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Imperial-lord/german-credit-risk-dataset-analysis.ipynb

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German Credit Risk Dataset Analysis.ipynb
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german-credit-risk-dataset-analysis.ipynb
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"display_name": "Python 3",
"language": "python",
"name": "python3"
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{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/Imperial-lord/7d91c0af8e471bace61a6ddc20e52972/german-credit-risk-dataset-analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KWcJJ_7FKopL"
},
"source": [
"# **German Credit Risk Dataset Analysis**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ZGu60WZxKopN"
},
"source": [
"\n",
"The goal is to predict if this loan credit would be a risk to the bank or not?\n",
"\n",
"In simple terms, if the loan amount is given to the applicant, will they pay back or become a defaulter?\n",
"\n",
"Since there are many applications which needs to be processed everyday, it will be helpful if there was a predictive model in place which can assist the executives to do their job by giving them a heads up about approval or rejection of a new loan application."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IE4aOG7AKopN"
},
"source": [
"# Import Libraries"
]
},
{
"cell_type": "code",
"metadata": {
"id": "iWaVc5dWKopO"
},
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "tb3yRspfKopP"
},
"source": [
"#Reading the data into python\n",
"df=pd.read_csv('/content/drive/MyDrive/German Credit Data/train.csv')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
},
"id": "zL0cfOveKopQ",
"outputId": "1b25778f-de77-45eb-be4e-90daa7c1019e"
},
"source": [
"df.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GoodCredit</th>\n",
" <th>checkingstatus</th>\n",
" <th>duration</th>\n",
" <th>history</th>\n",
" <th>purpose</th>\n",
" <th>amount</th>\n",
" <th>savings</th>\n",
" <th>employ</th>\n",
" <th>installment</th>\n",
" <th>status</th>\n",
" <th>others</th>\n",
" <th>residence</th>\n",
" <th>property</th>\n",
" <th>age</th>\n",
" <th>otherplans</th>\n",
" <th>housing</th>\n",
" <th>cards</th>\n",
" <th>job</th>\n",
" <th>liable</th>\n",
" <th>tele</th>\n",
" <th>foreign</th>\n",
" </tr>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>A11</td>\n",
" <td>6</td>\n",
" <td>A34</td>\n",
" <td>A43</td>\n",
" <td>1169</td>\n",
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" <td>67</td>\n",
" <td>A143</td>\n",
" <td>A152</td>\n",
" <td>2</td>\n",
" <td>A173</td>\n",
" <td>1</td>\n",
" <td>A192</td>\n",
" <td>A201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>A12</td>\n",
" <td>48</td>\n",
" <td>A32</td>\n",
" <td>A43</td>\n",
" <td>5951</td>\n",
" <td>A61</td>\n",
" <td>A73</td>\n",
" <td>2</td>\n",
" <td>A92</td>\n",
" <td>A101</td>\n",
" <td>2</td>\n",
" <td>A121</td>\n",
" <td>22</td>\n",
" <td>A143</td>\n",
" <td>A152</td>\n",
" <td>1</td>\n",
" <td>A173</td>\n",
" <td>1</td>\n",
" <td>A191</td>\n",
" <td>A201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0</td>\n",
" <td>A14</td>\n",
" <td>12</td>\n",
" <td>A34</td>\n",
" <td>A46</td>\n",
" <td>2096</td>\n",
" <td>A61</td>\n",
" <td>A74</td>\n",
" <td>2</td>\n",
" <td>A93</td>\n",
" <td>A101</td>\n",
" <td>3</td>\n",
" <td>A121</td>\n",
" <td>49</td>\n",
" <td>A143</td>\n",
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" <td>A201</td>\n",
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" <td>0</td>\n",
" <td>A11</td>\n",
" <td>42</td>\n",
" <td>A32</td>\n",
" <td>A42</td>\n",
" <td>7882</td>\n",
" <td>A61</td>\n",
" <td>A74</td>\n",
" <td>2</td>\n",
" <td>A93</td>\n",
" <td>A103</td>\n",
" <td>4</td>\n",
" <td>A122</td>\n",
" <td>45</td>\n",
" <td>A143</td>\n",
" <td>A153</td>\n",
" <td>1</td>\n",
" <td>A173</td>\n",
" <td>2</td>\n",
" <td>A191</td>\n",
" <td>A201</td>\n",
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" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>A11</td>\n",
" <td>24</td>\n",
" <td>A33</td>\n",
" <td>A40</td>\n",
" <td>4870</td>\n",
" <td>A61</td>\n",
" <td>A73</td>\n",
" <td>3</td>\n",
" <td>A93</td>\n",
" <td>A101</td>\n",
" <td>4</td>\n",
" <td>A124</td>\n",
" <td>53</td>\n",
" <td>A143</td>\n",
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" <td>2</td>\n",
" <td>A173</td>\n",
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"text/plain": [
" GoodCredit checkingstatus duration history ... job liable tele foreign\n",
"0 0 A11 6 A34 ... A173 1 A192 A201\n",
"1 1 A12 48 A32 ... A173 1 A191 A201\n",
"2 0 A14 12 A34 ... A172 2 A191 A201\n",
"3 0 A11 42 A32 ... A173 2 A191 A201\n",
"4 1 A11 24 A33 ... A173 2 A191 A201\n",
"\n",
"[5 rows x 21 columns]"
]
},
"metadata": {},
"execution_count": 416
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "NL8b5R0AKopQ"
},
"source": [
"# Data description(Data Dictionary)\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "IyfX4nbrKopR"
},
"source": [
"The business meaning of each column in the data \n",
"\n",
"* `GoodCredit`: Whether the issued loan was a good decision or bad\n",
"* `checkingstatus`: Status of existing checking account.\n",
"* `duration`: Duration of loan in months\n",
"* `history`: Credit history of the applicant\n",
"* `purpose`: Purpose for the loan\n",
"* `amount`: Credit amount\n",
"* `savings`: Savings account/bonds\n",
"* `employ`: Present employment since\n",
"* `installment`: Installment rate in percentage of disposable income\n",
"* `status`: Personal status and sex\n",
"* `others`: Other debtors / guarantors for the applicant\n",
"* `residence`: Present residence since\n",
"* `property`: Property type of applicant\n",
"* `age`: Age in years\n",
"* `otherplans`: Other installment plans\n",
"* `housing`: Housing\n",
"* `cards`: Number of existing credits at this bank\n",
"* `job`: Job\n",
"* `liable`: Number of people being liable to provide maintenance for\n",
"* `tele`: Is the Telephone registered or not\n",
"* `foreign`: Is the applicant a foreign worker"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "Daros_9eKopS"
},
"source": [
"# Data Exploration - Aimed at understanding the overall data"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "L5BZUhomKopS",
"outputId": "cea3bcc1-0d17-402b-b717-d32dde110af5"
},
"source": [
"#Number of rows and columns\n",
"df.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1000, 21)"
]
},
"metadata": {},
"execution_count": 417
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 403
},
"id": "09lB1ymBKopT",
"outputId": "9385d524-93ca-4ecd-d1ad-3bfcc9d9540b"
},
"source": [
"##Descriptive statistics of the data\n",
"df.describe(include='all')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>GoodCredit</th>\n",
" <th>checkingstatus</th>\n",
" <th>duration</th>\n",
" <th>history</th>\n",
" <th>purpose</th>\n",
" <th>amount</th>\n",
" <th>savings</th>\n",
" <th>employ</th>\n",
" <th>installment</th>\n",
" <th>status</th>\n",
" <th>others</th>\n",
" <th>residence</th>\n",
" <th>property</th>\n",
" <th>age</th>\n",
" <th>otherplans</th>\n",
" <th>housing</th>\n",
" <th>cards</th>\n",
" <th>job</th>\n",
" <th>liable</th>\n",
" <th>tele</th>\n",
" <th>foreign</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>1000.000000</td>\n",
" <td>1000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000</td>\n",
" <td>1000</td>\n",
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" <td>1000</td>\n",
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" <td>1000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000</td>\n",
" <td>1000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000</td>\n",
" <td>1000.000000</td>\n",
" <td>1000</td>\n",
" <td>1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>unique</th>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>5</td>\n",
" <td>10</td>\n",
" <td>NaN</td>\n",
" <td>5</td>\n",
" <td>5</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>NaN</td>\n",
" <td>4</td>\n",
" <td>NaN</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>top</th>\n",
" <td>NaN</td>\n",
" <td>A14</td>\n",
" <td>NaN</td>\n",
" <td>A32</td>\n",
" <td>A43</td>\n",
" <td>NaN</td>\n",
" <td>A61</td>\n",
" <td>A73</td>\n",
" <td>NaN</td>\n",
" <td>A93</td>\n",
" <td>A101</td>\n",
" <td>NaN</td>\n",
" <td>A123</td>\n",
" <td>NaN</td>\n",
" <td>A143</td>\n",
" <td>A152</td>\n",
" <td>NaN</td>\n",
" <td>A173</td>\n",
" <td>NaN</td>\n",
" <td>A191</td>\n",
" <td>A201</td>\n",
" </tr>\n",
" <tr>\n",
" <th>freq</th>\n",
" <td>NaN</td>\n",
" <td>394</td>\n",
" <td>NaN</td>\n",
" <td>530</td>\n",
" <td>280</td>\n",
" <td>NaN</td>\n",
" <td>603</td>\n",
" <td>339</td>\n",
" <td>NaN</td>\n",
" <td>548</td>\n",
" <td>907</td>\n",
" <td>NaN</td>\n",
" <td>332</td>\n",
" <td>NaN</td>\n",
" <td>814</td>\n",
" <td>713</td>\n",
" <td>NaN</td>\n",
" <td>630</td>\n",
" <td>NaN</td>\n",
" <td>596</td>\n",
" <td>963</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>0.300000</td>\n",
" <td>NaN</td>\n",
" <td>20.903000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3271.258000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.973000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.845000</td>\n",
" <td>NaN</td>\n",
" <td>35.546000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.407000</td>\n",
" <td>NaN</td>\n",
" <td>1.155000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.458487</td>\n",
" <td>NaN</td>\n",
" <td>12.058814</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2822.736876</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.118715</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.103718</td>\n",
" <td>NaN</td>\n",
" <td>11.375469</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.577654</td>\n",
" <td>NaN</td>\n",
" <td>0.362086</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>4.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>250.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>19.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>12.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1365.500000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.000000</td>\n",
" <td>NaN</td>\n",
" <td>27.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>0.000000</td>\n",
" <td>NaN</td>\n",
" <td>18.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2319.500000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3.000000</td>\n",
" <td>NaN</td>\n",
" <td>33.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>24.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>3972.250000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.000000</td>\n",
" <td>NaN</td>\n",
" <td>42.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2.000000</td>\n",
" <td>NaN</td>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>1.000000</td>\n",
" <td>NaN</td>\n",
" <td>72.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>18424.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.000000</td>\n",
" <td>NaN</td>\n",
" <td>75.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>4.000000</td>\n",
" <td>NaN</td>\n",
" <td>2.000000</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" GoodCredit checkingstatus duration ... liable tele foreign\n",
"count 1000.000000 1000 1000.000000 ... 1000.000000 1000 1000\n",
"unique NaN 4 NaN ... NaN 2 2\n",
"top NaN A14 NaN ... NaN A191 A201\n",
"freq NaN 394 NaN ... NaN 596 963\n",
"mean 0.300000 NaN 20.903000 ... 1.155000 NaN NaN\n",
"std 0.458487 NaN 12.058814 ... 0.362086 NaN NaN\n",
"min 0.000000 NaN 4.000000 ... 1.000000 NaN NaN\n",
"25% 0.000000 NaN 12.000000 ... 1.000000 NaN NaN\n",
"50% 0.000000 NaN 18.000000 ... 1.000000 NaN NaN\n",
"75% 1.000000 NaN 24.000000 ... 1.000000 NaN NaN\n",
"max 1.000000 NaN 72.000000 ... 2.000000 NaN NaN\n",
"\n",
"[11 rows x 21 columns]"
]
},
"metadata": {},
"execution_count": 418
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "AHz10MDoKopT",
"outputId": "1db227c3-c59f-4639-ee1f-8c107cbdbb68"
},
"source": [
"#Summarized information of data- Data types, Missing values based on number of non-null values Vs total rows etc.\n",
"df.info()"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 1000 entries, 0 to 999\n",
"Data columns (total 21 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 GoodCredit 1000 non-null int64 \n",
" 1 checkingstatus 1000 non-null object\n",
" 2 duration 1000 non-null int64 \n",
" 3 history 1000 non-null object\n",
" 4 purpose 1000 non-null object\n",
" 5 amount 1000 non-null int64 \n",
" 6 savings 1000 non-null object\n",
" 7 employ 1000 non-null object\n",
" 8 installment 1000 non-null int64 \n",
" 9 status 1000 non-null object\n",
" 10 others 1000 non-null object\n",
" 11 residence 1000 non-null int64 \n",
" 12 property 1000 non-null object\n",
" 13 age 1000 non-null int64 \n",
" 14 otherplans 1000 non-null object\n",
" 15 housing 1000 non-null object\n",
" 16 cards 1000 non-null int64 \n",
" 17 job 1000 non-null object\n",
" 18 liable 1000 non-null int64 \n",
" 19 tele 1000 non-null object\n",
" 20 foreign 1000 non-null object\n",
"dtypes: int64(8), object(13)\n",
"memory usage: 164.2+ KB\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "agCK1sGUKopU",
"outputId": "8b422986-979e-41f2-cda4-dc3586711aa5"
},
"source": [
"#Number of Unique variable in each column\n",
"df.nunique()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GoodCredit 2\n",
"checkingstatus 4\n",
"duration 33\n",
"history 5\n",
"purpose 10\n",
"amount 921\n",
"savings 5\n",
"employ 5\n",
"installment 4\n",
"status 4\n",
"others 3\n",
"residence 4\n",
"property 4\n",
"age 53\n",
"otherplans 3\n",
"housing 3\n",
"cards 4\n",
"job 4\n",
"liable 2\n",
"tele 2\n",
"foreign 2\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 420
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "aVXcuI2QKopU",
"outputId": "a02a0695-583b-46af-a7af-9201bf7be8ac"
},
"source": [
"#Any Null-Value\n",
"df.isnull().sum()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GoodCredit 0\n",
"checkingstatus 0\n",
"duration 0\n",
"history 0\n",
"purpose 0\n",
"amount 0\n",
"savings 0\n",
"employ 0\n",
"installment 0\n",
"status 0\n",
"others 0\n",
"residence 0\n",
"property 0\n",
"age 0\n",
"otherplans 0\n",
"housing 0\n",
"cards 0\n",
"job 0\n",
"liable 0\n",
"tele 0\n",
"foreign 0\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 421
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "LJN-NTM_KopU"
},
"source": [
"# Removing duplicate rows if any\n",
"df=df.drop_duplicates()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/"
},
"id": "7TOQp5mAKopV",
"outputId": "7a011575-228f-45d2-a41c-0d4aaccc7113"
},
"source": [
"#checking shape of Data after removing Duplicates\n",
"df.shape"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"(1000, 21)"
]
},
"metadata": {},
"execution_count": 423
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "UgjNYfGqKopV"
},
"source": [
"# Basic Data Exploration Results:"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ou4BLTasKopV"
},
"source": [
"Target Variable: **GoodCredit**\n",
"\n",
"**Predictors**: *duration, history, purpose, amount, savings*, etc.\n",
"\n",
"* GoodCredit = 1 means the loan was a good decision.\n",
"* GoodCredit = 0 means the loan was a bad decision.\n",
"\n",
"**Determining the type of Machine Learning -**\n",
"\n",
"Based on the problem statement I can understand that we need to create a supervised ML classification model, as the target variable is categorical."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "yK4jWEwEKopW"
},
"source": [
"# -Looking at the distribution of Target variable "
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 239
},
"id": "L3oqmPpoKopW",
"outputId": "9790062b-5a05-4661-b6f1-bbffe4907cc2"
},
"source": [
"# Creating Bar chart as the Target variable is Categorical\n",
"GroupedData=df.groupby('GoodCredit').size()\n",
"GroupedData.plot(kind='bar', figsize=(4,3))"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f669d266b50>"
]
},
"metadata": {},
"execution_count": 424
},
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 288x216 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "C6AgI5WbKopX"
},
"source": [
"The data distribution of the target variable is satisfactory to proceed further. There are sufficient number of rows for each category to learn from."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "L6T786GAKopX"
},
"source": [
"# Visual Exploratory Data Analysis\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pKfsXfpOKopX"
},
"source": [
"1. **Categorical variales**: `'checkingstatus', 'history', 'purpose','savings','employ', 'installment', 'status', 'others','residence', 'property', 'otherplans', 'housing', 'cards', 'job', 'liable', 'tele', 'foreign'`\n",
"\n",
"\n",
"2. **Continuous variables**: `'amount', 'age', 'duration'`."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "5uOI4_3OKopX"
},
"source": [
"# Plotting bar charts for categorical variable"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "O_EMnFFJKopX",
"outputId": "c6988223-631b-4b17-b919-55a69d02b06a"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['checkingstatus'])\n",
"plt.title('checkingstatus Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "2jlXZIOOKopY",
"outputId": "c47423fa-dc59-4a75-96fe-da972cc0c828"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['history'])\n",
"plt.title('history Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "hQOiRnO-KopY",
"outputId": "5fd9c97e-1f7d-4297-bbed-b281c1adcde6"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['purpose'])\n",
"plt.title('purpose Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "yM6bohxwKopY",
"outputId": "77b3dec0-3508-4128-ee14-c56c38878f54"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['savings'])\n",
"plt.title('savings Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "2euVKxlfKopZ",
"outputId": "2bc43158-078d-4d47-c3ea-8d42bdb71222"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['employ'])\n",
"plt.title('employ Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "PQ1MHZWFKopZ",
"outputId": "e8ad5b38-9fac-4dbd-dcd8-02998b2aa0c1"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['installment'])\n",
"plt.title('installment Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "TPgDopo0KopZ",
"outputId": "daeccea9-7872-4fa6-8660-1c5e0c655a19"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['status'])\n",
"plt.title('status Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "sruhuvjBKopZ",
"outputId": "c776035a-c24c-4a80-dcd0-698e0552799c"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['others'])\n",
"plt.title('others Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "8pud9aqFKopZ",
"outputId": "cb16591d-f835-4489-d611-ead0965296af"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['residence'])\n",
"plt.title('residence Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "JCIHk-RTKopa",
"outputId": "d6a6ad4d-358a-4ed5-c2b6-050a7fc9c0e9"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['property'])\n",
"plt.title('property Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "D4I3Y9V3Kopa",
"outputId": "1fa037ab-5b93-444b-f6b9-d385aa58ebc6"
},
"source": [
" plt.figsize = (20,20)\n",
"sns.countplot(x = df['otherplans'])\n",
"plt.title('otherplans Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "_DY3ApAWKopa",
"outputId": "e8d7f5eb-108e-43ab-b4c0-00ed07fd1852"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['housing'])\n",
"plt.title('housing Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "CGk5mysnKopa",
"outputId": "dae4e240-81bb-4e84-d623-12f0b84df071"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['cards'])\n",
"plt.title('cards Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "m2TDWL0hKopa",
"outputId": "ffeb5dff-bfac-40e8-a53a-02b14029f538"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['job'])\n",
"plt.title('job Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "8dGqjB0WKopb",
"outputId": "8160a410-6593-432f-d9a4-9919050627b1"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['liable'])\n",
"plt.title('liable Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "O2escUkMKopb",
"outputId": "8db45d4e-2a85-4c8c-d442-2a6d26ba8cc2"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['tele'])\n",
"plt.title('tele Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 295
},
"id": "rYOvw_hjKopb",
"outputId": "b59f0091-33c9-4ad1-a69d-13335025e84e"
},
"source": [
"plt.figsize = (20,20)\n",
"sns.countplot(x = df['foreign'])\n",
"plt.title('foreign Distribution')\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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W0ySe7/RE4JAkBwP3Bu4PnAzskmRFO3rYG9jU+m8C9gE2JlkB7IyfpSFJYzX2I4uqelVV7V1Vq4EjgPOr6nnABQyfkwGwFji7TZ/T5mnLz6+qGmPJkjT1ltN9Fq8Ejk+ygeGcxGmt/TRg99Z+PHDChOqTpKk10ceMV9WFwIVt+grgcXP0uZ3hKbeSpAlZTkcWkqRlyrCQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1jT0skuyT5IIklyb5RpI/a+27Jflsksvb911be5K8PcmGJF9L8phx1yxJ024SRxZ3AC+vqv2BA4EXJtkfOAE4r6oeCpzX5gGeDTy0fR0HvGv8JUvSdBt7WFTVNVX1lTZ9G3AZsApYA5zeup0OHNqm1wBn1OAiYJckDxxz2ZI01SZ6ziLJauDRwMXAnlV1TVt0LbBnm14FXDWy2sbWtuW2jkuyLsm6zZs3L1nNkjSNJhYWSe4LfAJ4aVXdOrqsqgqohWyvqk6tqpmqmlm5cuUiVipJmkhYJLknQ1B8sKo+2Zqvmx1eat+vb+2bgH1GVt+7tUmSxmQSV0MFOA24rKr+x8iic4C1bXotcPZI+1HtqqgDgVtGhqskSWOwYgKv+UTgj4GvJ7mktb0aOBH4WJJjge8Ch7dl5wIHAxuAHwLHjLdcSdLYw6KqvgBkK4sPmqN/AS9c0qIkSXfJO7glSV2GhSSpy7CQJHUZFpKkLsNCktRlWEiSugwLSVKXYSFJ6jIsJEldhoUkqcuwkCR1GRaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtJUpdhIUnqMiwkSV2GhSSpy7CQJHUZFpKkLsNCktS1YtIFSFqYK1//iEmXoGXoN//y60u6fY8sJEldhoUkqcuwkCR1GRaSpK5tJiySPCvJt5JsSHLCpOuRpGmyTYRFkh2AdwLPBvYHnptk/8lWJUnTY5sIC+BxwIaquqKqfgJ8BFgz4ZokaWpsK/dZrAKuGpnfCDx+tEOS44Dj2uz3k3xrTLVNgz2A7026iOUgb1076RJ0Z743Z702i7GVB21twbYSFl1VdSpw6qTr2B4lWVdVM5OuQ9qS783x2VaGoTYB+4zM793aJEljsK2ExZeBhybZN8mOwBHAOROuSZKmxjYxDFVVdyR5EfAZYAfgvVX1jQmXNU0c3tNy5XtzTFJVk65BkrTMbSvDUJKkCTIsJEldhsWUSnJokkry223+UUm+mOQbSb6W5L+M9N03ycXtUSsfbRcZkOQpSb6S5I4kh01qX7T9WaT35/FJLm39z0uy1XsI1GdYTK/nAl9o3wF+CBxVVQ8HngW8LckubdmbgZOqaj/gJuDY1n4lcDTwoXEVramxGO/PrwIzVfVI4CzgLeMqfntkWEyhJPcFnsTwQ3UEQFV9u6oub9NXA9cDK5MEeAbDDxvA6cChrd93quprwM/Huwfani3i+/OCqvpha7+I4f4s3U2GxXRaA3y6qr4N3JDkgNGFSR4H7Aj8P2B34OaquqMt3sjw+BVpqSzF+/NY4B+XruTtn2ExnZ7L8DBG2vfZQ32SPBA4Ezimqjxi0CQs6vszyZHADPA3i1znVNkmbsrT4kmyG8Nh+yOSFMNNjpXkFcD9gH8AXlNVF7VVbgB2SbKi/fXmo1a0ZBb7/ZnkmcBrgKdW1Y/HuCvbHY8sps9hwJlV9aCqWl1V+wD/BjwZ+BRwRlXNjv9Sw12bF7T1ANYCZ4+5Zk2PRXt/Jnk08D+BQ6rq+jHuw3bJO7inTJILgDdX1adH2l4CvIzhr7LRx6gcXVWXJHkww3DAbgxXmBxZVT9O8liGH+BdgduBa9vVKtLdssjvz88BjwCuaf2vrKpDxrEf2yPDQpLU5TCUJKnLsJAkdRkWkqQuw0KS1GVYSJK6DAtpAZK8JMllST74a27nkCQnLFZd0lLz0llpAZJ8E3hmVW2cR9/Zu4qlbZ5HFtI8JXk38GDgH5O8PMnftc9KuCjJI1uf1yU5M8m/AGcmWZnkE0m+3L6e2PodneSUNv2Qto2vJ3ljku+39qcluTDJWUm+meSD7Smr0tgZFtI8VdULgKuBpwOrga+2z0p4NXDGSNf9GY4+nguczPBZC48F/jPwnjk2fTJwclU9guGpqaMeDby0bfPBwBMXbYekBfBBgtLd8ySGX/5U1flJdk9y/7bsnKr6UZt+JrD/yAHB/dvnNYx6Au0zGBg+SOqtI8u+NDvkleQShpD6wmLuiDQfhoW0+H4wMn0P4MCqun20wwJGk0aflPoz/JnVhDgMJd09/ww8D4ZzC8D3qurWOfr9H+DFszNJHjVHn4toRym0T4aTlhvDQrp7XgcckORrwIkMj8aey0uAmXYi/FLgBXP0eSlwfNvWfsAtS1Cv9Gvx0llpwpLsBPyoqirJEcBzq2rNpOuSRjn+KU3eAcAp7bLYm4HnT7ge6Vd4ZCFJ6vKchSSpy7CQJHUZFpKkLsNCktRlWEiSuv4/5ppimgydJPcAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "CExsu6rUKopb"
},
"source": [
"# Bar Charts Interpretation\n",
"---\n",
"\n",
"These bar charts represent the frequencies of each category in the Y-axis and the category names in the X-axis.\n",
"\n",
"The ideal bar chart is where each category has comparable frequency. Hence, there are enough rows for each category in the data for the ML algorithm to learn.\n",
"\n",
"If there is a column which shows too skewed distribution where there is only one dominant bar and the other categories are present in very low numbers. These kind of columns may not be very helpful in machine learning. We will confirm this in the correlation analysis section and take a final call to select or reject the column.\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ngd05xB7Kopb"
},
"source": [
"# Plotting histograms for continuous variables\n",
"\n"
]
},
{
"cell_type": "code",
"metadata": {
"colab": {
"base_uri": "https://localhost:8080/",
"height": 695
},
"id": "C_8OdVbMKopc",
"outputId": "e4e2c5d4-b62b-4f77-9680-cc76a8a59194"
},
"source": [
"df.hist(['age', 'amount','duration'], figsize=(18,10), rwidth=0.95)"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"array([[<matplotlib.axes._subplots.AxesSubplot object at 0x7f6681857b10>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f6681ba7c50>],\n",
" [<matplotlib.axes._subplots.AxesSubplot object at 0x7f6681c42710>,\n",
" <matplotlib.axes._subplots.AxesSubplot object at 0x7f6682156ad0>]],\n",
" dtype=object)"
]
},
"metadata": {},
"execution_count": 442
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x720 with 4 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "KbDWTElQKopc"
},
"source": [
"# Histogram Interpretation:\n",
"Histograms shows us the data distribution for a single continuous variable.\n",
"\n",
"The X-axis shows the range of values and Y-axis represent the number of values in that range. \n"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GK1OTDdIKopc"
},
"source": [
"# Missing values treatment"
]
},
{
"cell_type": "code",
"metadata": {
"id": "yxUTL2OjKopc",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9a3555eb-5c3a-4ada-eed6-c13cabecd333"
},
"source": [
"df.isnull().sum()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"GoodCredit 0\n",
"checkingstatus 0\n",
"duration 0\n",
"history 0\n",
"purpose 0\n",
"amount 0\n",
"savings 0\n",
"employ 0\n",
"installment 0\n",
"status 0\n",
"others 0\n",
"residence 0\n",
"property 0\n",
"age 0\n",
"otherplans 0\n",
"housing 0\n",
"cards 0\n",
"job 0\n",
"liable 0\n",
"tele 0\n",
"foreign 0\n",
"dtype: int64"
]
},
"metadata": {},
"execution_count": 443
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "TFLfqRtFKopc"
},
"source": [
"We see that we have no `null` values so no treatment is needed"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "n50cxul0Kopc"
},
"source": [
"# Feature Selection:\n",
"---\n",
"\n",
"Now its time to finally choose the best columns(Features) which are correlated to the Target variable. \n",
"\n",
"We will do this by visualizing the relation between the Target variable and each of the predictors to get a better sense of data. \n",
"\n",
"Then we will directly measure the correlation values or ANOVA or Chi-Square tests."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "b2ZbuIfmKopc"
},
"source": [
"1. **Relationship exploration**: Box plots for Categorical Target Variable \"GoodCredit\" and continuous predictors"
]
},
{
"cell_type": "code",
"metadata": {
"id": "GKkX-BNkKopd",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 361
},
"outputId": "1461d129-5dd8-4152-e63c-9acabfc8d129"
},
"source": [
"ContinuousColsList=['age','amount', 'duration']\n",
"\n",
"fig, PlotCanvas=plt.subplots(nrows=1, ncols=len(ContinuousColsList), figsize=(18,5))\n",
"\n",
"# Creating box plots for each continuous predictor against the Target Variable \"GoodCredit\"\n",
"for PredictorCol , i in zip(np.array(ContinuousColsList), range(len(ContinuousColsList))):\n",
" df.boxplot(column=PredictorCol, by='GoodCredit', figsize=(5,5), vert=True, ax=PlotCanvas[i])"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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NFRERERERERGRlKXEhYiIiIiIiIikrKzBPNiECRN88uTJg3nIIa2pqYnc3Nyow5A0pmusd5555pmd7n5C1HEMJt23e0f/pmQg6frqPd235Wj070oGkq6v3uvuvj2oiYvJkyfz9NNPD+Yhh7S6ujoKCwujDkPSmK6x3jGzrVHHMNh03+4d/ZuSgaTrq/d035aj0b8rGUi6vnqvu/u2hoqIiIiIiIiISMpS4kJEREREREREUpYSFyIiIiIiIiKSspS4EBEREREREZGUpcSFiIiIiIiIiKQsJS5EREREREREJGUpcSEiIiIiIiIiKUuJixSUSCTIyckhHo+Tk5NDIpGIOiQRERGJSHV1NQUFBRQVFVFQUEB1dXXUIYmIiAyqrKgDkI4SiQRLliyhoqKCqVOnsmnTJhYsWABAZWVlxNGJiIjIYKqurqasrIyqqipaW1vJzMykpKQEgOLi4oijExERGRzqcZFili5dSkVFBfPnzycnJ4f58+dTUVHB0qVLow5NREREBll5eTlVVVXE43GysrKIx+NUVVVRXl4edWgiIiKDRomLFNPc3ExpaWmHttLSUpqbmyOKSERERKLS0NDAjBkzOrTNmDGDhoaGiCISEZGumNlhj3g83mW7mUUd7pCjxEWKyc7O5iMf+QgZGRnE43EyMjL4yEc+QnZ2dtShSRrReGkRkaEhFotRX1/foa2+vp5YLBZRRCIi0hV3P+xx2oJHumx396jDHXJU4yLFnHjiifzxj39k+vTpfPWrX+V73/seTz75JKecckrUoUma0HhpEZGho6ysjJKSkvZ7dm1tLSUlJRoqIiIiw4oSFymmsbGRM888k2eeeYaLLrqI7OxszjzzTDZt2hR1aJImksdL19XVUVhYSFVVFYlEQokLEZEU03ZfTiQSNDQ0EIvFKC8v1/06zZjZGcDKpKb3Af8C3BO2Twa2ABe7++7Bjk9EJGoaKpJi3J0nnniC/fv3U1tby/79+3niiSfUnUj6jcZLi4gMLcXFxWzcuJH169ezceNGJS3SkLv/wd0/7O4fBj4K7AMeAm4E1rv76cD6cFlEZNhR4iLFmBkLFy7s0LZw4UIVcJF+o/HSIiIiKa0IeMndtwIXAneH7XcDsyOLSkQkQhoqkmLOOeccFi9eDMC5557LvHnzWLx4MTNnzow4MkkXGi8tIiKS0i4F2qpm57n7jvD1q0BeNCGJiERLiYsUs3btWmbNmsWSJUtYvHgxZsbMmTNZu3Zt1KFJmtB4aRERkdRkZiOBC4CFnde5u5tZl2OHzewa4BqAvLw86urqBjLMtLJ3716dLxlQur76hxIXKagtSdFWOFGkvxUXF1NcXKxrTEREJLV8Bvidu78WLr9mZhPdfYeZTQRe72ond78LuAtg2rRprv/be06/C8mAemyNrq9+ohoXIiIpwMyWmdnrZrYxqW2lmT0bPraY2bNh+2Qzeydp3ZKkfT5qZv9tZi+a2b9bWCDHzMaZ2eNm9kL4fPzgf0oRETmKYt4dJgKwGpgbvp4LPDzoEYmIpAAlLkREUsNy4NPJDe5+SVKV+QeBnyatfqltnbuXJrUvBq4GTg8fbe+pyvQiIinMzHKBc+h4r78NOMfMXgA+FS6LiAw7GioiIpIC3P2XZja5q3Vhr4mLgbOP9B5hN+Jj3f1X4fI9BBXof0ZQmb4w3PRuoA5Y0PfIRUSkP7h7EzC+U9sbBLOMiIgMa+pxISKS+v4GeM3dX0hqm2JmG8zsF2b2N2HbJKAxaZvGsA1UmV5EREREhij1uBARSX2dxzzvAE519zfM7KPAKjM7s6dvdqTK9KDq9H2h6vQykHR9iYjIcKXEhYhICjOzLOBzwEfb2ty9GWgOXz9jZi8BHwC2AflJu+eHbdDDyvThe6o6/Xuk6vQykHR9iYjIcKWhIiIiqe1TwPPu3j4ExMxOMLPM8PX7CIpwvhwOBXnLzD4e1sX4Eu9WoFdlehEREREZkpS4EBFJAWZWDTwFnGFmjWZWEq66lI7DRADOAp4Lp0d9ACh1913hunnAD4EXgZcICnOCKtOLiIiIyBCloSIiIinA3Yu7ab+ii7YHCaZH7Wr7p4GCLtpVmV5EREREhqSj9rgwszPM7Nmkx1tm9g9mNs7MHjezF8Ln4wcjYBEREREREREZPo6auHD3P7j7h939wwTF4fYBDwE3Auvd/XRgfbgs/aC6upqCggKKioooKCigurpzL3ERERERERGR4aG3Q0WKgJfcfauZXQgUhu13A3XAgv4LbXiqrq6mrKyMqqoqWltbyczMpKQkGOpeXNxlT3IRERERERGRtNXb4pzJReLywgr2AK8Cef0W1TBWXl5OVVUV8XicrKws4vE4VVVVlJeXRx2aiIiIiIiIyKDrcY8LMxsJXAAs7LzO3d3MvJv9rgGuAcjLy6Ouru69RTpMNDQ00NraSl1dHXv37qWuro7W1lYaGhp07qTftV1jIiIiIiIiqao3Q0U+A/zO3V8Ll18zs4nuvsPMJgKvd7WTu98F3AUwbdo0Lyws7Eu8aS8Wi5GZmUlhYSF1dXUUFhZSW1tLLBZD5076W9s1JiIiIiIikqp6M1SkmHeHiQCsBuaGr+cCD/dXUMNZWVkZJSUl1NbW0tLSQm1tLSUlJZSVlUUdmoiIiIiIiMig61GPCzPLBc4B/i6p+TbgfjMrAbYCF/d/eMNPWwHORCJBQ0MDsViM8vJyFeYUERERERGRYalHiQt3bwLGd2p7g2CWEelnxcXFFBcXqxu/iIiIiIiIDHu9nVVERERERERERGTQKHGRgqqrqykoKKCoqIiCggKqq6uPvpNILyQSCXJycojH4+Tk5JBIJKIOSUREREREpEu9mVVEBkF1dTVlZWVUVVXR2tpKZmYmJSUlAKpzIf0ikUiwZMkSKioqmDp1Kps2bWLBggUAVFZWRhydiIiIiIhIR+pxkWLKy8upqqoiHo+TlZVFPB6nqqqK8vLyqEOTNLF06VIqKiqYP38+OTk5zJ8/n4qKCpYuXRp1aCIiIiIiIodR4iLFNDQ0MGPGjA5tM2bMoKGhIaKIJN00NzdTWlraoa20tJTm5uaIIhIRkSPREFIRERnulLhIMbFYjOnTp5ORkUE8HicjI4Pp06cTi8WiDk3SRHZ2NkuWLOnQtmTJErKzsyOKSGTo0xdLGShtQ0grKytZu3YtlZWVlJWV6RoTEZFhRTUuUkxGRgZPP/00F1xwAVdeeSU/+tGPWL16NR/84AejDk3SxNVXX91e02Lq1Kl897vfZcGCBYf1whCRnlFtIhlIyUNI26ZJr6qqIpFI6PoSEZFhQ4mLFLNx40aKiop46aWX+PznP08sFqOoqIiampqoQ5M00VaA86abbqK5uZns7GxKS0tVmFPkPSovL2fOnDkkEgkaGhqIxWLMmTOH8vJyfbGUPmtoaKCxsZGCgoL262vBggUaQioiIsOKEhcpxt158MEHOe6449r/svLmm28yduzYqEOTNFJZWUllZWX7NSYi792mTZtoampi2bJl7T0urrrqKrZu3Rp1aJIGTj75ZBYsWMC9997bfn1ddtllnHzyyVGHJiIiMmhU4yLFmBkLFy7s0LZw4ULMLKKIRETkSEaOHEkikegwG1QikWDkyJFRhyZpwt2PuCwiIpLulLhIMeeccw6LFy/GzIjH45gZixcv5pxzzok6NEkjiUSCnJwc4vE4OTk5JBKJqEMSGbIOHDjAnXfeSW1tLS0tLdTW1nLnnXdy4MCBqEOTNLB9+3Zuv/12EokEs2bNIpFIcPvtt7N9+/aoQxMRERk0GioiMswkEgmWLFlCRUUFU6dOZdOmTe3FOlXnQqT3pk6dyuzZsw+rcbFq1aqoQ5M0EIvFyM/PZ+PGje3D+2prazXbmIiIDCvqcZFiHn/8cb785S/j7tTW1uLufPnLX+bxxx+POjRJE0uXLqWiooL58+eTk5PD/PnzqaioYOnSpVGHJjIklZWVsWLFig7TVa5YsYKysrKoQ5M0UFZWRklJSYcePSUlJbq+RERkWFGPixTj7tx6660d2m699VYWL14cUUSSbpqbmw+b+rS0tJTrr78+oohEhra2mUOSe1xoRhHpL7q+hg8zGwv8ECgAHLgK+AOwEpgMbAEudvfdEYWYVsaMGUNTU1P7cm5uLnv37o0wIhE5EvW4SDEqzikDLTs7m2uuuYaCggKKioooKCjgmmuuITs7O+rQRIas4uJiNm7cyPr169m4caO+VEq/0vU1bNwBPObufwl8CGgAbgTWu/vpwPpwWfqoLWkxefJkfvzjHzN58mSampoYM2ZM1KGJSDeUuEgxbcU5582bx969e5k3b56Kc0q/+uQnP8m9997LWWedxcMPP8xZZ53Fvffeyyc/+cmoQxMRERmWzOw44CygCsDdD7j7HuBC4O5ws7uB2dFEmF7akhabN28mPz+fzZs3tycvRCQ1aahIilm7di2zZs1iyZIl7bOLzJw5k7Vr10YdmqSJbdu2MXv2bJYtW8bixYvJzs5m9uzZvPDCC1GHNqyZ2TLgfOB1dy8I224Grgb+HG52k7s/Gq5bCJQArcBX3H1t2P5pgr/aZQI/dPfbwvYpwH3AeOAZ4HJ317QXIiKpYQrBvf5HZvYhgvv0dUCeu+8It3kVyOtqZzO7BrgGIC8vj7q6ugEPeKi75ZZbqKurY+/evdTV1XHLLbdw+eWX69xJv9M11T+UuEgBRxoG4u6sW7fusG00h7u8Vw0NDWzYsIERI0a0V6g/ePAgOTk5UYc23C0H7gTu6dT+PXf/1+QGM5sKXAqcCZwM/NzMPhCu/j5wDtAI/NbMVrv7JqAifK/7zGwJQdJDxXNERFJDFvBXQMLdf21md9BpWIi7u5l1+Qugu98F3AUwbdo0LywsHOBwh75//ud/ZvPmze2/C02ZMgUAnTvpV4+t0TXVTzRUJAW4e5eP0xY80u06kfcqFotRX1/foa2+vl5T60XM3X8J7Orh5hcC97l7s7tvBl4EPhY+XnT3l8PeFPcBF1qQ+TwbeCDcX92NRYaQ6urqDnWJqqurow5J+l8j0Ojuvw6XHyBIZLxmZhMBwufXI4ovreTm5rJlyxamTJlCY2MjU6ZMYcuWLeTm5kYdmoh0Qz0uRIaZtqn1qqqqaG1tbZ9ar7y8POrQpGvXmtmXgKeB68Nq8pOAXyVt0xi2AbzSqf2vCYaH7HH3li62P4y6HL93bV2ORfrL+vXrufPOO8nOzsbd2blzJ/PmzWPTpk0UFRVFHZ70E3d/1cxeMbMz3P0PQBGwKXzMBW4Lnx+OMMy0sXfvXsaMGcOWLVu4/PLLAc0qIpLqlLgQGWY0td6Qshi4hWBavFuA7xBMjzeg1OX4vWvrcizSXy6//HJycnJYsWIFra2tZGZmMmfOHJYvX84tt9wSdXjSvxLAvWY2EngZuJKgd/T9ZlYCbAUujjC+tNKWpNB9W2RoUOJCZBgqLi6muLhY/1mnOHd/re21mS0FHgkXtwGnJG2aH7bRTfsbwFgzywp7XSRvLyIprLGxkXXr1hGPx9vv2ffccw8zZ86MOjTpZ+7+LDCti1XqWiMiw55qXIiIpKi2cc2hvwU2hq9XA5eaWXY4W8jpwG+A3wKnm9mU8C92lwKrPSiMUwt8Idxf3Y1FhpCampoONS5qamqiDklERGRQKXEhMgyp0FvqMbNq4CngDDNrDLsF325m/21mzwFx4KsA7v4/wP0EY58fA/7e3VvD3hTXAmuBBuD+cFuABcB8M3uRoOZF1SB+PBF5j8aNG8eiRYu46qqrWLNmDVdddRWLFi1i3LhxUYcmIiIyaDRURGSYqa6upqysrL04Z2ZmJiUlJQCqcxEhd+/q5HebXHD3cuCwiqru/ijwaBftLxPMOiIiQ8jo0aPZv38/N954IwcPHmTEiBFkZ2czevToqEMTEREZNOpxITLMlJeXU1VVRTweJysri3g8TlVVlWYVERFJQdu2bWP06NFMmjSJjIwMJk2axOjRo9m2TWVqRERk+FDiQmSYaWhoYMaMGR3aZsyYQUNDQ0QRiYhId0aOHMmsWbPIzc0FgikbZ82axciRIyOOTEREZPAocSEyzMRiMerr6zu01dfXE4vFIopIZOhT3RgZKM3NzcN6OMYAACAASURBVKxcubJDjYuVK1fS3NwcdWgiIiKDRjUuRIaZsrIySkpK2mtc1NbWUlJSoqEiIu+R6sbIQMrOzuYLX/gCy5Yto6GhgVgsxiWXXMIDDzwQdWgiIiKDRokLkWGm7YtUIpFo/yW4vLxcX7BE3qPkujF1dXUUFhZSVVVFIpHQvyvpswMHDrB27VrGjBkDQFNTE2vXruXAgQMRRyYiIjJ4lLgQGYaKi4spLi5u/5IlIu+d6sbIQJo0aRJ79+4FwN0BaGlpYdKkSVGGJSIiMqiUuBAREemDtrox8Xi8vU11Y6Q/5eTksGzZsvahSHPmzIk6JBERkUGlxIWIiEgfqG6MDKTt27ezfPnyDsP7br/9dq644oqoQxMRERk0SlyIiIj0QXFxMU8++SSf+cxnaG5uJjs7m6uvvlr1LaRfxGIx8vPz2bhxY/vwvtraWvXoERGRYUWJC5FhwMx6tX3bOGoRObrq6mrWrFnDz372sw6zikyfPl3JC+kz9egRERGBjJ5sZGZjzewBM3vezBrM7BNmNs7MHjezF8Ln4wc6WBF5b9y9y8dpCx7psl1Eei55VpGsrCzi8ThVVVX6Yin9ori4mPLychKJBLNmzSKRSGgmKJF+UF1dTUFBAUVFRRQUFFBdXR11SCJyBD3tcXEH8Ji7f8HMRgKjgZuA9e5+m5ndCNwILBigOEVERFKSZhWRgaaZoET6V3V1NWVlZe09mdp6ygFKCoqkqKP2uDCz44CzgCoAdz/g7nuAC4G7w83uBmYPVJAiIiKpqm1WkWSaVUREJHWpp5zI0NOToSJTgD8DPzKzDWb2QzPLBfLcfUe4zatA3kAFKSIikqraahDU1tbS0tLSXoOgrKws6tBERKQL6iknMvT0ZKhIFvBXQMLdf21mdxAMC2nn7m5mXQ6MN7NrgGsA8vLyqKur61vEw4zOlww0XWMifdPWrTh5ukrVIBARSV1tPeXi8Xh7m3rKiaS2niQuGoFGd/91uPwAQeLiNTOb6O47zGwi8HpXO7v7XcBdANOmTXONzeyFx9ZoLKsMLF1jIv1CNQhERIYOzdYjMvQcNXHh7q+a2Stmdoa7/wEoAjaFj7nAbeHzwwMaqYiIiIiISB+pp5zI0NPTWUUSwL3hjCIvA1cS1Me438xKgK3AxQMTooiIiIiISP9RTzmRoaVHiQt3fxaY1sWqov4NR0RERERERETkXT2ZVUREREREIlJdXU1BQQFFRUUUFBRQXV0ddUgiIiKDqqdDRURERERkkFVXV1NWVtZeRDAzM5OSkhIAjccXEZFhQz0uRERERFJUeXk5VVVVxONxsrKyiMfjVFVVafYDEREZVpS4EBER6SN15ZeB0tDQwIwZMzq0zZgxg4aGhogiEhERGXwaKiIiItIH6sovAykWi/GNb3yDVatWtU/bOHv2bGKxWNShiYiIDBr1uBARSQFmtszMXjezjUlti8zseTN7zsweMrOxYftkM3vHzJ4NH0uS9vmomf23mb1oZv9uZha2jzOzx83shfD5+MH/lOlJXfllIMXjcb797W/z/PPPc+jQIZ5//nm+/e1vE4/How5N+pmZbQnv38+a2dNhm+7dIiIocSEikiqWA5/u1PY4UODu/wv4I7Awad1L7v7h8FGa1L4YuBo4PXy0veeNwHp3Px1YHy5LP1BXfhlIK1asAOCEE04gIyODE044oUO7pJ14eF+fFi7r3i0ighIXIiIpwd1/Cezq1LbO3VvCxV8B+Ud6DzObCBzr7r9ydwfuAWaHqy8E7g5f353ULn0Ui8Wor6/v0FZfX6+u/NIvdu3aRUVFBTt27GD9+vXs2LGDiooKdu3adfSdJR3o3i0igmpciIgMFVcBK5OWp5jZBuAt4Gvu/l/AJKAxaZvGsA0gz913hK9fBfIGON5ho6ysjJKSkvYaF7W1tZSUlGioiPSbnTt3UlBQ0F7j4rzzzos6JBkYDqwzMwd+4O530cN7t5ldA1wDkJeXR11d3SCEmx727t2r8yUDStdX/1DiQkQkxZlZGdAC3Bs27QBOdfc3zOyjwCozO7On7+fuHv5i3N3x9AtwL0ycOJHLLruMq666ij/96U+ceuqpfPGLX2TixIk6d9JnZsaiRYsoLS3l5ptvpqamhkWLFmFmur7Szwx332ZmJwKPm9nzySuPdO8Okxx3AUybNs0LCwsHPNh0UVdXh86XDJjH1uj66idKXIiIpDAzuwI4HygKh3/g7s1Ac/j6GTN7CfgAsI2Ow0nywzaA18xsorvvCIeUvN7dMfULcO/t2LGD3NxcAHJzc5k6dap+UZF+cfzxx7N7924eeughfvCDH3DiiSe2t+saSy/uvi18ft3MHgI+Ri/u3SIi6Uw1LkREUpSZfRq4AbjA3fcltZ9gZpnh6/cRFOF8OexO/JaZfTycTeRLwMPhbquBueHruUnt0kfV1dVcd911NDU14e40NTVx3XXXUV1dHXVokgb27NlDaWkpu3fv5tChQ+zevZvS0lL27NkTdWjSj8ws18yOaXsNzAQ2onu3iAigxIWISEows2rgKeAMM2s0sxLgTuAYgi7DydOengU8Z2bPAg8Ape7eVqlvHvBD4EXgJeBnYfttwDlm9gLwqXBZ+sENN9xAZmYmy5YtY926dSxbtozMzExuuOGGqEOTNBCLxbjooovYv38/tbW17N+/n4suukjFX9NPHlBvZr8HfgOscffH0L1bRATQUBERkZTg7sVdNFd1s+2DwIPdrHsaKOii/Q2gqC8xStcaGxtZt24d8Xi8faz0Pffcw8yZM6MOTdJAWVkZl1xyCbm5uWzdupXTTjuNpqYm7rjjjqhDk37k7i8DH+qiXffuARJ0TOwoHJEpIilIPS5EREREUtj+/fvZtm0b7s62bdvYv39/1CGJDGnJSYuFCxd22S4iqUWJCxERkT7Iz89n7ty51NbW0tLSQm1tLXPnziU/P//oO4scxQ033EBGRgaTJk3q8KyhSCJ95+7MnDlTPS1EhgANFREREemD22+/nb/7u79j1qxZHDx4kBEjRpCTk8MPfvCDqEOTNNDY2MhJJ53EsmXLaG1tJTMzkzlz5tDY2Bh1aCJDWmlpKQUFBTQ0NBCLxSgtLWXJkiVH31FEIqHEhYiISB/l5OQwfvx4tm7dyqRJk2hqaoo6JEkj8+fP71BDZf78+epxIdJHS5Ysoaampj0hePbZZ0cdkogcgYaKiIiI9EF5eTkrV65k8+bN1NTUsHnzZlauXEl5eXnUoUmaKC8vZ8qUKRQVFTFlyhRdWyL95Oyzz6ampkZJC5EhQD0uRERE+qChoYEZM2Z0aJsxYwYNDQ0RRSTpZNy4cezatYu9e/dy6NAhXnnlFVpbWxk3blzUoYkMWRkZGRw6dAiAW2+9tUO7iKQm/esUERHpg1gsRn19fYe2+vp6YrFYRBFJusnIyGDChAkATJgwQV+uRPooFotRU1ODu1NbW4u7U1NTo/u2SArT/3wiIiJ9UFZWRklJSYdZRUpKSigrK4s6NEkDu3btYsGCBe0JiwkTJrBgwQJ27doVdWgiQ5bu2yJDj4aKiIiI9EFxcTEAiUSivTp9eXl5e7tIX3WeqlFTN4r0je7bIkOPEhciIiJ9VFxcTHFxcfusDyL9Zdy4cdx+++0sWrSIqVOnsmnTJv7pn/5JNS5E+kj3bZGhRUNFRERERFLU6NGjOfbYY6msrOTcc8+lsrKSY489ltGjR0cdmsiQVl1dTUFBAUVFRRQUFFBdXR11SCJyBOpxISIiIpKitm/fzvLly6moqMDMyM3N5Zvf/CZXXHFF1KGJDFnV1dWUlZVRVVVFa2srmZmZlJSUAGi4iEiKUo8LERGRPtJf7mSgxGIx8vPz2bhxI+vXr2fjxo3k5+dr9gORPigvL6eqqop4PE5WVhbxeJyqqirKy8ujDk1EuqEeFyIiIn2gv9zJQCorK+PCCy9k//79HDx4kBEjRpCTk8MPfvCDqEMTGbIaGhqYMWNGh7YZM2bQ0NAQUUQicjTqcSEiItIH5eXlzJkzh0QiwaxZs0gkEsyZM0d/uZN+8eSTT9LU1MS4ceMwM8aNG0dTUxNPPvlk1KGJDFmxWIz6+voObfX19erJJJLClLgQERHpg02bNrFixQoqKytZu3YtlZWVrFixgk2bNkUdmqSBpUuXsmjRIl599VVqamp49dVXWbRoEUuXLo06NJEhq6ysjJKSEmpra2lpaaG2tpaSkhLKysqiDk1EuqGhIiIiIn0wcuRIpk+fTiKRoKGhgVgsxvTp09m+fXvUoUkaaG5uprS0tENbaWkp119/fUQRiQx9bcP4ku/b5eXlGt4nksLU40JERKQPDhw4wIoVK9i5cyeHDh1i586drFixggMHDkQdmqSB7OxslixZ0qFtyZIlZGdnRxSRSHooLi7uUPRWSQuR1KYeFyIiIn2QmZlJdnY2o0aNIiMjg1GjRjF69Giam5ujDk3SwNVXX82CBQsAmDp1Kt/97ndZsGDBYb0wRERE0pkSFyIiIn3Q0tJCbm5uh7asrCyampoiikjSSWVlJQA33XQTzc3NZGdnU1pa2t4uIiIyHGioiIiISB+ZGQDu3mFZpD9UVlayf/9+amtr2b9/v5IWIiIy7PSox4WZbQHeBlqBFnefZmbjgJXAZGALcLG77x6YMEVERFJTVlYWBw8eBN5NWBw8eJCsLHVqFBEREekPvelxEXf3D7v7tHD5RmC9u58OrA+XRUREhpXW1lb27dvHO++8g7vzzjvvsG/fPlpbW6MOTURERCQt9GWoyIXA3eHru4HZfQ9HRERkaGmbDnXPnj24O3v27GH69OmMHDky6tBERCRkZl0+4vF4t+tEJHX0NHHhwDoze8bMrgnb8tx9R/j6VSCv36MTERFJcc3NzTz11FOMHTsWgLFjx/LUU09pVhERkRTi7l0+TlvwSLfrRCR19HQA7gx332ZmJwKPm9nzySvd3c2sy3/dYaLjGoC8vDzq6ur6Eu+wo/MlA03XWOows2XA+cDr7l4QtnVZT8iCPwXdAZwL7AOucPffhfvMBb4Wvu233P3usP2jwHJgFPAocJ3rN7M+y8rKOmw61FGjRilxIX3S27/26p+yiIiksx4lLtx9W/j8upk9BHwMeM3MJrr7DjObCLzezb53AXcBTJs2zQsLC/sl8GHhsTXofMmA0jWWapYDdwL3JLW11RO6zcxuDJcXAJ8BTg8ffw0sBv46THR8HZhG0FvuGTNbHRZPXgxcDfyaIHHxaeBng/C50lpLSwsTJkxg2bJltLa2kpmZyZw5czQdqvRJV4mIyTeuYctt50UQjYiISLSOOlTEzHLN7Ji218BMYCOwGpgbbjYXeHigghQRGQ7c/ZfArk7N3dUTuhC4xwO/AsaGSeRZwOPuvitMVjwOfDpcd6y7/yrsZXEPqk3Ub6688koSiQSzZs0ikUhw5ZVXRh2SiAxBZpZpZhvM7JFweYqZ/drMXjSzlWam4jkiMiz1pMZFHlBvZr8HfgOscffHgNuAc8zsBeBT4bKIiPSv7uoJTQJeSdquMWw7UntjF+3SR/n5+SxevLi9h0VTUxOLFy8mPz8/4shEZAi6DmhIWq4Avufu7wd2AyWRRCUiErGjDhVx95eBD3XR/gZQNBBBiYjI4Y5UT6g/qTZR70ybNo3Vq1djZrg7b775Jm+99RaFhYU6d9LvdE2lLzPLB84DyoH5YS2js4E54SZ3AzcTDPsTERlWelqcU0REotFdPaFtwClJ2+WHbduAwk7tdWF7fhfbH0a1iXrn2muvZeHChaxatYo333yTk08+mXnz5rFq1SrVkJH+pbpE6e7fgBuAY8Ll8cAed28Jl9VTTkSGLSUuRERSW1s9odvoWE9oNXCtmd1HUJzzzTC5sRb4tpkdH243E1jo7rvM7C0z+zhBcc4vAZWD+UHSVUNDAxs2bOBb3/oWdXV1FBYWcvDgQW699daoQxORIcLM2maUesbMCt/D/uop1wc6XzKQdH31DyUuRERShJlVE/SWmGBmjQSzg9wG3G9mJcBW4OJw80cJpkJ9kWA61CsBwgTFLcBvw+2+6e5tBT/n8e50qD9DM4r0i1gsxje+8Q1WrVpFQ0MDsViM2bNnE4vFog5NRIaO/wNcYGbnAjnAsQRTXo81s6yw14V6yg0E9WSSgaTrq98ocSEikiLcvbibVYfVEwpnBvn7bt5nGbCsi/angYK+xCiHi8fj3HrrrZxwwgm4Ozt37uTWW29l3rx5UYcmIkOEuy8EFgKEPS7+0d0vM7OfAF8A7kOz+InIMNaTWUVERESkG6tWreLYY49l1KhRmBmjRo3i2GOPZdWqVVGHJiJD3wKCQp0vEtS8qIo4HhGRSChxISIi0geNjY3cf//9bN68mfXr17N582buv/9+Ghsbj76ziEgn7l7n7ueHr19294+5+/vd/SJ3b446PhGRKGioiIiISB/deeedfPazn6W5uZns7GxmzZoVdUgiIiIiaUM9LkRERPogNzeX1atXM3r0aABGjx7N6tWryc3NjTgyERERkfSgxIWIiEgf7N+/H4CRI0d2eG5rFxEREZG+UeJCRESkD1pbWzn//PPZs2cPAHv27OH888+ntbU14shERERE0oMSFyIiIn1UW1vLxIkTycjIYOLEidTW1kYdkoiIiEjaUHFOERGRPjAzmpqaGDlyJIcOHeLNN9+kqakJM4s6NBEREZG0oB4XIiIifeDuALz99tsdntvaRURERKRvlLgQERHpo+LiYs444wwyMjI444wzKC4ujjokERERkbShoSIiIiJ9VFNTQ3V1Na2trWRmZipxISIiItKPlLgQERHpg/z8fF5//XXOPvvs9raRI0eSn58fYVQiIiIyED70jXW8+c7BHm8/+cY1Pd72uFEj+P3XZ76XsNKeEhciIiJ9MHXqVBobGzu0HThwgKlTp0YUkYiIiAyUN985yJbbzuvRtnV1dRQWFvb4vXuT5BhuVONCRESkD9avX4+ZcdJJJ5GRkcFJJ52EmbF+/fqoQxMRERFJC0pciIiI9EFraysXX3wx48ePB2D8+PFcfPHFtLa2RhyZiIiISHrQUBEREZE+WrduHQ8++GB7cc7Pf/7zUYckIiIikjbU40JERKSP9uzZw4YNG2hpaWHDhg3s2bMn6pBERERE0oZ6XIiIiPSBmeHuXH/99Ye1i4iIiEjfqceFiIhIH0yaNIkRI0Z0aBsxYgSTJk2KKCIRERGR9KLEhYiISB/s27ePlpYW8vLyMDPy8vJoaWlh3759UYcmIiIikhaUuBAREemDXbt2kZOTw65du3D3DssiIiIi0ndKXIiIiPTRoUOHjrgsIiIiIu+dEhciIiJ91NzczJgxYwAYM2YMzc3NEUckIiIikj6UuBAREekHu3fv7vAsIiIiIv1DiQsRERERERERSVlZUQcw3HzoG+t4852DPd5+8o1rerTdcaNG8Puvz3yvYYlIijKzM4CVSU3vA/4FGAtcDfw5bL/J3R8N91kIlACtwFfcfW3Y/mngDiAT+KG73zYoH2KYyMjI4NChQ+3PIiIiItI/lLgYZG++c5Att53Xo23r6uooLCzs0bY9TXCIyNDi7n8APgxgZpnANuAh4Erge+7+r8nbm9lU4FLgTOBk4Odm9oFw9feBc4BG4LdmttrdNw3KBxkG2pIVSlqIiIiI9C8lLkREho4i4CV332pm3W1zIXCfuzcDm83sReBj4boX3f1lADO7L9xWiQsRERERSWmqcSEiMnRcClQnLV9rZs+Z2TIzOz5smwS8krRNY9jWXbuIiIiISEpTjwsRkSHAzEYCFwALw6bFwC2Ah8/fAa7qp2NdA1wDkJeXR11dXX+87bCkcyf9TddUejKzHOCXQDbB7+cPuPvXzWwKcB8wHngGuNzdD0QXqYhINJS4EBEZGj4D/M7dXwNoewYws6XAI+HiNuCUpP3ywzaO0N6Bu98F3AUwbdo072mtHTmczp30q8fW6JpKX83A2e6+18xGAPVm9jNgPkE9o/vMbAlB4eXFUQYqIhKFHg8VMbNMM9tgZo+Ey1PM7Ndm9qKZrQz/GigiIgOjmKRhImY2MWnd3wIbw9ergUvNLDv8S93pwG+A3wKnh/fukQTDTlYPSuTDxPHHH9/hWUSkpzywN1wcET4cOBt4IGy/G5gdQXgiIpHrTY2L64CGpOUKggzw+4HdBBlgERHpZ2aWSzAbyE+Tmm83s/82s+eAOPBVAHf/H+B+gqKbjwF/7+6t7t4CXAusJbiX3x9uK/1k9+7dHZ5FRHoj/CPhs8DrwOPAS8Ce8P4Nqk0kIsNYj4aKmFk+cB5QDsy3oJz92cCccJO7gZtR1zURkX7n7k0E45uT2y4/wvblBPfrzu2PAo/2e4AiItJn7t4KfNjMxhJMe/2XPd1XtYn6RudLequn18zevXt7fX3peuxaT2tc/BtwA3BMuDyeHmaAdSM93EBd6Dq38vfrm2g62Lt9Jt+4pkfb5Y6A7xflvoeoRNKbmeHuHH/88ezevbv9+QhT1oqIdMvd95hZLfAJYKyZZYW/c6s20UBQ7RjprV5cM3V1db27vnQ9duuoiQszOx943d2fMbPC3h5AN9JOBupC10UuQNNja9hy23k93r4319jkG3WNiXRn1KhRHYaKjBo1iv3790cclYgMFWZ2AnAwTFqMIhgeWAHUAl8gmFlkLvBwdFGKiESnJz0u/g9wgZmdC+QAxwJ30MMMsIiISLrp3JvinXfe6XI5eTt3H/jARGSomgjcbWaZBDXo7nf3R8xsE3CfmX0L2ABURRmkiEhUjlqc090Xunu+u08mqEJf4+6X8W4GGJQBFhGRYcTd2x8rVqxgypQp1NTUcOo/rqKmpoYpU6awYsWKDtuJiHTH3Z9z94+4+/9y9wJ3/2bY/rK7f8zd3+/uF7l7c9SxiohEoac1LrqyAGWARURkmCsuLgYgkUjwp00NJH4Wo7y8vL1dRERERPqmV4kLd68D6sLXLwMf6/+QREREhpbi4mKKi4uZfOMaNvaizoyIiIiIHN1Rh4qIiIiIiIiIiERFiQsRERERERERSVlKXIiIiIiIiIhIylLiQkRERERERERSlhIXIiIiIiIiIpKylLgQERERERERkZSlxIWIiIiIiIiIpCwlLkREREREREQkZSlxISIiIiIiIiIpS4kLERH5/+3dfaxlVXnH8e8PGOvUISit3iBMHVJHBUXA3IANxo4gBMUWTIxBG8RKO42BKNE23tqmiNZ0TFqMGopOhTKm4khUysTBAUq5oSSgA4i8jZaRlzBkBC2KXCHy4tM/7p54gXtnzpk55559zvl+kpO799pr7/PsnXVW4Jm115IkSZJay8SFJEmSJElqLRMXkiRJkiSptUxcSJIkSZKk1jJxIUmSJEmSWmufQQcgSZIkSb1w+LlX8egTT3V1zoqpjR3V22/pEn5wzgm7E5ZGyL6HTHHYuqnOT1jXzbUBTuo2pLFg4kKSJEnSSHj0iae4b03n/+M3PT3NqlWrOqrbaYJDo+2xLWs6bmPdtC+wje2Mr4pIkiRJkqTWMnEhSZIkSZJay8SFJEmSJElqLRMXktRySe5LcnuSW5Pc1JTtn+TqJHc3f1/SlCfJ55NsTXJbkjfMuc7pTf27k5w+qPuRJEmSuuHknJI0HN5SVT+bsz8FXFNVa5JMNfsfA94GrGw+RwMXAEcn2R84B5gECrg5yYaq+vli3oSkZ+t2BYRuJm5zBQRJ0qgwcbHI+rV8jkvnSGPnZGBVs70OmGY2cXEy8JWqKuDGJC9OckBT9+qqegQgydXAicDXFjdsSXN1swKCs9NLksaViYtF1q/lc/yPE2mkFXBVkgK+VFVrgYmq2t4c/wkw0WwfCDww59xtTdlC5c+TZDWwGmBiYoLp6eke3cZ48HmpW522mZmZma7bl+1RkjQKTFxIUvu9qaoeTPIy4OokP5x7sKqqSWr0RJMYWQswOTlZ3fwL79jbtLGrfxGXumkz3Y64sD1KkkaFk3NKUstV1YPN34eBy4CjgIeaV0Bo/j7cVH8QWD7n9IOasoXKJUmSpFYzcSFJLZbkRUn23bENnADcAWwAdqwMcjpwebO9AXhfs7rIG4FHm1dKrgROSPKSZgWSE5oySdKAJVme5NokdyW5M8mHm/J5V5CSpHHjqyKS1G4TwGVJYLbPvqSqNiXZDFya5AzgfuDdTf0rgLcDW4HHgT8HqKpHknwK2NzU++SOiTolSQP3NPDRqrqlSVbf3Eyi/H7mX0FKksaKiQtJarGqugc4fJ7y/wOOm6e8gDMXuNZFwEW9jlGStGeakXHbm+3HkmxhdgLlhVaQkqSx4qsikiRJUkskWQEcCXyXhVeQkqSx4ogLSZIkqQWSLAO+CZxdVb9sXhMEdr6ClMtYP1s399/tMsPj/mw1y2WsF5+JC0mSJGnAkixhNmnx1ar6VlP8UJIDqmr7c1aQehaXsZ6jy2WAu1pm2CWGBS5jPSC+KiJJkiQNUGaHVlwIbKmq8+YcWmgFKUkaK464kCRJkgbrGOA04PYktzZlHwfWMP8KUpI0VkxcSJIkSQNUVdcDWeDw81aQkqRxY+JCGiH7HjLFYeumujtpXafXBjip25AkSZIkaY/sMnGR5IXAdcDvNPW/UVXnJDkYWA/8HnAzcFpVPdnPYCXt3GNb1nDfms6TC91MGLRiauNuRiVJkiRJu6+TERe/Bo6tqplmtuPrk3wH+Ajw2apan+SLwBnABX2MVZIkSZIW5OhTaTTtMnFRVQXMNLtLmk8BxwLvbcrXAZ/AxIUkSZKkAXH0qTSaOprjIsnezL4O8krgfODHwC+q6ummyjbgwAXOXQ2sBpiYmGB6enoPQx5+nT6DmZmZrp6Xz1bQXTuwjUmSJElqu44SF1X1DHBEkhcDlwGv6fQLqmotsBZgcnKyOs1ojqxNGzvO6naTAe7muhph2N8yuwAADL5JREFUXbYD25gkSZKktturm8pV9QvgWuCPgBcn2ZH4OAh4sMexSZIkSZKkMdfJqiIvBZ6qql8kWQocD3yG2QTGu5hdWeR04PJ+BipJ0iAcfu5VPPrEUx3X7/Qd6P2WLuEH55ywu2FJkiSNjU5eFTkAWNfMc7EXcGlVfTvJXcD6JP8IfB+4sI9xSpI0EI8+8VTHE705yZskSVLvdbKqyG3AkfOU3wMc1Y+gJEmSJEmSoMs5LiRJkiRJkhaTiQtJkiRJktRaJi4kSZIkSVJrmbiQJEmSJEmtZeJCkiRJkiS1lokLSZIkSZLUWiYuJEmSJElSa+0z6ADG0YqpjZ1X3tRZ3f2WLtnNaCRJkiRJai8TF4vsvjUndVx3xdTGrupLGj1JlgNfASaAAtZW1eeSfAL4S+CnTdWPV9UVzTl/C5wBPAN8qKqubMpPBD4H7A18uarWLOa9SJIkSbvDxIUktdvTwEer6pYk+wI3J7m6OfbZqvrnuZWTHAqcCrwWeDnwX0le1Rw+Hzge2AZsTrKhqu5alLuQJEmSdpOJC0lqsaraDmxvth9LsgU4cCennAysr6pfA/cm2Qoc1RzbWlX3ACRZ39Q1cSFJkqRWM3EhSUMiyQrgSOC7wDHAWUneB9zE7KiMnzOb1Lhxzmnb+G2i44HnlB/d55Al7cK+h0xx2Lqpzk9Y1821AXzlVJI0/ExcSNIQSLIM+CZwdlX9MskFwKeYnffiU8C/AB/o0XetBlYDTExMMD093YvLDrVOn8HMzExXz8tnq8e2rOHiE1/UUd2ZmRmWLVvW8bXfv+lXtjFJ0kgwcSFJLZdkCbNJi69W1bcAquqhOcf/Dfh2s/sgsHzO6Qc1Zeyk/Fmqai2wFmBycrJWrVq15zcxzDZtpNNnMD093XHdbq6rEdav9tXltSVJajMTF5LUYkkCXAhsqarz5pQf0Mx/AfBO4I5mewNwSZLzmJ2ccyXwPSDAyiQHM5uwOBV47+LchSRJi2fF1MbuTtjUWf39li7ZjWg0irpqYx22L7CN7YyJC0lqt2OA04Dbk9zalH0ceE+SI5h9VeQ+4K8AqurOJJcyO+nm08CZVfUMQJKzgCuZXQ71oqq6czFvRJKkfrtvTXfzuqyY2tj1ORpv3bQX21fvmLiQRoz/yjBaqup6ZkdLPNcVOznn08Cn5ym/YmfnaX79mjzRiRMlSZI6Y+JCGiH+K4PUe49tWdPx76SbOQi6TjJKGllJLgLeATxcVa9ryvYHvg6sYHZk3bub1aMkaezsNegAJEmSpDF3MXDic8qmgGuqaiVwTbMvSWPJxIUkSZI0QFV1HfDIc4pP5rcvn60DTlnUoCSpRUxcSJIkSe0zMWf1qJ8AE4MMRpIGyTkuJEmSpBarqkpSCx1PshpYDTAxMcH09PRihTYSfF7qJ9tXb5i4kCRJktrnoSQHVNX2JAcADy9UsarWAmsBJicnq9NJggVs2tjxpMpS12xfPeOrIpIkSVL7bABOb7ZPBy4fYCySNFAmLiRJkqQBSvI14Abg1Um2JTkDWAMcn+Ru4K3NviSNJV8VkSRJkgaoqt6zwKHjFjUQSWopR1xIkiRJkqTWcsSFJEm7sGJqY+eVN3VWd7+lS3YzGkmSpPFi4kKSpJ24b81JHdddMbWxq/oS9CcxBibHJEmjw8SFJEnSgJgYkyRp15zjQpIkSZIktZaJC0mSJEmS1FomLiRJkiRJUmuZuJAkSZIkSa21y8RFkuVJrk1yV5I7k3y4Kd8/ydVJ7m7+vqT/4UqSJEmSpHHSyYiLp4GPVtWhwBuBM5McCkwB11TVSuCaZl+SJEmSpLGS5Hmf+z/zjnnLkww63KGzy8RFVW2vqlua7ceALcCBwMnAuqbaOuCUfgUpSZIkSVJbVdXzPtdee+285VU16HCHTldzXCRZARwJfBeYqKrtzaGfABM9jUySJEmSJI29fTqtmGQZ8E3g7Kr65dzhLVVVSeZNGyVZDawGmJiYYHp6eo8CHjc+L/WbbUySJElSm3WUuEiyhNmkxVer6ltN8UNJDqiq7UkOAB6e79yqWgusBZicnKxVq1btedTjYtNGfF7qK9uYJEmSpJbbZeIis0MrLgS2VNV5cw5tAE4H1jR/L+9LhJIkSWNmoYnb8pn56/u+tCRplHUyx8UxwGnAsUlubT5vZzZhcXySu4G3NvuSJEnaQ07yJknSb+1yxEVVXQ8stF7Lcb0NR5LUT0lOBD4H7A18uapMOkuSJKnVOp6cU/2zs3V8HRKqXui2jdm+RlOSvYHzgeOBbcDmJBuq6q7BRjZ87LclabjYb0vDravlUNUfCw37dEioeqXbNqaRdRSwtaruqaongfXAyQOOaSjZb0vScLHfloabiQtJGh8HAg/M2d/WlEmSJEmt5asikqRnSbIaWA0wMTHB9PT0YAMaIjMzMz4v9Y3tS5I0rkxcSNL4eBBYPmf/oKbsWapqLbAWYHJyslatWrUowY2C6elpfF7qF9uXJGlc+aqIJI2PzcDKJAcneQFwKrBhwDFJkiRJO+WIC0kaE1X1dJKzgCuZXQ71oqq6c8BhSZIkSTtl4kKSxkhVXQFcMeg4JEmSpE75qogkSZIkSWotExeSJEmSJKm1TFxIkiRJkqTWMnEhSZIkSZJaK1W1eF+W/BS4f9G+cPj9PvCzQQehkWYb684rquqlgw5iMdlvd83flPrJ9tU9+23tir8r9ZPtq3vz9tuLmrhQd5LcVFWTg45Do8s2JvWWvyn1k+1L6j1/V+on21fv+KqIJEmSJElqLRMXkiRJkiSptUxctNvaQQegkWcbk3rL35T6yfYl9Z6/K/WT7atHnONCkiRJkiS1liMuJEmSJElSa5m4aKkkJyb5UZKtSaYGHY9GR5KLkjyc5I5BxyKNCvts9ZP9ttR79tvqJ/vt3jNx0UJJ9gbOB94GHAq8J8mhg41KI+Ri4MRBByGNCvtsLYKLsd+WesZ+W4vgYuy3e8rERTsdBWytqnuq6klgPXDygGPSiKiq64BHBh2HNELss9VX9ttSz9lvq6/st3vPxEU7HQg8MGd/W1MmSWof+2xJGi7229KQMXEhSZIkSZJay8RFOz0ILJ+zf1BTJklqH/tsSRou9tvSkDFx0U6bgZVJDk7yAuBUYMOAY5Ikzc8+W5KGi/22NGRMXLRQVT0NnAVcCWwBLq2qOwcblUZFkq8BNwCvTrItyRmDjkkaZvbZ6jf7bam37LfVb/bbvZeqGnQMkiRJkiRJ83LEhSRJkiRJai0TF5IkSZIkqbVMXEiSJEmSpNYycSFJkiRJklrLxIUkSZIkSWotExfquyQTSS5Jck+Sm5PckOSdPbjudJLJZntZki8l+XHzHdNJjt6Da38iyV83259M8tZm++wkv7unsUtSm9lvS9Jwsd/WqNtn0AFotCUJ8J/Auqp6b1P2CuBPe/xVXwbuBVZW1W+SHAwcOk8sqarfdHPhqvqHObtnA/8BPL6H8UpSK9lvS9Jwsd/WOHDEhfrtWODJqvrijoKqur+qvpDkhUn+PcntSb6f5C0AOylfmmR9ki1JLgOWNuV/CBwN/P2OTrKq7q2qjUlWJPlRkq8AdwDLk/xNks1Jbkty7o64kvxdkv9Ncj3w6jnlFyd5V5IPAS8Hrk1ybZ+fmyQNiv22JA0X+22NPEdcqN9eC9yywLEzgaqqw5K8Brgqyat2Uv5B4PGqOiTJ6+dc97XArVX1zALfsxI4vapuTHJCs38UEGBDkjcDvwJOBY5g9ndxC3Dz3ItU1eeTfAR4S1X9bDeehSQNA/ttSRou9tsaeSYutKiSnA+8CXgS2AZ8AaCqfpjkfuBVzfH5yt8MfL4pvy3JbR1+7f1VdWOzfULz+X6zv4zZjnVf4LKqeryJc8Oe3KckjQr7bUkaLvbbGkW+KqJ+uxN4w46dqjoTOA54aY+/4/Akey9w/FdztgP8U1Ud0XxeWVUX9jAWSRp29tuSNFzstzXyTFyo3/4beGGSD84p2zFL8P8AfwbQDE37A+BHOym/Dtgx4dDrgNcDVNWPgZuAc5OkOb4iyUnzxHMl8IEky5p6ByZ5WXPtU5r3+vYF/mSB+3mM2WyxJI0q+21JGi722xp5Ji7UV1VVwCnAHye5N8n3gHXAx4B/BfZKcjvwdeD9VfXrnZRfACxLsgX4JM9+J+4vgAlga5I7gIuBh+eJ5yrgEuCG5vrfAPatqlua7/oB8B1g8wK3tBbY5GRBkkaV/bYkDRf7bY2DzLZzSZIkSZKk9nHEhSRJkiRJai0TF5IkSZIkqbVMXEiSJEmSpNYycSFJkiRJklrLxIUkSZIkSWotExeSJEmSJKm1TFxIkiRJkqTWMnEhSZIkSZJa6/8BgiZl1HgRUnAAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 1296x360 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9izJ4gW9Kopd"
},
"source": [
"# Box-Plots interpretation: \n",
"These plots gives an idea about the data distribution of continuous predictor in the Y-axis for each of the category in the X-Axis.\n",
"\n",
"If the distribution looks similar for each category(Boxes are in the same line), that means the the continuous variable has NO effect on the target variable. Hence, the variables are not correlated to each other."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "arhQTh_hKopd"
},
"source": [
"# Statistical Feature Selection using ANOVA test"
]
},
{
"cell_type": "code",
"metadata": {
"id": "lO673aFZKopd"
},
"source": [
"# Defining a function to find the statistical relationship with all the categorical variables\n",
"\n",
"def FunctionAnova(inpData, TargetVariable, ContinuousPredictorList):\n",
" from scipy.stats import f_oneway\n",
"\n",
" # Creating an empty list of final selected predictors\n",
" SelectedPredictors=[]\n",
" \n",
" print('ANOVA Test Results \\n')\n",
" for predictor in ContinuousPredictorList:\n",
" CategoryGroupLists=inpData.groupby(TargetVariable)[predictor].apply(list)\n",
" AnovaResults = f_oneway(*CategoryGroupLists)\n",
" \n",
" \n",
" if (AnovaResults[1] < 0.05):\n",
" print(predictor, 'is correlated with', TargetVariable, '| P-Value:', AnovaResults[1])\n",
" SelectedPredictors.append(predictor)\n",
" else:\n",
" print(predictor, 'is NOT correlated with', TargetVariable, '| P-Value:', AnovaResults[1])\n",
" \n",
" return(SelectedPredictors)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "dMyjFrw8Kopd",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "77bfc205-faa4-40cf-9b1a-488a63a263b2"
},
"source": [
"# Calling the function to check which categorical variables are correlated with target\n",
"ContinuousVariables=['age', 'amount','duration']\n",
"FunctionAnova(inpData=df, TargetVariable='GoodCredit', ContinuousPredictorList=ContinuousVariables)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"ANOVA Test Results \n",
"\n",
"age is correlated with GoodCredit | P-Value: 0.003925339398278295\n",
"amount is correlated with GoodCredit | P-Value: 8.797572373533373e-07\n",
"duration is correlated with GoodCredit | P-Value: 6.488049877187189e-12\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['age', 'amount', 'duration']"
]
},
"metadata": {},
"execution_count": 446
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "R1NTHk2UKopd"
},
"source": [
"All three columns are correlated with GoodCredit but the P-Value of \"age\", it is just at the boundry of the threshold."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "378L8Lm4Kope"
},
"source": [
"# Relationship exploration: \n",
"Grouped Bar Charts for Categorical Target Variable \"GoodCredit\" and Categorical predictors"
]
},
{
"cell_type": "code",
"metadata": {
"id": "6fEc76t2Kope"
},
"source": [
"CategoricalColsList=['checkingstatus', 'history', 'purpose','savings','employ',\n",
" 'installment', 'status', 'others','residence', 'property',\n",
" 'otherplans', 'housing', 'cards', 'job', 'liable', 'tele', 'foreign']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "jrzBDgCBKope",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 201
},
"outputId": "995fe54b-524f-44e5-ccfb-d2d0910366ba"
},
"source": [
"CrossTabResult=pd.crosstab(index=df['checkingstatus'], columns=df['GoodCredit'])\n",
"CrossTabResult"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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" 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>GoodCredit</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>checkingstatus</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A11</th>\n",
" <td>139</td>\n",
" <td>135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A12</th>\n",
" <td>164</td>\n",
" <td>105</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A13</th>\n",
" <td>49</td>\n",
" <td>14</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A14</th>\n",
" <td>348</td>\n",
" <td>46</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"GoodCredit 0 1\n",
"checkingstatus \n",
"A11 139 135\n",
"A12 164 105\n",
"A13 49 14\n",
"A14 348 46"
]
},
"metadata": {},
"execution_count": 448
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dnrcY_NUKope",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 231
},
"outputId": "f341987a-942c-484d-f404-793854829a59"
},
"source": [
"CrossTabResult1=pd.crosstab(index=df['history'], columns=df['GoodCredit'])\n",
"CrossTabResult1"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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>GoodCredit</th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <th>history</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>A30</th>\n",
" <td>15</td>\n",
" <td>25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A31</th>\n",
" <td>21</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A32</th>\n",
" <td>361</td>\n",
" <td>169</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A33</th>\n",
" <td>60</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>A34</th>\n",
" <td>243</td>\n",
" <td>50</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"GoodCredit 0 1\n",
"history \n",
"A30 15 25\n",
"A31 21 28\n",
"A32 361 169\n",
"A33 60 28\n",
"A34 243 50"
]
},
"metadata": {},
"execution_count": 449
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "CXO0Zq8sKope",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
},
"outputId": "20aede94-a978-4e82-b1ae-340441763641"
},
"source": [
"# Creating Grouped bar plots for each categorical predictor against the Target Variable \"GoodCredit\"\n",
"\n",
"fig, PlotCanvas=plt.subplots(nrows=len(CategoricalColsList), ncols=1, figsize=(10,90))\n",
"\n",
"for CategoricalCol , i in zip(CategoricalColsList, range(len(CategoricalColsList))):\n",
" CrossTabResult=pd.crosstab(index=df[CategoricalCol], columns=df['GoodCredit'])\n",
" CrossTabResult.plot.bar(color=['red','green'], ax=PlotCanvas[i])"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x6480 with 17 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "eiof8zCmKope"
},
"source": [
"# Grouped Bar charts Interpretation: \n",
"These grouped bar charts show the frequency in the Y-Axis and the category in the X-Axis.\n",
"\n",
"If the ratio of bars is similar across all categories, then the two columns are not correlated."
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "odUFbXSFKopf"
},
"source": [
"# Statistical Feature Selection using Chi-Square Test\n",
"\n",
"Chi-Square test is conducted to check the correlation between two categorical variables"
]
},
{
"cell_type": "code",
"metadata": {
"id": "9VyiY9_CKopf"
},
"source": [
"# Writing a function to find the correlation of all categorical variables with the Target variable\n",
"\n",
"def FunctionChisq(inpData, TargetVariable, CategoricalVariablesList):\n",
" from scipy.stats import chi2_contingency\n",
" \n",
" # Creating an empty list of final selected predictors\n",
" SelectedPredictors=[]\n",
" print('Chi Square Test Results \\n')\n",
"\n",
" for predictor in CategoricalVariablesList:\n",
" CrossTabResult=pd.crosstab(index=inpData[TargetVariable], columns=inpData[predictor])\n",
" ChiSqResult = chi2_contingency(CrossTabResult)\n",
" \n",
" \n",
" if (ChiSqResult[1] < 0.05):\n",
" print(predictor, 'is correlated with', TargetVariable, '| P-Value:', ChiSqResult[1])\n",
" SelectedPredictors.append(predictor)\n",
" else:\n",
" print(predictor, 'is NOT correlated with', TargetVariable, '| P-Value:', ChiSqResult[1]) \n",
" \n",
" return(SelectedPredictors) "
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "QdL0n-aQKopf",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "cde6e9fb-fe3b-4df9-a6e5-c47d6ea5e5b2"
},
"source": [
"FunctionChisq(inpData=df, \n",
" TargetVariable='GoodCredit',\n",
" CategoricalVariablesList= CategoricalColsList)"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"Chi Square Test Results \n",
"\n",
"checkingstatus is correlated with GoodCredit | P-Value: 1.2189020722893755e-26\n",
"history is correlated with GoodCredit | P-Value: 1.2791872956751013e-12\n",
"purpose is correlated with GoodCredit | P-Value: 0.00011574910079691586\n",
"savings is correlated with GoodCredit | P-Value: 2.7612142385682596e-07\n",
"employ is correlated with GoodCredit | P-Value: 0.0010454523491402541\n",
"installment is NOT correlated with GoodCredit | P-Value: 0.1400333122128481\n",
"status is correlated with GoodCredit | P-Value: 0.02223800546926877\n",
"others is correlated with GoodCredit | P-Value: 0.036055954027247226\n",
"residence is NOT correlated with GoodCredit | P-Value: 0.8615521320413175\n",
"property is correlated with GoodCredit | P-Value: 2.8584415733250017e-05\n",
"otherplans is correlated with GoodCredit | P-Value: 0.0016293178186473534\n",
"housing is correlated with GoodCredit | P-Value: 0.00011167465374597684\n",
"cards is NOT correlated with GoodCredit | P-Value: 0.4451440800083001\n",
"job is NOT correlated with GoodCredit | P-Value: 0.5965815918843431\n",
"liable is NOT correlated with GoodCredit | P-Value: 1.0\n",
"tele is NOT correlated with GoodCredit | P-Value: 0.27887615430357426\n",
"foreign is correlated with GoodCredit | P-Value: 0.015830754902852885\n"
]
},
{
"output_type": "execute_result",
"data": {
"text/plain": [
"['checkingstatus',\n",
" 'history',\n",
" 'purpose',\n",
" 'savings',\n",
" 'employ',\n",
" 'status',\n",
" 'others',\n",
" 'property',\n",
" 'otherplans',\n",
" 'housing',\n",
" 'foreign']"
]
},
"metadata": {},
"execution_count": 452
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "WzFm1D73Kopf"
},
"source": [
"Based on the results of Chi-Square test, below categorical columns are selected as predictors for Machine Learning\n",
"\n",
"'checkingstatus', 'history', 'purpose', 'savings', 'employ', 'status', 'others', 'property', 'otherplans', 'housing', 'foreign'"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "7VFUpupCKopf"
},
"source": [
"# Selecting final predictors for Machine Learning"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Iqy0ghOGKopg",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 201
},
"outputId": "bcfb1b01-2d17-4889-8dc3-cc8d8a30aac8"
},
"source": [
"#Based on all the above tests, selecting the final columns for machine learning\n",
"\n",
"SelectedColumns=['checkingstatus','history','purpose','savings','employ',\n",
" 'status','others','property','otherplans','housing','foreign',\n",
" 'age', 'amount', 'duration']\n",
"\n",
"# Selecting final columns\n",
"DataForML=df[SelectedColumns]\n",
"DataForML.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"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>checkingstatus</th>\n",
" <th>history</th>\n",
" <th>purpose</th>\n",
" <th>savings</th>\n",
" <th>employ</th>\n",
" <th>status</th>\n",
" <th>others</th>\n",
" <th>property</th>\n",
" <th>otherplans</th>\n",
" <th>housing</th>\n",
" <th>foreign</th>\n",
" <th>age</th>\n",
" <th>amount</th>\n",
" <th>duration</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>A11</td>\n",
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" <td>A152</td>\n",
" <td>A201</td>\n",
" <td>67</td>\n",
" <td>1169</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>A12</td>\n",
" <td>A32</td>\n",
" <td>A43</td>\n",
" <td>A61</td>\n",
" <td>A73</td>\n",
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" <td>A152</td>\n",
" <td>A201</td>\n",
" <td>22</td>\n",
" <td>5951</td>\n",
" <td>48</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>A14</td>\n",
" <td>A34</td>\n",
" <td>A46</td>\n",
" <td>A61</td>\n",
" <td>A74</td>\n",
" <td>A93</td>\n",
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" <td>A143</td>\n",
" <td>A152</td>\n",
" <td>A201</td>\n",
" <td>49</td>\n",
" <td>2096</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>A11</td>\n",
" <td>A32</td>\n",
" <td>A42</td>\n",
" <td>A61</td>\n",
" <td>A74</td>\n",
" <td>A93</td>\n",
" <td>A103</td>\n",
" <td>A122</td>\n",
" <td>A143</td>\n",
" <td>A153</td>\n",
" <td>A201</td>\n",
" <td>45</td>\n",
" <td>7882</td>\n",
" <td>42</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>A11</td>\n",
" <td>A33</td>\n",
" <td>A40</td>\n",
" <td>A61</td>\n",
" <td>A73</td>\n",
" <td>A93</td>\n",
" <td>A101</td>\n",
" <td>A124</td>\n",
" <td>A143</td>\n",
" <td>A153</td>\n",
" <td>A201</td>\n",
" <td>53</td>\n",
" <td>4870</td>\n",
" <td>24</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" checkingstatus history purpose savings ... foreign age amount duration\n",
"0 A11 A34 A43 A65 ... A201 67 1169 6\n",
"1 A12 A32 A43 A61 ... A201 22 5951 48\n",
"2 A14 A34 A46 A61 ... A201 49 2096 12\n",
"3 A11 A32 A42 A61 ... A201 45 7882 42\n",
"4 A11 A33 A40 A61 ... A201 53 4870 24\n",
"\n",
"[5 rows x 14 columns]"
]
},
"metadata": {},
"execution_count": 453
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2nuBQZ7iKopg"
},
"source": [
"# Saving this final data for reference during deployment\n",
"DataForML.to_pickle('DataForML.pkl')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ZJsYVbq_Kopg"
},
"source": [
"# Supressing the warning messages\n",
"import warnings\n",
"warnings.filterwarnings('ignore')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "FCImNyecKopg"
},
"source": [
"# Data Pre-processing for Machine Learning"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ARPzTVLoKopg"
},
"source": [
"##### 1. Converting Ordinal variables to numeric using business mapping"
]
},
{
"cell_type": "code",
"metadata": {
"id": "BDkAjJ19Kopg"
},
"source": [
"# Treating the Ordinal variable first\n",
"DataForML['employ'].replace({'A71':1, 'A72':2,'A73':3, 'A74':4,'A75':5 }, inplace=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "JDs7IYqSKopg"
},
"source": [
"##Converting the binary nominal variable to numeric using 1/0 mapping\n",
"# Treating the binary nominal variable\n",
"\n",
"DataForML['foreign'].replace({'A201':1, 'A202':0}, inplace=True)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "p6ULwAT4Koph"
},
"source": [
"##### 2. Converting nominal variables to numeric using get_dummies()"
]
},
{
"cell_type": "code",
"metadata": {
"id": "DfEIHDK8Koph"
},
"source": [
"# Treating all the nominal variables at once using dummy variables\n",
"DataForML_Numeric=pd.get_dummies(DataForML)\n",
"\n",
"# Adding Target Variable to the data\n",
"DataForML_Numeric['GoodCredit']=df['GoodCredit']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ohGRg61aKoph",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 221
},
"outputId": "85f08533-fcd1-4744-a795-e513c32f5129"
},
"source": [
"## Looking at data after all the treatments\n",
"DataForML_Numeric.head()"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>employ</th>\n",
" <th>foreign</th>\n",
" <th>age</th>\n",
" <th>amount</th>\n",
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" <th>savings_A64</th>\n",
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" <th>property_A122</th>\n",
" <th>property_A123</th>\n",
" <th>property_A124</th>\n",
" <th>otherplans_A141</th>\n",
" <th>otherplans_A142</th>\n",
" <th>otherplans_A143</th>\n",
" <th>housing_A151</th>\n",
" <th>housing_A152</th>\n",
" <th>housing_A153</th>\n",
" <th>GoodCredit</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
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" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>53</td>\n",
" <td>4870</td>\n",
" <td>24</td>\n",
" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" employ foreign age ... housing_A152 housing_A153 GoodCredit\n",
"0 5 1 67 ... 1 0 0\n",
"1 3 1 22 ... 1 0 1\n",
"2 4 1 49 ... 1 0 0\n",
"3 4 1 45 ... 0 1 0\n",
"4 3 1 53 ... 0 1 1\n",
"\n",
"[5 rows x 47 columns]"
]
},
"metadata": {},
"execution_count": 459
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "2Qf3_0REKoph",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "707e352f-b24e-4138-d0ee-de9a619da646"
},
"source": [
"# Printing all the column names for our reference\n",
"DataForML_Numeric.columns"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"Index(['employ', 'foreign', 'age', 'amount', 'duration', 'checkingstatus_A11',\n",
" 'checkingstatus_A12', 'checkingstatus_A13', 'checkingstatus_A14',\n",
" 'history_A30', 'history_A31', 'history_A32', 'history_A33',\n",
" 'history_A34', 'purpose_A40', 'purpose_A41', 'purpose_A410',\n",
" 'purpose_A42', 'purpose_A43', 'purpose_A44', 'purpose_A45',\n",
" 'purpose_A46', 'purpose_A48', 'purpose_A49', 'savings_A61',\n",
" 'savings_A62', 'savings_A63', 'savings_A64', 'savings_A65',\n",
" 'status_A91', 'status_A92', 'status_A93', 'status_A94', 'others_A101',\n",
" 'others_A102', 'others_A103', 'property_A121', 'property_A122',\n",
" 'property_A123', 'property_A124', 'otherplans_A141', 'otherplans_A142',\n",
" 'otherplans_A143', 'housing_A151', 'housing_A152', 'housing_A153',\n",
" 'GoodCredit'],\n",
" dtype='object')"
]
},
"metadata": {},
"execution_count": 460
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "XltmQ7g1Koph"
},
"source": [
"# Separate Target Variable and Predictor Variables\n",
"TargetVariable='GoodCredit'\n",
"Predictors=['employ', 'foreign', 'age', 'amount', 'duration', 'checkingstatus_A11','checkingstatus_A12', 'checkingstatus_A13', 'checkingstatus_A14','history_A30', 'history_A31', 'history_A32', 'history_A33',\n",
" 'history_A34', 'purpose_A40', 'purpose_A41', 'purpose_A410','purpose_A42', 'purpose_A43', 'purpose_A44', 'purpose_A45', 'purpose_A46', 'purpose_A48', 'purpose_A49', 'savings_A61',\n",
" 'savings_A62', 'savings_A63', 'savings_A64', 'savings_A65','status_A91', 'status_A92', 'status_A93', 'status_A94', 'others_A101','others_A102', 'others_A103', 'property_A121', 'property_A122',\n",
" 'property_A123', 'property_A124', 'otherplans_A141', 'otherplans_A142','otherplans_A143', 'housing_A151', 'housing_A152', 'housing_A153']"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "I8HiWQe2Koph"
},
"source": [
"X=DataForML_Numeric[Predictors].values\n",
"y=DataForML_Numeric[TargetVariable].values"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "R1Vi7loWKopi"
},
"source": [
"# Splitting the data into training and testing set\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=428)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "_x_KRoSQKopi"
},
"source": [
"# Normalization of data\n"
]
},
{
"cell_type": "code",
"metadata": {
"id": "TiDkOU_dKopi"
},
"source": [
"from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
"\n",
"PredictorScaler=MinMaxScaler()\n",
"\n",
"# Storing the fit object for later reference\n",
"PredictorScalerFit=PredictorScaler.fit(X)\n",
"\n",
"# Generating the standardized values of X\n",
"X=PredictorScalerFit.transform(X)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "yq02uV96Kopi"
},
"source": [
"# Split the data into training and testing set\n",
"\n",
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "bDofmcthKopi",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "5023329c-29da-4e4b-cbec-590ba120e69d"
},
"source": [
"# Sanity check for the sampled data\n",
"print(X_train.shape)\n",
"print(y_train.shape)\n",
"print(X_test.shape)\n",
"print(y_test.shape)\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"(700, 46)\n",
"(700,)\n",
"(300, 46)\n",
"(300,)\n"
]
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "_h1RKlbLKopi"
},
"source": [
"# **After all the treatments and splitting the final data into test and train we will now starting modelling diffrent classification models.**"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "p3RZE7tEKopi"
},
"source": [
"# Classifier 1- Logistic Regression"
]
},
{
"cell_type": "code",
"metadata": {
"id": "Yrzqb4shKopi",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "d4621dc2-2c4e-443c-d375-faf72397ed48"
},
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"\n",
"clf = LogisticRegression(C=1,penalty='l2', solver='newton-cg')\n",
"clf"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"LogisticRegression(C=1, solver='newton-cg')"
]
},
"metadata": {},
"execution_count": 467
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "36nQzIcmKopj"
},
"source": [
"# Creating the model on Training Data\n",
"LOG=clf.fit(X_train,y_train)\n",
"prediction=LOG.predict(X_test)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8rWoOTW4Kopj",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "07e2e6f7-f528-4869-c2a0-c7ee68dd5630"
},
"source": [
"# Measuring accuracy on Testing Data\n",
"from sklearn import metrics\n",
"print(metrics.classification_report(y_test, prediction))\n",
"print(metrics.confusion_matrix(y_test, prediction))\n",
"\n",
"# the Overall Accuracy of the model\n",
"F1_Score=metrics.f1_score(y_test, prediction, average='weighted')\n",
"print('Accuracy of the model on Testing Sample Data:', round(F1_Score,2))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.80 0.90 0.85 209\n",
" 1 0.68 0.47 0.56 91\n",
"\n",
" accuracy 0.77 300\n",
" macro avg 0.74 0.69 0.70 300\n",
"weighted avg 0.76 0.77 0.76 300\n",
"\n",
"[[189 20]\n",
" [ 48 43]]\n",
"Accuracy of the model on Testing Sample Data: 0.76\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "iCFX_kqiKopj",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "62b6b4bb-ff5f-4be6-ab94-25e07995d0ca"
},
"source": [
"# Importing cross validation function from sklearn\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"# Running Cross validation\n",
"\n",
"Accuracy_Values=cross_val_score(LOG, X , y, cv=10, scoring='f1_weighted')\n",
"print('\\nAccuracy values for 10-fold Cross Validation:\\n',Accuracy_Values)\n",
"print('\\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Accuracy values for 10-fold Cross Validation:\n",
" [0.78666667 0.66403326 0.75159817 0.71776316 0.76028751 0.80460526\n",
" 0.63733333 0.77519841 0.77229833 0.7343254 ]\n",
"\n",
"Final Average Accuracy of the model: 0.74\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "j9AlGM9xKopj",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "9a1edd58-3d08-4056-b8de-462c65428471"
},
"source": [
"#Calculating roc_auc_score\n",
"from sklearn.metrics import roc_auc_score\n",
"roc_auc_score(y_test ,prediction) "
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.6884168463115832"
]
},
"metadata": {},
"execution_count": 471
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "eqEYa6LvKopj"
},
"source": [
"pred_proba = clf.predict_proba(X_test)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "OYGXLghhKopj",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"outputId": "0b6a9dc3-4ca2-4baf-afea-f6f679646923"
},
"source": [
"from sklearn.metrics import roc_curve\n",
"\n",
"pred_proba = clf.predict_proba(X_test)[::,1]\n",
"\n",
"fpr, tpr, _ = roc_curve(y_test, prediction)\n",
"\n",
"auc = roc_auc_score(y_test, prediction)\n",
"plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
"plt.legend(loc=4)\n",
"plt.show()"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ezxgXMf1Kopk"
},
"source": [
"For more exploration - we can use oversampling, undersampling, smote and adasym"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "oDIBbVZKKopk"
},
"source": [
"# Classifier 2 - Decision Tree Classifier"
]
},
{
"cell_type": "code",
"metadata": {
"id": "vAL5cnN6Kopk"
},
"source": [
"from sklearn import tree\n",
"from sklearn.tree import DecisionTreeClassifier"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "V-_2UZh9Kopk"
},
"source": [
"clf = tree.DecisionTreeClassifier(max_depth=4,criterion='gini')\n",
"\n",
"\n",
"# Creating the model on Training Data\n",
"DTree=clf.fit(X_train,y_train)\n",
"prediction=DTree.predict(X_test)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "V1SyEst-Kopk",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "fec1c765-1f4f-42c5-c9f9-adb9f29cd214"
},
"source": [
"# Measuring accuracy on Testing Data\n",
"from sklearn import metrics\n",
"print(metrics.classification_report(y_test, prediction))\n",
"print(metrics.confusion_matrix(y_test, prediction))\n",
"\n",
"# Printing the Overall Accuracy of the model\n",
"F1_Score=metrics.f1_score(y_test, prediction, average='weighted')\n",
"print('Accuracy of the model on Testing Sample Data:', round(F1_Score,2))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
" precision recall f1-score support\n",
"\n",
" 0 0.74 0.85 0.79 209\n",
" 1 0.48 0.33 0.39 91\n",
"\n",
" accuracy 0.69 300\n",
" macro avg 0.61 0.59 0.59 300\n",
"weighted avg 0.66 0.69 0.67 300\n",
"\n",
"[[177 32]\n",
" [ 61 30]]\n",
"Accuracy of the model on Testing Sample Data: 0.67\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "F0f6yVImKopk",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "ee56c6a3-de72-454b-de24-423aa936803a"
},
"source": [
"from sklearn import tree\n",
"from sklearn.model_selection import cross_val_score\n",
"\n",
"# Running Cross validation\n",
"Accuracy_Values=cross_val_score(DTree, X , y, cv=10, scoring='f1_weighted')\n",
"print('\\nAccuracy values for 10-fold Cross Validation:\\n',Accuracy_Values)\n",
"print('\\nFinal Average Accuracy of the model:', round(Accuracy_Values.mean(),2))"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"name": "stdout",
"text": [
"\n",
"Accuracy values for 10-fold Cross Validation:\n",
" [0.73734823 0.68 0.7343254 0.65257937 0.66798419 0.64715447\n",
" 0.70133333 0.72 0.71433083 0.70133333]\n",
"\n",
"Final Average Accuracy of the model: 0.7\n"
]
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "F3eW9tRtKopk",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 282
},
"outputId": "8d72d844-af3a-4daf-afa8-923947e8a0bb"
},
"source": [
"# Plotting the feature importance for Top 10 most important columns\n",
"%matplotlib inline\n",
"feature_importances = pd.Series(DTree.feature_importances_, index=Predictors)\n",
"feature_importances.nlargest(10).plot(kind='barh')"
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f6681af2610>"
]
},
"metadata": {},
"execution_count": 478
},
{
"output_type": "display_data",
"data": {
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a9mRgLfAOYALVA5L3Ax4DzgSOoHpw8qcaujgIOAZ4KXCspOZnjr0VmAwcABwOzJG0C3AeMLPEMJHqIdHPWIEj6SRJXZK61q5aMSRjjoiISlax9u11wBTgZkkAWwP/AzwJXF3qdAOrba+R1A10NLS/xvbvACR9BzgYaHyq8cHAN22vBR6RdC3wcttXSvqKpJ2pEuxltp9qDs72PGAewNhd9t40nnwdETFKJEH2TcDXbf/jeoXSR233JKSngdUAtp+W1PieNietwSSxC4F3AscDJwwq6oiI2Gi5xNq3HwLTJT0HQNKzJe0xiPZHlDZbA9OAG5qOXw8cV+5r7gwcCtxUjl0AfBjA9p0bMYaIiNgAmUH2wfadkj4GfF/SFsAaYDAPTL4JuAx4PnCR7a6m45cDU4HbqGaXf2/71+Xcj0i6C7hiI4cREREbIAmyH7bnA/ObirdpOH5GU/1tGnYftj2NJj11ymXa08prPZLGA3sD39zQ2CMiYsMlQY5Ckg6nWsn6BdsDWp46adeJdOVn1CIihkwS5DCxfQHVfcQNafsDYDD3OiMiYohlkU5ERESNJMiIiIgaSZARERE1kiAjIiJqJEFGRETUSIKMiIiokQQZERFRIwkyIiKiRhJkREREjfySziaie/kKOmY945nKMQDL8hN9EVEjM8iIiIgaSZARERE1kiBHgKQtJf1G0uxejn9J0sqG/bGS5ku6X9KNkjpGKtaIiKhsNglSUivvtx4B3AscK0mNByR1Ajs01X8v8AfbLwS+AHx2RKKMiIg/a6sEKalD0t2SLpZ0l6RLJY2XtEzSTqVOp6SFZfsMSf8u6Qbg3yXNlPRdSQsl3Sfpkw19f0TS0vL6cCmbIGmBpNtK+XGlfIqkayUtlvQ9Sbv0E/oMYC7wc2BqwznHAHOAv2+qfxTw9bJ9KfC65sRa2p8kqUtS19pVA3psZEREDFA7rmJ9MfBe2zdI+jfgA/3U3xc42PYTkmYCBwH7A6uAmyUtAAycALwCEHCjpGuBFwC/tP1GAEkTJT0LOAs4yvZvStL8DHBi3ckljQMOB/4G2J4qWf60HP4gcKXtXzXlv12BXwDYfkrSCmBH4LeNlWzPA+YBjN1lb/fzPkRExCC01Qyy+IXtG8r2RcDB/dS/0vYTDfvX2P5dKftOaX8wcLntx22vLOWHAN3AEZI+K+kQ2yuoEvT+wDWSlgAfA57fx/nfBPy4nO8yYJqkMZKeBxxLlWwjImKUaccZZPNMycBTrEv245qOPz6A9vUnsu+VdCDwBuBMST8ELgfusD21t3ZNZgAHS1pW9ncEXgtsBbwQuL/MHsdLur/cd1wO7AY8XO6dTgR+N8DzRUTEEGjHGeTuknqS09uBnwDLgCml7Jh+2h8h6dmStgamATcA11PN7MZLmgAcDVxfZnmrbF9Eda/wQOAeYOeeGCQ9S9J+dSeStB3VTHR32x22O4BTgBm2F9j+Xw3lq0pyBLgSeE/Zng78yHYuoUZEjKB2nEHeA5xS7j/eCXwVuAk4T9KngYX9tL+J6lLn84GLbHcBSLqgHAM41/atkl4PzJH0NLAGeL/tJyVNB74kaSLVe/hF4I6acx1NldxWN5R9F/icpLFN5Y3Oo1pUdD/we+D4fsbEpF0n0pVfhImIGDJqp4lJ+T7gVbb338D2M4FO2x8cwrBGhc7OTnd1dbU6jIiItiJpse3OumPteIk1IiJi2LXVJVbby6hWkG5o+wuAC4YonPVI+jLw6qbiubbPH47zRUTE8GqrBDma2T6l1TFERMTQySXWiIiIGkmQERERNZIgIyIiaiRBRkRE1EiCjIiIqJEEGRERUSNf89hEdC9fQcesBa0OY5O2LD/lF7FZyQwyIiKiRhJkREREjSTIUU7SNEn7tjqOiIjNTRLk6DcNSIKMiBhhSZB9kHSFpMWS7pB0UilbKWlOKfuBpIMkLZT0oKS3lDrjJJ0vqVvSrZJeU8pnSjq7of+rJB3W0O9nJN0maZGk50p6FfAWqmdSLpG014i/CRERm6kkyL6daHsK0AmcKmlHYALVQ5D3Ax4DzgSOoHo48qdKu1MA254EzAC+LmlcP+eaACyyfQBwHfA+2z8FrgROsz3Z9gONDSSdJKlLUtfaVSuGZMAREVFJguzbqZJuAxYBuwF7A08CV5fj3cC1tteU7Y5SfjBwEYDtu4GfAS/q51xPAleV7cUNffXK9jzbnbY7x4yfOMAhRUTEQOR7kL0olz4PB6baXiVpITAOWGPbpdrTwGoA209L6u/9fIr1/1HSOKts7Hct+WwiIloqM8jeTQT+UJLjPsArB9H2euAdAJJeBOwO3AMsAyZL2kLSbsBBA+jrMWDbwQQeEREbLwmyd1cDW0q6C5hNdZl1oL4CbCGpG5gPzLS9GrgBeAi4E/gScMsA+roEOK0s9skinYiIEaJ1V/WinXV2drqrq6vVYUREtBVJi2131h3LDDIiIqJGEmRERESNJMiIiIgaSZARERE1kiAjIiJqJEFGRETUSIKMiIiokQQZERFRIwkyIiKiRhJkREREjTwxYhPRvXwFHbMWtDqMzdKy2W9sdQgRMQwyg4yIiKiRBBkREVFjk0iQkjokLa0p/5Skw/toN03SvsMbHUjaUtJvJM1uKj9P0m2Sbpd0qaRtmo4fI8mSan9pPiIihs8mkSB7Y/sTtn/QR5VpwKASpKQNuW97BHAvcKwkNZT/ne0DbL8U+DnwwYbzbAv8LXDjBpwvIiI20qaUIMdIOkfSHZK+L2lrSRdImg4gabakO8ts7fOSXgW8BZgjaYmkvSRNlrSo1Llc0g6l7UJJX5TUBZwu6SFJzyrHtmvc78UMYC5VEpzaU2j7j6UPAVsDjQ/n/DTwWeBPvXUq6SRJXZK61q5aMfh3LCIierUpJci9gS/b3g94FDim54CkHYGjgf3KbO1M2z8FrgROsz3Z9gPAhcA/lDrdwCcb+t/KdqftfwIWAj1LF48HvmN7TV1QksYBhwP/AXyTKlk2Hj8f+DWwD3BWKTsQ2M12n8tSbc8rMXWOGT+x73cnIiIGZVNKkA/ZXlK2FwMdDcdWUM3EzpP0VmBVc2NJE4HtbV9bir4OHNpQZX7D9rnACWX7BOD8PuJ6E/Bj208AlwHTJI3pOWj7BOB5wF3AcZK2AP4v8L/76DMiIobZppQgVzdsr6XhO562nwIOAi6lSlhXb0D/jzf0dwPQIekwYIztZywQajADOFzSMqrEvSPw2sYKttcCl1DNercF9gcWljavBK7MQp2IiJG1KSXIXpXVoRNt/yfwd8AB5dBjVAkJ2yuAP0g6pBx7F3Btc18NLgS+QR+zR0nbAYcAu9vusN0BnALMUOWFpZ6o7ofebXuF7Z0a6i8C3mK7awOGHhERG2hz+SWdbYHvlvuBAj5Syi8BzpF0KjAdeA/wNUnjgQdZdxm1zsXAmVT3FXtzNPAj242z2+8CnwO2Ar5ekqiA24D3D3ZgERExPGS7/1rxDGV17FG239XqWAA6Ozvd1ZVJZkTEYEhabLv2FtbmMoMcUpLOAv4KeEOrY4mIiOGRBLkBbH+ouUzSl4FXNxXPtd3XCteIiBilkiCHiO1TWh1DREQMnc1iFWtERMRgJUFGRETUSIKMiIiokQQZERFRIwkyIiKiRhJkREREjSTIiIiIGvke5Caie/kKOmb1+fjI2EQsm/3G/itFxEbLDDIiIqJGEmQvJJ0h6aND0M/2kj7QsP88SZdubL8RETG8kiCHgKS+LlVvD/w5Qdr+pe3pwx9VRERsjCTIBpJOl3SvpJ8ALy5lCyV1lu2dJC0r2zMlXSnpR8APJW0j6YeSbpHULemo0u1sYC9JSyTNkdQhaWnpY5yk80v9WyW9pqHv70i6WtJ9kj43wm9FRMRmL4t0CklTgOOByVTvyy3A4n6aHQi81PbvyyzyaNt/lLQTsEjSlcAsYH/bk8t5OhranwLY9iRJ+wDfl/Sicmwy8DJgNXCPpLNs/6Ip5pOAkwDGbLfzBo48IiLqZAa5ziHA5bZX2f4jcOUA2lxj+/dlW8A/S7od+AGwK/DcftofDFwEYPtu4GdAT4L8oe0Vtv8E3Ans0dzY9jzbnbY7x4yfOIBwIyJioDKD7N9TrPuHxLimY483bL8D2BmYYntNuRTbXH8wVjdsryWfVUTEiMoMcp3rgGmStpa0LfDmUr4MmFK2+1pcMxH4n5IcX8O6Gd9jwLa9tLmeKrFSLq3uDtyzwSOIiIghkwRZ2L4FmA/cBvwXcHM59Hng/ZJuBXbqo4uLgU5J3cC7gbtLv78DbpC0VNKcpjZfAbYobeYDM22vJiIiWk62Wx1DDIGxu+ztXd7zxVaHESMgv6QTMXQkLbbdWXcs97U2EZN2nUhX/uKMiBgyucQaERFRIwkyIiKiRhJkREREjSTIiIiIGkmQERERNZIgIyIiaiRBRkRE1EiCjIiIqJEEGRERUSO/pLOJ6F6+go5ZC1odRkTEiBrOn17MDDIiIqJGEmRERESNJMiIiIgaG5QgJV0gqa+HB29UP5LOlbTvxvbf1Of/Gcp6G3D+KyQtaio7VNItkp7q5X3YTtLDks4ejpgiIqJ3o3IGafuvbd85xN0ONPENeYKUtD0wBZgo6QUNh34OzAS+0UvTTwPXDXU8ERHRvwElSEnvlnS7pNsk/XspPlTSTyU92Dj7kXSapJtL/X/qp4/Gc3y6zCjHSFooqbOUr5T0mdJukaTnlvK9yn63pDMlrSzlu0i6TtISSUslHSJpNrB1Kbu41LtC0mJJd0g6qZStV09Sh6SlDTF+VNIZZftUSXeWMV3Sz1v4VuA/gEuA43sKbS+zfTvwdM37MQV4LvD93jqVdJKkLklda1et6CeEiIgYjH4TpKT9gI8Br7V9APC35dAuwMHAm4DZpe5fAnsDBwGTgSnlMmJvffScYw6wM3CC7bVNIUwAFpV21wHvK+Vzgbm2JwEPN9R/O/A925OBA4AltmcBT9iebPsdpd6JtqcAncCpknbspV5vZgEvs/1S4OR+6s4AvlleM/qpi6QtgH8FPtpXPdvzbHfa7hwzfmJ/3UZExCAMZAb5WuDbtn8LYPv3pfwK20+XS6HPLWV/WV63ArcA+1AlzN76APg4MNH2ybZdc/4ngavK9mKgo2xPBb5dthsvUd4MnFBmepNsP9bLuE6VdBuwCNitxDkYtwMXS3on8FRvlcqMd2/gJ7bvBdZI2r+fvj8A/Kfth/upFxERw2Rj7kGubthWw5//UmZgk22/0PZ5/fRzM9VM89m9HF/TkDjX0s+PG9i+DjgUWA5cIOndzXUkHQYcDkwtM9NbgXE13T3F+u9RY503Al8GDgRultRbXG8DdgAekrSMKsH3N4ucCnyw1P888O5y+TciIkbIQBLkj4BjJe0I0EciA/gecKKkbUrdXSU9p58+rqa6RLtA0raDiH0RcEzZ/vN9PUl7AI/YPgc4lyqBQTVze1bZngj8wfYqSfsAr2zot7HeI8BzJO0oaSzV5eSeS6C72f4x8A+lv216iXMGcKTtDtsdVIt1ju+lLgC232F791L/o8CF5fJvRESMkH5/as72HZI+A1wraS3VbKu3ut+X9BLgvyUBrATe2UsfMxvafbskxyslvWGAsX8YuEjS6VRJtmeVymHAaZLWlPP3zCDnAbdLugU4EThZ0l3APVTJluZ6tt8h6VPATVQz0rtLnTHl3BOpZs1fsv1oc4CSOoA9Gvu3/ZCkFZJeQbU453KqGeabJf2T7f0GOP71TNp1Il3D+JNLERGbG9Xf9hv9JI2nWlBjSccDM2wf1eq4WqWzs9NdXV2tDiMioq1IWmy7s+5YO/9Y+RTgbFVT1UepZoURERFDom0TpO3rqb7GMSpIOoGmr68AN9g+pRXxRETExmnbBDna2D4fOL/VcURExNAYlT81FxER0Wptu0gn1ifpMaoVue1sJ+C3rQ5iI2UMrdfu8UPGMJL2sL1z3YFcYt103NPbSqx2IakrY2i9dh9Du8cPGcNokUusERERNZIgIyIiaiRBbjrmtTqAIZAxjA7tPoZ2jx8yhlEhi3QiIiJqZAYZERFRIwkyIiKiRhJkG5B0pKR7JN0v6RmPvZI0VtL8cvzG8hSRnmP/WMrvkfT6kUNUL/UAAAONSURBVIy7KcYNGoOkDklPSFpSXl8b6dhLHP3Ff6ikWyQ9JWl607H3SLqvvN4zclE/I8aNGcPahs/gypGL+hkx9jeGj0i6U9Ltkn5YHn/Xc6xdPoe+xtDyz2EA8Z8sqbvE+BNJ+zYcGxV/Hw2Y7bxG8Yvq0VoPAC8AtgJuA/ZtqvMB4Gtl+3hgftnet9QfC+xZ+hnTZmPoAJa2wWfQAbwUuBCY3lD+bODB8ucOZXuHdhpDObaylZ/BIMbwGmB82X5/w39H7fQ51I5hNHwOA4x/u4bttwBXl+1R8ffRYF6ZQY5+BwH3237Q9pPAJUDzY72OAr5eti8FXleecnIUcInt1bYfAu4v/Y20jRnDaNBv/LaX2b6d6hmfjV4PXGP797b/AFwDHDkSQTfZmDGMFgMZw49tryq7i4Dnl+12+hx6G8NoMJD4/9iwOwHoWQk6Wv4+GrAkyNFvV+AXDfsPl7LaOrafonp49I4DbDsSNmYMAHtKulXStZIOGe5ga2zM+9hOn0FfxknqkrRI0rShDW3ABjuG9wL/tYFth8vGjAFa/zkMKH5Jp0h6APgccOpg2o4m+am5GO1+Bexu+3eSpgBXSNqv6V+pMfz2sL1c0guAH0nqtv1Aq4PqjaR3Ap3AX7Q6lg3Vyxja4nOw/WXgy5LeDnwMaNk9342RGeTotxzYrWH/+aWsto6kLYGJwO8G2HYkbPAYyuWY3wHYXkx13+JFwx5xL7EVg3kf2+kz6JXt5eXPB4GFwMuGMrgBGtAYJB0OnA68xfbqwbQdARszhtHwOQz2fbwE6JnpjpbPYOBafRM0r75fVLP8B6luavfcFN+vqc4prL/A5Vtlez/Wvyn+IK1ZpLMxY9i5J2aqhQHLgWePtvgb6l7AMxfpPES1MGSHsj2i8Q/BGHYAxpbtnYD7aFqYMVrGQJUwHgD2bipvm8+hjzG0/HMYYPx7N2y/Gegq26Pi76NBjbfVAeQ1gA8J3gDcW/6nOb2UfYrqX5cA44BvU930vgl4QUPb00u7e4C/arcxAMcAdwBLgFuAN4/S+F9OdU/lcarZ+x0NbU8s47ofOGEUfwa1YwBeBXSXv9y6gfeO4jH8AHik/PeyBLiyDT+H2jGMls9hAPHPbfh/9sc0JNDR8vfRQF/5qbmIiIgauQcZERFRIwkyIiKiRhJkREREjSTIiIiIGkmQERERNZIgIyIiaiRBRkRE1Pj/5B4bhUV081UAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "abB9WCz3Kopl",
"colab": {
"base_uri": "https://localhost:8080/"
},
"outputId": "86a3f34e-ee46-479e-c6ad-46f969eaaa7a"
},
"source": [
"#Calculating roc_auc_score\n",
"from sklearn.metrics import roc_auc_score\n",
"\n",
"roc_auc_score(y_test ,prediction) "
],
"execution_count": null,
"outputs": [
{
"output_type": "execute_result",
"data": {
"text/plain": [
"0.5882801409117199"
]
},
"metadata": {},
"execution_count": 479
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "QoUh7bhxKopl",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 265
},
"outputId": "07e7e8a8-362f-44c7-fb7e-e056244954cc"
},
"source": [
"from sklearn.metrics import roc_curve\n",
"\n",
"pred_proba = clf.predict_proba(X_test)[::,1]\n",
"\n",
"fpr, tpr, _ = roc_curve(y_test, prediction)\n",
"\n",
"auc = roc_auc_score(y_test, prediction)\n",
"plt.plot(fpr,tpr,label=\"data 1, auc=\"+str(auc))\n",
"plt.legend(loc=4)\n",
"plt.show()\n"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "dB9b1fz9Kopl",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 669
},
"outputId": "94a4a167-e59d-4422-9312-aa6e178e52b9"
},
"source": [
"from dtreeplt import dtreeplt\n",
"dtree = dtreeplt(model=clf, feature_names=Predictors, target_names=TargetVariable)\n",
"fig = dtree.view()\n",
"currentFigure=plt.gcf()\n",
"currentFigure.set_size_inches(80,40)"
],
"execution_count": null,
"outputs": [
{
"output_type": "display_data",
"data": {
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