n",
+    "clf_RF4 = RandomForestClassifier(n_estimators=100, criterion='entropy', max_depth=15,\n",
+    "                                min_samples_split=5, min_samples_leaf=1,\n",
+    "                                max_features='sqrt', random_state=42)\n",
+    "# Fitting the model\n",
+    "clf_RF4.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_RF4 = clf_RF4.predict(X_val)\n",
+    "\n",
+    "tmp4 = pd.Series({'Model': \" Random Forest with Entropy (sqrt) \",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_RF4),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_RF4),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_RF4),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_RF4),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_RF4)})\n",
+    "\n",
+    "model_rt4_report = models_report.append(tmp4, ignore_index = True)\n",
+    "model_rt4_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 187,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with GINI (log2)</td>\n",
+       "      <td>0.531524</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.082192</td>\n",
+       "      <td>0.924786</td>\n",
+       "      <td>0.089317</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             Model  ROC Score  Precision Score  Recall Score  \\\n",
+       "0   Random Forest with GINI (log2)   0.531524         0.222222      0.082192   \n",
+       "\n",
+       "   Accuracy Score  Kappa Score  \n",
+       "0        0.924786     0.089317  "
+      ]
+     },
+     "execution_count": 187,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a Decision Tree model using 'gini' solver and 'log2' max_features\n",
+    "clf_RF5 = RandomForestClassifier(n_estimators=250, criterion='gini', max_depth=15,\n",
+    "                                min_samples_split=11, min_samples_leaf=1,\n",
+    "                                max_features='log2',random_state=42)\n",
+    "# Fitting the model\n",
+    "clf_RF5.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_RF5 = clf_RF5.predict(X_val)\n",
+    "\n",
+    "tmp5 = pd.Series({'Model': \" Random Forest with GINI (log2)\",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_RF5),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_RF5),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_RF5),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_RF5),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_RF5)})\n",
+    "\n",
+    "model_rt5_report = models_report.append(tmp5, ignore_index = True)\n",
+    "model_rt5_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 181,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy (log2)</td>\n",
+       "      <td>0.520105</td>\n",
+       "      <td>0.2</td>\n",
+       "      <td>0.054795</td>\n",
+       "      <td>0.92735</td>\n",
+       "      <td>0.060818</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Model  ROC Score  Precision Score  \\\n",
+       "0   Random Forest with Entropy (log2)    0.520105              0.2   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.054795         0.92735     0.060818  "
+      ]
+     },
+     "execution_count": 181,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Building a Decision Tree model using 'entropy' solver and 'log2' max_features\n",
+    "clf_RF6 = RandomForestClassifier(n_estimators=250, criterion='entropy', max_depth=15,\n",
+    "                                min_samples_split=11, min_samples_leaf=1,\n",
+    "                                max_features='log2',random_state=42)\n",
+    "# Fitting the model\n",
+    "clf_RF6.fit(X_train, y_train)\n",
+    "# Predicting the model\n",
+    "y_pred_RF6 = clf_RF6.predict(X_val)\n",
+    "\n",
+    "tmp6 = pd.Series({'Model': \" Random Forest with Entropy (log2) \",\n",
+    "                 'ROC Score' : metrics.roc_auc_score(y_val, y_pred_RF6),\n",
+    "                 'Precision Score': metrics.precision_score(y_val, y_pred_RF6),\n",
+    "                 'Recall Score': metrics.recall_score(y_val, y_pred_RF6),\n",
+    "                 'Accuracy Score': metrics.accuracy_score(y_val, y_pred_RF6),\n",
+    "                 'Kappa Score':metrics.cohen_kappa_score(y_val, y_pred_RF6)})\n",
+    "\n",
+    "model_rt6_report = models_report.append(tmp6, ignore_index = True)\n",
+    "model_rt6_report"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 194,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with GINI</td>\n",
+       "      <td>0.535183</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.095890</td>\n",
+       "      <td>0.919658</td>\n",
+       "      <td>0.092948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy</td>\n",
+       "      <td>0.529701</td>\n",
+       "      <td>0.193548</td>\n",
+       "      <td>0.082192</td>\n",
+       "      <td>0.921368</td>\n",
+       "      <td>0.081209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy (sqrt)</td>\n",
+       "      <td>0.519649</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.054795</td>\n",
+       "      <td>0.926496</td>\n",
+       "      <td>0.058870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy (sqrt)</td>\n",
+       "      <td>0.519649</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.054795</td>\n",
+       "      <td>0.926496</td>\n",
+       "      <td>0.058870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with GINI (log2)</td>\n",
+       "      <td>0.531524</td>\n",
+       "      <td>0.222222</td>\n",
+       "      <td>0.082192</td>\n",
+       "      <td>0.924786</td>\n",
+       "      <td>0.089317</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy (log2)</td>\n",
+       "      <td>0.520105</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.054795</td>\n",
+       "      <td>0.927350</td>\n",
+       "      <td>0.060818</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                 Model  ROC Score  Precision Score  \\\n",
+       "0             Random Forest with GINI    0.535183         0.200000   \n",
+       "0          Random Forest with Entropy    0.529701         0.193548   \n",
+       "0   Random Forest with Entropy (sqrt)    0.519649         0.190476   \n",
+       "0   Random Forest with Entropy (sqrt)    0.519649         0.190476   \n",
+       "0       Random Forest with GINI (log2)   0.531524         0.222222   \n",
+       "0   Random Forest with Entropy (log2)    0.520105         0.200000   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.095890        0.919658     0.092948  \n",
+       "0      0.082192        0.921368     0.081209  \n",
+       "0      0.054795        0.926496     0.058870  \n",
+       "0      0.054795        0.926496     0.058870  \n",
+       "0      0.082192        0.924786     0.089317  \n",
+       "0      0.054795        0.927350     0.060818  "
+      ]
+     },
+     "execution_count": 194,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Comparison of Random Forest based on criterion and max_features\n",
+    "cols = ['Model', 'ROC Score', 'Precision Score', 'Recall Score','Accuracy Score','Kappa Score']\n",
+    "model_rf = pd.DataFrame(columns = cols)\n",
+    "model_rf = model_rf.append([model_rt1_report,model_rt2_report,model_rt3_report,model_rt4_report,model_rt5_report,model_rt6_report])\n",
+    "model_rf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 186,
+   "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>Model</th>\n",
+       "      <th>ROC Score</th>\n",
+       "      <th>Precision Score</th>\n",
+       "      <th>Recall Score</th>\n",
+       "      <th>Accuracy Score</th>\n",
+       "      <th>Kappa Score</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'liblinear' Solver</td>\n",
+       "      <td>0.673318</td>\n",
+       "      <td>0.128852</td>\n",
+       "      <td>0.630137</td>\n",
+       "      <td>0.711111</td>\n",
+       "      <td>0.123106</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'newton-cg' Solver</td>\n",
+       "      <td>0.670115</td>\n",
+       "      <td>0.129310</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.717094</td>\n",
+       "      <td>0.123351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'lbfgs' Solver</td>\n",
+       "      <td>0.669659</td>\n",
+       "      <td>0.128940</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.716239</td>\n",
+       "      <td>0.122736</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'sag' Solver</td>\n",
+       "      <td>0.670115</td>\n",
+       "      <td>0.129310</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.717094</td>\n",
+       "      <td>0.123351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Logistic Regression with 'saga' Solver</td>\n",
+       "      <td>0.670115</td>\n",
+       "      <td>0.129310</td>\n",
+       "      <td>0.616438</td>\n",
+       "      <td>0.717094</td>\n",
+       "      <td>0.123351</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (GINI &amp; BEST)</td>\n",
+       "      <td>0.539816</td>\n",
+       "      <td>0.107438</td>\n",
+       "      <td>0.178082</td>\n",
+       "      <td>0.856410</td>\n",
+       "      <td>0.060932</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (GINI &amp; RANDOM)</td>\n",
+       "      <td>0.575923</td>\n",
+       "      <td>0.123529</td>\n",
+       "      <td>0.287671</td>\n",
+       "      <td>0.828205</td>\n",
+       "      <td>0.093722</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (ENTROPY &amp; BEST)</td>\n",
+       "      <td>0.522459</td>\n",
+       "      <td>0.090090</td>\n",
+       "      <td>0.136986</td>\n",
+       "      <td>0.859829</td>\n",
+       "      <td>0.036137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Decision Tree with (ENTROPY &amp; RANDOM)</td>\n",
+       "      <td>0.610650</td>\n",
+       "      <td>0.135922</td>\n",
+       "      <td>0.383562</td>\n",
+       "      <td>0.809402</td>\n",
+       "      <td>0.119600</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with GINI</td>\n",
+       "      <td>0.535183</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.095890</td>\n",
+       "      <td>0.919658</td>\n",
+       "      <td>0.092948</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy</td>\n",
+       "      <td>0.529701</td>\n",
+       "      <td>0.193548</td>\n",
+       "      <td>0.082192</td>\n",
+       "      <td>0.921368</td>\n",
+       "      <td>0.081209</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy (sqrt)</td>\n",
+       "      <td>0.519649</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.054795</td>\n",
+       "      <td>0.926496</td>\n",
+       "      <td>0.058870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy (sqrt)</td>\n",
+       "      <td>0.519649</td>\n",
+       "      <td>0.190476</td>\n",
+       "      <td>0.054795</td>\n",
+       "      <td>0.926496</td>\n",
+       "      <td>0.058870</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy (log2)</td>\n",
+       "      <td>0.520105</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.054795</td>\n",
+       "      <td>0.927350</td>\n",
+       "      <td>0.060818</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Random Forest with Entropy (log2)</td>\n",
+       "      <td>0.520105</td>\n",
+       "      <td>0.200000</td>\n",
+       "      <td>0.054795</td>\n",
+       "      <td>0.927350</td>\n",
+       "      <td>0.060818</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           Model  ROC Score  Precision Score  \\\n",
+       "0    Logistic Regression with 'liblinear' Solver   0.673318         0.128852   \n",
+       "0   Logistic Regression with 'newton-cg' Solver    0.670115         0.129310   \n",
+       "0        Logistic Regression with 'lbfgs' Solver   0.669659         0.128940   \n",
+       "0          Logistic Regression with 'sag' Solver   0.670115         0.129310   \n",
+       "0         Logistic Regression with 'saga' Solver   0.670115         0.129310   \n",
+       "0              Decision Tree with (GINI & BEST)    0.539816         0.107438   \n",
+       "0            Decision Tree with (GINI & RANDOM)    0.575923         0.123529   \n",
+       "0           Decision Tree with (ENTROPY & BEST)    0.522459         0.090090   \n",
+       "0         Decision Tree with (ENTROPY & RANDOM)    0.610650         0.135922   \n",
+       "0                       Random Forest with GINI    0.535183         0.200000   \n",
+       "0                    Random Forest with Entropy    0.529701         0.193548   \n",
+       "0             Random Forest with Entropy (sqrt)    0.519649         0.190476   \n",
+       "0             Random Forest with Entropy (sqrt)    0.519649         0.190476   \n",
+       "0              Random Forest with Entropy (log2)   0.520105         0.200000   \n",
+       "0             Random Forest with Entropy (log2)    0.520105         0.200000   \n",
+       "\n",
+       "   Recall Score  Accuracy Score  Kappa Score  \n",
+       "0      0.630137        0.711111     0.123106  \n",
+       "0      0.616438        0.717094     0.123351  \n",
+       "0      0.616438        0.716239     0.122736  \n",
+       "0      0.616438        0.717094     0.123351  \n",
+       "0      0.616438        0.717094     0.123351  \n",
+       "0      0.178082        0.856410     0.060932  \n",
+       "0      0.287671        0.828205     0.093722  \n",
+       "0      0.136986        0.859829     0.036137  \n",
+       "0      0.383562        0.809402     0.119600  \n",
+       "0      0.095890        0.919658     0.092948  \n",
+       "0      0.082192        0.921368     0.081209  \n",
+       "0      0.054795        0.926496     0.058870  \n",
+       "0      0.054795        0.926496     0.058870  \n",
+       "0      0.054795        0.927350     0.060818  \n",
+       "0      0.054795        0.927350     0.060818  "
+      ]
+     },
+     "execution_count": 186,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Comparison of various model\n",
+    "cols = ['Model', 'ROC Score', 'Precision Score', 'Recall Score','Accuracy Score','Kappa Score']\n",
+    "clas_model = pd.DataFrame(columns = cols)\n",
+    "clas_model = clas_model.append([model_log,model_DT,model_rf])\n",
+    "clas_model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# CONCLUSION\n",
+    "\n",
+    "As we can see in the table with the comparision of various models, Out of all the models that we have created, RandomForest model gives the best accuracy of 92 %.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/Classification/Caravan_Insurance_Claim_Prediction/caravan_insurance (1).csv b/Classification/Caravan_Insurance_Claim_Prediction/caravan_insurance (1).csv
new file mode 100644
index 0000000..49150e5
--- /dev/null
+++ b/Classification/Caravan_Insurance_Claim_Prediction/caravan_insurance (1).csv	
@@ -0,0 +1,5823 @@
+Customer Subtype,Number of houses,vg size household,Avg age,Customer main type,Roman catholic,Protestant,Other religion,No religion,Married,Living together,Other relation,Singles,Household without children,Household with children,High level education,Medium level education,Lower level education,High status,Entrepreneur,Farmer,Middle management,Skilled labourers,Unskilled labourers,Social class A,Social class B1,Social class B2,Social class C,Social class D,Rented house,Home owners,car,cars,No car,National Health Service,Private Health Service,Income < 30.000,Income 30-45.000,Income 45-75.000,Income 75-122.000,Income >123.000,Average income,Purchasing power class,Contribution private third party insurance,Contribution third party insurance (firms),Contribution third party insurane (agriculture),Contribution car policies,Contribution delivery van policies,Contribution motorcycle/scooter policies,Contribution lorry policies,Contribution trailer policies,Contribution tractor policies,Contribution agricultural machines policies ,Contribution moped policies,Contribution life insurances,Contribution private accident insurance policies,Contribution family accidents insurance policies,Contribution disability insurance policies,Contribution fire policies,Contribution surfboard policies,Contribution boat policies,Contribution bicycle policies,Contribution property insurance policies,Contribution social security insurance policies,Number of private third party insurance,Number of third party insurance (firms) ,Number of third party insurane (agriculture),Number of car policies,Number of delivery van policies,Number of motorcycle/scooter policies,Number of lorry policies,Number of trailer policies,Number of tractor policies,Number of agricultural machines policies,Number of moped policies,Number of life insurances,Number of private accident insurance policies,Number of family accidents insurance policies,Number of disability insurance policies,Number of fire policies,Number of surfboard policies,Number of boat policies,Number of bicycle policies,Number of property insurance policies,Number of social security insurance policies,Number of mobile home policies
+33,1,3,2,8,0,5,1,3,7,0,2,1,2,6,1,2,7,1,0,1,2,5,2,1,1,2,6,1,1,8,8,0,1,8,1,0,4,5,0,0,4,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,5,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
+37,1,2,2,8,1,4,1,4,6,2,2,0,4,5,0,5,4,0,0,0,5,0,4,0,2,3,5,0,2,7,7,1,2,6,3,2,0,5,2,0,5,4,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,2,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
+37,1,2,2,8,0,4,2,4,3,2,4,4,4,2,0,5,4,0,0,0,7,0,2,0,5,0,4,0,7,2,7,0,2,9,0,4,5,0,0,0,3,4,2,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
+9,1,3,3,3,2,3,2,4,5,2,2,2,3,4,3,4,2,4,0,0,3,1,2,3,2,1,4,0,5,4,9,0,0,7,2,1,5,3,0,0,4,4,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,2,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
+40,1,4,2,10,1,4,1,4,7,1,2,2,4,4,5,4,0,0,5,4,0,0,0,9,0,0,0,0,4,5,6,2,1,5,4,0,0,9,0,0,6,3,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0
+23,1,2,1,5,0,5,0,5,0,6,3,3,5,2,0,5,4,2,0,0,4,2,2,2,2,2,4,2,9,0,5,3,3,9,0,5,2,3,0,0,3,3,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
+39,2,3,2,9,2,2,0,5,7,2,0,0,3,6,0,4,5,0,0,0,4,1,5,0,1,4,5,0,6,3,8,0,1,9,0,4,3,3,0,0,3,5,0,0,0,6,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
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