Project 3 | Ensemble Technique Project - Predict Loan Defaulters.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Y3vvEXP1_0og"
   },
   "outputs": [],
   "source": [
    "# Import important library\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7O83uMqA_0ol"
   },
   "source": [
    "### Q1 Read the input file and check the data dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1brbOfG_Enzy"
   },
   "outputs": [],
   "source": [
    "#Read input file and understand the data\n",
    "# \"default\" is my dependent variable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_LGo1xnK_0ow"
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
       "      <th>account_check_status</th>\n",
       "      <th>duration_in_month</th>\n",
       "      <th>credit_history</th>\n",
       "      <th>purpose</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>savings</th>\n",
       "      <th>present_emp_since</th>\n",
       "      <th>installment_as_income_perc</th>\n",
       "      <th>personal_status_sex</th>\n",
       "      <th>...</th>\n",
       "      <th>present_res_since</th>\n",
       "      <th>property</th>\n",
       "      <th>age</th>\n",
       "      <th>other_installment_plans</th>\n",
       "      <th>housing</th>\n",
       "      <th>credits_this_bank</th>\n",
       "      <th>job</th>\n",
       "      <th>people_under_maintenance</th>\n",
       "      <th>telephone</th>\n",
       "      <th>foreign_worker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>6</td>\n",
       "      <td>critical account/ other credits existing (not ...</td>\n",
       "      <td>domestic appliances</td>\n",
       "      <td>1169</td>\n",
       "      <td>unknown/ no savings account</td>\n",
       "      <td>.. &gt;= 7 years</td>\n",
       "      <td>4</td>\n",
       "      <td>male : single</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>real estate</td>\n",
       "      <td>67</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1</td>\n",
       "      <td>yes, registered under the customers name</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0 &lt;= ... &lt; 200 DM</td>\n",
       "      <td>48</td>\n",
       "      <td>existing credits paid back duly till now</td>\n",
       "      <td>domestic appliances</td>\n",
       "      <td>5951</td>\n",
       "      <td>... &lt; 100 DM</td>\n",
       "      <td>1 &lt;= ... &lt; 4 years</td>\n",
       "      <td>2</td>\n",
       "      <td>female : divorced/separated/married</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>real estate</td>\n",
       "      <td>22</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>1</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>no checking account</td>\n",
       "      <td>12</td>\n",
       "      <td>critical account/ other credits existing (not ...</td>\n",
       "      <td>(vacation - does not exist?)</td>\n",
       "      <td>2096</td>\n",
       "      <td>... &lt; 100 DM</td>\n",
       "      <td>4 &lt;= ... &lt; 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>male : single</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>real estate</td>\n",
       "      <td>49</td>\n",
       "      <td>none</td>\n",
       "      <td>own</td>\n",
       "      <td>1</td>\n",
       "      <td>unskilled - resident</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>42</td>\n",
       "      <td>existing credits paid back duly till now</td>\n",
       "      <td>radio/television</td>\n",
       "      <td>7882</td>\n",
       "      <td>... &lt; 100 DM</td>\n",
       "      <td>4 &lt;= ... &lt; 7 years</td>\n",
       "      <td>2</td>\n",
       "      <td>male : single</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>if not A121 : building society savings agreeme...</td>\n",
       "      <td>45</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>1</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>&lt; 0 DM</td>\n",
       "      <td>24</td>\n",
       "      <td>delay in paying off in the past</td>\n",
       "      <td>car (new)</td>\n",
       "      <td>4870</td>\n",
       "      <td>... &lt; 100 DM</td>\n",
       "      <td>1 &lt;= ... &lt; 4 years</td>\n",
       "      <td>3</td>\n",
       "      <td>male : single</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>unknown / no property</td>\n",
       "      <td>53</td>\n",
       "      <td>none</td>\n",
       "      <td>for free</td>\n",
       "      <td>2</td>\n",
       "      <td>skilled employee / official</td>\n",
       "      <td>2</td>\n",
       "      <td>none</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   default account_check_status  duration_in_month  \\\n",
       "0        0               < 0 DM                  6   \n",
       "1        1    0 <= ... < 200 DM                 48   \n",
       "2        0  no checking account                 12   \n",
       "3        0               < 0 DM                 42   \n",
       "4        1               < 0 DM                 24   \n",
       "\n",
       "                                      credit_history  \\\n",
       "0  critical account/ other credits existing (not ...   \n",
       "1           existing credits paid back duly till now   \n",
       "2  critical account/ other credits existing (not ...   \n",
       "3           existing credits paid back duly till now   \n",
       "4                    delay in paying off in the past   \n",
       "\n",
       "                        purpose  credit_amount                      savings  \\\n",
       "0           domestic appliances           1169  unknown/ no savings account   \n",
       "1           domestic appliances           5951                 ... < 100 DM   \n",
       "2  (vacation - does not exist?)           2096                 ... < 100 DM   \n",
       "3              radio/television           7882                 ... < 100 DM   \n",
       "4                     car (new)           4870                 ... < 100 DM   \n",
       "\n",
       "    present_emp_since  installment_as_income_perc  \\\n",
       "0       .. >= 7 years                           4   \n",
       "1  1 <= ... < 4 years                           2   \n",
       "2  4 <= ... < 7 years                           2   \n",
       "3  4 <= ... < 7 years                           2   \n",
       "4  1 <= ... < 4 years                           3   \n",
       "\n",
       "                   personal_status_sex  ... present_res_since  \\\n",
       "0                        male : single  ...                 4   \n",
       "1  female : divorced/separated/married  ...                 2   \n",
       "2                        male : single  ...                 3   \n",
       "3                        male : single  ...                 4   \n",
       "4                        male : single  ...                 4   \n",
       "\n",
       "                                            property age  \\\n",
       "0                                        real estate  67   \n",
       "1                                        real estate  22   \n",
       "2                                        real estate  49   \n",
       "3  if not A121 : building society savings agreeme...  45   \n",
       "4                              unknown / no property  53   \n",
       "\n",
       "   other_installment_plans   housing credits_this_bank  \\\n",
       "0                     none       own                 2   \n",
       "1                     none       own                 1   \n",
       "2                     none       own                 1   \n",
       "3                     none  for free                 1   \n",
       "4                     none  for free                 2   \n",
       "\n",
       "                           job people_under_maintenance  \\\n",
       "0  skilled employee / official                        1   \n",
       "1  skilled employee / official                        1   \n",
       "2         unskilled - resident                        2   \n",
       "3  skilled employee / official                        2   \n",
       "4  skilled employee / official                        2   \n",
       "\n",
       "                                   telephone foreign_worker  \n",
       "0  yes, registered under the customers name             yes  \n",
       "1                                       none            yes  \n",
       "2                                       none            yes  \n",
       "3                                       none            yes  \n",
       "4                                       none            yes  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "credit_data = pd.read_csv(\"german_credit.csv\")\n",
    "credit_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`The dataset tells about the credit history and other financial status of the customer, and also wether they defaulted in paying credit bills\n",
    "The data here has 20 dimension.Since, default is the dependent variable.Separating that column to a different dataframe.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gKaiZis__0o0"
   },
   "outputs": [],
   "source": [
    "# Lets build a Ensemble model but need to modify the dataset first"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "utYEGBER_0o4"
   },
   "source": [
    "### Q2.Prepare the model data by converting non-numeric to dummy ( 1 Marks)\n",
    "##### Hint: Use get_dummies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wjyTJcA6_0o6",
    "outputId": "e6bf7e9d-5732-4836-c0bb-fe03ba34683e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 21)"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print Shape of model data\n",
    "credit_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>default</th>\n",
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       "      <th>account_check_status_0 &lt;= ... &lt; 200 DM</th>\n",
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       "      <th>...</th>\n",
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       "      <th>job_management/ self-employed/ highly qualified employee/ officer</th>\n",
       "      <th>job_skilled employee / official</th>\n",
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       "      <th>telephone_none</th>\n",
       "      <th>telephone_yes, registered under the customers name</th>\n",
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       "      <th>0</th>\n",
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       "      <td>1</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
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       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>7882</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>4870</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   default  duration_in_month  credit_amount  installment_as_income_perc  \\\n",
       "0        0                  6           1169                           4   \n",
       "1        1                 48           5951                           2   \n",
       "2        0                 12           2096                           2   \n",
       "3        0                 42           7882                           2   \n",
       "4        1                 24           4870                           3   \n",
       "\n",
       "   present_res_since  age  credits_this_bank  people_under_maintenance  \\\n",
       "0                  4   67                  2                         1   \n",
       "1                  2   22                  1                         1   \n",
       "2                  3   49                  1                         2   \n",
       "3                  4   45                  1                         2   \n",
       "4                  4   53                  2                         2   \n",
       "\n",
       "   account_check_status_0 <= ... < 200 DM  account_check_status_< 0 DM  ...  \\\n",
       "0                                       0                            1  ...   \n",
       "1                                       1                            0  ...   \n",
       "2                                       0                            0  ...   \n",
       "3                                       0                            1  ...   \n",
       "4                                       0                            1  ...   \n",
       "\n",
       "   housing_own  housing_rent  \\\n",
       "0            1             0   \n",
       "1            1             0   \n",
       "2            1             0   \n",
       "3            0             0   \n",
       "4            0             0   \n",
       "\n",
       "   job_management/ self-employed/ highly qualified employee/ officer  \\\n",
       "0                                                  0                   \n",
       "1                                                  0                   \n",
       "2                                                  0                   \n",
       "3                                                  0                   \n",
       "4                                                  0                   \n",
       "\n",
       "   job_skilled employee / official  job_unemployed/ unskilled - non-resident  \\\n",
       "0                                1                                         0   \n",
       "1                                1                                         0   \n",
       "2                                0                                         0   \n",
       "3                                1                                         0   \n",
       "4                                1                                         0   \n",
       "\n",
       "   job_unskilled - resident  telephone_none  \\\n",
       "0                         0               0   \n",
       "1                         0               1   \n",
       "2                         1               1   \n",
       "3                         0               1   \n",
       "4                         0               1   \n",
       "\n",
       "   telephone_yes, registered under the customers name   foreign_worker_no  \\\n",
       "0                                                  1                    0   \n",
       "1                                                  0                    0   \n",
       "2                                                  0                    0   \n",
       "3                                                  0                    0   \n",
       "4                                                  0                    0   \n",
       "\n",
       "   foreign_worker_yes  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   1  \n",
       "3                   1  \n",
       "4                   1  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dummy_data = pd.get_dummies(credit_data)\n",
    "dummy_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ytl4nlUW_0pA"
   },
   "source": [
    "### Check for highly correlated variables but don't required any treatment for this use case"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "0W02DKRx_0pA"
   },
   "source": [
    "`We can find correalation matrix and use heatmap to visualize the correlation between the variables.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>duration_in_month</th>\n",
       "      <th>credit_amount</th>\n",
       "      <th>installment_as_income_perc</th>\n",
       "      <th>present_res_since</th>\n",
       "      <th>age</th>\n",
       "      <th>credits_this_bank</th>\n",
       "      <th>people_under_maintenance</th>\n",
       "      <th>account_check_status_0 &lt;= ... &lt; 200 DM</th>\n",
       "      <th>account_check_status_&lt; 0 DM</th>\n",
       "      <th>account_check_status_&gt;= 200 DM / salary assignments for at least 1 year</th>\n",
       "      <th>...</th>\n",
       "      <th>housing_own</th>\n",
       "      <th>housing_rent</th>\n",
       "      <th>job_management/ self-employed/ highly qualified employee/ officer</th>\n",
       "      <th>job_skilled employee / official</th>\n",
       "      <th>job_unemployed/ unskilled - non-resident</th>\n",
       "      <th>job_unskilled - resident</th>\n",
       "      <th>telephone_none</th>\n",
       "      <th>telephone_yes, registered under the customers name</th>\n",
       "      <th>foreign_worker_no</th>\n",
       "      <th>foreign_worker_yes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6</td>\n",
       "      <td>1169</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>67</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>48</td>\n",
       "      <td>5951</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>2096</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>49</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>42</td>\n",
       "      <td>7882</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>45</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24</td>\n",
       "      <td>4870</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   duration_in_month  credit_amount  installment_as_income_perc  \\\n",
       "0                  6           1169                           4   \n",
       "1                 48           5951                           2   \n",
       "2                 12           2096                           2   \n",
       "3                 42           7882                           2   \n",
       "4                 24           4870                           3   \n",
       "\n",
       "   present_res_since  age  credits_this_bank  people_under_maintenance  \\\n",
       "0                  4   67                  2                         1   \n",
       "1                  2   22                  1                         1   \n",
       "2                  3   49                  1                         2   \n",
       "3                  4   45                  1                         2   \n",
       "4                  4   53                  2                         2   \n",
       "\n",
       "   account_check_status_0 <= ... < 200 DM  account_check_status_< 0 DM  \\\n",
       "0                                       0                            1   \n",
       "1                                       1                            0   \n",
       "2                                       0                            0   \n",
       "3                                       0                            1   \n",
       "4                                       0                            1   \n",
       "\n",
       "   account_check_status_>= 200 DM / salary assignments for at least 1 year  \\\n",
       "0                                                  0                         \n",
       "1                                                  0                         \n",
       "2                                                  0                         \n",
       "3                                                  0                         \n",
       "4                                                  0                         \n",
       "\n",
       "   ...  housing_own  housing_rent  \\\n",
       "0  ...            1             0   \n",
       "1  ...            1             0   \n",
       "2  ...            1             0   \n",
       "3  ...            0             0   \n",
       "4  ...            0             0   \n",
       "\n",
       "   job_management/ self-employed/ highly qualified employee/ officer  \\\n",
       "0                                                  0                   \n",
       "1                                                  0                   \n",
       "2                                                  0                   \n",
       "3                                                  0                   \n",
       "4                                                  0                   \n",
       "\n",
       "   job_skilled employee / official  job_unemployed/ unskilled - non-resident  \\\n",
       "0                                1                                         0   \n",
       "1                                1                                         0   \n",
       "2                                0                                         0   \n",
       "3                                1                                         0   \n",
       "4                                1                                         0   \n",
       "\n",
       "   job_unskilled - resident  telephone_none  \\\n",
       "0                         0               0   \n",
       "1                         0               1   \n",
       "2                         1               1   \n",
       "3                         0               1   \n",
       "4                         0               1   \n",
       "\n",
       "   telephone_yes, registered under the customers name   foreign_worker_no  \\\n",
       "0                                                  1                    0   \n",
       "1                                                  0                    0   \n",
       "2                                                  0                    0   \n",
       "3                                                  0                    0   \n",
       "4                                                  0                    0   \n",
       "\n",
       "   foreign_worker_yes  \n",
       "0                   1  \n",
       "1                   1  \n",
       "2                   1  \n",
       "3                   1  \n",
       "4                   1  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = dummy_data['default']\n",
    "X = dummy_data.iloc[:,1:62]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2fc57c88>"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corr = credit_data.corr()\n",
    "sns.heatmap(corr,cmap=sns.diverging_palette(20, 220, n=256),\n",
    "    square=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "LYeShbCO_0pI"
   },
   "source": [
    "`From the heatmap, we can see that credit_amount and duration_in_month are highly correlated features.\n",
    "Also,present_res_since and age have high correlation.`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "XLtFcI1m_0pQ"
   },
   "source": [
    "### Q3 Split Train/Test data 70:30 ratio( 1 Marks)\n",
    "##### Hint:from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train,X_test,Y_train,Y_test = train_test_split(X,Y,train_size=0.70,random_state=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wWTpd4Ng_0pW"
   },
   "source": [
    "### Q4 Build Random Forest Model( 2 Marks)\n",
    "#### Hint:from sklearn.ensemble import RandomForestClassifier using n_jobs=2,n_estimators=500,criterion=\"entropy\",random_state=9999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "AHX5tgQm_0pY"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='entropy',\n",
       "                       max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, n_estimators=500, n_jobs=2,\n",
       "                       oob_score=False, random_state=9999, verbose=0,\n",
       "                       warm_start=False)"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "randomForest_clf = RandomForestClassifier(n_jobs=2,n_estimators=500,criterion=\"entropy\",random_state=9999)\n",
    "randomForest_clf.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predicted = randomForest_clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "LuQwgG7T_0pa"
   },
   "source": [
    "### Q5 Calculate Confusion Matrix and Accuracy score (1 Marks)\n",
    "##### Hint: Use confusion_matrix and accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YgczdMEB_0pc"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score of the random forest model is:0.7433333333333333\n"
     ]
    }
   ],
   "source": [
    "import sklearn.metrics as metrics\n",
    "print(\"The accuracy score of the random forest model is:\"+str(metrics.accuracy_score(Y_test,Y_predicted)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The confusion matrix of the random forest model is:\n",
      "[[197  17]\n",
      " [ 60  26]]\n"
     ]
    }
   ],
   "source": [
    "print(\"The confusion matrix of the random forest model is:\")\n",
    "print(str(metrics.confusion_matrix(Y_test,Y_predicted)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "x2wDGYcC_0pf"
   },
   "source": [
    "### Q6 Show the list of the features importance( 1 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "bEWKBXX8_0ph"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0720369 , 0.10011299, 0.03698526, 0.03336023, 0.07812153,\n",
       "       0.01887908, 0.01134648, 0.01671098, 0.03092904, 0.00733814,\n",
       "       0.0447593 , 0.00769965, 0.02938331, 0.00989199, 0.01463478,\n",
       "       0.01253093, 0.00565399, 0.01148948, 0.01805625, 0.01189063,\n",
       "       0.01544127, 0.00444066, 0.0015188 , 0.01262918, 0.002495  ,\n",
       "       0.00095491, 0.00854398, 0.02014251, 0.00914877, 0.00585681,\n",
       "       0.01370746, 0.01401708, 0.01406608, 0.0150978 , 0.01236615,\n",
       "       0.00753987, 0.01564968, 0.00650298, 0.00716742, 0.01598798,\n",
       "       0.00792537, 0.00874017, 0.01067193, 0.01268978, 0.01412935,\n",
       "       0.01491938, 0.01014396, 0.01428347, 0.01744218, 0.00769151,\n",
       "       0.00703641, 0.01458963, 0.01045272, 0.0115418 , 0.01510074,\n",
       "       0.00197804, 0.01112365, 0.01424959, 0.0143988 , 0.00275009,\n",
       "       0.00305608])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "randomForest_clf.feature_importances_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "U7VnUE71_0pk"
   },
   "source": [
    "### Q7 K-fold cross-validation( 2 Marks)\n",
    "##### k-fold cross validation( without stratification)\n",
    "##### Usually k is set as 10-20 in practical settings, depends on data set size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Pf6rIo-c_0pm"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import cross_val_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SLIiX3BP_0pp"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KFold(n_splits=5, random_state=2, shuffle=False)"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Use below values\n",
    "num_folds = 10\n",
    "seed = 77\n",
    "kfold = KFold(n_splits=5,random_state=2)\n",
    "kfold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lDyCh_OG_0pt"
   },
   "outputs": [],
   "source": [
    "#Validate the Random Forest model build above using k fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fvxAgXik_0p0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.325     , 0.53658537, 0.32      , 0.34285714, 0.375     ])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = cross_val_score(randomForest_clf, X_train, Y_train, cv=kfold, scoring='recall')\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6r8p73dT_0p6"
   },
   "outputs": [],
   "source": [
    "#Calculate Mean score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qtGEiGuu_0qA"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean score of the random forest model is:0.3798885017421603\n"
     ]
    }
   ],
   "source": [
    "print(\"The mean score of the random forest model is:\"+str(np.mean(results)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Rr132f7H_0qD"
   },
   "outputs": [],
   "source": [
    "# Calculate score standard deviation using std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "44JxPbHG_0qH"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The standard deviation of the random forest model is:0.08068271993054187\n"
     ]
    }
   ],
   "source": [
    "print(\"The standard deviation of the random forest model is:\"+str(np.std(results)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "kB0x5yLs_0qM"
   },
   "source": [
    "# Q8 Build Ada boosting model, check the accuracy, and print confusion matrix ( 1 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "WiS5l8Cn_0qO"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AdaBoostClassifier(algorithm='SAMME.R', base_estimator=None, learning_rate=1.0,\n",
       "                   n_estimators=200, random_state=0)"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import AdaBoostClassifier\n",
    "ada_classifier = AdaBoostClassifier(random_state=0,\n",
    "    n_estimators=200\n",
    ")\n",
    "ada_classifier.fit(X_train,Y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_predict = ada_classifier.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy score of the Ada Boosting Model is:0.7333333333333333\n"
     ]
    }
   ],
   "source": [
    "print(\"The accuracy score of the Ada Boosting Model is:\"+str(metrics.accuracy_score(Y_test,Y_predict)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The confusion matrix of the Ada Boosting model is:\n",
      "[[184  30]\n",
      " [ 50  36]]\n"
     ]
    }
   ],
   "source": [
    "print(\"The confusion matrix of the Ada Boosting model is:\")\n",
    "print(str(metrics.confusion_matrix(Y_test,Y_predict)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "uMLxrkNg_0qR"
   },
   "source": [
    "# Q9. Build Gradient boosting model, check the accuracy, and print confusion matrix ( 1 Marks)\n",
    "percentage of correct predictions and Calculate sensitivity (or True Positive Rate or Recall) and Precision.\n",
    "( 1 Marks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "QUG5Y_li_0qT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Learning rate:  0.05\n",
      "Accuracy score (training): 0.694\n",
      "Accuracy score (validation): 0.713\n",
      "\n",
      "Learning rate:  0.075\n",
      "Accuracy score (training): 0.696\n",
      "Accuracy score (validation): 0.713\n",
      "\n",
      "Learning rate:  0.1\n",
      "Accuracy score (training): 0.703\n",
      "Accuracy score (validation): 0.713\n",
      "\n",
      "Learning rate:  0.25\n",
      "Accuracy score (training): 0.746\n",
      "Accuracy score (validation): 0.730\n",
      "\n",
      "Learning rate:  0.5\n",
      "Accuracy score (training): 0.793\n",
      "Accuracy score (validation): 0.723\n",
      "\n",
      "Learning rate:  0.75\n",
      "Accuracy score (training): 0.797\n",
      "Accuracy score (validation): 0.723\n",
      "\n",
      "Learning rate:  1\n",
      "Accuracy score (training): 0.806\n",
      "Accuracy score (validation): 0.720\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "lr_list = [0.05, 0.075, 0.1, 0.25, 0.5, 0.75, 1]\n",
    "\n",
    "for learning_rate in lr_list:\n",
    "    gb_clf = GradientBoostingClassifier(n_estimators=20, learning_rate=learning_rate, max_features=2, max_depth=2, random_state=0)\n",
    "    gb_clf.fit(X_train, Y_train)\n",
    "    \n",
    "    print(\"Learning rate: \", learning_rate)\n",
    "    print(\"Accuracy score (training): {0:.3f}\".format(gb_clf.score(X_train, Y_train)))\n",
    "    print(\"Accuracy score (validation): {0:.3f}\".format(gb_clf.score(X_test, Y_test)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "IS9CZaZr_0qX"
   },
   "source": [
    "`We're mainly interested in the classifier's accuracy on the validation set, but it looks like\n",
    "a learning rate of 0.25 gives us the best performance on the validation set and good performance on the training set.`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion Matrix:\n",
      "[[207   7]\n",
      " [ 74  12]]\n"
     ]
    }
   ],
   "source": [
    "gb_clf2 = GradientBoostingClassifier(n_estimators=20, learning_rate=0.25, max_features=2, max_depth=2, random_state=0)\n",
    "gb_clf2.fit(X_train, Y_train)\n",
    "predictions = gb_clf2.predict(X_test)\n",
    "\n",
    "print(\"Confusion Matrix:\")\n",
    "print(metrics.confusion_matrix(Y_test, predictions))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BZ43LYtQ_0q9"
   },
   "source": [
    "### Bootstrapping ( Bonus)\n",
    "##### Given a dataset of size n, a bootstrap sample is created by sampling n instances uniformly from the data (with/without replacement)\n",
    "##### Create a model with each bootstrap sample and validate it with the test set\n",
    "##### Final result is calculated by averaging the accuracy of models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "8KwXPTNE_0q9"
   },
   "outputs": [],
   "source": [
    "# Number of iterations for bootstrapping\n",
    "bootstrap_iteration = 10\n",
    "accuracy = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rVQTCij9_0rA"
   },
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "for i in range(bootstrap_iteration):\n",
    "    X_, y_ = resample(X_train, Y_train)\n",
    "    randomForest_clf.fit(X_, y_)\n",
    "    y_pred = randomForest_clf.predict(X_test)\n",
    "    \n",
    "    acc = accuracy_score(y_pred, Y_test)\n",
    "    accuracy.append(acc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "lRtfc6p7_0rD",
    "outputId": "6b6e9b14-0652-4550-83e0-7bb2ca3f0481"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy Score\n",
      "Avearge:  0.7553333333333334\n",
      "Standard deviation:  0.00702376916856849\n"
     ]
    }
   ],
   "source": [
    "accuracy = np.array(accuracy)\n",
    "print('Accuracy Score')\n",
    "print('Avearge: ', accuracy.mean())\n",
    "print('Standard deviation: ', accuracy.std())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "7ItbXIpd_0rH"
   },
   "outputs": [],
   "source": []
  }
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