Bank_Marketing_Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bank Marketing Project"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Team Members:\n",
    "Chaitra Hedge (cvh255)\n",
    "\n",
    "Aakash Kaku (ark576)\n",
    "\n",
    "Neelang Parghi (np788)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Description:\n",
    "The project (Code) analyzes the prior marketing campaign data of a Portugeuse bank and aims to predict whether the customer will subscribe to fixed-term deposit products the bank is offering. The code cleans the data, imputes the missing values, and analyzes and compares different predictive models. The best predictive model is used for the prediction exercise.\n",
    "\n",
    "Comments and Explanations (in the form of Markdown) are included in the notebook to improve the readability of the code, to explain the rationale of doing certain steps and to discuss the results and inferences from the executed code.\n",
    "\n",
    "The project was carried out as a part of course requirement of DS-GA:1007 Programming for Pyhton."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(41188, 20)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>campaign</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>56</td>\n",
       "      <td>housemaid</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.4y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</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>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>57</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>unknown</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</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>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</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>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>admin.</td>\n",
       "      <td>married</td>\n",
       "      <td>basic.6y</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</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>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56</td>\n",
       "      <td>services</td>\n",
       "      <td>married</td>\n",
       "      <td>high.school</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>telephone</td>\n",
       "      <td>may</td>\n",
       "      <td>mon</td>\n",
       "      <td>1</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>4.857</td>\n",
       "      <td>5191.0</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age        job  marital    education  default housing loan    contact  \\\n",
       "0   56  housemaid  married     basic.4y       no      no   no  telephone   \n",
       "1   57   services  married  high.school  unknown      no   no  telephone   \n",
       "2   37   services  married  high.school       no     yes   no  telephone   \n",
       "3   40     admin.  married     basic.6y       no      no   no  telephone   \n",
       "4   56   services  married  high.school       no      no  yes  telephone   \n",
       "\n",
       "  month day_of_week  campaign  pdays  previous     poutcome  emp.var.rate  \\\n",
       "0   may         mon         1    999         0  nonexistent           1.1   \n",
       "1   may         mon         1    999         0  nonexistent           1.1   \n",
       "2   may         mon         1    999         0  nonexistent           1.1   \n",
       "3   may         mon         1    999         0  nonexistent           1.1   \n",
       "4   may         mon         1    999         0  nonexistent           1.1   \n",
       "\n",
       "   cons.price.idx  cons.conf.idx  euribor3m  nr.employed   y  \n",
       "0          93.994          -36.4      4.857       5191.0  no  \n",
       "1          93.994          -36.4      4.857       5191.0  no  \n",
       "2          93.994          -36.4      4.857       5191.0  no  \n",
       "3          93.994          -36.4      4.857       5191.0  no  \n",
       "4          93.994          -36.4      4.857       5191.0  no  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv(\"bank-additional-full.csv\",sep=';')\n",
    "df=df.drop(['duration'],axis=1)\n",
    "print(df.shape)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have dropped the 'duration' variable as the value of the variable will only be known at the end of the call. Hence, at that time we will also know the outcome of the call. The 'duration' variable will lead to leakage in the data and the prediction model will not be realistic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exploratory Analysis:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categorical Variables:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first start the exploratory analysis of the categorical variables and see what are the categories and are there any missing values for these categories. Here, we used the seaborn package to create the bar graphs below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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U1WPA14GDWpu9gSuq6g7gU8AnWkyvbsejxfyTqnoucCqw0UgnmeTQJLOTzL7n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\n56vik5fnmdkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8864d358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8907ba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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Ik9xdd9016C5IkiStUgzIkiRJUocBWZIkSeowIEuSJEkdBmRJkiSpw4AsSZIk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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd890996a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd89436c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd89099cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8881b588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd89099d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd895edfd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd896311d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ek8V53pvNpO/Tz0/yiWmGBzT39T+z+W0y2y+3/2muLM4Z7vuu48Jm7utdfOu6ZPHuya9k\nce7RK+dax1a6ZPGu3C9Nl2v2zS2L87E+nuT66eOJ0/ZK8pZpxlcl2dUe63eyeFPD3iQvmvu5zTjT\nf87ix6Xfy+L/jn93lfNMsiuLg9ENSf4u02+vPBIum8z23dPsrszi4Hxyu/8rpzldl/bO6M2OFdN/\nD5+fZv4vSY6d+zkfxtn+ShY/2rwyyRXT5QL77lpna99dzXyfmuSL0xyvTvKqZTNJ8uDp873T7Y87\n2Llv98uS2X5i2nevTvKe/OhfunBcWHLxq6YBAKDxm/QAAKARyAAA0AhkAABoBDIAADQCGQAAGoEM\nAACNQAYAgOb/AU8IeoTNsWTEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd89538780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorcial_variables = ['job', 'marital', 'education', 'default', 'loan', 'contact', 'month', 'day_of_week', 'poutcome','y']\n",
    "for col in categorcial_variables:\n",
    "    plt.figure(figsize=(10,4))\n",
    "    sns.barplot(df[col].value_counts().values, df[col].value_counts().index)\n",
    "    plt.title(col)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## List of normalized relative frequency of the target class per category. \n",
    "\n",
    "Normalized distribution of each class per feature and plotted difference between positive and negative frequencies. Positive values imply this category favors clients that will subscribe and negative values categories that favor not buying the product."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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FJvfuQtBcGRkZqdHR0UGHIUmapk9+4ZnTav/aV5w2T5FImg9JVlbVlN9n4TfpSZIkSR0m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\n6e2Aj1bVjsAvgJd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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd896ae5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd898396a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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JqUmuTPKtJA/orsAm2TfJNUl+kOSkJF/pXLttkguTXJ/kqH6DJzkhyVVJliT5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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd89890470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd897695f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8a92e9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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8Sdb3xGzeI69O8qi2XeKMLMeGe2GMcdUY47Ik/7uKAVmqzeyHN40xPre4+7YkX7XkGVdK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\nAXZ1UAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd894c7208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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4PgdZkiRJahiQJUmSpIYBWZIkSWr08pg3SZK0ntr5hAPGXYI08ZxBliRJkhoGZEmSJKlh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\nliRJkhr/Hyk/5lidWY32AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd89833588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8aa3ae48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8968af98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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EIvnnJKfX7DcKHJvZAr9kzK4YvyKz62yT5Lwk6zkjvZVdktlxJmsf792us5qi58D1tc9K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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8a9115c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categorcial_variables = ['job', 'marital', 'education', 'default', 'loan', 'contact', 'month', 'day_of_week', 'poutcome','y']\n",
    "for col in categorcial_variables:\n",
    "    plt.figure(figsize=(10,4))\n",
    "    #Returns counts of unique values for each outcome for each feature.\n",
    "    pos_counts = df.loc[df.y.values == 'yes', col].value_counts() \n",
    "    neg_counts = df.loc[df.y.values == 'no', col].value_counts()\n",
    "    \n",
    "    all_counts = list(set(list(pos_counts.index) + list(neg_counts.index)))\n",
    "    \n",
    "    #Counts of how often each outcome was recorded.\n",
    "    freq_pos = (df.y.values == 'yes').sum()\n",
    "    freq_neg = (df.y.values == 'no').sum()\n",
    "    \n",
    "    pos_counts = pos_counts.to_dict()\n",
    "    neg_counts = neg_counts.to_dict()\n",
    "    \n",
    "    all_index = list(all_counts)\n",
    "    all_counts = [pos_counts.get(k, 0) / freq_pos - neg_counts.get(k, 0) / freq_neg for k in all_counts]\n",
    "\n",
    "    sns.barplot(all_counts, all_index)\n",
    "    plt.title(col)\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference/Result**: There are unknown values for many variables in the Data set. There are many ways to handle missing data. One of the ways is to discard the row but that would lead to reduction of data set and hence would not serve our purpose of building an accurate and realistic prediction model.\n",
    "\n",
    "Other method is to smartly infer the value of the unknown variable from the other variables. This a way of doing an imputation where we use other independent variables to infer the value of the missing variable. This doesn't gurantee that all missing values will be addressed but majority of them will have a reasonable which can be useful in the prediction. \n",
    "\n",
    "Variables with unknown/missing values are : 'education', 'job', 'housing', 'loan', 'deafult', and 'marital'. But the significant ones are 'education', 'job', 'housing', and 'loan'. The number of unknowns  for 'marital' is very low. The unknown for 'default' variable are considered to be recorded as unknown. It may be possible that customer is not willing to disclose this information to the banking representative. Hence the unknown value in 'default' is actually a separate value.\n",
    "\n",
    "Therefore, we start with creating new variables for the unknown values in 'education', 'job', 'housing' and 'loan'. We do this to see if the values are missing at random or is there a pattern in the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Creating new variables (variable name + '_un') to capture the information if the missing values are at random or is there\n",
    "## a pattern in the missing values.\n",
    "significant_cat_variables = ['education','job','housing','loan']\n",
    "for var in significant_cat_variables:\n",
    "    #df[var + '_un'] = 0\n",
    "    #df[var + '_un'][df[var]=='unknown'] = 1\n",
    "    #FIXME one-line coding\n",
    "    df[var + '_un'] = (df[var] == 'unknown').astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imputation:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, to infer the missing values in 'job' and 'education', we make use of the cross-tabulation between 'job' and 'education'. Our hypothesis here is that 'job' is influenced by the 'education' of a person. Hence, we can infer 'job' based on the education of the person. Moreover, since we are just filling the missing values, we are not much concerned about the causal inference. We, therefore, can use the job to predict the education."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cross_tab(df,f1,f2):\n",
    "    jobs=list(df[f1].unique())\n",
    "    edu=list(df[f2].unique())\n",
    "    dataframes=[]\n",
    "    for e in edu:\n",
    "        dfe=df[df[f2]==e]\n",
    "        dfejob=dfe.groupby(f1).count()[f2]\n",
    "        dataframes.append(dfejob)\n",
    "    xx=pd.concat(dataframes,axis=1)\n",
    "    xx.columns=edu\n",
    "    xx=xx.fillna(0)\n",
    "    return xx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>basic.4y</th>\n",
       "      <th>high.school</th>\n",
       "      <th>basic.6y</th>\n",
       "      <th>basic.9y</th>\n",
       "      <th>professional.course</th>\n",
       "      <th>unknown</th>\n",
       "      <th>university.degree</th>\n",
       "      <th>illiterate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>admin.</th>\n",
       "      <td>77</td>\n",
       "      <td>3329</td>\n",
       "      <td>151</td>\n",
       "      <td>499</td>\n",
       "      <td>363</td>\n",
       "      <td>249</td>\n",
       "      <td>5753</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>2318</td>\n",
       "      <td>878</td>\n",
       "      <td>1426</td>\n",
       "      <td>3623</td>\n",
       "      <td>453</td>\n",
       "      <td>454</td>\n",
       "      <td>94</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>137</td>\n",
       "      <td>234</td>\n",
       "      <td>71</td>\n",
       "      <td>210</td>\n",
       "      <td>135</td>\n",
       "      <td>57</td>\n",
       "      <td>610</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>474</td>\n",
       "      <td>174</td>\n",
       "      <td>77</td>\n",
       "      <td>94</td>\n",
       "      <td>59</td>\n",
       "      <td>42</td>\n",
       "      <td>139</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>100</td>\n",
       "      <td>298</td>\n",
       "      <td>85</td>\n",
       "      <td>166</td>\n",
       "      <td>89</td>\n",
       "      <td>123</td>\n",
       "      <td>2063</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>597</td>\n",
       "      <td>276</td>\n",
       "      <td>75</td>\n",
       "      <td>145</td>\n",
       "      <td>241</td>\n",
       "      <td>98</td>\n",
       "      <td>285</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self-employed</th>\n",
       "      <td>93</td>\n",
       "      <td>118</td>\n",
       "      <td>25</td>\n",
       "      <td>220</td>\n",
       "      <td>168</td>\n",
       "      <td>29</td>\n",
       "      <td>765</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>132</td>\n",
       "      <td>2682</td>\n",
       "      <td>226</td>\n",
       "      <td>388</td>\n",
       "      <td>218</td>\n",
       "      <td>150</td>\n",
       "      <td>173</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>26</td>\n",
       "      <td>357</td>\n",
       "      <td>13</td>\n",
       "      <td>99</td>\n",
       "      <td>43</td>\n",
       "      <td>167</td>\n",
       "      <td>170</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>58</td>\n",
       "      <td>873</td>\n",
       "      <td>87</td>\n",
       "      <td>384</td>\n",
       "      <td>3320</td>\n",
       "      <td>212</td>\n",
       "      <td>1809</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>112</td>\n",
       "      <td>259</td>\n",
       "      <td>34</td>\n",
       "      <td>186</td>\n",
       "      <td>142</td>\n",
       "      <td>19</td>\n",
       "      <td>262</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>52</td>\n",
       "      <td>37</td>\n",
       "      <td>22</td>\n",
       "      <td>31</td>\n",
       "      <td>12</td>\n",
       "      <td>131</td>\n",
       "      <td>45</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               basic.4y  high.school  basic.6y  basic.9y  professional.course  \\\n",
       "admin.               77         3329       151       499                  363   \n",
       "blue-collar        2318          878      1426      3623                  453   \n",
       "entrepreneur        137          234        71       210                  135   \n",
       "housemaid           474          174        77        94                   59   \n",
       "management          100          298        85       166                   89   \n",
       "retired             597          276        75       145                  241   \n",
       "self-employed        93          118        25       220                  168   \n",
       "services            132         2682       226       388                  218   \n",
       "student              26          357        13        99                   43   \n",
       "technician           58          873        87       384                 3320   \n",
       "unemployed          112          259        34       186                  142   \n",
       "unknown              52           37        22        31                   12   \n",
       "\n",
       "               unknown  university.degree  illiterate  \n",
       "admin.             249               5753         1.0  \n",
       "blue-collar        454                 94         8.0  \n",
       "entrepreneur        57                610         2.0  \n",
       "housemaid           42                139         1.0  \n",
       "management         123               2063         0.0  \n",
       "retired             98                285         3.0  \n",
       "self-employed       29                765         3.0  \n",
       "services           150                173         0.0  \n",
       "student            167                170         0.0  \n",
       "technician         212               1809         0.0  \n",
       "unemployed          19                262         0.0  \n",
       "unknown            131                 45         0.0  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_tab(df,'job','education')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "retired          678\n",
       "housemaid         54\n",
       "admin.            47\n",
       "technician        34\n",
       "management        30\n",
       "unknown           21\n",
       "blue-collar       20\n",
       "self-employed      9\n",
       "entrepreneur       8\n",
       "unemployed         7\n",
       "services           2\n",
       "Name: job, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['job'][df['age']>60].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Inferring education from jobs **: From the cross-tabulation, it can be seen that people with management jobs usually have a university degree. Hence wherever 'job' = management and 'education' = unknown, we can replace 'education' with 'university.degree'. Similarly, 'job' = 'services' --> 'education' = 'high.school' and 'job' = 'housemaid' --> 'education' = 'basic.4y'.\n",
    "\n",
    "** Inferring jobs from education **: If 'education' = 'basic.4y' or 'basic.6y' or 'basic.9y' then the 'job' is usually 'blue-collar'. If 'education' = 'professional.course', then the 'job' = 'technician'.\n",
    "\n",
    "** Inferring jobs from age**: As we see, if 'age' > 60, then the 'job' is 'retired,' which makes sense.\n",
    "\n",
    "While imputing the values for job and education, we were cognizant of the fact that the correlations should make real world sense. If it didn't make real world sense, we didn't replace the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df.loc[(df['age']>60) & (df['job']=='unknown'), 'job'] = 'retired'\n",
    "df.loc[(df['education']=='unknown') & (df['job']=='management'), 'education'] = 'university.degree'\n",
    "df.loc[(df['education']=='unknown') & (df['job']=='services'), 'education'] = 'high.school'\n",
    "df.loc[(df['education']=='unknown') & (df['job']=='housemaid'), 'education'] = 'basic.4y'\n",
    "df.loc[(df['job'] == 'unknown') & (df['education']=='basic.4y'), 'job'] = 'blue-collar'\n",
    "df.loc[(df['job'] == 'unknown') & (df['education']=='basic.6y'), 'job'] = 'blue-collar'\n",
    "df.loc[(df['job'] == 'unknown') & (df['education']=='basic.9y'), 'job'] = 'blue-collar'\n",
    "df.loc[(df['job']=='unknown') & (df['education']=='professional.course'), 'job'] = 'technician'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>basic.4y</th>\n",
       "      <th>high.school</th>\n",
       "      <th>basic.6y</th>\n",
       "      <th>basic.9y</th>\n",
       "      <th>professional.course</th>\n",
       "      <th>unknown</th>\n",
       "      <th>university.degree</th>\n",
       "      <th>illiterate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>admin.</th>\n",
       "      <td>77.0</td>\n",
       "      <td>3329</td>\n",
       "      <td>151.0</td>\n",
       "      <td>499.0</td>\n",
       "      <td>363.0</td>\n",
       "      <td>249.0</td>\n",
       "      <td>5753</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>blue-collar</th>\n",
       "      <td>2366.0</td>\n",
       "      <td>878</td>\n",
       "      <td>1448.0</td>\n",
       "      <td>3654.0</td>\n",
       "      <td>453.0</td>\n",
       "      <td>454.0</td>\n",
       "      <td>94</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>entrepreneur</th>\n",
       "      <td>137.0</td>\n",
       "      <td>234</td>\n",
       "      <td>71.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>610</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>housemaid</th>\n",
       "      <td>516.0</td>\n",
       "      <td>174</td>\n",
       "      <td>77.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>139</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>management</th>\n",
       "      <td>100.0</td>\n",
       "      <td>298</td>\n",
       "      <td>85.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2186</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>retired</th>\n",
       "      <td>601.0</td>\n",
       "      <td>276</td>\n",
       "      <td>75.0</td>\n",
       "      <td>145.0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>286</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>self-employed</th>\n",
       "      <td>93.0</td>\n",
       "      <td>118</td>\n",
       "      <td>25.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>765</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>services</th>\n",
       "      <td>132.0</td>\n",
       "      <td>2832</td>\n",
       "      <td>226.0</td>\n",
       "      <td>388.0</td>\n",
       "      <td>218.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>173</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>student</th>\n",
       "      <td>26.0</td>\n",
       "      <td>357</td>\n",
       "      <td>13.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>170</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>technician</th>\n",
       "      <td>58.0</td>\n",
       "      <td>873</td>\n",
       "      <td>87.0</td>\n",
       "      <td>384.0</td>\n",
       "      <td>3330.0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>1809</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unemployed</th>\n",
       "      <td>112.0</td>\n",
       "      <td>259</td>\n",
       "      <td>34.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>262</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unknown</th>\n",
       "      <td>0.0</td>\n",
       "      <td>37</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>44</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               basic.4y  high.school  basic.6y  basic.9y  professional.course  \\\n",
       "admin.             77.0         3329     151.0     499.0                363.0   \n",
       "blue-collar      2366.0          878    1448.0    3654.0                453.0   \n",
       "entrepreneur      137.0          234      71.0     210.0                135.0   \n",
       "housemaid         516.0          174      77.0      94.0                 59.0   \n",
       "management        100.0          298      85.0     166.0                 89.0   \n",
       "retired           601.0          276      75.0     145.0                243.0   \n",
       "self-employed      93.0          118      25.0     220.0                168.0   \n",
       "services          132.0         2832     226.0     388.0                218.0   \n",
       "student            26.0          357      13.0      99.0                 43.0   \n",
       "technician         58.0          873      87.0     384.0               3330.0   \n",
       "unemployed        112.0          259      34.0     186.0                142.0   \n",
       "unknown             0.0           37       0.0       0.0                  0.0   \n",
       "\n",
       "               unknown  university.degree  illiterate  \n",
       "admin.           249.0               5753         1.0  \n",
       "blue-collar      454.0                 94         8.0  \n",
       "entrepreneur      57.0                610         2.0  \n",
       "housemaid          0.0                139         1.0  \n",
       "management         0.0               2186         0.0  \n",
       "retired          112.0                286         3.0  \n",
       "self-employed     29.0                765         3.0  \n",
       "services           0.0                173         0.0  \n",
       "student          167.0                170         0.0  \n",
       "technician       212.0               1809         0.0  \n",
       "unemployed        19.0                262         0.0  \n",
       "unknown          117.0                 44         0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_tab(df,'job','education')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see, we are able to reduce the number of unknowns and enhance our data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "jobhousing=cross_tab(df,'job','housing')\n",
    "jobloan=cross_tab(df,'job','loan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Imputations for house and loan **: We are again using cross-tabulation between 'house' and 'job' and between 'loan' and 'job.' Our hypothesis is that housing loan status (Yes or No) should be in the proportion of each job category. Hence using the prior known distribution of the housing loan for each job category, the house loan for unknown people will be predicted such that the prior distribution (% House = Yes's and No's for each job category remains the same). Similarly, we have filled the missing values in the 'loan' variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fillhousing(df,jobhousing):\n",
    "    \"\"\"Function for imputation via cross-tabulation to fill missing values for the 'housing' categorical feature\"\"\"\n",
    "    jobs=['housemaid','services','admin.','blue-collar','technician','retired','management','unemployed','self-employed','entrepreneur','student']\n",
    "    house=[\"no\",\"yes\"]\n",
    "    for j in jobs:\n",
    "        ind=df[np.logical_and(np.array(df['housing']=='unknown'),np.array(df['job']==j))].index\n",
    "        mask=np.random.rand(len(ind))<((jobhousing.loc[j]['no'])/(jobhousing.loc[j]['no']+jobhousing.loc[j]['yes']))\n",
    "        ind1=ind[mask]\n",
    "        ind2=ind[~mask]\n",
    "        df.loc[ind1,\"housing\"]='no'\n",
    "        df.loc[ind2,\"housing\"]='yes'\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fillloan(df,jobloan):\n",
    "    \"\"\"Function for imputation via cross-tabulation to fill missing values for the 'loan' categorical feature\"\"\"\n",
    "    jobs=['housemaid','services','admin.','blue-collar','technician','retired','management','unemployed','self-employed','entrepreneur','student']\n",
    "    loan=[\"no\",\"yes\"]\n",
    "    for j in jobs:\n",
    "        ind=df[np.logical_and(np.array(df['loan']=='unknown'),np.array(df['job']==j))].index\n",
    "        mask=np.random.rand(len(ind))<((jobloan.loc[j]['no'])/(jobloan.loc[j]['no']+jobloan.loc[j]['yes']))\n",
    "        ind1=ind[mask]\n",
    "        ind2=ind[~mask]\n",
    "        df.loc[ind1,\"loan\"]='no'\n",
    "        df.loc[ind2,\"loan\"]='yes'\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=fillhousing(df,jobhousing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df=fillloan(df,jobloan)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Numerical variables:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let see the summary of the data in order to understand the numerical variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>campaign</th>\n",
       "      <th>pdays</th>\n",
       "      <th>previous</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>41188.00000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "      <td>41188.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>40.02406</td>\n",
       "      <td>2.567593</td>\n",
       "      <td>962.475454</td>\n",
       "      <td>0.172963</td>\n",
       "      <td>0.081886</td>\n",
       "      <td>93.575664</td>\n",
       "      <td>-40.502600</td>\n",
       "      <td>3.621291</td>\n",
       "      <td>5167.035911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.42125</td>\n",
       "      <td>2.770014</td>\n",
       "      <td>186.910907</td>\n",
       "      <td>0.494901</td>\n",
       "      <td>1.570960</td>\n",
       "      <td>0.578840</td>\n",
       "      <td>4.628198</td>\n",
       "      <td>1.734447</td>\n",
       "      <td>72.251528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>17.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-3.400000</td>\n",
       "      <td>92.201000</td>\n",
       "      <td>-50.800000</td>\n",
       "      <td>0.634000</td>\n",
       "      <td>4963.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>32.00000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.800000</td>\n",
       "      <td>93.075000</td>\n",
       "      <td>-42.700000</td>\n",
       "      <td>1.344000</td>\n",
       "      <td>5099.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>38.00000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.100000</td>\n",
       "      <td>93.749000</td>\n",
       "      <td>-41.800000</td>\n",
       "      <td>4.857000</td>\n",
       "      <td>5191.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>47.00000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>93.994000</td>\n",
       "      <td>-36.400000</td>\n",
       "      <td>4.961000</td>\n",
       "      <td>5228.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>98.00000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.400000</td>\n",
       "      <td>94.767000</td>\n",
       "      <td>-26.900000</td>\n",
       "      <td>5.045000</td>\n",
       "      <td>5228.100000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               age      campaign         pdays      previous  emp.var.rate  \\\n",
       "count  41188.00000  41188.000000  41188.000000  41188.000000  41188.000000   \n",
       "mean      40.02406      2.567593    962.475454      0.172963      0.081886   \n",
       "std       10.42125      2.770014    186.910907      0.494901      1.570960   \n",
       "min       17.00000      1.000000      0.000000      0.000000     -3.400000   \n",
       "25%       32.00000      1.000000    999.000000      0.000000     -1.800000   \n",
       "50%       38.00000      2.000000    999.000000      0.000000      1.100000   \n",
       "75%       47.00000      3.000000    999.000000      0.000000      1.400000   \n",
       "max       98.00000     56.000000    999.000000      7.000000      1.400000   \n",
       "\n",
       "       cons.price.idx  cons.conf.idx     euribor3m   nr.employed  \n",
       "count    41188.000000   41188.000000  41188.000000  41188.000000  \n",
       "mean        93.575664     -40.502600      3.621291   5167.035911  \n",
       "std          0.578840       4.628198      1.734447     72.251528  \n",
       "min         92.201000     -50.800000      0.634000   4963.600000  \n",
       "25%         93.075000     -42.700000      1.344000   5099.100000  \n",
       "50%         93.749000     -41.800000      4.857000   5191.000000  \n",
       "75%         93.994000     -36.400000      4.961000   5228.100000  \n",
       "max         94.767000     -26.900000      5.045000   5228.100000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numerical_variables = ['age','campaign', 'pdays', 'previous', 'emp.var.rate', 'cons.price.idx','cons.conf.idx','euribor3m',\n",
    "                      'nr.employed']\n",
    "df[numerical_variables].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Missing Values: **From the source of the data (U.C. Irvine ML Repository), we're told that the missing values, or NaNs, are encoded as '999'. From the above table, it is clear that only 'pdays' has missing values. Moreover, a majority of the values for 'pdays' are missing.\n",
    "\n",
    "**Outliers:** Outliers are defined as 1.5 x Q3 value (75th percentile). From the above table, it can be seen that only 'age' and 'campaign' have outliers as max('age') and max('campaign') > 1.5*Q3('age') and >1.5*Q3('campaign') respectively.\n",
    "\n",
    "But we also see that the value of these outliers are not so unrealistic (max('age')=98 and max('campaign')=56). Hence, we need not remove them since the prediction model should represent the real world. This improves the generalizability of the model and makes it robust for real world situations. The outliers, therefore, are not removed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Missing Values in Numerical Variables\n",
    "Let's examine the missing values in 'pdays'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def drawhist(df,feature):\n",
    "    plt.hist(df[feature])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtered out missing values encoded with an out-of-range value when plotting the histogram of values in order to properly understand the distribution of the known values. Here, histograms were created using matplotlib."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd89774f98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd896eec88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawhist(df,'pdays')\n",
    "plt.show()\n",
    "\n",
    "plt.hist(df.loc[df.pdays != 999, 'pdays'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>poutcome</th>\n",
       "      <th>failure</th>\n",
       "      <th>nonexistent</th>\n",
       "      <th>success</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pdays</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000364</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000631</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.001481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.010561</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.002816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.000097</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001020</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000607</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.000146</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.000583</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000971</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000073</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000607</td>\n",
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       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000316</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.001093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000194</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.000219</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000121</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
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       "      <td>0.000000</td>\n",
       "      <td>0.000049</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.000049</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.000024</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>0.099786</td>\n",
       "      <td>0.863431</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "poutcome   failure  nonexistent   success\n",
       "pdays                                    \n",
       "0         0.000000     0.000000  0.000364\n",
       "1         0.000000     0.000000  0.000631\n",
       "2         0.000000     0.000000  0.001481\n",
       "3         0.000097     0.000000  0.010561\n",
       "4         0.000049     0.000000  0.002816\n",
       "5         0.000097     0.000000  0.001020\n",
       "6         0.000607     0.000000  0.009396\n",
       "7         0.000364     0.000000  0.001093\n",
       "8         0.000146     0.000000  0.000291\n",
       "9         0.000583     0.000000  0.000971\n",
       "10        0.000170     0.000000  0.001093\n",
       "11        0.000073     0.000000  0.000607\n",
       "12        0.000316     0.000000  0.001093\n",
       "13        0.000194     0.000000  0.000680\n",
       "14        0.000121     0.000000  0.000364\n",
       "15        0.000219     0.000000  0.000364\n",
       "16        0.000049     0.000000  0.000219\n",
       "17        0.000121     0.000000  0.000073\n",
       "18        0.000121     0.000000  0.000049\n",
       "19        0.000024     0.000000  0.000049\n",
       "20        0.000024     0.000000  0.000000\n",
       "21        0.000049     0.000000  0.000000\n",
       "22        0.000000     0.000000  0.000073\n",
       "25        0.000024     0.000000  0.000000\n",
       "26        0.000000     0.000000  0.000024\n",
       "27        0.000000     0.000000  0.000024\n",
       "999       0.099786     0.863431  0.000000"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df['pdays'],df['poutcome'], values=df['age'], aggfunc='count', normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As we can see from the above table, the majority of the values for 'pdays' are missing. The majority of these missing values occur when the 'poutcome' is 'non-existent'. This means that the majority of the values in 'pdays' are missing because the customer was never contacted before. To deal with this variable, we removed the numerical variable 'pdays' and replaced it with categorical variables with following categories: p_days_missing, pdays_less_5, pdays_bet_5_15, and pdays_greater_15."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Add new categorical variables to our dataframe.\n",
    "df['pdays_missing'] = 0\n",
    "df['pdays_less_5'] = 0\n",
    "df['pdays_greater_15'] = 0\n",
    "df['pdays_bet_5_15'] = 0\n",
    "df['pdays_missing'][df['pdays']==999] = 1\n",
    "df['pdays_less_5'][df['pdays']<5] = 1\n",
    "df['pdays_greater_15'][(df['pdays']>15) & (df['pdays']<999)] = 1\n",
    "df['pdays_bet_5_15'][(df['pdays']>=5)&(df['pdays']<=15)]= 1\n",
    "df_dropped_pdays = df.drop('pdays', axis=1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since we have many categorical variables, dummy variables needs to be created for those vaiables. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_with_dummies=pd.get_dummies(df_dropped_pdays)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Drop one of the dummy variables as for $n$ categories we only need $n$-1 dummy variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def dropfeature(df,f):\n",
    "    \"\"\"Drops one of the dummy variables.\"\"\"\n",
    "    df=df.drop(f,axis=1)\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "features_dropped = ['default_no','housing_no','loan_no','y_no','marital_single','contact_cellular',\n",
    "                    'education_unknown','job_unknown','housing_unknown','loan_unknown', 'pdays_less_5']\n",
    "df_clean = dropfeature(df_with_dummies, features_dropped)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some helper functions to build and evaluate the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def drawheatmap(df):\n",
    "    '''Builds the heat map for the given data'''\n",
    "    f, ax = plt.subplots(figsize=(15, 15))\n",
    "    sns.heatmap(df.corr(method='spearman'), annot=False, cmap='coolwarm')\n",
    "    \n",
    "def drawhist(df,feature):\n",
    "    '''Draws an histogram for a feature in a data frame (df)'''\n",
    "    plt.hist(df[feature])\n",
    "\n",
    "def functionreplace(df,fea,val1,val2):\n",
    "    '''Replaces value (val1) with value (val2) in the data frame (df) for a feature (fea)'''\n",
    "    df[fea].replace(val1,val2)\n",
    "    return df\n",
    "\n",
    "def drawbarplot(df,x,y):\n",
    "    '''Draws a bar plot for a given feature x and y in a data frame'''\n",
    "    sns.barplot(x=x, y=y, data=df)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VntIuT+MBDgROiYjDU33eKmk2lS7Gz606ujsSmB4R+wKvAf9B5SbTRwPfKrzg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\nZN3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ztv+nl//XGyppKEWPgf+yvUxSs7xzjiM5eedsNygumP6Z4oJXXy2u27uuW7zt30ra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\nZmZmU9L/B45ELD4k8ua6AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8acea6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drawheatmap(df_clean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inferences**: From the above heat map we can see that 'y_yes' (our target variable) has good correlation with **'previous', 'emp.var.rate', 'euribor3m', 'nr.employed', 'pdays_missing', 'poutcome_success' , 'poutcome_nonexistent'and 'pdays_bet_5_15'**. We expect to see these independent variables as significant while building the models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def getmeanauc(aucs,model):\n",
    "    ''' Calculates the mean AUC for all the cross-validated samples and computes the value of C (Regularization Parameter) \n",
    "    for which max mean auc is obtained'''\n",
    "    #Initialize empty array to hold mean AUC values.\n",
    "    meanauc = []\n",
    "    maxmean = 0 #Initial value for maximum mean AUC\n",
    "    models_with_1_param = ['Logistic_Regression' , 'Ada_Boost']\n",
    "    models_with_2_params = ['Decision_Tree' , 'Random_Forest', 'Grad_Boost']\n",
    "    for c in aucs: #For loop to append AUC value to meanauc array.\n",
    "        meanauc.append(np.mean(aucs[c]))\n",
    "        if maxmean < np.mean(aucs[c]):\n",
    "            maxmean = np.mean(aucs[c]) #Adjust value of maxmean\n",
    "            cval = c\n",
    "    if model in models_with_1_param:\n",
    "        print(\"C value for max auc is: \",cval)\n",
    "        print(\"Max Mean Auc corresponding to the optimal value of C = \", maxmean)\n",
    "        return meanauc,cval\n",
    "    if model in models_with_2_params:\n",
    "        listSL=cval.split('L')\n",
    "        splitval=int(listSL[0]) #Stores minimum split value for max AUC\n",
    "        leafval=int(listSL[1]) #Stores minimum leaf value for max AUC\n",
    "        print(\"min_sample_split value for max auc is:\",splitval)\n",
    "        print(\"min_sample_leaf value for max auc is:\",leafval)\n",
    "        print(\"Max mean AUC corresponding to optimal leaf and split value = \",maxmean)\n",
    "        return meanauc,splitval,leafval\n",
    "\n",
    "def plot_mean_auc_LR(aucs,cs, label):\n",
    "    '''Plots different values of mean auc versus the hyperparameter C'''\n",
    "    plt.plot(np.log10(cs),aucs, label = label )\n",
    "    plt.xlabel(\"C (Regularization Parameter)\")\n",
    "    plt.ylabel(\"Mean AUC\")\n",
    "    plt.legend()\n",
    "        \n",
    "def plotfeatureimportances(train, importance):\n",
    "    '''Plots feature importance in a sorted order and shows the most significant variables at the top'''\n",
    "    X = list(train.columns)\n",
    "    X.remove('y_yes')\n",
    "    feature_importance_df = pd.DataFrame(data = importance, index = X, columns=['coefficient_values'])\n",
    "    feature_importance_df['sort'] = feature_importance_df.coefficient_values.abs()\n",
    "    sorted_feature_imp_df = feature_importance_df.sort_values(by='sort', ascending=False).drop('sort', axis=1)\n",
    "    fig, ax = plt.subplots()\n",
    "    fig.set_size_inches(10, 15)\n",
    "    sns.barplot(np.array(sorted_feature_imp_df.coefficient_values), np.array(sorted_feature_imp_df.index.values))\n",
    "    plt.title('Feature Importances')\n",
    "    plt.xlabel('Coefficients')\n",
    "    plt.ylabel('Feature Names')\n",
    "    \n",
    "def plotfeatureimp(fl,col):\n",
    "    '''Plots the feature importance of all the independent variables in the model'''\n",
    "    f=plt.figure(figsize=(10,15))\n",
    "    plt.barh(range(len(fl)),fl)\n",
    "    plt.yticks(range(len(col[:-1])),col[:-1])\n",
    "    \n",
    "def plotAUCDTRF(aucs,leafs,splits):\n",
    "    '''Plots AUC for each value of Leaf and Split combination'''\n",
    "    for i in range(len(splits)):\n",
    "        plt.plot(leafs,aucs[len(leafs)*i:len(leafs)*i+len(leafs)], label = 'Split value= ' + str(splits[i]))\n",
    "    plt.legend()\n",
    "    plt.xlabel('Leaf Values')\n",
    "    plt.ylabel('Mean AUC')\n",
    "    \n",
    "def plot_mean_auc_Ada_Boost(aucs, estimators, label):\n",
    "    '''Plots different values of mean auc versus the Estimators for AdaBoosting'''\n",
    "    plt.plot(estimators,aucs, label = label )\n",
    "    plt.xlabel(\"Estimators\")\n",
    "    plt.ylabel(\"Mean AUC\")\n",
    "    plt.legend()\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Building and Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Train Test Split **: First we start with splitting the data into training and testing. For all the models, five-fold cross validation will be used.\n",
    "\n",
    "Used stratification (keeping the target distribution unchanged) since dataset is highly imbalanced. A random train/test split will probably change the target distribution quite a bit."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original: 0.112654171118 Train: 0.110682852807 Test: 0.120538965768\n",
      "Original: 0.112654171118 Train: 0.112655538695 Test: 0.112648701141\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train, test = train_test_split(df_clean, train_size=0.8, random_state=5)\n",
    "print('Original:', (df_clean.y_yes).mean(), 'Train:', (train.y_yes).mean(), 'Test:', (test.y_yes).mean())\n",
    "\n",
    "train, test = train_test_split(df_clean, train_size=0.8, stratify=df_clean.y_yes.values, random_state=5)\n",
    "print('Original:', (df_clean.y_yes).mean(), 'Train:', (train.y_yes).mean(), 'Test:', (test.y_yes).mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression Model\n",
    "Here, we build the Logistic Regression model and evaluate it using the ROC curve and the AUC values. Since we know that the positive and the negative class are heavily imbalanced, the ROC curve and the AUC values are appropriate evaluation metrics as compared to the accuracy. For all the future models, the ROC curve and the AUC values are used as the evaluation metric.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "def LRmodel(train , validate , c, l_1 = False):\n",
    "    '''Fits the Logistic Regression Model on the training data set and calculates evaluation metrics on the validation set\n",
    "    with the regularization constant C'''\n",
    "    X=list(train.columns) #Create list of column labels from training data\n",
    "    Y='y_yes'\n",
    "    X.remove('y_yes')\n",
    "    scaler = StandardScaler().fit(train[X]) #Standardize features by removing the mean and scaling to unit variance\n",
    "    train_std = scaler.transform(train[X]) #Compute the mean and standard deviation on training set\n",
    "    validate_std = scaler.transform(validate[X])\n",
    "    xtrain = train_std\n",
    "    ytrain = train[Y]\n",
    "    xval = validate_std\n",
    "    yval = validate[Y]\n",
    "    if l_1:\n",
    "        logreg = LogisticRegression(C = c, penalty= 'l1') #Apply logistic regression on L1 penalty\n",
    "    else:\n",
    "        logreg = LogisticRegression(C=c)\n",
    "    logreg.fit(xtrain,ytrain)\n",
    "    pred_proba_val = logreg.predict_proba(xval)[:,1]\n",
    "    auc = metrics.roc_auc_score(yval, pred_proba_val)\n",
    "    fpr, tpr, threshold = metrics.roc_curve(yval, pred_proba_val)\n",
    "    accuracy = metrics.accuracy_score(yval, logreg.predict(xval))\n",
    "    return auc,logreg.coef_, tpr, fpr, threshold, accuracy\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross-Validation and Tuning the hyperparameters for Logistic Regression:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tuning of the models is done simulataneously with cross-validation. Tuning is basically done to decide the optimal regularization parameter for Logistic Regression and Linear SVM.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "\n",
    "def kfoldlr(dataset , k , cs, l1_penalty = False):\n",
    "    '''Performs kfold cross validation for the dataset and also searches for the optimal regularixation parameter '''\n",
    "    aucs = {}\n",
    "    kf = KFold(len(dataset),k) #Provides indices to split data in train/test sets\n",
    "    for train_idx, vali_idx in kf: #For loop to extract and append AUC values to array from training data\n",
    "        cv_train,cv_validate=dataset.iloc[train_idx,:], dataset.iloc[vali_idx,:]\n",
    "        for c in cs:\n",
    "            auc, f_imp, tpr, fpr, threshold, accuracy = LRmodel(cv_train,cv_validate,c, l_1 = l1_penalty)\n",
    "            if c in aucs:\n",
    "                aucs[c].append(auc)\n",
    "            else:\n",
    "                aucs[c]=[]\n",
    "                aucs[c].append(auc)\n",
    "    return aucs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The possible values of Cs (Regularization Parameter) are taken between $10^{-4}$ to $10^{5}$. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cs = np.logspace(-4,5,10)\n",
    "aucs_l2=kfoldlr(train , 5 , cs)\n",
    "aucs_l1=kfoldlr(train , 5 , cs, l1_penalty= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C value for max auc is:  0.1\n",
      "Max Mean Auc corresponding to the optimal value of C =  0.79007410954\n",
      "C value for max auc is:  0.1\n",
      "Max Mean Auc corresponding to the optimal value of C =  0.790285187028\n"
     ]
    }
   ],
   "source": [
    "means_l2,cval_l2 = getmeanauc(aucs_l2,'Logistic_Regression')\n",
    "means_l1,cval_l1 = getmeanauc(aucs_l1,'Logistic_Regression')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inferences**: The Logistic regression is performing well and from the above run, its evident that LR is performing better with L1 regularization parameter than with L2 regularization parameter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8ac4f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_mean_auc_LR(means_l2,cs, label = 'L2')\n",
    "plot_mean_auc_LR(means_l1,cs, label = 'L1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Decision Tree Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn import metrics\n",
    "def DTmodel(train,validation,leaf=1,split=2):\n",
    "    '''Decision Tree model to fit the train set to decision tree and predict AUC on test/validation set'''\n",
    "    X=list(train.columns) #Create list of column labels from training data\n",
    "    Y='y_yes'\n",
    "    X.remove('y_yes')\n",
    "    xtrain=train[X] \n",
    "    ytrain=train[Y]\n",
    "    xval=validation[X]\n",
    "    yval=validation[Y]\n",
    "    \n",
    "    #Runs sklearn's decision tree algorithm on the training data with min leaf size 1 and split size 2\n",
    "    dt=DecisionTreeClassifier(criterion='entropy',min_samples_leaf=leaf,min_samples_split=split) \n",
    "    \n",
    "    #Train the model\n",
    "    dt.fit(xtrain,ytrain)\n",
    "    predict_proba_val=dt.predict_proba(xval)[:,1] #Predict class probabilities of input validation data\n",
    "    auc=metrics.roc_auc_score(yval,predict_proba_val) #Compute ROC AUC from prediction scores\n",
    "    fpr,tpr,threshold=metrics.roc_curve(yval,predict_proba_val) #Compute ROC\n",
    "    accuracy=metrics.accuracy_score(yval,dt.predict(xval)) #Compute subset accuracy classification score\n",
    "    \n",
    "    #Feature importances. The higher the score, the more important the feature. \n",
    "    #Computed as the normalized total reduction of the criterion brought by that feature.\n",
    "    imp=dt.feature_importances_ \n",
    "    return auc,imp,tpr,fpr,threshold,accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation and Hyper Parameter Tuning for Decision Tree:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/neelangparghi/anaconda/lib/python3.5/site-packages/sklearn/cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "\n",
    "def kfoldDTRF(dataset, k,split,leaf,model):\n",
    "    '''K-fold validation function for Decision tree and Random Forest. K=5, hence input data set is divided \n",
    "    into 5 datasets out of which 4 of them will be used to fit model and one to validate the model in each iteration '''\n",
    "    aucs={}\n",
    "    kf=KFold(len(dataset),k) #Provides indices to split data in train/test sets\n",
    "    for train_idx, vali_idx in kf: \n",
    "        cv_train,cv_validate=dataset.iloc[train_idx,:], dataset.iloc[vali_idx,:]\n",
    "        for sp in split:\n",
    "            for lf in leaf:\n",
    "                if model=='DT':\n",
    "                    #Run decision tree function above based on user input\n",
    "                    core,fimp,tpr,fpr,threshold,accuracy=DTmodel(cv_train,cv_validate,lf,sp)\n",
    "                if model=='RF':\n",
    "                    #Run random forest function below based on user input\n",
    "                    core,fimp,tpr,fpr,threshold,accuracy=randomforrest(cv_train,cv_validate,lf,sp)\n",
    "                \n",
    "                if str(sp)+\"L\"+str(lf) in aucs:\n",
    "                    aucs[str(sp)+\"L\"+str(lf)].append(core)\n",
    "                else:\n",
    "                    aucs[str(sp)+\"L\"+str(lf)]=[]\n",
    "                    aucs[str(sp)+\"L\"+str(lf)].append(core)\n",
    "    return aucs\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min_sample_split_value and min_sample_leaf are hyper paremeters for Decision Tree and Random Forest indicating minimum split size and minimum leaf size. The range of value taken for split is [10,2000] and the range for leaf is [5,200]. Hence, there are 100 combination of split and leaf values. For each of these combinations, the AUC is calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "split = np.geomspace(10,2000,num=10,dtype=int)\n",
    "leaf = np.geomspace(5,200,num=10,dtype=int)\n",
    "aucsdt=kfoldDTRF(train,5,split,leaf,'DT')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_sample_split value for max auc is: 1110\n",
      "min_sample_leaf value for max auc is: 132\n",
      "Max mean AUC corresponding to optimal leaf and split value =  0.791968413497\n"
     ]
    },
    {
     "data": {
      "image/png": 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ALGBNt+T9wO1CiJ8S81AuAB5Ooq2jhpC/ky1vrGTD316mraGelIxM5n3qBqZf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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8ac75a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "meanauc,splitval,leafval=getmeanauc(aucsdt,'Decision_Tree')\n",
    "plotAUCDTRF(meanauc,leaf,split)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference: **It's evident that the Decision Tree is performing slightly better than Logistic Regression."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Forest Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "def randomforrest(train,validation,leaf=1,split=2):\n",
    "    '''Random Forest model to fit the training set to random forest and compute AUC on test/validation set'''\n",
    "    X=list(train.columns) #Provides indices to split data in train/test sets\n",
    "    Y='y_yes'\n",
    "    X.remove('y_yes')\n",
    "    xtrain=train[X]\n",
    "    ytrain=train[Y]\n",
    "    xval=validation[X]\n",
    "    yval=validation[Y]\n",
    "    \n",
    "    #Run sklearn's random forest algorithm with 128 trees and a minimum split size of 2 and minimum leaf size 1\n",
    "    random_forest = RandomForestClassifier(n_estimators=128,min_samples_split=split,min_samples_leaf=leaf)\n",
    "    \n",
    "    #Train the model\n",
    "    random_forest.fit(xtrain,ytrain)\n",
    "    \n",
    "    #Predict target values\n",
    "    Y_pred = random_forest.predict(xval)\n",
    "    \n",
    "    #Predict class probabilities of input validation data\n",
    "    proba_val=random_forest.predict_proba(xval)[:,1]\n",
    "    \n",
    "    #Compute ROC\n",
    "    fpr, tpr, thresholds=metrics.roc_curve(yval,proba_val)\n",
    "    \n",
    "    #Compute ROC AUC scores from prediction values\n",
    "    score=metrics.roc_auc_score(yval,proba_val)\n",
    "    \n",
    "    #Feature importances. The higher the score, the more important the feature. \n",
    "    #Computed as the normalized total reduction of the criterion brought by that feature.\n",
    "    importance=random_forest.feature_importances_\n",
    "    accuracy=metrics.accuracy_score(yval,random_forest.predict(xval))\n",
    "    return score,importance,tpr,fpr,thresholds,accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation and Hyper Parameter Tuning for Random Forest:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "min_sample_split_value and min_sample_leaf are hyper parameters for random forest indicating minimum split size and minimum leaf size. The range of value taken for split is [10,2000] and the range for leaf is [5,200]. hence, there are 100 combination of split and leaf values. For each of these combinations, the AUC is calculated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "RF_split = np.geomspace(10,2000,num=10,dtype=int)\n",
    "RF_leaf = np.geomspace(5,200,num=10,dtype=int)\n",
    "\n",
    "RF_auc=kfoldDTRF(train,5,RF_split,RF_leaf,'RF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_sample_split value for max auc is: 189\n",
      "min_sample_leaf value for max auc is: 7\n",
      "Max mean AUC corresponding to optimal leaf and split value =  0.797996817722\n"
     ]
    },
    {
     "data": {
      "image/png": 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M+qANYz9uR/shjXD01FR7AF2pktN3WiDWjmbs+P4SKTfr1t1VQqI2SDbyD7eRFwSBnBzj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agjiCaSbmyt1kSSTET4Selw3CI84JzNde8x9gF5pdTgpYmkG9trInhdFsMyBkb5+MNBRLi1ZBgu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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8ec33438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "RF_aucs,RF_splitval,RF_leafval=getmeanauc(RF_auc,'Random_Forest')\n",
    "plotAUCDTRF(RF_aucs,RF_leaf,RF_split)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference**: It's evident that Random Forest is performing better than both the Logistic Regression and Decision Tree models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gradient Boosting Model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "def GBoosting(train,validation,leaf=1,split=2):\n",
    "    \"\"\"Function for gradient boosting to minimize loss via weak learners\"\"\"\n",
    "    X=list(train.columns)\n",
    "    Y='y_yes'\n",
    "    X.remove('y_yes')\n",
    "    xtrain=train[X]\n",
    "    ytrain=train[Y]\n",
    "    xval=validation[X]\n",
    "    yval=validation[Y]\n",
    "    \n",
    "    GB = GradientBoostingClassifier(min_samples_split=split,min_samples_leaf=leaf)\n",
    "    \n",
    "    #Train the model\n",
    "    GB.fit(xtrain,ytrain)\n",
    "    \n",
    "    #Predict target values\n",
    "    Y_pred = GB.predict(xval)\n",
    "    \n",
    "    #Predict class probabilities of input validation data\n",
    "    gbplot=GB.predict_proba(xval)\n",
    "    gbpre=gbplot[:,1]\n",
    "    \n",
    "    #Compute ROC\n",
    "    gbfpr, gbtpr, gbthresholds=metrics.roc_curve(yval,gbpre)\n",
    "    \n",
    "    #Compute ROC AUC scores from prediction values\n",
    "    gbscore=metrics.roc_auc_score(yval,gbpre)\n",
    "    gbaccuracy=metrics.accuracy_score(yval,GB.predict(xval))\n",
    "    \n",
    "    #Feature importances. The higher the score, the more important the feature. \n",
    "    importance=GB.feature_importances_\n",
    "    \n",
    "    return gbscore,importance,gbtpr,gbfpr,gbthresholds,gbaccuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation and Hyper Paremeter Tuning for Gradient Boosting: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "def kfoldGB(dataset, k,split,leaf):\n",
    "    aucs={}\n",
    "    kf=KFold(len(dataset),k) #Provides indices to split data in train/test sets\n",
    "    for sp in split:\n",
    "        for lf in leaf:\n",
    "            for train_idx, vali_idx in kf:\n",
    "                cv_train,cv_validate=dataset.iloc[train_idx,:], dataset.iloc[vali_idx,:]\n",
    "                #Run Gradient Boosted Trees function defined above based on user input\n",
    "                gbscore,importance,gbtpr,gbfpr,gbthresholds,gbaccuracy= GBoosting(cv_train,cv_validate,lf,sp)\n",
    "                \n",
    "                #Storing the AUC scores in the aucs dictionary for all split values (sp) and leaf values (lf)\n",
    "                if str(sp)+\"L\"+str(lf) in aucs:\n",
    "                    aucs[str(sp)+\"L\"+str(lf)].append(gbscore)\n",
    "                else:\n",
    "                    aucs[str(sp)+\"L\"+str(lf)]=[]\n",
    "                    aucs[str(sp)+\"L\"+str(lf)].append(gbscore)\n",
    "    return aucs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Gradient Boosting also takes split and leaf as hyper parameters. We are taking the range (10,2000) for split values and the range (5,2000) for leaf values to see which combination of gives the optimal AUC when Gradient Boosting is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "gbsplit = np.geomspace(10,1500, num=8, dtype=int)\n",
    "gbleaf = np.geomspace(5,750, num=6, dtype=int)\n",
    "gbaucs=kfoldGB(train,5,gbsplit,gbleaf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min_sample_split value for max auc is: 85\n",
      "min_sample_leaf value for max auc is: 37\n",
      "Max mean AUC corresponding to optimal leaf and split value =  0.800653715711\n"
     ]
    },
    {
     "data": {
      "image/png": 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BAwfyxhtv4O3tbZNv7YncI2mCyKRB9BwwmB5lYRjV7uiCreOUFwKJoW6N9qZK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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8ac6fc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "gbauc,gbsplitval,gbleafval=getmeanauc(gbaucs,'Grad_Boost')\n",
    "plotAUCDTRF(gbauc,gbleaf,gbsplit)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Inference: ** We see from the above output that Gradient Boosting performs better than Logistic Regression, Decision Tree, and Random Forest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Ada Boost Model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def adaboost(settrain,settest, nestimator = 100):\n",
    "    X=list(settrain.columns)\n",
    "    Y='y_yes'\n",
    "    X.remove('y_yes')\n",
    "    xtrain=settrain[X]\n",
    "    ytrain=settrain[Y]\n",
    "    xtest=settest[X]\n",
    "    ytest=settest[Y]\n",
    "    #Instantiate a Decision Stump\n",
    "    dt_stump = DecisionTreeClassifier(max_depth=1, min_samples_leaf=1)\n",
    "    \n",
    "    #Instantitate an AdaBoostClassifier using the decision stump defined above\n",
    "    ad = AdaBoostClassifier(base_estimator=dt_stump, n_estimators=nestimator)\n",
    "    \n",
    "    #fit the AdaBoostClassifier on the training data\n",
    "    ad.fit(xtrain,ytrain)\n",
    "    \n",
    "    #Predict the Y values for the test/validation data\n",
    "    Y_pred = ad.predict(xtest)\n",
    "    \n",
    "    #Predict class probabilities of input validation data\n",
    "    adplot=ad.predict_proba(xtest)\n",
    "    \n",
    "    \n",
    "    adpre=adplot[:,1]\n",
    "    \n",
    "    #Computation to compute AUC score\n",
    "    adfpr, adtpr, adthresholds=metrics.roc_curve(ytest,adpre)\n",
    "    adscore=metrics.roc_auc_score(ytest,adpre)\n",
    "    \n",
    "    #Feature importances. The higher the score, the more important the feature.\n",
    "    ii=ad.feature_importances_\n",
    "    return adscore,ii"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cross Validation and Hyper Parameter Tuning for Adaboost Model: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import KFold\n",
    "from sklearn import metrics\n",
    "from sklearn import preprocessing\n",
    "def kfoldadaboost(dataset, k, estimators):\n",
    "    aucs ={}\n",
    "    kf=KFold(len(dataset),k) #Provides indices to split data in train/test sets\n",
    "    for e in estimators:\n",
    "        for train_idx, vali_idx in kf:\n",
    "            cv_train,cv_validate=dataset.iloc[train_idx,:], dataset.iloc[vali_idx,:]\n",
    "            \n",
    "            #Run AdaBoostClassifier function defined above based on user input\n",
    "            core,f= adaboost(cv_train,cv_validate, nestimator = e) \n",
    "            \n",
    "            #storing the auc Scores in the aucs dictionary for all the estimator values.\n",
    "            aucs[e] = []\n",
    "            aucs[e].append(core)\n",
    "    return aucs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For Adaboost, n_estimators is the hyper parameter which corresponds to maximum number of estimators. we are taking the range of values (50,2000) to see which of these values gives highest AUC when Adaboost is used"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "estimators = np.array([50,100,400,600,1000,2000])\n",
    "adaauc = kfoldadaboost(train, 5, estimators)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C value for max auc is:  1000\n",
      "Max Mean Auc corresponding to the optimal value of C =  0.815741466281\n"
     ]
    },
    {
     "data": {
      "image/png": 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DgNMIJmy8GPgrMNPdl3VEYV3B3fOK2Dsvh0uOSt1SviIiqZbwYru717n739z9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\nRKRNFCQiItIm/x+9Z+3wsTV+7wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8d637358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adaaucs,adac=getmeanauc(adaauc,'Ada_Boost')\n",
    "plot_mean_auc_Ada_Boost(adaaucs,estimators,'Adaboost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By comparing the mean AUC obtained by k-fold validation for each previous model, its clear that Adaboost is performing better than Logistic Regression, Decision Tree, Random Forest, and Gradient Boosting.\n",
    "Hence, we select Adaboost with selected features as our final model and run it on the test set to make suggestions to the marketing team of Banco de Portugal to guide them in targeting optimal clients."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Rki6WNEHSPEkfl/RjSXMkXV+zW+k8SWdJmlp+Ni/lbfV7gqTzyvFmku6SNE3S9yQtKuXD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\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1fd8dbefc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "adaauc_test,adafea=adaboost(train,test,adac)\n",
    "plotfeatureimportances(train,adafea)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Final Test AUC Score =  0.803608144842\n"
     ]
    }
   ],
   "source": [
    "print('Final Test AUC Score = ',adaauc_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the above Feature Importance chart, we see that Europe's Libor rate ('euribor3m'), client age ('age'), employment variability index ('emp.var.rate'), number of client contacts during the current marketing campaign ('campaign'), consumer price index ('cons.price.idx'), consumer confidence index ('cons.price.idx'), and mode of contact via phone ('contact_telephone') are the imporatant features that drive the decision of the customer to purchase the term deposit or not. "
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}