Main.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the neccesary libraries\n",
    "import pandas as pd\n",
    "import seaborn as sb\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "from sklearn import preprocessing\n",
    "from itertools import islice\n",
    "import seaborn as sns\n",
    "from scipy.stats import binom\n",
    "from scipy.stats import norm\n",
    "import statsmodels.stats.api as sms\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Project Classes\n",
    "The data module is concerned with pre-processing and data exploration while the model class is concerned with model generation and comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modules from this project\n",
    "import data\n",
    "from model import Model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_frame = pd.read_csv(\"dataset/bank-additional-full.csv\",sep=';'  , engine='python')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating Instances of the preprocessing and model classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize the modelling and preprocessing classes\n",
    "processor = data.PreProcessing(data_frame)\n",
    "model = Model()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explanatory Data Analysis\n",
    "### Target Imbalance\n",
    "\n",
    "The following plot shows the dataset is a victim of class imbalance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b5167cbfc8>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use the plot_target_imbalance method from the preprocessing class\n",
    "processor.plot_target_imbalance()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Outlier Detection\n",
    "The following plots show we have many outliers in age and duration. The box plots of both classes overlap quite a lot for age, this show age is not such a separating column. The difference b/n medians of yes and no for euribor3m shows that it is a separating class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x2b51375ca08>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b5136bd288>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b517290c08>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b51372bb88>]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1116x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1116x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processor.detect_outliers_boxplot(['duration','age','cons.conf.idx','euribor3m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Treating outliers by replacing them with medians. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "processor.handle_outliers(['duration','age','cons.conf.idx'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check if outliers are treated\n",
    "The following plots show that the outliers have been treated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x2b515626308>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b515227f88>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b5148b7888>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b5141e6588>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1116x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1116x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processor.detect_outliers_boxplot(['duration','age','cons.conf.idx','euribor3m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Effects of categorical variables on the target variable.\n",
    "In the subsequent plots the counts of all people who subscribed and those who didn't are shown as per each category of the catagorical variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x2b513804288>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b51382de08>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b5138ce408>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b514227648>]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# use the plot_multiple_categorical_against_target method of the PreProcessing class\n",
    "# to plot the count of each categorical variable with the target as a hue\n",
    "processor.plot_multiple_categorical_against_target(['poutcome','marital','loan','housing'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x2b5142c5fc8>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b514336248>]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processor.plot_multiple_categorical_against_target(['default','contact'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count of Yes/No by Education"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b5143c53c8>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processor.plot_single_categorical_against_target('education')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The following plot shows day of week does not help very much when it comes to predicting the target variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b51442fc48>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processor.plot_single_categorical_against_target('day_of_week')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Histograms of numerical columns against the target column per each class\n",
    "Looking at the age plot, the number of people who responded in the affirmative to the campaign goes up between 23 and 40 age, but not nearly as dramatically as negative responses do."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x2b514d79788>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b5151eea88>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b515930f08>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b51596f188>]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1116x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1116x540 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processor.plot_hist_against_target(['age','duration','campaign','euribor3m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correlation b/n numerical variables\n",
    "The emp.var.rate, cons.price.idx, euribor3m and nr.employed features have very high correlation. With euribor3m and nr.employed having the highest correlation of 0.95!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2b515cd9f48>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the correlation heat_map\n",
    "processor.plot_correletion_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distribution plots of numerical columns\n",
    "The following are histograms of some numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: Odd number of variables provided. One plot will be empty\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.axes._subplots.AxesSubplot at 0x2b515dc4288>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b515e11f88>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b515eaa988>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b515f54bc8>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b51602bc48>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x2b516061e08>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "processor.plot_distribution(['age','duration','campaign','emp.var.rate','euribor3m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Engineerinig\n",
    "### Assign a new feature 'Year' to each row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>job</th>\n",
       "      <th>marital</th>\n",
       "      <th>education</th>\n",
       "      <th>default</th>\n",
       "      <th>housing</th>\n",
       "      <th>loan</th>\n",
       "      <th>contact</th>\n",
       "      <th>month</th>\n",
       "      <th>day_of_week</th>\n",
       "      <th>...</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</th>\n",
       "      <th>poutcome</th>\n",
       "      <th>emp.var.rate</th>\n",
       "      <th>cons.price.idx</th>\n",
       "      <th>cons.conf.idx</th>\n",
       "      <th>euribor3m</th>\n",
       "      <th>nr.employed</th>\n",
       "      <th>y</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17504</th>\n",
       "      <td>43.0</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>unknown</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jul</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11600</th>\n",
       "      <td>28.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.959</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17475</th>\n",
       "      <td>54.0</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jul</td>\n",
       "      <td>mon</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21789</th>\n",
       "      <td>33.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>university.degree</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>cellular</td>\n",
       "      <td>aug</td>\n",
       "      <td>wed</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.444</td>\n",
       "      <td>-36.1</td>\n",
       "      <td>4.964</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27600</th>\n",
       "      <td>48.0</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>divorced</td>\n",
       "      <td>professional.course</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>nov</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-0.1</td>\n",
       "      <td>93.200</td>\n",
       "      <td>-42.0</td>\n",
       "      <td>4.021</td>\n",
       "      <td>5195.8</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2456</th>\n",
       "      <td>48.0</td>\n",
       "      <td>blue-collar</td>\n",
       "      <td>married</td>\n",
       "      <td>unknown</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>tue</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.1</td>\n",
       "      <td>93.994</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>4.856</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8339</th>\n",
       "      <td>43.0</td>\n",
       "      <td>unemployed</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.9y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>tue</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.864</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12895</th>\n",
       "      <td>25.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>cellular</td>\n",
       "      <td>jul</td>\n",
       "      <td>tue</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>93.918</td>\n",
       "      <td>-42.7</td>\n",
       "      <td>4.962</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34928</th>\n",
       "      <td>41.0</td>\n",
       "      <td>entrepreneur</td>\n",
       "      <td>divorced</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>92.893</td>\n",
       "      <td>-46.2</td>\n",
       "      <td>1.250</td>\n",
       "      <td>5099.1</td>\n",
       "      <td>no</td>\n",
       "      <td>2009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9194</th>\n",
       "      <td>39.0</td>\n",
       "      <td>admin.</td>\n",
       "      <td>single</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>jun</td>\n",
       "      <td>fri</td>\n",
       "      <td>...</td>\n",
       "      <td>999</td>\n",
       "      <td>0</td>\n",
       "      <td>nonexistent</td>\n",
       "      <td>1.4</td>\n",
       "      <td>94.465</td>\n",
       "      <td>-41.8</td>\n",
       "      <td>4.967</td>\n",
       "      <td>5228.1</td>\n",
       "      <td>no</td>\n",
       "      <td>2008</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        age           job   marital            education  default housing  \\\n",
       "17504  43.0      services   married             basic.6y  unknown     yes   \n",
       "11600  28.0        admin.    single    university.degree       no     yes   \n",
       "17475  54.0   blue-collar   married             basic.4y       no      no   \n",
       "21789  33.0        admin.   married    university.degree       no     yes   \n",
       "27600  48.0    unemployed  divorced  professional.course       no     yes   \n",
       "2456   48.0   blue-collar   married              unknown  unknown      no   \n",
       "8339   43.0    unemployed   married             basic.9y       no      no   \n",
       "12895  25.0        admin.    single          high.school       no      no   \n",
       "34928  41.0  entrepreneur  divorced              unknown       no      no   \n",
       "9194   39.0        admin.    single          high.school       no     yes   \n",
       "\n",
       "      loan    contact month day_of_week  ...  pdays  previous     poutcome  \\\n",
       "17504  yes   cellular   jul         mon  ...    999         0  nonexistent   \n",
       "11600   no  telephone   jun         fri  ...    999         0  nonexistent   \n",
       "17475   no   cellular   jul         mon  ...    999         0  nonexistent   \n",
       "21789   no   cellular   aug         wed  ...    999         0  nonexistent   \n",
       "27600   no  telephone   nov         fri  ...    999         0  nonexistent   \n",
       "2456    no  telephone   may         tue  ...    999         0  nonexistent   \n",
       "8339    no  telephone   jun         tue  ...    999         0  nonexistent   \n",
       "12895  yes   cellular   jul         tue  ...    999         0  nonexistent   \n",
       "34928   no  telephone   may         fri  ...    999         0  nonexistent   \n",
       "9194    no  telephone   jun         fri  ...    999         0  nonexistent   \n",
       "\n",
       "       emp.var.rate cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \\\n",
       "17504           1.4         93.918          -42.7      4.962       5228.1  no   \n",
       "11600           1.4         94.465          -41.8      4.959       5228.1  no   \n",
       "17475           1.4         93.918          -42.7      4.962       5228.1  no   \n",
       "21789           1.4         93.444          -36.1      4.964       5228.1  no   \n",
       "27600          -0.1         93.200          -42.0      4.021       5195.8  no   \n",
       "2456            1.1         93.994          -36.4      4.856       5191.0  no   \n",
       "8339            1.4         94.465          -41.8      4.864       5228.1  no   \n",
       "12895           1.4         93.918          -42.7      4.962       5228.1  no   \n",
       "34928          -1.8         92.893          -46.2      1.250       5099.1  no   \n",
       "9194            1.4         94.465          -41.8      4.967       5228.1  no   \n",
       "\n",
       "       Year  \n",
       "17504  2008  \n",
       "11600  2008  \n",
       "17475  2008  \n",
       "21789  2008  \n",
       "27600  2008  \n",
       "2456   2008  \n",
       "8339   2008  \n",
       "12895  2008  \n",
       "34928  2009  \n",
       "9194   2008  \n",
       "\n",
       "[10 rows x 22 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "processor.assign_years()\n",
    "processor.data_frame.sample(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Distinguish b/n numerical and categorical columns\n",
    "Create lists holding the categorical and numerical columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create lists holding the categorical columns\n",
    "categorical_columns = ['job','marital','education',\n",
    "                                         'default','loan','housing','contact',\n",
    "                                         'month','poutcome']\n",
    "#Create lists holding the numerical columns\n",
    "numerical_columns = ['age','pdays','previous','emp.var.rate','cons.price.idx',\n",
    "                                         'cons.conf.idx','euribor3m','nr.employed','Year']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create a column transformer\n",
    "The following cell creates a column transformer that is responsible for both one hot encoding of categorical variables and standardization of numerical variables.This is then passed to the pipeline."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# a column transformer that handles both one hot encoding and standardization\n",
    "column_transformer = processor.get_column_transformer(categorical_columns,numerical_columns,['duration','day_of_week'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train/Test Split \n",
    "Split the data using the train_test_split() method of preprocessor class  which has a default of 20% for the test size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#split the data using the train_test_split() method of preprocessor class \n",
    "X_train,X_test,y_train,y_test=  processor.train_test_split()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Add classifiers to be compared\n",
    "The classifiers to be compared are Random Forest, Logistic Regressor, XGBoost and Multi Layer Perceptron. Then a pipe is created by making use of the above column transformer and the classifiers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add 4 classifier models to be compared\n",
    "model.add_classifier(model.get_random_forest_classifier(),'Random Forest')\n",
    "model.add_classifier(model.get_logistic_classifier(),'Logistic Regressor')\n",
    "model.add_classifier(model.get_xgboost_classifier(),'XGBoost')\n",
    "model.add_classifier(model.get_multi_perceptron(),'MLP')\n",
    "\n",
    "#create a pipeline using the transformer and the above classifiers\n",
    "model.create_pipes(column_transformer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create K-Fold splitter\n",
    "The K-Fold splitter will be used to compare models using cross_validation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#use K-fold to split the training/validation data in 80:20 ratio\n",
    "kfold = model.get_cross_validation_splitter(is_strattified = False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.64194254\n",
      "Iteration 2, loss = 0.36559022\n",
      "Iteration 3, loss = 0.33276312\n",
      "Iteration 4, loss = 0.32101801\n",
      "Iteration 5, loss = 0.31457806\n",
      "Iteration 6, loss = 0.31046307\n",
      "Iteration 7, loss = 0.30757475\n",
      "Iteration 8, loss = 0.30535965\n",
      "Iteration 9, loss = 0.30370498\n",
      "Iteration 10, loss = 0.30239766\n",
      "Iteration 11, loss = 0.30133397\n",
      "Iteration 12, loss = 0.30049993\n",
      "Iteration 13, loss = 0.29977899\n",
      "Iteration 14, loss = 0.29920778\n",
      "Iteration 15, loss = 0.29867679\n",
      "Iteration 16, loss = 0.29828348\n",
      "Iteration 17, loss = 0.29785900\n",
      "Iteration 18, loss = 0.29753992\n",
      "Iteration 19, loss = 0.29718536\n",
      "Iteration 20, loss = 0.29688828\n",
      "Iteration 21, loss = 0.29661353\n",
      "Iteration 22, loss = 0.29635134\n",
      "Iteration 23, loss = 0.29609275\n",
      "Iteration 24, loss = 0.29584829\n",
      "Iteration 25, loss = 0.29564254\n",
      "Iteration 26, loss = 0.29543760\n",
      "Iteration 27, loss = 0.29520910\n",
      "Iteration 28, loss = 0.29503713\n",
      "Iteration 29, loss = 0.29484560\n",
      "Iteration 30, loss = 0.29467123\n",
      "Iteration 31, loss = 0.29450737\n",
      "Iteration 32, loss = 0.29434916\n",
      "Iteration 33, loss = 0.29419017\n",
      "Iteration 34, loss = 0.29404227\n",
      "Iteration 35, loss = 0.29394953\n",
      "Iteration 36, loss = 0.29378288\n",
      "Iteration 37, loss = 0.29363938\n",
      "Iteration 38, loss = 0.29352497\n",
      "Iteration 39, loss = 0.29338755\n",
      "Iteration 40, loss = 0.29327699\n",
      "Iteration 41, loss = 0.29317798\n",
      "Iteration 42, loss = 0.29304206\n",
      "Iteration 43, loss = 0.29297069\n",
      "Iteration 44, loss = 0.29286049\n",
      "Iteration 45, loss = 0.29275684\n",
      "Iteration 46, loss = 0.29265287\n",
      "Iteration 47, loss = 0.29256936\n",
      "Iteration 48, loss = 0.29247050\n",
      "Iteration 49, loss = 0.29240062\n",
      "Iteration 50, loss = 0.29227447\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63927170\n",
      "Iteration 2, loss = 0.36312255\n",
      "Iteration 3, loss = 0.33164192\n",
      "Iteration 4, loss = 0.32019176\n",
      "Iteration 5, loss = 0.31396991\n",
      "Iteration 6, loss = 0.30995604\n",
      "Iteration 7, loss = 0.30714860\n",
      "Iteration 8, loss = 0.30499688\n",
      "Iteration 9, loss = 0.30337571\n",
      "Iteration 10, loss = 0.30210506\n",
      "Iteration 11, loss = 0.30110766\n",
      "Iteration 12, loss = 0.30031758\n",
      "Iteration 13, loss = 0.29956418\n",
      "Iteration 14, loss = 0.29898871\n",
      "Iteration 15, loss = 0.29848824\n",
      "Iteration 16, loss = 0.29803265\n",
      "Iteration 17, loss = 0.29762446\n",
      "Iteration 18, loss = 0.29728876\n",
      "Iteration 19, loss = 0.29696682\n",
      "Iteration 20, loss = 0.29666509\n",
      "Iteration 21, loss = 0.29640121\n",
      "Iteration 22, loss = 0.29613626\n",
      "Iteration 23, loss = 0.29592781\n",
      "Iteration 24, loss = 0.29573604\n",
      "Iteration 25, loss = 0.29551959\n",
      "Iteration 26, loss = 0.29531609\n",
      "Iteration 27, loss = 0.29515365\n",
      "Iteration 28, loss = 0.29495564\n",
      "Iteration 29, loss = 0.29477968\n",
      "Iteration 30, loss = 0.29462553\n",
      "Iteration 31, loss = 0.29447879\n",
      "Iteration 32, loss = 0.29432638\n",
      "Iteration 33, loss = 0.29417243\n",
      "Iteration 34, loss = 0.29404337\n",
      "Iteration 35, loss = 0.29389778\n",
      "Iteration 36, loss = 0.29375037\n",
      "Iteration 37, loss = 0.29363760\n",
      "Iteration 38, loss = 0.29350245\n",
      "Iteration 39, loss = 0.29339510\n",
      "Iteration 40, loss = 0.29330985\n",
      "Iteration 41, loss = 0.29318552\n",
      "Iteration 42, loss = 0.29306305\n",
      "Iteration 43, loss = 0.29296609\n",
      "Iteration 44, loss = 0.29286971\n",
      "Iteration 45, loss = 0.29276172\n",
      "Iteration 46, loss = 0.29267127\n",
      "Iteration 47, loss = 0.29257334\n",
      "Iteration 48, loss = 0.29249463\n",
      "Iteration 49, loss = 0.29241347\n",
      "Iteration 50, loss = 0.29236495\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.62620238\n",
      "Iteration 2, loss = 0.36424802\n",
      "Iteration 3, loss = 0.33241862\n",
      "Iteration 4, loss = 0.32045275\n",
      "Iteration 5, loss = 0.31386359\n",
      "Iteration 6, loss = 0.30945947\n",
      "Iteration 7, loss = 0.30621133\n",
      "Iteration 8, loss = 0.30375903\n",
      "Iteration 9, loss = 0.30186240\n",
      "Iteration 10, loss = 0.30040491\n",
      "Iteration 11, loss = 0.29926932\n",
      "Iteration 12, loss = 0.29840130\n",
      "Iteration 13, loss = 0.29769740\n",
      "Iteration 14, loss = 0.29719314\n",
      "Iteration 15, loss = 0.29674105\n",
      "Iteration 16, loss = 0.29639397\n",
      "Iteration 17, loss = 0.29605974\n",
      "Iteration 18, loss = 0.29581000\n",
      "Iteration 19, loss = 0.29554539\n",
      "Iteration 20, loss = 0.29530913\n",
      "Iteration 21, loss = 0.29507781\n",
      "Iteration 22, loss = 0.29486883\n",
      "Iteration 23, loss = 0.29469145\n",
      "Iteration 24, loss = 0.29452782\n",
      "Iteration 25, loss = 0.29433127\n",
      "Iteration 26, loss = 0.29419900\n",
      "Iteration 27, loss = 0.29400718\n",
      "Iteration 28, loss = 0.29385941\n",
      "Iteration 29, loss = 0.29371727\n",
      "Iteration 30, loss = 0.29358819\n",
      "Iteration 31, loss = 0.29347631\n",
      "Iteration 32, loss = 0.29334316\n",
      "Iteration 33, loss = 0.29319411\n",
      "Iteration 34, loss = 0.29311891\n",
      "Iteration 35, loss = 0.29301178\n",
      "Iteration 36, loss = 0.29288474\n",
      "Iteration 37, loss = 0.29280768\n",
      "Iteration 38, loss = 0.29269547\n",
      "Iteration 39, loss = 0.29260215\n",
      "Iteration 40, loss = 0.29249228\n",
      "Iteration 41, loss = 0.29244628\n",
      "Iteration 42, loss = 0.29234609\n",
      "Iteration 43, loss = 0.29225510\n",
      "Iteration 44, loss = 0.29219053\n",
      "Iteration 45, loss = 0.29213474\n",
      "Iteration 46, loss = 0.29204970\n",
      "Iteration 47, loss = 0.29198705\n",
      "Iteration 48, loss = 0.29192584\n",
      "Iteration 49, loss = 0.29184403\n",
      "Iteration 50, loss = 0.29180549\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63971445\n",
      "Iteration 2, loss = 0.36442413\n",
      "Iteration 3, loss = 0.33223785\n",
      "Iteration 4, loss = 0.32076061\n",
      "Iteration 5, loss = 0.31454600\n",
      "Iteration 6, loss = 0.31058061\n",
      "Iteration 7, loss = 0.30773252\n",
      "Iteration 8, loss = 0.30564010\n",
      "Iteration 9, loss = 0.30403853\n",
      "Iteration 10, loss = 0.30278485\n",
      "Iteration 11, loss = 0.30180170\n",
      "Iteration 12, loss = 0.30099176\n",
      "Iteration 13, loss = 0.30029492\n",
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      "Iteration 27, loss = 0.29585197\n",
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      "Iteration 36, loss = 0.29461599\n",
      "Iteration 37, loss = 0.29448551\n",
      "Iteration 38, loss = 0.29438229\n",
      "Iteration 39, loss = 0.29426285\n",
      "Iteration 40, loss = 0.29418137\n",
      "Iteration 41, loss = 0.29406287\n",
      "Iteration 42, loss = 0.29396701\n",
      "Iteration 43, loss = 0.29386912\n",
      "Iteration 44, loss = 0.29380520\n",
      "Iteration 45, loss = 0.29370364\n",
      "Iteration 46, loss = 0.29361868\n",
      "Iteration 47, loss = 0.29354228\n",
      "Iteration 48, loss = 0.29345951\n",
      "Iteration 49, loss = 0.29336993\n",
      "Iteration 50, loss = 0.29329942\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63667783\n",
      "Iteration 2, loss = 0.36317407\n",
      "Iteration 3, loss = 0.33141790\n",
      "Iteration 4, loss = 0.31988062\n",
      "Iteration 5, loss = 0.31360110\n",
      "Iteration 6, loss = 0.30953233\n",
      "Iteration 7, loss = 0.30664514\n",
      "Iteration 8, loss = 0.30455103\n",
      "Iteration 9, loss = 0.30292931\n",
      "Iteration 10, loss = 0.30166914\n",
      "Iteration 11, loss = 0.30068499\n",
      "Iteration 12, loss = 0.29986469\n",
      "Iteration 13, loss = 0.29918163\n",
      "Iteration 14, loss = 0.29860370\n",
      "Iteration 15, loss = 0.29815235\n",
      "Iteration 16, loss = 0.29773543\n",
      "Iteration 17, loss = 0.29735397\n",
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      "Iteration 19, loss = 0.29672541\n",
      "Iteration 20, loss = 0.29642966\n",
      "Iteration 21, loss = 0.29617991\n",
      "Iteration 22, loss = 0.29594404\n",
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      "Iteration 24, loss = 0.29549102\n",
      "Iteration 25, loss = 0.29531770\n",
      "Iteration 26, loss = 0.29514234\n",
      "Iteration 27, loss = 0.29494691\n",
      "Iteration 28, loss = 0.29478785\n",
      "Iteration 29, loss = 0.29464278\n",
      "Iteration 30, loss = 0.29450080\n",
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      "Iteration 32, loss = 0.29426370\n",
      "Iteration 33, loss = 0.29410451\n",
      "Iteration 34, loss = 0.29401239\n",
      "Iteration 35, loss = 0.29387021\n",
      "Iteration 36, loss = 0.29378220\n",
      "Iteration 37, loss = 0.29366275\n",
      "Iteration 38, loss = 0.29356197\n",
      "Iteration 39, loss = 0.29344423\n",
      "Iteration 40, loss = 0.29334758\n",
      "Iteration 41, loss = 0.29326039\n",
      "Iteration 42, loss = 0.29317440\n",
      "Iteration 43, loss = 0.29308253\n",
      "Iteration 44, loss = 0.29297363\n",
      "Iteration 45, loss = 0.29289317\n",
      "Iteration 46, loss = 0.29276103\n",
      "Iteration 47, loss = 0.29270759\n",
      "Iteration 48, loss = 0.29261631\n",
      "Iteration 49, loss = 0.29254811\n",
      "Iteration 50, loss = 0.29246652\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.64194265\n",
      "Iteration 2, loss = 0.36559029\n",
      "Iteration 3, loss = 0.33276318\n",
      "Iteration 4, loss = 0.32101807\n",
      "Iteration 5, loss = 0.31457812\n",
      "Iteration 6, loss = 0.31046313\n",
      "Iteration 7, loss = 0.30757482\n",
      "Iteration 8, loss = 0.30535972\n",
      "Iteration 9, loss = 0.30370504\n",
      "Iteration 10, loss = 0.30239771\n",
      "Iteration 11, loss = 0.30133402\n",
      "Iteration 12, loss = 0.30049998\n",
      "Iteration 13, loss = 0.29977903\n",
      "Iteration 14, loss = 0.29920782\n",
      "Iteration 15, loss = 0.29867683\n",
      "Iteration 16, loss = 0.29828352\n",
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      "Iteration 18, loss = 0.29753995\n",
      "Iteration 19, loss = 0.29718539\n",
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      "Iteration 21, loss = 0.29661357\n",
      "Iteration 22, loss = 0.29635139\n",
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      "Iteration 25, loss = 0.29564258\n",
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      "Iteration 28, loss = 0.29503701\n",
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      "Iteration 35, loss = 0.29394964\n",
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      "Iteration 37, loss = 0.29363957\n",
      "Iteration 38, loss = 0.29352510\n",
      "Iteration 39, loss = 0.29338779\n",
      "Iteration 40, loss = 0.29327717\n",
      "Iteration 41, loss = 0.29317797\n",
      "Iteration 42, loss = 0.29304245\n",
      "Iteration 43, loss = 0.29297062\n",
      "Iteration 44, loss = 0.29286104\n",
      "Iteration 45, loss = 0.29275707\n",
      "Iteration 46, loss = 0.29265304\n",
      "Iteration 47, loss = 0.29256945\n",
      "Iteration 48, loss = 0.29247053\n",
      "Iteration 49, loss = 0.29240025\n",
      "Iteration 50, loss = 0.29227440\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63927175\n",
      "Iteration 2, loss = 0.36312258\n",
      "Iteration 3, loss = 0.33164184\n",
      "Iteration 4, loss = 0.32019172\n",
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      "Iteration 38, loss = 0.29350237\n",
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      "Iteration 43, loss = 0.29296632\n",
      "Iteration 44, loss = 0.29286949\n",
      "Iteration 45, loss = 0.29276170\n",
      "Iteration 46, loss = 0.29267148\n",
      "Iteration 47, loss = 0.29257322\n",
      "Iteration 48, loss = 0.29249401\n",
      "Iteration 49, loss = 0.29241281\n",
      "Iteration 50, loss = 0.29236412\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.62620230\n",
      "Iteration 2, loss = 0.36424797\n",
      "Iteration 3, loss = 0.33241857\n",
      "Iteration 4, loss = 0.32045270\n",
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      "Iteration 43, loss = 0.29225485\n",
      "Iteration 44, loss = 0.29218955\n",
      "Iteration 45, loss = 0.29213394\n",
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      "Iteration 47, loss = 0.29198617\n",
      "Iteration 48, loss = 0.29192428\n",
      "Iteration 49, loss = 0.29184288\n",
      "Iteration 50, loss = 0.29180403\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63971443\n",
      "Iteration 2, loss = 0.36442411\n",
      "Iteration 3, loss = 0.33223784\n",
      "Iteration 4, loss = 0.32076060\n",
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      "Iteration 25, loss = 0.29621779\n",
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      "Iteration 44, loss = 0.29380545\n",
      "Iteration 45, loss = 0.29370369\n",
      "Iteration 46, loss = 0.29361909\n",
      "Iteration 47, loss = 0.29354260\n",
      "Iteration 48, loss = 0.29345987\n",
      "Iteration 49, loss = 0.29337071\n",
      "Iteration 50, loss = 0.29329967\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63667800\n",
      "Iteration 2, loss = 0.36317406\n",
      "Iteration 3, loss = 0.33141789\n",
      "Iteration 4, loss = 0.31988061\n",
      "Iteration 5, loss = 0.31360109\n",
      "Iteration 6, loss = 0.30953232\n",
      "Iteration 7, loss = 0.30664514\n",
      "Iteration 8, loss = 0.30455103\n",
      "Iteration 9, loss = 0.30292930\n",
      "Iteration 10, loss = 0.30166913\n",
      "Iteration 11, loss = 0.30068499\n",
      "Iteration 12, loss = 0.29986469\n",
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      "Iteration 14, loss = 0.29860375\n",
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      "Iteration 25, loss = 0.29531789\n",
      "Iteration 26, loss = 0.29514245\n",
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      "Iteration 29, loss = 0.29464362\n",
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      "Iteration 33, loss = 0.29410586\n",
      "Iteration 34, loss = 0.29401363\n",
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      "Iteration 37, loss = 0.29366483\n",
      "Iteration 38, loss = 0.29356454\n",
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      "Iteration 40, loss = 0.29335098\n",
      "Iteration 41, loss = 0.29326403\n",
      "Iteration 42, loss = 0.29317873\n",
      "Iteration 43, loss = 0.29308836\n",
      "Iteration 44, loss = 0.29298009\n",
      "Iteration 45, loss = 0.29290054\n",
      "Iteration 46, loss = 0.29276950\n",
      "Iteration 47, loss = 0.29271616\n",
      "Iteration 48, loss = 0.29262678\n",
      "Iteration 49, loss = 0.29256119\n",
      "Iteration 50, loss = 0.29248195\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "#compare the models based on roc_auc\n",
    "models_k_fold = model.compare_models(kfold , X_train, y_train,scoring = 'roc_auc')\n",
    "#compare the models based on accuracy\n",
    "models_k_fold_accuracy = model.compare_models(kfold , X_train, y_train,scoring='accuracy')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Label Binarizer\n",
    "Fit target to Label Binarizer to identify which target class is positive so that recall, precision and f1 are done"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.64194264\n",
      "Iteration 2, loss = 0.36559029\n",
      "Iteration 3, loss = 0.33276318\n",
      "Iteration 4, loss = 0.32101806\n",
      "Iteration 5, loss = 0.31457811\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63927169\n",
      "Iteration 2, loss = 0.36312229\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.62620248\n",
      "Iteration 2, loss = 0.36424808\n",
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      "Iteration 50, loss = 0.29180565\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63971436\n",
      "Iteration 2, loss = 0.36442431\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63667787\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.64194262\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63927175\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.62620237\n",
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      "Iteration 47, loss = 0.29198638\n",
      "Iteration 48, loss = 0.29192467\n",
      "Iteration 49, loss = 0.29184298\n",
      "Iteration 50, loss = 0.29180390\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63971440\n",
      "Iteration 2, loss = 0.36442409\n",
      "Iteration 3, loss = 0.33223782\n",
      "Iteration 4, loss = 0.32076058\n",
      "Iteration 5, loss = 0.31454597\n",
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      "Iteration 8, loss = 0.30563990\n",
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      "Iteration 44, loss = 0.29380274\n",
      "Iteration 45, loss = 0.29370130\n",
      "Iteration 46, loss = 0.29361597\n",
      "Iteration 47, loss = 0.29353880\n",
      "Iteration 48, loss = 0.29345571\n",
      "Iteration 49, loss = 0.29336580\n",
      "Iteration 50, loss = 0.29329421\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63667785\n",
      "Iteration 2, loss = 0.36317408\n",
      "Iteration 3, loss = 0.33141790\n",
      "Iteration 4, loss = 0.31988063\n",
      "Iteration 5, loss = 0.31360111\n",
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      "Iteration 7, loss = 0.30664515\n",
      "Iteration 8, loss = 0.30455102\n",
      "Iteration 9, loss = 0.30292930\n",
      "Iteration 10, loss = 0.30166913\n",
      "Iteration 11, loss = 0.30068498\n",
      "Iteration 12, loss = 0.29986469\n",
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      "Iteration 41, loss = 0.29326032\n",
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      "Iteration 44, loss = 0.29297358\n",
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      "Iteration 46, loss = 0.29276098\n",
      "Iteration 47, loss = 0.29270767\n",
      "Iteration 48, loss = 0.29261628\n",
      "Iteration 49, loss = 0.29254767\n",
      "Iteration 50, loss = 0.29246636\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.64194266\n",
      "Iteration 2, loss = 0.36559029\n",
      "Iteration 3, loss = 0.33276327\n",
      "Iteration 4, loss = 0.32101815\n",
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      "Iteration 11, loss = 0.30133399\n",
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      "Iteration 49, loss = 0.29240076\n",
      "Iteration 50, loss = 0.29227483\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63927171\n",
      "Iteration 2, loss = 0.36312256\n",
      "Iteration 3, loss = 0.33164193\n",
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      "Iteration 48, loss = 0.29249419\n",
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      "Iteration 50, loss = 0.29236410\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.62620237\n",
      "Iteration 2, loss = 0.36424801\n",
      "Iteration 3, loss = 0.33241861\n",
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      "Iteration 48, loss = 0.29192496\n",
      "Iteration 49, loss = 0.29184365\n",
      "Iteration 50, loss = 0.29180514\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63971448\n",
      "Iteration 2, loss = 0.36442415\n",
      "Iteration 3, loss = 0.33223787\n",
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      "Iteration 48, loss = 0.29345943\n",
      "Iteration 49, loss = 0.29337003\n",
      "Iteration 50, loss = 0.29329903\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63667785\n",
      "Iteration 2, loss = 0.36317408\n",
      "Iteration 3, loss = 0.33141790\n",
      "Iteration 4, loss = 0.31988062\n",
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      "Iteration 46, loss = 0.29276102\n",
      "Iteration 47, loss = 0.29270756\n",
      "Iteration 48, loss = 0.29261632\n",
      "Iteration 49, loss = 0.29254814\n",
      "Iteration 50, loss = 0.29246693\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "# Label Binarizer to identify which target class is positive\n",
    "lb = preprocessing.LabelBinarizer()\n",
    "y_train = np.array([number[0] for number in lb.fit_transform(y_train)])\n",
    "\n",
    "#compare the models based on recall\n",
    "models_k_fold_recall = model.compare_models(kfold , X_train, y_train,scoring='recall')\n",
    "\n",
    "#compare the models based on precision\n",
    "models_k_fold_precision = model.compare_models(kfold , X_train, y_train,scoring='precision')\n",
    "\n",
    "#compare the models based on f1_score\n",
    "models_k_fold_f1 = model.compare_models(kfold , X_train, y_train,scoring='f1')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compare Classifiers\n",
    "The compare_models() method of the model class returns average scores (of the 5 folds) along side the name of the classifier. The returned data is used to create a table that shows the scores of classifiers based on different metrics.\n",
    "Looking at the tables below one can observe that XGBoost is the best performer according to the ROC_AUC with a score of around 0.764.\n",
    "ROC_AUC score is chosen as the most reliable metric because it doesn't have any bias toward models that perform well on the majority class at the expense of the minority class - a property that is quite attractive when dealing with imbalanced data such as the one under consideration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Auc</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Classifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.751058</td>\n",
       "      <td>0.887739</td>\n",
       "      <td>0.267709</td>\n",
       "      <td>0.506298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regressor</th>\n",
       "      <td>0.758243</td>\n",
       "      <td>0.897633</td>\n",
       "      <td>0.180830</td>\n",
       "      <td>0.676593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.781935</td>\n",
       "      <td>0.898452</td>\n",
       "      <td>0.188443</td>\n",
       "      <td>0.687912</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLP</th>\n",
       "      <td>0.764142</td>\n",
       "      <td>0.897906</td>\n",
       "      <td>0.193783</td>\n",
       "      <td>0.666967</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Auc  Accuracy    Recall  Precision\n",
       "Classifier                                                 \n",
       "Random Forest       0.751058  0.887739  0.267709   0.506298\n",
       "Logistic Regressor  0.758243  0.897633  0.180830   0.676593\n",
       "XGBoost             0.781935  0.898452  0.188443   0.687912\n",
       "MLP                 0.764142  0.897906  0.193783   0.666967"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifiers = models_k_fold['classifiers']\n",
    "scores_auc = models_k_fold['scores']\n",
    "scores_accuracy = models_k_fold_accuracy['scores']\n",
    "scores_recall = models_k_fold_recall['scores']\n",
    "scores_precision = models_k_fold_precision['scores']\n",
    "scores_f1 = models_k_fold_f1['scores']\n",
    "data = {'Classifier':classifiers , 'Auc':scores_auc, 'Accuracy': scores_accuracy,\n",
    "        'Recall':scores_recall, 'Precision':scores_precision } \n",
    "performance_df = pd.DataFrame(data)\n",
    "performance_df = performance_df.set_index('Classifier')\n",
    "performance_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified K-Fold\n",
    "Stratified K-Fold is used if there is a significant change."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63816974\n",
      "Iteration 2, loss = 0.36266845\n",
      "Iteration 3, loss = 0.33049719\n",
      "Iteration 4, loss = 0.31911288\n",
      "Iteration 5, loss = 0.31299267\n",
      "Iteration 6, loss = 0.30905395\n",
      "Iteration 7, loss = 0.30624534\n",
      "Iteration 8, loss = 0.30418257\n",
      "Iteration 9, loss = 0.30260438\n",
      "Iteration 10, loss = 0.30141368\n",
      "Iteration 11, loss = 0.30043066\n",
      "Iteration 12, loss = 0.29964569\n",
      "Iteration 13, loss = 0.29893847\n",
      "Iteration 14, loss = 0.29840287\n",
      "Iteration 15, loss = 0.29788718\n",
      "Iteration 16, loss = 0.29745189\n",
      "Iteration 17, loss = 0.29709983\n",
      "Iteration 18, loss = 0.29676406\n",
      "Iteration 19, loss = 0.29644895\n",
      "Iteration 20, loss = 0.29618613\n",
      "Iteration 21, loss = 0.29592805\n",
      "Iteration 22, loss = 0.29566264\n",
      "Iteration 23, loss = 0.29545514\n",
      "Iteration 24, loss = 0.29524756\n",
      "Iteration 25, loss = 0.29501302\n",
      "Iteration 26, loss = 0.29481709\n",
      "Iteration 27, loss = 0.29463218\n",
      "Iteration 28, loss = 0.29444801\n",
      "Iteration 29, loss = 0.29428050\n",
      "Iteration 30, loss = 0.29419517\n",
      "Iteration 31, loss = 0.29400901\n",
      "Iteration 32, loss = 0.29387979\n",
      "Iteration 33, loss = 0.29373672\n",
      "Iteration 34, loss = 0.29359443\n",
      "Iteration 35, loss = 0.29348796\n",
      "Iteration 36, loss = 0.29337913\n",
      "Iteration 37, loss = 0.29325600\n",
      "Iteration 38, loss = 0.29315850\n",
      "Iteration 39, loss = 0.29304040\n",
      "Iteration 40, loss = 0.29293596\n",
      "Iteration 41, loss = 0.29284944\n",
      "Iteration 42, loss = 0.29275102\n",
      "Iteration 43, loss = 0.29263275\n",
      "Iteration 44, loss = 0.29254788\n",
      "Iteration 45, loss = 0.29244475\n",
      "Iteration 46, loss = 0.29237623\n",
      "Iteration 47, loss = 0.29229485\n",
      "Iteration 48, loss = 0.29223417\n",
      "Iteration 49, loss = 0.29215049\n",
      "Iteration 50, loss = 0.29206571\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63540682\n",
      "Iteration 2, loss = 0.36302923\n",
      "Iteration 3, loss = 0.33104470\n",
      "Iteration 4, loss = 0.31940467\n",
      "Iteration 5, loss = 0.31312201\n",
      "Iteration 6, loss = 0.30908365\n",
      "Iteration 7, loss = 0.30623313\n",
      "Iteration 8, loss = 0.30406903\n",
      "Iteration 9, loss = 0.30243955\n",
      "Iteration 10, loss = 0.30116723\n",
      "Iteration 11, loss = 0.30015604\n",
      "Iteration 12, loss = 0.29930559\n",
      "Iteration 13, loss = 0.29862346\n",
      "Iteration 14, loss = 0.29805039\n",
      "Iteration 15, loss = 0.29753819\n",
      "Iteration 16, loss = 0.29708759\n",
      "Iteration 17, loss = 0.29668925\n",
      "Iteration 18, loss = 0.29636293\n",
      "Iteration 19, loss = 0.29601273\n",
      "Iteration 20, loss = 0.29572795\n",
      "Iteration 21, loss = 0.29548158\n",
      "Iteration 22, loss = 0.29518802\n",
      "Iteration 23, loss = 0.29493777\n",
      "Iteration 24, loss = 0.29469967\n",
      "Iteration 25, loss = 0.29449754\n",
      "Iteration 26, loss = 0.29429373\n",
      "Iteration 27, loss = 0.29410695\n",
      "Iteration 28, loss = 0.29391148\n",
      "Iteration 29, loss = 0.29372255\n",
      "Iteration 30, loss = 0.29355917\n",
      "Iteration 31, loss = 0.29338896\n",
      "Iteration 32, loss = 0.29324965\n",
      "Iteration 33, loss = 0.29310677\n",
      "Iteration 34, loss = 0.29297338\n",
      "Iteration 35, loss = 0.29280974\n",
      "Iteration 36, loss = 0.29268852\n",
      "Iteration 37, loss = 0.29256614\n",
      "Iteration 38, loss = 0.29247384\n",
      "Iteration 39, loss = 0.29234660\n",
      "Iteration 40, loss = 0.29221220\n",
      "Iteration 41, loss = 0.29206963\n",
      "Iteration 42, loss = 0.29199136\n",
      "Iteration 43, loss = 0.29188717\n",
      "Iteration 44, loss = 0.29176747\n",
      "Iteration 45, loss = 0.29167916\n",
      "Iteration 46, loss = 0.29156919\n",
      "Iteration 47, loss = 0.29147147\n",
      "Iteration 48, loss = 0.29139237\n",
      "Iteration 49, loss = 0.29127635\n",
      "Iteration 50, loss = 0.29118389\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63637766\n",
      "Iteration 2, loss = 0.36522515\n",
      "Iteration 3, loss = 0.33274794\n",
      "Iteration 4, loss = 0.32104875\n",
      "Iteration 5, loss = 0.31475269\n",
      "Iteration 6, loss = 0.31073371\n",
      "Iteration 7, loss = 0.30788697\n",
      "Iteration 8, loss = 0.30577847\n",
      "Iteration 9, loss = 0.30415159\n",
      "Iteration 10, loss = 0.30289084\n",
      "Iteration 11, loss = 0.30188920\n",
      "Iteration 12, loss = 0.30107560\n",
      "Iteration 13, loss = 0.30039500\n",
      "Iteration 14, loss = 0.29981042\n",
      "Iteration 15, loss = 0.29934196\n",
      "Iteration 16, loss = 0.29893403\n",
      "Iteration 17, loss = 0.29855246\n",
      "Iteration 18, loss = 0.29819842\n",
      "Iteration 19, loss = 0.29790213\n",
      "Iteration 20, loss = 0.29760537\n",
      "Iteration 21, loss = 0.29734021\n",
      "Iteration 22, loss = 0.29710142\n",
      "Iteration 23, loss = 0.29687732\n",
      "Iteration 24, loss = 0.29668375\n",
      "Iteration 25, loss = 0.29648032\n",
      "Iteration 26, loss = 0.29630795\n",
      "Iteration 27, loss = 0.29610943\n",
      "Iteration 28, loss = 0.29594393\n",
      "Iteration 29, loss = 0.29580428\n",
      "Iteration 30, loss = 0.29566680\n",
      "Iteration 31, loss = 0.29552301\n",
      "Iteration 32, loss = 0.29535833\n",
      "Iteration 33, loss = 0.29523070\n",
      "Iteration 34, loss = 0.29509666\n",
      "Iteration 35, loss = 0.29496216\n",
      "Iteration 36, loss = 0.29484846\n",
      "Iteration 37, loss = 0.29470004\n",
      "Iteration 38, loss = 0.29457706\n",
      "Iteration 39, loss = 0.29447177\n",
      "Iteration 40, loss = 0.29435380\n",
      "Iteration 41, loss = 0.29423218\n",
      "Iteration 42, loss = 0.29414251\n",
      "Iteration 43, loss = 0.29403375\n",
      "Iteration 44, loss = 0.29395166\n",
      "Iteration 45, loss = 0.29384364\n",
      "Iteration 46, loss = 0.29376126\n",
      "Iteration 47, loss = 0.29366283\n",
      "Iteration 48, loss = 0.29356886\n",
      "Iteration 49, loss = 0.29348584\n",
      "Iteration 50, loss = 0.29339750\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.62660206\n",
      "Iteration 2, loss = 0.36664687\n",
      "Iteration 3, loss = 0.33420255\n",
      "Iteration 4, loss = 0.32190616\n",
      "Iteration 5, loss = 0.31509034\n",
      "Iteration 6, loss = 0.31043199\n",
      "Iteration 7, loss = 0.30700835\n",
      "Iteration 8, loss = 0.30446274\n",
      "Iteration 9, loss = 0.30248090\n",
      "Iteration 10, loss = 0.30100891\n",
      "Iteration 11, loss = 0.29984839\n",
      "Iteration 12, loss = 0.29898735\n",
      "Iteration 13, loss = 0.29833223\n",
      "Iteration 14, loss = 0.29784771\n",
      "Iteration 15, loss = 0.29744569\n",
      "Iteration 16, loss = 0.29707046\n",
      "Iteration 17, loss = 0.29675645\n",
      "Iteration 18, loss = 0.29648231\n",
      "Iteration 19, loss = 0.29622503\n",
      "Iteration 20, loss = 0.29596403\n",
      "Iteration 21, loss = 0.29577784\n",
      "Iteration 22, loss = 0.29555590\n",
      "Iteration 23, loss = 0.29531637\n",
      "Iteration 24, loss = 0.29511914\n",
      "Iteration 25, loss = 0.29496141\n",
      "Iteration 26, loss = 0.29478976\n",
      "Iteration 27, loss = 0.29459705\n",
      "Iteration 28, loss = 0.29445050\n",
      "Iteration 29, loss = 0.29428883\n",
      "Iteration 30, loss = 0.29413969\n",
      "Iteration 31, loss = 0.29398970\n",
      "Iteration 32, loss = 0.29389773\n",
      "Iteration 33, loss = 0.29375661\n",
      "Iteration 34, loss = 0.29364962\n",
      "Iteration 35, loss = 0.29351727\n",
      "Iteration 36, loss = 0.29342249\n",
      "Iteration 37, loss = 0.29329833\n",
      "Iteration 38, loss = 0.29319171\n",
      "Iteration 39, loss = 0.29309628\n",
      "Iteration 40, loss = 0.29300104\n",
      "Iteration 41, loss = 0.29290599\n",
      "Iteration 42, loss = 0.29279712\n",
      "Iteration 43, loss = 0.29272550\n",
      "Iteration 44, loss = 0.29267772\n",
      "Iteration 45, loss = 0.29256591\n",
      "Iteration 46, loss = 0.29251791\n",
      "Iteration 47, loss = 0.29241428\n",
      "Iteration 48, loss = 0.29233035\n",
      "Iteration 49, loss = 0.29226484\n",
      "Iteration 50, loss = 0.29218505\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63974980\n",
      "Iteration 2, loss = 0.36568092\n",
      "Iteration 3, loss = 0.33286638\n",
      "Iteration 4, loss = 0.32107674\n",
      "Iteration 5, loss = 0.31464123\n",
      "Iteration 6, loss = 0.31052845\n",
      "Iteration 7, loss = 0.30754317\n",
      "Iteration 8, loss = 0.30534970\n",
      "Iteration 9, loss = 0.30367401\n",
      "Iteration 10, loss = 0.30237800\n",
      "Iteration 11, loss = 0.30133163\n",
      "Iteration 12, loss = 0.30048858\n",
      "Iteration 13, loss = 0.29981255\n",
      "Iteration 14, loss = 0.29923880\n",
      "Iteration 15, loss = 0.29872430\n",
      "Iteration 16, loss = 0.29830755\n",
      "Iteration 17, loss = 0.29789165\n",
      "Iteration 18, loss = 0.29755940\n",
      "Iteration 19, loss = 0.29724534\n",
      "Iteration 20, loss = 0.29691981\n",
      "Iteration 21, loss = 0.29665741\n",
      "Iteration 22, loss = 0.29642229\n",
      "Iteration 23, loss = 0.29619038\n",
      "Iteration 24, loss = 0.29597428\n",
      "Iteration 25, loss = 0.29578146\n",
      "Iteration 26, loss = 0.29557402\n",
      "Iteration 27, loss = 0.29540362\n",
      "Iteration 28, loss = 0.29524963\n",
      "Iteration 29, loss = 0.29510551\n",
      "Iteration 30, loss = 0.29492336\n",
      "Iteration 31, loss = 0.29479267\n",
      "Iteration 32, loss = 0.29465077\n",
      "Iteration 33, loss = 0.29452804\n",
      "Iteration 34, loss = 0.29439931\n",
      "Iteration 35, loss = 0.29429941\n",
      "Iteration 36, loss = 0.29417949\n",
      "Iteration 37, loss = 0.29407572\n",
      "Iteration 38, loss = 0.29397271\n",
      "Iteration 39, loss = 0.29386626\n",
      "Iteration 40, loss = 0.29375921\n",
      "Iteration 41, loss = 0.29366349\n",
      "Iteration 42, loss = 0.29357314\n",
      "Iteration 43, loss = 0.29349490\n",
      "Iteration 44, loss = 0.29339713\n",
      "Iteration 45, loss = 0.29334883\n",
      "Iteration 46, loss = 0.29325561\n",
      "Iteration 47, loss = 0.29317251\n",
      "Iteration 48, loss = 0.29309862\n",
      "Iteration 49, loss = 0.29299462\n",
      "Iteration 50, loss = 0.29293798\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63816978\n",
      "Iteration 2, loss = 0.36266848\n",
      "Iteration 3, loss = 0.33049721\n",
      "Iteration 4, loss = 0.31911290\n",
      "Iteration 5, loss = 0.31299270\n",
      "Iteration 6, loss = 0.30905397\n",
      "Iteration 7, loss = 0.30624536\n",
      "Iteration 8, loss = 0.30418259\n",
      "Iteration 9, loss = 0.30260440\n",
      "Iteration 10, loss = 0.30141370\n",
      "Iteration 11, loss = 0.30043069\n",
      "Iteration 12, loss = 0.29964571\n",
      "Iteration 13, loss = 0.29893849\n",
      "Iteration 14, loss = 0.29840292\n",
      "Iteration 15, loss = 0.29788727\n",
      "Iteration 16, loss = 0.29745199\n",
      "Iteration 17, loss = 0.29709984\n",
      "Iteration 18, loss = 0.29676409\n",
      "Iteration 19, loss = 0.29644887\n",
      "Iteration 20, loss = 0.29618612\n",
      "Iteration 21, loss = 0.29592803\n",
      "Iteration 22, loss = 0.29566269\n",
      "Iteration 23, loss = 0.29545521\n",
      "Iteration 24, loss = 0.29524757\n",
      "Iteration 25, loss = 0.29501314\n",
      "Iteration 26, loss = 0.29481721\n",
      "Iteration 27, loss = 0.29463231\n",
      "Iteration 28, loss = 0.29444817\n",
      "Iteration 29, loss = 0.29428063\n",
      "Iteration 30, loss = 0.29419530\n",
      "Iteration 31, loss = 0.29400922\n",
      "Iteration 32, loss = 0.29387993\n",
      "Iteration 33, loss = 0.29373683\n",
      "Iteration 34, loss = 0.29359455\n",
      "Iteration 35, loss = 0.29348817\n",
      "Iteration 36, loss = 0.29337923\n",
      "Iteration 37, loss = 0.29325613\n",
      "Iteration 38, loss = 0.29315851\n",
      "Iteration 39, loss = 0.29304036\n",
      "Iteration 40, loss = 0.29293601\n",
      "Iteration 41, loss = 0.29284957\n",
      "Iteration 42, loss = 0.29275144\n",
      "Iteration 43, loss = 0.29263294\n",
      "Iteration 44, loss = 0.29254812\n",
      "Iteration 45, loss = 0.29244496\n",
      "Iteration 46, loss = 0.29237631\n",
      "Iteration 47, loss = 0.29229509\n",
      "Iteration 48, loss = 0.29223408\n",
      "Iteration 49, loss = 0.29215077\n",
      "Iteration 50, loss = 0.29206559\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63540687\n",
      "Iteration 2, loss = 0.36302928\n",
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      "Iteration 50, loss = 0.29118554\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63637769\n",
      "Iteration 2, loss = 0.36522518\n",
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      "Iteration 50, loss = 0.29339711\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.62660192\n",
      "Iteration 2, loss = 0.36664692\n",
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      "Iteration 50, loss = 0.29218550\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63974989\n",
      "Iteration 2, loss = 0.36568115\n",
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      "Iteration 50, loss = 0.29293778\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63816975\n",
      "Iteration 2, loss = 0.36266846\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63540672\n",
      "Iteration 2, loss = 0.36302917\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63637766\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.62660177\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63974978\n",
      "Iteration 2, loss = 0.36568108\n",
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      "Iteration 50, loss = 0.29293789\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63816975\n",
      "Iteration 2, loss = 0.36266846\n",
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      "Iteration 50, loss = 0.29206620\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63540677\n",
      "Iteration 2, loss = 0.36302920\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63637767\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.62660170\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63974979\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63816998\n",
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      "Iteration 46, loss = 0.29237635\n",
      "Iteration 47, loss = 0.29229527\n",
      "Iteration 48, loss = 0.29223501\n",
      "Iteration 49, loss = 0.29215111\n",
      "Iteration 50, loss = 0.29206655\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63540678\n",
      "Iteration 2, loss = 0.36302921\n",
      "Iteration 3, loss = 0.33104469\n",
      "Iteration 4, loss = 0.31940466\n",
      "Iteration 5, loss = 0.31312200\n",
      "Iteration 6, loss = 0.30908363\n",
      "Iteration 7, loss = 0.30623312\n",
      "Iteration 8, loss = 0.30406902\n",
      "Iteration 9, loss = 0.30243954\n",
      "Iteration 10, loss = 0.30116721\n",
      "Iteration 11, loss = 0.30015597\n",
      "Iteration 12, loss = 0.29930505\n",
      "Iteration 13, loss = 0.29862285\n",
      "Iteration 14, loss = 0.29804995\n",
      "Iteration 15, loss = 0.29753806\n",
      "Iteration 16, loss = 0.29708752\n",
      "Iteration 17, loss = 0.29668916\n",
      "Iteration 18, loss = 0.29636286\n",
      "Iteration 19, loss = 0.29601304\n",
      "Iteration 20, loss = 0.29572790\n",
      "Iteration 21, loss = 0.29548134\n",
      "Iteration 22, loss = 0.29518791\n",
      "Iteration 23, loss = 0.29493735\n",
      "Iteration 24, loss = 0.29469898\n",
      "Iteration 25, loss = 0.29449664\n",
      "Iteration 26, loss = 0.29429240\n",
      "Iteration 27, loss = 0.29410509\n",
      "Iteration 28, loss = 0.29390940\n",
      "Iteration 29, loss = 0.29372129\n",
      "Iteration 30, loss = 0.29355780\n",
      "Iteration 31, loss = 0.29338776\n",
      "Iteration 32, loss = 0.29324851\n",
      "Iteration 33, loss = 0.29310600\n",
      "Iteration 34, loss = 0.29297255\n",
      "Iteration 35, loss = 0.29280918\n",
      "Iteration 36, loss = 0.29268829\n",
      "Iteration 37, loss = 0.29256580\n",
      "Iteration 38, loss = 0.29247373\n",
      "Iteration 39, loss = 0.29234678\n",
      "Iteration 40, loss = 0.29221249\n",
      "Iteration 41, loss = 0.29206983\n",
      "Iteration 42, loss = 0.29199185\n",
      "Iteration 43, loss = 0.29188752\n",
      "Iteration 44, loss = 0.29176757\n",
      "Iteration 45, loss = 0.29167927\n",
      "Iteration 46, loss = 0.29156932\n",
      "Iteration 47, loss = 0.29147242\n",
      "Iteration 48, loss = 0.29139362\n",
      "Iteration 49, loss = 0.29127751\n",
      "Iteration 50, loss = 0.29118487\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63637772\n",
      "Iteration 2, loss = 0.36522520\n",
      "Iteration 3, loss = 0.33274798\n",
      "Iteration 4, loss = 0.32104879\n",
      "Iteration 5, loss = 0.31475277\n",
      "Iteration 6, loss = 0.31073383\n",
      "Iteration 7, loss = 0.30788721\n",
      "Iteration 8, loss = 0.30577874\n",
      "Iteration 9, loss = 0.30415187\n",
      "Iteration 10, loss = 0.30289113\n",
      "Iteration 11, loss = 0.30188943\n",
      "Iteration 12, loss = 0.30107563\n",
      "Iteration 13, loss = 0.30039505\n",
      "Iteration 14, loss = 0.29981073\n",
      "Iteration 15, loss = 0.29934177\n",
      "Iteration 16, loss = 0.29893370\n",
      "Iteration 17, loss = 0.29855205\n",
      "Iteration 18, loss = 0.29819802\n",
      "Iteration 19, loss = 0.29790190\n",
      "Iteration 20, loss = 0.29760583\n",
      "Iteration 21, loss = 0.29734091\n",
      "Iteration 22, loss = 0.29710200\n",
      "Iteration 23, loss = 0.29687822\n",
      "Iteration 24, loss = 0.29668494\n",
      "Iteration 25, loss = 0.29648085\n",
      "Iteration 26, loss = 0.29630863\n",
      "Iteration 27, loss = 0.29610999\n",
      "Iteration 28, loss = 0.29594478\n",
      "Iteration 29, loss = 0.29580560\n",
      "Iteration 30, loss = 0.29566741\n",
      "Iteration 31, loss = 0.29552357\n",
      "Iteration 32, loss = 0.29535865\n",
      "Iteration 33, loss = 0.29523092\n",
      "Iteration 34, loss = 0.29509655\n",
      "Iteration 35, loss = 0.29496250\n",
      "Iteration 36, loss = 0.29484872\n",
      "Iteration 37, loss = 0.29470017\n",
      "Iteration 38, loss = 0.29457719\n",
      "Iteration 39, loss = 0.29447165\n",
      "Iteration 40, loss = 0.29435384\n",
      "Iteration 41, loss = 0.29423213\n",
      "Iteration 42, loss = 0.29414248\n",
      "Iteration 43, loss = 0.29403349\n",
      "Iteration 44, loss = 0.29395153\n",
      "Iteration 45, loss = 0.29384352\n",
      "Iteration 46, loss = 0.29376130\n",
      "Iteration 47, loss = 0.29366300\n",
      "Iteration 48, loss = 0.29356870\n",
      "Iteration 49, loss = 0.29348576\n",
      "Iteration 50, loss = 0.29339726\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.62660170\n",
      "Iteration 2, loss = 0.36664803\n",
      "Iteration 3, loss = 0.33420444\n",
      "Iteration 4, loss = 0.32190968\n",
      "Iteration 5, loss = 0.31509500\n",
      "Iteration 6, loss = 0.31043658\n",
      "Iteration 7, loss = 0.30701134\n",
      "Iteration 8, loss = 0.30446631\n",
      "Iteration 9, loss = 0.30248488\n",
      "Iteration 10, loss = 0.30101261\n",
      "Iteration 11, loss = 0.29985069\n",
      "Iteration 12, loss = 0.29898874\n",
      "Iteration 13, loss = 0.29833275\n",
      "Iteration 14, loss = 0.29784842\n",
      "Iteration 15, loss = 0.29744638\n",
      "Iteration 16, loss = 0.29707039\n",
      "Iteration 17, loss = 0.29675646\n",
      "Iteration 18, loss = 0.29648231\n",
      "Iteration 19, loss = 0.29622504\n",
      "Iteration 20, loss = 0.29596392\n",
      "Iteration 21, loss = 0.29577833\n",
      "Iteration 22, loss = 0.29555625\n",
      "Iteration 23, loss = 0.29531686\n",
      "Iteration 24, loss = 0.29511955\n",
      "Iteration 25, loss = 0.29496161\n",
      "Iteration 26, loss = 0.29479004\n",
      "Iteration 27, loss = 0.29459701\n",
      "Iteration 28, loss = 0.29445110\n",
      "Iteration 29, loss = 0.29428916\n",
      "Iteration 30, loss = 0.29413999\n",
      "Iteration 31, loss = 0.29398984\n",
      "Iteration 32, loss = 0.29389834\n",
      "Iteration 33, loss = 0.29375645\n",
      "Iteration 34, loss = 0.29364970\n",
      "Iteration 35, loss = 0.29351662\n",
      "Iteration 36, loss = 0.29342208\n",
      "Iteration 37, loss = 0.29329717\n",
      "Iteration 38, loss = 0.29319104\n",
      "Iteration 39, loss = 0.29309625\n",
      "Iteration 40, loss = 0.29300075\n",
      "Iteration 41, loss = 0.29290605\n",
      "Iteration 42, loss = 0.29279691\n",
      "Iteration 43, loss = 0.29272516\n",
      "Iteration 44, loss = 0.29267777\n",
      "Iteration 45, loss = 0.29256595\n",
      "Iteration 46, loss = 0.29251807\n",
      "Iteration 47, loss = 0.29241487\n",
      "Iteration 48, loss = 0.29233010\n",
      "Iteration 49, loss = 0.29226481\n",
      "Iteration 50, loss = 0.29218523\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1, loss = 0.63974977\n",
      "Iteration 2, loss = 0.36568106\n",
      "Iteration 3, loss = 0.33286664\n",
      "Iteration 4, loss = 0.32107693\n",
      "Iteration 5, loss = 0.31464129\n",
      "Iteration 6, loss = 0.31052858\n",
      "Iteration 7, loss = 0.30754330\n",
      "Iteration 8, loss = 0.30534980\n",
      "Iteration 9, loss = 0.30367408\n",
      "Iteration 10, loss = 0.30237807\n",
      "Iteration 11, loss = 0.30133166\n",
      "Iteration 12, loss = 0.30048865\n",
      "Iteration 13, loss = 0.29981261\n",
      "Iteration 14, loss = 0.29923898\n",
      "Iteration 15, loss = 0.29872452\n",
      "Iteration 16, loss = 0.29830778\n",
      "Iteration 17, loss = 0.29789190\n",
      "Iteration 18, loss = 0.29755970\n",
      "Iteration 19, loss = 0.29724564\n",
      "Iteration 20, loss = 0.29692010\n",
      "Iteration 21, loss = 0.29665770\n",
      "Iteration 22, loss = 0.29642266\n",
      "Iteration 23, loss = 0.29619067\n",
      "Iteration 24, loss = 0.29597447\n",
      "Iteration 25, loss = 0.29578172\n",
      "Iteration 26, loss = 0.29557424\n",
      "Iteration 27, loss = 0.29540377\n",
      "Iteration 28, loss = 0.29524973\n",
      "Iteration 29, loss = 0.29510557\n",
      "Iteration 30, loss = 0.29492340\n",
      "Iteration 31, loss = 0.29479274\n",
      "Iteration 32, loss = 0.29465084\n",
      "Iteration 33, loss = 0.29452804\n",
      "Iteration 34, loss = 0.29439932\n",
      "Iteration 35, loss = 0.29429939\n",
      "Iteration 36, loss = 0.29417951\n",
      "Iteration 37, loss = 0.29407573\n",
      "Iteration 38, loss = 0.29397264\n",
      "Iteration 39, loss = 0.29386621\n",
      "Iteration 40, loss = 0.29375924\n",
      "Iteration 41, loss = 0.29366350\n",
      "Iteration 42, loss = 0.29357317\n",
      "Iteration 43, loss = 0.29349495\n",
      "Iteration 44, loss = 0.29339726\n",
      "Iteration 45, loss = 0.29334875\n",
      "Iteration 46, loss = 0.29325570\n",
      "Iteration 47, loss = 0.29317229\n",
      "Iteration 48, loss = 0.29309841\n",
      "Iteration 49, loss = 0.29299430\n",
      "Iteration 50, loss = 0.29293750\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:571: ConvergenceWarning: Stochastic Optimizer: Maximum iterations (50) reached and the optimization hasn't converged yet.\n",
      "  % self.max_iter, ConvergenceWarning)\n"
     ]
    }
   ],
   "source": [
    "# using a stratified K-Fold for splitting the training data to training and validation sets to be used for cross validation\n",
    "strattified_fold = model.get_cross_validation_splitter(is_strattified = True)\n",
    "models_k_fold = model.compare_models(strattified_fold , X_train, y_train,scoring = 'roc_auc')\n",
    "models_k_fold_accuracy = model.compare_models(strattified_fold , X_train, y_train,scoring='accuracy')\n",
    "models_k_fold_recall = model.compare_models(strattified_fold , X_train, y_train,scoring='recall')\n",
    "models_k_fold_precision = model.compare_models(strattified_fold , X_train, y_train,scoring='precision')\n",
    "models_k_fold_f1 = model.compare_models(strattified_fold , X_train, y_train,scoring='f1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified K-Fold Results\n",
    "Even though, I expected a visible differrence in performace between Normal K-Fold and Stratified K-Fold the performances "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Auc</th>\n",
       "      <th>Accuracy</th>\n",
       "      <th>Recall</th>\n",
       "      <th>Precision</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Classifier</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Random Forest</th>\n",
       "      <td>0.752560</td>\n",
       "      <td>0.886434</td>\n",
       "      <td>0.268743</td>\n",
       "      <td>0.499207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Logistic Regressor</th>\n",
       "      <td>0.758293</td>\n",
       "      <td>0.897633</td>\n",
       "      <td>0.179787</td>\n",
       "      <td>0.674620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>XGBoost</th>\n",
       "      <td>0.780859</td>\n",
       "      <td>0.898483</td>\n",
       "      <td>0.186776</td>\n",
       "      <td>0.681788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MLP</th>\n",
       "      <td>0.764948</td>\n",
       "      <td>0.897572</td>\n",
       "      <td>0.196986</td>\n",
       "      <td>0.653005</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         Auc  Accuracy    Recall  Precision\n",
       "Classifier                                                 \n",
       "Random Forest       0.752560  0.886434  0.268743   0.499207\n",
       "Logistic Regressor  0.758293  0.897633  0.179787   0.674620\n",
       "XGBoost             0.780859  0.898483  0.186776   0.681788\n",
       "MLP                 0.764948  0.897572  0.196986   0.653005"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# using stratified K-Fold to compare the classifiers based on three metrics.\n",
    "classifiers = models_k_fold['classifiers']\n",
    "scores_auc = models_k_fold['scores']\n",
    "scores_accuracy = models_k_fold_accuracy['scores']\n",
    "scores_recall = models_k_fold_recall['scores']\n",
    "scores_precision = models_k_fold_precision['scores']\n",
    "scores_f1 = models_k_fold_f1['scores']\n",
    "data = {'Classifier':classifiers , 'Auc':scores_auc, 'Accuracy': scores_accuracy,\n",
    "        'Recall':scores_recall, 'Precision':scores_precision } \n",
    "performance_stratified_df = pd.DataFrame(data)\n",
    "performance_stratified_df = performance_stratified_df.set_index('Classifier')\n",
    "performance_stratified_df"
   ]
  }
 ],
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