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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",
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"<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",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
" .dataframe thead th {\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",
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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>4</td>\n",
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" <td>4</td>\n",
" <td>A121</td>\n",
" <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>A191</td>\n",
" <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",
" .dataframe tbody tr th:only-of-type {\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",
" <td>1000.000000</td>\n",
" <td>1000</td>\n",
" <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",
" <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": "iVBORw0KGgoAAAANSUhEUgAAAQcAAADMCAYAAAB+82ZkAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAM4ElEQVR4nO3de6xl5V3G8e8DFMGCpYXpBIHpEJmCoGXACdK0QQGLXJRigg3YWKyYMQ3VEryAl6g0JtJ/rKWhKIEW0AJFFCVQKYRCamO5zADlNlCGy1gIl4ECbSEWW37+sd8Dm+M7zD7MPufM2fP9JDtnrXfts/d7OvTJWmuv/axUFZI03VbzPQFJmyfDQVKX4SCpy3CQ1GU4SOraZr4nALDLLrvU0qVL53sa0hZn9erVz1TVot62zSIcli5dyqpVq+Z7GtIWJ8m6DW3zsEJSl+EgqctwkNS10XBIsneSO4ce301yapJ3JLk+yYPt59vb85Pk7CRrk9yV5MDZ/zMkjdtGw6GqHqiq5VW1HPg54CXgSuAM4IaqWgbc0NYBjgKWtcdK4NzZmLik2TXTw4rDgYeqah3wQeCiNn4RcFxb/iBwcQ3cDOyUZNexzFbSnJnpR5knAJe25cVV9URbfhJY3JZ3A7499DuPtbEnhsZIspLBngVLliyZ4TQ2zdIzrpnT95trj551zHxPQRNg5D2HJNsCxwL/PH1bDb73PaPvflfVeVW1oqpWLFrUvQZD0jyayWHFUcDtVfVUW39q6nCh/Xy6jT8O7DH0e7u3MUkLyEzC4UReO6QAuAo4qS2fBPz70PhH2qcWBwMvDB1+SFogRjrnkOStwAeA3x0aPgu4PMnJwDrgQ238y8DRwFoGn2x8dGyzlTRnRgqHqnoR2Hna2LMMPr2Y/twCThnL7CTNG6+QlNRlOEjqMhwkdRkOkroMB0ldhoOkLsNBUpfhIKnLcJDUZThI6jIcJHUZDpK6DAdJXSOFQ5KdklyR5P4ka5K81/ZpabKNuufwGeDaqtoH2B9Yg+3T0kQb5b4VbwMOAS4AqKqXq+p5bJ+WJtooew57AuuBLyS5I8n5rRlqpu3Tr5NkZZJVSVatX7/+zf8FkmbFKOGwDXAgcG5VHQC8yGuHEIDt09IkGiUcHgMeq6pb2voVDMLC9mlpgo1yO7wngW8n2bsNHQ7ch+3T0kQb9Y5Xvwd8sd3Y5mEGjdJbYfu0NLFGbZ++E1jR2WT7tDShvEJSUpfhIKnLcJDUZThI6jIcJHUZDpK6DAdJXYaDpC7DQVKX4SCpy3CQ1GU4SOoyHCR1jdo+/WiSu5PcmWRVG7N9WppgM9lzOLSqllfV1Fe3bZ+WJtimHFbYPi1NsFHDoYDrkqxOsrKN2T4tTbBRa+LeX1WPJ3kncH2S+4c3VlUlmXH7NHAewIoVK2b0u5Jm30h7DlX1ePv5NHAlcBC2T0sTbZQ7Xr01yY5Ty8ARwD3YPi1NtFEOKxYDVyaZev4lVXVtktuwfVqaWBsNh6p6mMHNc6ePP4vt09LE8gpJSV2Gg6Quw0FSl+EgqctwkNRlOEjqMhwkdRkOkroMB0ldhoOkLsNBUpfhIKnLcJDUZThI6ho5HJJsneSOJFe39T2T3NIq6L+UZNs2/mNtfW3bvnR2pi5pNs1kz+ETwJqh9U8Bn66qvYDngJPb+MnAc2380+15khaYUW9qsztwDHB+Ww9wGHBFe8r0avqpyvorgMPb8yUtIKPuOfwd8MfAK219Z+D5qvphWx+un3+1mr5tf6E9/3Wsppc2b6MUzP4K8HRVrR7nG1fVeVW1oqpWLFq0aJwvLWkMRimYfR9wbJKjge2AnwA+w+BOVtu0vYPh+vmpavrHkmwDvA14duwzlzSrNrrnUFV/UlW7V9VS4ATgq1X1YeBG4Pj2tOnV9FOV9ce353vTGmmB2ZTrHE4HTkuylsE5hQva+AXAzm38NF67wa6kBWTU2+EBUFU3ATe15YcZ3Plq+nP+B/j1McxN0jzyCklJXYaDpC7DQVKX4SCpy3CQ1GU4SOoyHCR1GQ6SugwHSV2Gg6Quw0FSl+EgqctwkNQ1ShPUdkluTfLNJPcmObON2z4tTbBR9hx+ABxWVfsDy4EjkxyM7dPSRBulCaqq6vtt9S3tUdg+LU20kcpekmwNrAb2As4BHmLE9ukkU+3Tz0x7zZXASoAlS5Zs2l+hLcrSM66Z7ynMmkfPOma+p/CqkU5IVtWPqmo5gyLZg4B9NvWNbZ+WNm8z+rSiqp5nUCz7Xlr7dNvUa5/G9mlp4Rrl04pFSXZqy9sDH2BwWzzbp6UJNso5h12Bi9p5h62Ay6vq6iT3AZcl+WvgDl7fPv2PrX36Owzq7CUtMBsNh6q6CzigM277tDTBvEJSUpfhIKnLcJDUZThI6jIcJHUZDpK6DAdJXYaDpC7DQVKX4SCpy3CQ1GU4SOoyHCR1jdLnsEeSG5Pc19qnP9HG35Hk+iQPtp9vb+NJcnZrn74ryYGz/UdIGr9R9hx+CPxBVe0LHAyckmRf4AzghqpaBtzQ1gGOApa1x0rg3LHPWtKsG6V9+omqur0tf49BC9RuvL5lenr79MWttfpmBnVyu4595pJm1YzOObQb1BwA3AIsrqon2qYngcVt+dX26Wa4mXr4tVYmWZVk1fr162c4bUmzbeRwSLID8C/AqVX13eFtrSNyRj2Rtk9Lm7eRwiHJWxgEwxer6l/b8FNThwvt59Nt/NX26Wa4mVrSAjHKpxVhUBq7pqr+dmjTcMv09Pbpj7RPLQ4GXhg6/JC0QIzSPv0+4DeBu5Pc2cb+FDgLuDzJycA64ENt25eBo4G1wEvAR8c6Y0lzYpT26a8DG7rX5eGd5xdwyibOS9I88wpJSV2Gg6Quw0FSl+EgqctwkNRlOEjqMhwkdRkOkroMB0ldhoOkLsNBUpfhIKnLcJDUNUqfw+eTPJ3knqExm6elCTfKnsOFwJHTxmyelibcKO3TXwO+M23Y5mlpwr3Zcw6b1DwNtk9Lm7tNPiH5Zpqn2+/ZPi1txt5sONg8LU24NxsONk9LE26jBbNJLgV+EdglyWPAX2LztDTxRmmfPnEDm2yeliaYV0hK6jIcJHUZDpK6DAdJXYaDpC7DQVKX4SCpy3CQ1GU4SOoyHCR1GQ6SugwHSV2Gg6SuWQmHJEcmeaC1UJ+x8d+QtLkZezgk2Ro4h0ET9b7AiUn2Hff7SJpds7HncBCwtqoerqqXgcsYtFJLWkA2WvbyJvQaqH9++pOSrGRwbwuA7yd5YBbmsrnYBXhmrt4sn5qrd9oiTPq/3bs2tGE2wmEkVXUecN58vf9cSrKqqlbM9zw0c1vyv91sHFbYQC1NgNkIh9uAZUn2TLItcAKDVmpJC8jYDyuq6odJPg58Bdga+HxV3Tvu91lgtojDpwm1xf7bZVAYLUmv5xWSkroMB0ldhoOkLsNBUte8XQQ1qZLsw+By8d3a0OPAVVW1Zv5mJc2cew5jlOR0Bt8lCXBrewS41G+nLnxJtqgbQ/tR5hgl+RawX1X977TxbYF7q2rZ/MxM45Dkv6tqyXzPY654WDFerwA/CaybNr5r26bNXJK7NrQJWDyXc5lvhsN4nQrckORBXvtm6hJgL+Dj8zYrzcRi4JeB56aNB/ivuZ/O/DEcxqiqrk3ybgadFsMnJG+rqh/N38w0A1cDO1TVndM3JLlp7qczfzznIKnLTyskdRkOkroMhwmVZHGSS5I8nGR1km8k+bUxvO5NSVa05R2S/EOSh9p73JTk/1UCzuC1/yrJH7blTyb5pbZ8apIf39S5a2Y8ITmBkgT4N+CiqvqNNvYu4Ngxv9X5wCPAsqp6JcmeDBrHp88lVTWjj3Kr6i+GVk8F/gl4aRPnqxlwz2EyHQa8XFV/PzVQVeuq6rNJtkvyhSR3J7kjyaEAbzC+fZLLkqxJciWwfRv/KQbFwX8+9X/8qnqkqq5JsrTdt+Ri4B5gjyR/lOS2JHclOXNqXkn+LMm3knwd2Hto/MIkxyf5fQbXjtyY5MZZ/t9NQ9xzmEz7AbdvYNspQFXVz7bvgVzXPn7d0PjHgJeq6qeTvGfodfcD7nyDj2iXASdV1c1JjmjrBzG4XuCqJIcALzKoEVzO4L/F24HVwy9SVWcnOQ04tKrmrAVahsMWIck5wPuBlxncKuCzAFV1f5J1wLvb9t74IcDZbfyuN7iCcLp1VXVzWz6iPe5o6zswCIsdgSur6qU2T7tGNyMeVkyme4EDp1aq6hTgcGDRmN9j/3aHs54Xh5YD/E1VLW+PvarqgjHORbPAcJhMXwW2S/KxobGps/3/CXwYoB02LAEeeIPxrwFTJzV/BngPQFU9BKwCzmwnHWnnGo7pzOcrwG8n2aE9b7ck72yvfVw7r7Ej8Ksb+Hu+x2AvQ3PIcJhANbjs9TjgF5I8kuRW4CLgdOBzwFZJ7ga+BPxWVf3gDcbPBXZIsgb4JK8/J/A7DL6LsDbJPcCFwNOd+VwHXAJ8o73+FcCOVXV7e69vAv/B4LYGPecB13pCcm55+bSkLvccJHUZDpK6DAdJXYaDpC7DQVKX4SCpy3CQ1PV/iIhZgkGZHGoAAAAASUVORK5CYII=\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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lSeu3iYbC45JsNTbT9hQmupchSVpPTPSD/e+AK5J8qc2/HvjoYEqSJI3KRM9oPifJAnpnIwO8rqpuHFxZkqRRmPAQUAsBg0CSNmCTvnS2JGnDZShIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjoDC4UkOyb5bpIbk/w0yTtb+9ZJLklyc3vcqrUnyclJFia5Lsk+g6pNkjS+Qe4pPAK8t6r2APYHjk6yB3AscGlV7Qpc2uYBXgHs2n7mAqcNsDZJ0jgGFgpVdUdV/VObvh+4CdgBOAg4u3U7G3htmz4IOKd6rgS2TLLdoOqTJP2hoRxTSDILeA5wFTCjqu5oi5YAM9r0DsDtfU9b1NpWXtfcJAuSLFi2bNnAapakjdHAQyHJk4AvA++qql/3L6uqAmoy66uqeVU1u6pmT58+fR1WKkkaaCgk2ZReIJxbVV9pzXeODQu1x6WtfTGwY9/TZ7Y2SdKQDPLbRwHOAG6qqv/dt2g+MKdNzwEu6ms/vH0LaX/gvr5hJknSEEwb4LqfD7wFuD7Jta3tr4CPARckORK4DTikLbsYeCWwEHgQOGKAtUmSxjGwUKiqHwBZxeIDx+lfwNGDqkeStGaD3FMYqee+/5xRl7DOXX3i4aMuQdIGzstcSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqTOwUEjy2SRLk9zQ17Z1kkuS3Nwet2rtSXJykoVJrkuyz6DqkiSt2iD3FM4CXr5S27HApVW1K3Bpmwd4BbBr+5kLnDbAuiRJqzBtUCuuqsuTzFqp+SDgJW36bOAy4IOt/ZyqKuDKJFsm2a6q7hhUfZI05tT3fn3UJaxzx/zdq9fqecM+pjCj74N+CTCjTe8A3N7Xb1Fr+wNJ5iZZkGTBsmXLBlepJG2ERnague0V1Fo8b15Vza6q2dOnTx9AZZK08Rp2KNyZZDuA9ri0tS8GduzrN7O1SZKGaNihMB+Y06bnABf1tR/evoW0P3CfxxMkafgGdqA5yRfpHVTeJski4CPAx4ALkhwJ3AYc0rpfDLwSWAg8CBwxqLokSas2yG8fHbaKRQeO07eAowdViyRpYjyjWZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUGdh5CtJU9PxTnj/qEta5H77jh6MuQRsQ9xQkSR1DQZLUMRQkSR1DQZLU8UCztJH63otePOoS1rkXX/69UZew3nNPQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUmVKhkOTlSX6eZGGSY0ddjyRtbKZMKCTZBPgU8ApgD+CwJHuMtipJ2rhMmVAA9gMWVtUtVfU74DzgoBHXJEkblVTVqGsAIMnBwMur6qg2/xbgT6rqmJX6zQXmttndgJ8PtdDxbQPcNeoipgi3RY/bYTm3xXJTZVvsVFXTx1uw3t15rarmAfNGXUe/JAuqavao65gK3BY9bofl3BbLrQ/bYioNHy0Gduybn9naJElDMpVC4cfArkl2TrIZ8AZg/ohrkqSNypQZPqqqR5IcA3wL2AT4bFX9dMRlTdSUGs4aMbdFj9thObfFclN+W0yZA82SpNGbSsNHkqQRMxQkSR1DYQKSvDZJJdm9r+3pSb6d5KYkNyaZ1drPSvIvSa5tP3uPqu5BmOS2OKZdsqSSbDOqmgdlktvijCQ/SXJdkguTPGlUdQ/CZLZF3/KTkzww7FoHbZL/L85tl/a5Iclnk2w6qrrHGAoTcxjwg/Y45hzgxKr6d/TOxl7at+z9VbV3+7l2iHUOw2S2xQ+BlwK3DbXC4ZnMtnh3Ve1VVc8G/hVY4aTMDcCkfkeSzAa2GmqFwzOZbXEusDuwJ/B44Kgh1jkuQ2EN2l90LwCOpPc1Wdo1maZV1SUAVfVAVT04uiqHY7LboqquqapbR1TuQK3Ftvh16xN6v/wbzDc8Jrst2nXOTgQ+MJqKB2ct/l9cXA3wI3rnZ42UobBmBwHfrKp/Bu5O8lzgWcC9Sb6S5JokJ7b/6GM+2oYJPpFk85FUPRhrsy02VJPeFknOBJbQ+8vwlJFUPRiT3RbHAPOr6o5RFTxAa/U70oaN3gJ8c/glr8hQWLPD6F2cj/Z4GL3zO14IvA/YF3gG8NbW50P0fun3BbYGPjjEWgdtsttiQzbpbVFVRwDbAzcBhw6x1kGb8LZIsj3wejasUOy3tr8jnwYur6rvD6fMVZsyJ69NRUm2Bg4A9kxS9E6qK+DLwLVVdUvr9zVgf+CMvr9+ftv+Mnzf8Ctf99ZmW4yq1kF7LNuiqh5Nch69oZMzh137urYW22IJsAuwsDeSxhOSLKyqXUZR/7q0tv8vknwEmA68fRR1r8w9hdU7GPhcVe1UVbOqakfgX4DNgC2TjF1l8ADgRoAk27XHAK8Fbhh+2QMx6W2xAZvUtkjPLtD9v3gN8LNRFD4Ak9oWVfUPVbVt6zsLeHBDCIRmbT4vjgJeBhxWVb8fRdErMxRW7zDgqyu1fZneAaT3AZcmuR4I8Jm2/NzWdj29y+SeMKRaB23S2yLJf02yiN7Bs+uSnD7EegdpstsiwNl9/y+2A44fXrkDtTa/IxuqtdkWfw/MAK5I7yvsHx5WsaviZS4kSR33FCRJHUNBktQxFCRJHUNBktQxFCRJHUNBWseSnN6udyOtd/xKqiSp456C1CfJE5P8Q7v3wQ1JDk3y4SQ/bvPz2hnKuyf5Ud/zZrUTk0hyWbs0NEkeSPLRtr4rk8xo7c9s89cnOSHtvgJJtktyeTuR6YYkLxzFdtDGy1CQVvRy4Jft3gf/nt5VK0+tqn3b/OOBV1XVz4DNkuzcnncocP4463sicGVV7QVcDvxFaz8JOKmq9gQW9fV/I/Ctqtob2AvY0O7HoSnOUJBWdD3wZ0k+nuSFVXUf8KdJrmp7AgcAf9z6XsDyq52uKhR+B3yjTV8NzGrTzwO+1Ka/0Nf/x8ARSY4D9qyq+x/7W5ImzlCQ+rTr4O9DLxxOaNei+TRwcPur/jPAFq37+cAhSZ7Ve2rdPM4qH67lB+4eZQ1XJq6qy4EXAYuBs5Ic/ljfkzQZhoLUp13v/8Gq+jy9u4Pt0xbdld5dtQ4e61tVv6D3Qf/fGH8vYXWuBP5zm35D3+vvBNxZVZ8BTu97fWkovJ+CtKI9gROT/B54GPhLll8CfQm94Z1+59MLj52ZnHcBn0/y1/SOW9zX2l8CvD/Jw8ADgHsKGiq/kiqNQJInAL+pqkryBnrX0z9o1HVJ7ilIo/Fc4NR20517gbeNuB4JcE9BktTHA82SpI6hIEnqGAqSpI6hIEnqGAqSpM7/B473uJzW4tbOAAAAAElFTkSuQmCC\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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hSdoDpnum8F+BbyV5T5L3AP8H+M872qGq/qSqFlfVUuBk4NKqeh3wFQbXJABWARe29kVtmzZ+qbf/lKSZNd1PNJ+TZJTBRWKAV1bVtbv5M98BfDrJe4HvAme3/rOBTyYZA25mECSSpBk03eUjWgjsVhBU1VeBr7b29cCxU8y5B3j17hxfkrRn7PJXZ0uS9l6GgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSpYyhIkjq9hUKSRyX5dpLvJbkmybtb/xFJLk8yluQzSQ5o/Y9s22NtfGlftUmSptbnmcK9wEuq6ijgaODlSY4D3g+cUVVPBW4BVrf5q4FbWv8ZbZ4kaQb1Fgo1cGfb3L89isEtPc9v/euAE1t7Zdumja9Ikr7qkyRtq9drCkn2S3IVsBW4BPgJcGtV3dembAQWtfYiYANAG78NePwUx1yTZDTJ6Pj4eJ/lS9I+p9dQqKr7q+poYDGD+zI/Yw8cc21VLa+q5SMjIw+5RknSA2bk3UdVdSvwFeC5wPwk89rQYmBTa28ClgC08YOBm2aiPknSQJ/vPhpJMr+1Hw28DLiOQTi8qk1bBVzY2he1bdr4pVVVfdUnSdrWvJ1P2W2HAeuS7McgfM6rqi8kuRb4dJL3At8Fzm7zzwY+mWQMuBk4ucfaJElT6C0Uqupq4NlT9F/P4PrC5P57gFf3VY8kaef8RLMkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6hoIkqWMoSJI6fd6Oc0mSryS5Nsk1Sd7S+g9NckmSH7fnQ1p/kpyZZCzJ1UmO6as2SdLU+jxTuA/4d1V1JHAccGqSI4HTgPVVtQxY37YBjgeWtcca4Kwea5MkTaG3UKiqzVV1ZWvfAVwHLAJWAuvatHXAia29EjinBi4D5ic5rK/6JEnbmpFrCkmWMrhf8+XAwqra3Ia2AAtbexGwYWi3ja1v8rHWJBlNMjo+Pt5bzZK0L+o9FJIcCHwOeGtV3T48VlUF1K4cr6rWVtXyqlo+MjKyByuVJPUaCkn2ZxAI51bV51v3jRPLQu15a+vfBCwZ2n1x65MkzZA+330U4Gzguqr6y6Ghi4BVrb0KuHCo/5T2LqTjgNuGlpkkSTNgXo/Hfj7wB8D3k1zV+v4UOB04L8lq4AbgpDZ2MXACMAbcDbyxx9okSVPoLRSq6ptAtjO8Yor5BZzaVz2SpJ3zE82SpI6hIEnqGAqSpI6hIEnqGAqSpE6fb0mV9BD8/C/+8WyXoDnoSX/+/V6P75mCJKljKEiSOoaCJKljKEiSOoaCJKljKEiSOoaCJKljKEiSOoaCJKnT553XPpZka5IfDPUdmuSSJD9uz4e0/iQ5M8lYkquTHNNXXZKk7evzTOETwMsn9Z0GrK+qZcD6tg1wPLCsPdYAZ/VYlyRpO3oLhar6OnDzpO6VwLrWXgecONR/Tg1cBsxPclhftUmSpjbT1xQWVtXm1t4CLGztRcCGoXkbW982kqxJMppkdHx8vL9KJWkfNGsXmts9mWs39ltbVcuravnIyEgPlUnSvmumQ+HGiWWh9ry19W8ClgzNW9z6JEkzaKZD4SJgVWuvAi4c6j+lvQvpOOC2oWUmSdIM6e0mO0k+BbwYWJBkI/BO4HTgvCSrgRuAk9r0i4ETgDHgbuCNfdUlSdq+3kKhql67naEVU8wt4NS+apEkTY+faJYkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVLHUJAkdQwFSVJnToVCkpcn+VGSsSSnzXY9krSvmTOhkGQ/4K+B44EjgdcmOXJ2q5KkfcucCQXgWGCsqq6vql8BnwZWznJNkrRP6e0ezbthEbBhaHsj8M8mT0qyBljTNu9M8qMZqG1fsQD45WwXMRfkA6tmuwQ9mP82J7wze+IoT97ewFwKhWmpqrXA2tmuY2+UZLSqls92HdJk/tucOXNp+WgTsGRoe3HrkyTNkLkUCt8BliU5IskBwMnARbNckyTtU+bM8lFV3Zfk3wD/AOwHfKyqrpnlsvY1LstprvLf5gxJVc12DZKkOWIuLR9JkmaZoSBJ6hgKIsnHkmxN8oPZrkUalmRJkq8kuTbJNUneMts17e28piCSvAi4Ezinqp412/VIE5IcBhxWVVcmOQi4Ajixqq6d5dL2Wp4piKr6OnDzbNchTVZVm6vqyta+A7iOwbcfqCeGgqSHhSRLgWcDl89uJXs3Q0HSnJfkQOBzwFur6vbZrmdvZihImtOS7M8gEM6tqs/Pdj17O0NB0pyVJMDZwHVV9ZezXc++wFAQST4FfAt4epKNSVbPdk1S83zgD4CXJLmqPU6Y7aL2Zr4lVZLU8UxBktQxFCRJHUNBktQxFCRJHUNBktQxFKRpSHJnez48yfk7mfviJF/YztjPkizoo0ZpT5gzt+OUHg6q6hfAq2a7DqkvnilIuyDJ0on7TrT2N5Jc2R7PG5r6uCRfTPKjJB9Jss3/a0len+Tb7QNZ/y3JfjP2i0jbYShIu28r8LKqOgZ4DXDm0NixwJuBI4GnAK8c3jHJM9s+z6+qo4H7gdfNRNHSjrh8JO2+/YEPJ5n4o/60obFvV9X10H2NyAuA4WsRK4DnAN8ZfL0Pj2YQMtKsMhSk3fc24EbgKAZn3fcMjU3+/pjJ2wHWVdWf9FeetOtcPpJ238HA5qr6NYMvbRu+JnBskiPatYTXAN+ctO964FVJngCQ5NAkT56JoqUdMRSk3fc3wKok3wOeAdw1NPYd4MMMbh/5U+CC4R3bPYb/I/DlJFcDlwCHzUTR0o74LamSpI5nCpKkjqEgSeoYCpKkjqEgSeoYCpKkjqEgSeoYCpKkzv8Hvls3LY/WJwAAAAAASUVORK5CYII=\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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\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": {
"image/png": 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6G9n/ha4s7SUe2JQ0iZLMA44H/hZ4n78hdH8kORL4OPCbVXV86zySNLb+t+iHgecCC6rq8saRNIMl2YyunHbl3sWddAUwi4FTq+rWhvEkSZIkSdIcZvGLJEmSJEmSJEmSJN1Lki2APVi14GVP4C6mF7yc58Fj/SJJNgIOoDskMEV3s/DKm2JPrqqbGsaTpFEkeQxwCN06eBBwGcNa+M2qurthPM0SSbalKzo4G/idqrqrcSRJGl2S+cBngLdU1eda59HskmRv4Fi6wqC/ap1HklpI8hbgbcALqupbrfNo5uvLvPekK4CZAp4KfJ1+X6OqLm4YT5IkSZIkzTEWv0iSJEmSJEmSJEmaSEk2AXZnesHLxkwveFleVdc2iqpZIslODLfB7gt8m+4gwGJgWfmCXtIc19+kvjfDWrgDcCLdOrikqq5uGE+zUJInA8cBHwU+6HeppEmU5A3Ae4Bfr6ozWufR7JRkB2Ah3bPZH1TVPY0jSdLokjwf+Hvg9VV1bOs8ml2SbA48j2HP41a6YtvFwGlVdVvDeJIkSZIkaZaz+EWSJEmSJEmSJEnSnJZkfWAXphe8bAVcwPSSlys9VKz7I8mGwP4Mw/4b0d/4CpxUVT9rl06SxpFkK+AQurXwYOAKhrXwrKq6q2E8zWJJDgM+Bbyxqo5pnUeSxpZkLeADwBHAoVV1SeNImuWSPAz4InAL8LKq+nnjSJI0uiRPBb4MfBj4P+4D68FIEuDXGPaFnwScQV8C7nObJEmSJEl6oCx+kSRJkiRJkiRJkjQnJFkbeALTC152AC6jK3VZxlDwcqk3XOuBSvIEumH+KWA/4LsMBQff97CIpLmu/759OsNa+ATgZLq1cElV/ahhPM0RSd4EvAN4YVWd1TqPJI0tyUbA0cCWdGvhDY0jaY5Ish7wceCJwOFVdVXjSJI0uiTbAcfTFXW8uarubhxJs1xfrnYQ3T7JFHAzw57xV6vq9obxJEmSJEnSLGDxiyRJkiRJkiRJkqRZpb9Nc1u6Upd5DAUvuwI/Zih2Wfm5sKruaJNWs12SDYDnMNzeuin9za3AiVV1Y8N4kjSKJFsC8+nWwYOAq+gOLy0GvlFVdzWMpzmkLxb6EP2/t6q6rHEkSRpdkq2A44CLgdf6e1arW7+v8nbgvwOHVdX3G0eSpNEl2Qz4AnAP8NKqurlxJM0RSdaiK1hbuZ/8a8Dp9PsoVXVpw3iSJEmSJGmGsvhFkiRJkiRJkiRJ0oyV5FEMxS73/tzC9IKX86rq542iag5JsgPdUP4U8Gzg+ww3tH6vqlY0jCdJa1xfvrEXw1q4M3AKffFVVV3ZMJ7mqCQbA/9MV7J2ZFX9tHEkSRpdkt2B44GjgfeUQ75ag5IcBXwUeGVVLWmdR5LGlmRd4K+BZwCH+ltXa0KSh9OV6C4ADgFuZNhr/polf5IkSZIkCSx+kSRJkiRJkiRJkjQD9Des7sH0gpf1gGWsWvCyvKpuaBRVc1CS9ekKXlbewvpw+nID4MSq+knDeJI0iiSPBObTrYXzgWvob6MGvl5VdzaMpzkuydbAV+jK1t7gvzdJkyjJgcDngD+sqqNb59FkSLIv8EXg3VX18dZ5JGlsSQK8FXgL8Pyq+k7jSJrDkqwFPJlhH3pP4Kv0+y9V9YN26SRJkiRJUksWv0iSJEmSJEmSJEkaTZINgV2ZXvDySOA8ppe8XO0N51oTkjyObsB+Ctif7t/byptW/72qVrTKJklj6A8b7cWwFu4GnEq3Di6pqisaxtMESTIPOB74W+B9PvtJmkRJfhN4P/CSqvpq6zyaLEl2BBbSlbC9zd/DkiZRkiOBjwO/WVXHt86jyZBkC+AguhKYQ4AbGEp4T6+qOxrGkyRJkiRJI7L4RZIkSZIkSZIkSdJql2QdYEe6Upd5DAUv2wGXMBS7LKcre7nMg0Vak5KsD+xHN0Q/RVc2tIRuiH5pVd3QMJ4kjaI/UHQw3Vo4H7iObh1cDJzhgSKNLcl84DPAW6rqc63zSNLY+iK29wJHAYdW1QWNI2lCJXkEcCxwPfCKqrq1cSRJGl2SvenWwvdV1V+1zqPJ0j8XPpWhoHd34DT6IhgLeiVJkiRJmtssfpEkSZIkSZIkSZL0oPXDyNsxFLus/OwM/Iih4GXl5+KqurNNWk2aJNvRDckvAPYHzme4MfXblg1Jmuv67+mnMKyFe7DqoaHL26XTpEvyBuA9wK9X1Rmt80jS2JJsAPwj3W/q51fVdW0TadL1hal/T7enc0RVXdM4kiSNLskOwELgROAPquqexpE0oZI8kq68dwo4BLiWYW/7DN+zSJIkSZI0t1j8IkmSJEmSJEmSJOm/lCTAVkwveNkDuJHpBS/nezu0xpZkPWA/hltRtwJOoBuIX1pV1zeMJ0mjSPIIVj0Y9BO6Q0GLgNOr6o6G8aSVhUTvB14ALKiqSxpHkqTRJXkU8CXgSuBVVXV740gS8J/7P/8LeDVwaFWd1zaRJI0vycOALwK3AC+rqp83jqQJl2Rt4Kl0pb5TwK7AKXT7PYur6ocN40mSJEmSpNXA4hdJkiRJkiRJkiRJq0jycLpCl/uWvEBX6rKMoeBleVXd2CKnBJBkW7ph9wXAAcBFDDefnuOtvJLmur5E40kMa+E84Gv0a2FVXdYwnrSKJBsBRwNbAi+sqhsaR5Kk0SXZBVgI/Cvwzqpa0TiSNE2SVwAfois8OKl1HkkaW18w/XHgicDhVXVV40jSf+pLBOfT7QMdDFzNsCf+9aq6q2E8SZIkSZL0IFj8IkmSJEmSJEmSJE2o/uDtbnSlLvMYCl42B5YzlLus/FxTvmBUY0nWBfalKziYArYGltINtp9QVdc1jCdJo+hL2g6iWwcPAW6iO9yzCPhaVd3eMJ70CyXZCjgOuBh4bVXd0TiSJI0uyXPoCl/eXlX/0DqP9Kv471XSpEsS4O3AfwcOq6rvN44kTZNkbeBpdCUwU8BOwMl0+0SLq+pHDeNJkiRJkqT7yeIXSZIkSZIkSZIkaY7rizJ2Zih2WfnZBriI6QUvV3jjuGaSJNvQFRssAA6kOzC+uP+cXVX3NIwnSWtcf9jsiXQHeBb0/306wyGe/2gYT/ovJdkdOB44GniPZYKSJlGSVwAfAl5WVSe1ziPdH0l2ARbSFcC80/0iSZMoyVHAR4FXVtWS1nmkX6UvXZ1Pt4d0MHAlQ1nwmVV1V8N4kiRJkiTpl7D4RZIkSZIkSZIkSZojkqwF7MD0gpcdgcuZXvBySVXd3Sat9Mv1ZUX7MBQcbAucSDecfkJVXdMwniSNIsnmwEF0a+EUcAvdOrgY+GpV3dYwnnS/JTkQ+Bzwh1V1dOs8kjS2vsDtT4BXAYdV1fLGkaQHJMmjgC8BPwReXVW3N44kSaNLsi9wDPDuqvrb1nmk+yPJOsDTGfbZHw+cRLe3tKSqrmoYT5IkSZIk3YvFL5IkSZIkSZIkSdIs0x8a25rpBS+7ATfQlbosYyh4ucBDOZrpkmwNHEI3hP484FKGm0jPtqRI0lzXf7/PozuIMwU8BTiDbi1cXFUXN4wnPShJXgO8D3hJVX21dR5JGluS9YG/B3YGjrDEUrNVkg2AfwQeC7ygqq5rm0iSxpdkR2AhcBzwx1W1onEk6QFJ8miGPfiD6C4MWLkHf5Z78JIkSZIktWPxiyRJkiRJkiRJkjSDJdkC2IOu2GUeQ8nLnQzFLis/y6vqpkZRpQekv230GQwFB9sDJzLcNvrjhvEkaRRJNqMru1pAd/DmDrrDNouB06rq1obxpActyVrAe4GjgAVVdWHjSJI0uiSPAI4Frgde4fe6Zrt7fb+/FDjU73dJk8jvd80V99qfn6Lbl9oeOIluX8r9eUmSJEmSRmbxiyRJkiRJkiRJkjQDJNkE2J2h2GXlZ2N+ccHLtY2iSg+aN4pKmnRJQvf9PtV/9gK+wbAWXlwO82iWS7IB8I/AdsDzq+q6tokkaXxJdgQWAscBf1xVKxpHklabJK8B3ge8pKq+2jqPJI0tyfrA3wM7A0dU1TWNI0kPWZKtGfbunwdcSrdftRj4pnv3kiRJkiStWRa/SJIkSZIkSZIkSSPqh8J3YXrBy6OB85le8nKlB8A1WyVZG9ib7sbQKeDxdLeGLqa7NfSqhvEkaRRJNqU7MLOy7OVuupKXxcCpVXVLw3jSapXkUcCXgCuBV1XV7Y0jSdLokuwLHAO8p6o+3jqPtCYkORD4HPDWqvpM6zySNLa+2PV/Aa8GDq2q89omklafJOsC+9DtYy0AtgVOZNjXt+xIkiRJkqTVzOIXSZIkSZIkSZIkaQ3oCy+ewPSClx2Ay+hKXZYxFLxcWlX3tEkrrT5JtqS7GXQBcBDdwe+VBQdnVtVdDeNJ0hrXH/7aneFwzNOAsxjWwgstddNclGQXYCHwr8A7q2pF40iSNLokRwEfBV5ZVUta55HWpCS70333Hw2822dcSZMoySuADwEvq6qTWueR1oQk2zDs+R8IXMKwz3W277YkSZIkSXroLH6RJEmSJEmSJEmSHoL+cPe2dKUu8xgKXnYFfsxQ7LLyc2FV3dEmrbT69SVHT6Mb+p4CdgJOZrj988qG8SRpFEk2AZ7LsBYW3Tq4CDi1qn7eMJ60xiV5Dl3hy9ur6h9a55GksfV7A28H3gAcXlXfbxxJGkWSrYDjgIuB17rnJWkS+XtIkyTJusAzGfbAtgaWMrwPuK5hPEmSJEmSZi2LXyRJkiRJkiRJkqT7KcmjGIpd7v25hekFL+d5yFtzVf+3MJ9uuPtg4GqGGz6/UVV3NownSWtcf7h7V7oDLguAvYGzGdbC88uhHE0Ib7iXNOmSrAd8HHgScFhVXdU4kjSqJBsBRwNbAi+sqhsaR5Kk0SXZBVhIVwDzzqpa0TiSNIokjwUOodsfey5wIcP+2DlVdU/DeJIkSZIkzRoWv0iSJEmSJEmSJEn3kWQzYA+mF7ysByxj1YKX5R5o0VyXZG1gL7qCgym6soNT6Ia3F1fVDxvGk6RRJNmY7gDLyrKXtejXQeDkqrq5YTxpdH0B0p8Ar6IrOljeOJIkjS7Jw4Av0hXCvswCWE2qJGsB7weeDxxaVZc0jiRJo+vLsr8E/BB4dVXd3jiSNKq+EHFfun2zKWAr4AS6vbMTqur6hvEkSZIkSZrRLH6RJEmSJEmSJEnSxEqyIV2BxX0LXrYAzmPVgpdzgavLF2yaEEkeCcynG9CeD1zLcFPnGVV1Z8N4krTG9aUWOzMcVtkH+BZD2ctynws0qZKsD/w93d/IEVV1TeNIkjS6JDsAC4GlwFur6p7GkaTmkrwBeA9wZFV9vXUeSRpbkg2AfwQeC7ygqq5rm0hqJ8l2DGXyBwDn0+2pLQK+XVUrGsaTJEmSJGlGsfhFkiRJkiRJkiRJc16SdYAd6Upd5jEUvGwHXMJQ7LKs/98fOHCqSdPfzv1UuiHsBcBuwGl0Q9hLqurydukkaRxJNqI7iLJyLVyP4UDKyVV1U8N40oyQ5BHAscD1wCuq6tbGkSRpdEn2plsL31dVf9U6jzSTJJkPfAZ4S1V9rnUeSRpbv8/6XuClwKFVdWHjSFJzfYHsfgwFy48EltDtuy2tqhsaxpMkSZIkqTmLXyRJkiRJkiRJkjRn9APV2zEUu6z87Az8iKHgZeXn4qq6s01aqb0kWwAH0w1aH0J3gHtlwcEZVXVHw3iSNIokOzEcOtkX+DbDWnhuOVwj/ackOwILgeOAP7YsUdIkSnIk8HHgN6vq+NZ5pJkoyTzgeODvgD/3mVrSJEryGuB9wEuq6qut80gzSZLH0e3FTQH7072zW9x/vuN+gyRJkiRp0lj8IkmSJEmSJEmSpFknSYCtmF7wsgdwI9MLXs6vqlvbpJVmjr4c6cl0w9QL6P5uTqMfqK6qHzQLJ0kjSbIh3YGSlWUvG9GVvCwGTqqqn7VLJ81cSfYFjgHeU1Ufb51HksbW70X8IfAW4Iiq+k7jSNKMlmRr4CvA94E3WL4saRIlORD4HPDWqvpM6zzSTJRkA+BZDO8tHg4soduvW1pVP20YT5IkSZKkUVj8IkmSJEmSJEmSpBktycPpCl3uW/ICsIxVC16WV9WNLXJKM1X/N3Qw3dD0IXTlSIvphqZPr6rbG8aTpFEk2ZHhFuFnAf/OsBZ+vxygkX6lJEcBHwVeWVVLWueRpLElWRf4a+AZwGFV9cPGkaRZIcnGwD8DmwJHenBb0iRKsjuwEDgaeLd7ENKvluTxDPt4z6YrkVvcf75bVSsaxpMkSZIkaY2w+EWSJEmSJEmSJEkzQpKNgN2ZXvCyObCcodxlZdnLtQ5IS9P1t9A/ie5mzCng14Cv0Q9GV9WlDeNJ0ij6m4Kfw7AWbspwQOREi+Kk+6d/rng78Abg8Kr6fuNIkjS6JJsBXwDuAV5aVTc3jiTNKknWBj5IV0a7oKouaxxJkkaXZCvgOOBi4LVVdUfjSNKscK89vim6fb5NgSV0Zc7u8UmSJEmS5gyLXyRJkiRJkiRJkjSq/pbsnVm13GUesA1wIUPBy8rPFd7eJ/1qSR4GHMRwC+bNdOUGi4CvVdVtDeNJ0ijudRvwAuBZDLcBLwK+5/OE9MAkWQ/4OF2h3GFVdVXjSJI0uiTbAccDZwBvrqq7G0eSZq0kbwL+J/CiqjqrdR5JGltffn80sCXwwqq6oXEkadZJ8gRW3f/7Lt3e32K6/T8PyUmSJEmSZiWLXyRJkiRJkiRJkrRGJFkL2IFVC172BHYELmd6wcslHqCS7p8kAZ7IUPTyZOB0uuHmxVV1ScN4kjSK/sbfZzOshQ+ju/F3Md2Nvz9pGE+a1fpSuS8CtwAvq6qfN44kSaNL8lTgOOBDwIc9RCo9dEkOAz4FvLGqjmmdR5LG1r83eT/wfOBQ93GlBy/JhsD+DEUwG7Lq3uDP2qWTJEmSJOmBsfhFkiRJkiRJkiRJD0lfQLE10wtedgOuZ3rBywVVdXubtNLslWRz4Hl0A8yHALcx3GR5WlXd1jCeJI0iyeMYDnM8h+7ZYuVa+O9VtaJZOGmOSLIDsBA4EfiDqrqncSRJGl2SI4BPAq+vqmNb55HmkiRPpitV+ijwQUuVJE2iJG8A3gMcWVVfb51HmguS7MSwb7gv8B2GfcNlPnNIkiRJkmYyi18kSZIkSZIkSZJ0vyXZgukFL3sCd7Jqucsy4LyquqlRVGnW60uV5jEMKj8F+DrdkPKiqrq4YTxJGkWS9YFnMayFW9Dd3LuI7ubeGxrGk+acJHsDxwLvr6qPts4jSWPrf4e9GXgb8IKq+lbjSNKclGRb4Hjgm8CbququxpEkaXRJ5gOfAd5SVZ9rnUeaS5JsBOxPt5+4AFiP7t3KYuAk319KkiRJkmYai18kSZIkSZIkSZI0TZJNgN1ZtdxlHrARqxa8nAssr6prG0WV5pQkmwHPoys4mKIrVVp5I+WpVXVrw3iSNIok2zOsg/sD5zOshd+uqhXt0klzV5IjgY8Dv1lVx7fOI0ljS7IO8GHgAODQqrq8cSRpTkuyKfB5YB3gJVX1s8aRJGl0SebRFWH9HfDn5QEfabXryx13ZiiW3gc4h75kn+49p397kiRJkqSmLH6RJEmSJEmSJEmaYEnWB3Zh1YKXPYGtgAuYXvJypcOP0urTDxzvwTBwvBdwJkPBwUX+zUma65KsB+zHsBZuCZxAtxYurarrG8aT5rz+eeQPgbcAR1TVdxpHkqTR9QW4nwfWB37dAgppHH3h0keAZ9MVLl3ROJIkjS7J1sBXgO8Db6iqOxtHkua0JBvTlT0uoNuPXIfhnczJVXVzw3iSJEmSpAll8YskSZIkSZIkSdIESLI28ASmF7zsAFzK9IKXS6vqnjZppbmtv9H6QLqB4ilgBcNQ8SlVdUvDeJI0iiSPZVgHn0tXOLe4/5zjc4g0jv6w9V/T3XZ9WFX9sHEkSRpdkm2A44FvA2+sqrsaR5ImSl9C93t0RXRHVNW3G0eSpNH1RRT/DGwCHFlVNzaOJE2E/jlkV4Z9ymcAZ9PtUS4CzrecX5IkSZI0BotfJEmSJEmSJEmS5pB+QPGxTC942RX4MauWuywDLqqqO9qklSZD/3e5G93Q8ALg6cA3GcpeLnBwWNJcl2RdYF+Gm3QfAyylWwtPqKrrGsaTJlKSzYB/pSuhe6k3WkuaREmeCHwF+BvgL/xtJrWT5AXAJ4Dfqqovt84jSWPrC/w/CMwHDq2qyxpHkiZOkk3oSqpXvs+BoQTmlKr6eatskiRJkqS5zeIXSZIkSZIkSZKkWSrJo1i13GUesAdwC6sWvJwLnOcwojQeh4MlCZJsw3Bb7oHAxQxr4beq6p6G8aSJlmQ74HjgDODNVXV340iSNLokC4B/BH6nqr7QOI4kIMlewJeBvwQ+YhmTpEmU5E3A/wReVFVntc4jTap7lfqvLLJ+OnAW3f6mpf6SJEmSpNXK4hdJkiRJkiRJkqQZLslmdIUue97nsx6wjFULXpZX1Q2NokoTqx8A3oVhAPgZwLfoyg0W05Uv+XJW0pyWZF1gH4a1cFvgRLq18ISquqZhPEm9JE8FjgM+BHzYZxRJkyjJbwPvojtQfWbrPJIGSbYHFgKnAb9nQZ2kSZTkMOBTwBur6pjWeSRBkk3pyq1XFv7fzVByfWpV3dIwniRJkiRplrP4RZIkSZIkSZIkaYZIsiGwK9MLXrYAzmPVgpdzgas9pCm1k2Rj4ACGgoN1GIZ8T66qmxvGk6RRJNkaOIRuLTwQuJRhLTzbQ5rSzJLkCOCTwOur6tjWeSRpbEnWBv4COBRYUFWXNo4k6RdIsjnwBeAu4Cj3WCRNoiRPpivt/CjwQd8HSTNHfxnAHgzvh/YCzmS4DOAi/2YlSZIkSQ+ExS+SJEmSJEmSJEkjS7IOsBPTC162Ay5hKHZZ1v/vD6pqRZu0klbqB3l3phvinQKeCZxDN8S7GDjXQV5Jc13/HLMPw1q4PXAi3Tq4pKp+3DCepF+if455M/A24AVV9a3GkSRpdH155z8BmwMvqqqfNo4k6VdIsi7wMeBpwGFVdWXjSJI0uiTbAscD3wTeVFV3NY4k6RdIshnwPLr90gXAHQwlMKdW1a0N40mSJEmSZgGLXyRJkiRJkiRJktaQJGvRlbncu9xlHl1xxJUMBS8rPxdX1Z1t0kr6RZJsBOzPcGvjBgzDuidV1U3t0knSOJI8GjiEbi18HnA5w1p4VlXd3TCepP9CX9j0YeAA4NCqurxxJEkaXf888xVgOfB691+k2aEvr/sj4HeBw6vqu40jSdLokmwKfB5YB3hJVf2scSRJv0L//DKPoQTmKcDX6fdTq+rihvEkSZIkSTOUxS+SJEmSJEmSJEkPUT/AtxWrFrzsCewB3Mj0gpfzvdlNmrmS7EQ3kDsF7Ad8h67cYBGwrHzJKmmO60si9mZYCx8PnES3Fi6pqqsaxpP0ACTZhO6A4PrAr3tAUNIkSrIncDzwSeDP/E0nzT5JXgx8DHh1VS1snUeSxtbv1XwEeDZdoecVjSNJup+SPIyuTHvlXuutDKXap1XVbQ3jSZIkSZJmCItfJEmSJEmSJEmSHoAkD6crdLlvyQvAMlYteFleVTe2yCnp/kuyIfAcupsXp4CN6QZuFwMn+XcsaRIk2Qo4hG4dPAi4kuEAwplVdVfDeJIehCTb0BUdfBt4o3/HkiZRkoOBzwK/X1X/1DqPpAcvyT7Av9EVOP1N6zySNLb+EoLfA/4QOKKqvt04kqQHqP87fiLdHuyC/r/PoL98oKr+o2E8SZIkSVJDFr9IkiRJkiRJkiT9Akk2AnZnesHL5sByhnKXlWUv13pjtDR7JHkCw2DtfsD36Adrge/59yxprkuyNvB0hrVwR+BkurVwSVVd2TCepIcoyROBrwAfAz7gs42kSZTkdcB7gRdX1emt80h66JI8HljrKGO+AAAgAElEQVRI97vlj6rqnsaRJGl0SV4AfAL4rar6cus8kh68/sKRg+j2aA8BbmYo4/5qVd3eMJ4kSZIkaUQWv0iSJEmSJEmSpImWZF1gZ1Ytd5kHbANcyFDwsvJzRVWtaJNW0oOVZAPgOQwFB5vRDc4uBk6sqp82jCdJo0iyJTCfbi2cD1zFcJDgG1V1Z8N4klaTJAuATwO/U1X/2jqPJI0tyVrAnwNHAodW1UWNI0lajfoD0l8EbgJeXlW3NI4kSaNL8jTgS8BfAh+x7FOa/frfMU+i27udAn4N+Br9u6yqurRhPEmSJEnSGmbxiyRJkiRJkiRJmgj9sNwOrFrwsiewI3A50wteLqmqu9uklbQ6JNmBruRlCng2sIyh4OC7ljhJmuuSrA3sxbAW7gKcQrcWLqmqHzaMJ2kNSPLbwLuAF1XVma3zSNLYkmwIHA08GnhhVV3fOJKkNSDJesDf0hV4H15VVzeOJEmjS7I9sBA4Dfg932lJc0uSRwAH0e3tHgL8lO791iLga1V1R8N4kiRJkqTVzOIXSZIkSZIkSZI0pyQJsDXTC152A65nesHLBVV1e5u0klanJOvTFbxM0Q3CPhxYQjcIu7SqftIwniSNIskjgfl0a+F84BqGAwFfr6o7G8aTtIb0RU9/ARwKLPAWaEmTKMmWwJeBy4DXuN8jzW39PvA7gNcBh1XVssaRJGl0STYHvgDcBRxVVTc3jiRpDegvOHkyQ8H3nsBX6S87qKoftEsnSZIkSVodLH6RJEmSJEmSJEmzVpItmF7wsidwB9MLXs6rqpsaRZW0hiR5HN2Q6xSwP7CcftAV+E5VrWiVTZLG0A/978WwFu4OnMow9H9Fw3iSRpBkY+CzwMOAF1XVTxtHkqTRJdkNWEi3Hv5JORwrTYwkLwP+L/AbVbW0dR5JGluSdYGP0e0PHVZVP2ocSdIa1r8jP5huP/gQustPFvef06vqjobxJEmSJEkPgsUvkiRJkiRJkiRpxkuyCd0h5nuXu8wDNmJ6wcu5VXVdo6iS1rAk6wP7Mdxq+EjgBLph1qVVdX3DeJI0insN9i8A5jMM9i8CznCwX5ocSR4NfIWu/O71VXVn40iSNLokBwCfB95WVZ9unUfS+JLsBxwDvKuqPtE6jySNLUmAPwJ+Fzi8qr7bOJKkkfTF4E9leG+2G3AaFoNLkiRJ0qxi8YskSZIkSZIkSZox+kKHXVi14GVPYCvgAqaXvFzpDc7S3JdkO7ph1SngAOB8hpsLz6mqFQ3jSdIa1w/vP4VhLdyTbnh/Md3w/g+ahZPUTJI9geOBTwJ/5m8jSZMoyauBDwBHVdWpjeNIaijJTsBC4Fjg7e4XSZpESV4MfAx4dVUtbJ1H0viSPIquNHwKOAT4McM7tTMsDZYkSZKkmcniF0mSJEmSJEmSNLokawNPYHrByw7ApUwveLm0qu5pk1bS2JKsB+zLcDvho4ET6G4nXFpV1zWMJ0mjSPJwugH9BXQD+j+lG85fBJxeVbc3jCepsSQHA58Ffr+q/ql1HkkaW5IAfwq8HDi0qs5vHEnSDJBkC+BLwDXAK6rqtsaRJGl0SfYB/o2uIPRvWueR1E7/Tn4vhvdtuwCn0O0xL66qKxvGkyRJkiTdi8UvkiRJkiRJkiRpjekP4TyW6QUvuwJXM73g5aKquqNNWkktJdmWbuh0AfBc4EKGgoNzLH+SNNclWQt4EsNaOA/4Gv1trFV1acN4kmaQJK8D3gu8uKpOb51HksaWZAPgk8DjgedX1bWNI0maQZKsD/wDXfH4Ea4RkiZRkscDC+n2lf7I/XVJAEm2BObT7UEfTPe+fhHdWvH1qrqrYTxJkiRJmmgWv0iSJEmSJEmSpNWiHxS7b8HLHsAtTC94Oa+qft4oqqQZIMm6wL50w6VTwDbACXTDpSd4KEfSJEjycOAgunXwEOBmhkH7r1bV7Q3jSZph+oKoPweOBA6tqosaR5Kk0SV5JHAs3QHFV1XVbY0jSZqB+kLydwOvAA6rqvPaJpKk8fX7Tl8EbgJeXlW3NI4kaQZJsjbwdIYi8h2BkxmKyH/UMJ4kSZIkTRyLXyRJkiRJkiRJ0gOSZDO6Qpf7lrysByxj1YKX5VV1Q6OokmaYJNvQFRssAA4ELqEbIF0EnO2to5Lmuv7w4RMZhumfBJzOMEx/ScN4kmawJBsCRwOPBl5YVdc3jiRJo0uyE93vx2OAd1TVisaRJM1wSV4J/CXw36rqlNZ5JGlsSdYD/haYBxxeVVc3jiRphkqyFTCfbt/6YOCHDO/wzqyquxrGkyRJkqQ5z+IXSZIkSZIkSZL0C/UHC3dlesHLFsB5rFrwci5wdfniQdK9JFkX2Ieh4OCxwFK6QdETqurHDeNJ0iiSbA4cRLcWTgG30g3LLwZOq6rbGsaTNAsk2RL4MnAZ8Jqqur1xJEkaXZJnAV8A3lVVn2idR9LskWR/4F+A/1FVn2ocR5JG1xcRvwN4HXBYVS1rHEnSDJdkHWBvhvd7OwAn0e1pL6mqqxrGkyRJkqQ5yeIXSZIkSZIkSZImXD+4tRPTC162Ay5mesHLD7xRWdIvk+QxwCF0g6DPozugvPJGwG9W1d0N40nSGtcfpplHtw5OAU8Bvk5f9lJVFzeMJ2mWSbIbsBD4LPAnlm1KmkRJXg58GHh5VZ3YOo+k2SfJrnTPVJ+jK5DymUrSxEnyMuD/Ar9RVUtb55E0e/Tv/ubT7XkfBPyA4d3fWb77kyRJkqSHzuIXSZIkSZIkSZImRJK1gO2ZXvCyM3Al0wteLqqqu9qklTRb9OVRz2C49e9xwIkMt/5d3S6dJI0jyWZ0ZVdT/edO+qIX4NSqurVhPEmzVJIDgM8Db6uqT7fOI0lj6wv13gm8Fjisqs5tHEnSLJbkUcCX6Q4qv6aqbm+bSJLGl2Q/4Bi6EqxPtM4jafbp3wvuw7AXvj2rvhf8ccN4kiRJkjRrWfwiSZIkSZIkSdIc0x+K2YrpBS97ADcyveDlfA8jS3ogkjwaOIRuoPMg4AqGgoMzvdlP0lzXP2/tQVd4NQXsBZzJsBZe5A3ykh6KJK8GPgAcVVWnNo4jSaNLsh7wCWB34HAPD0paHZJsCHwa2Bp4QVVd3ziSJI0uyU7AQuBY4O1VtaJxJEmzWJKt6d4ZLqArR/8Puj3yRcA3q+qehvEkSZIkadaw+EWSJEmSJEmSpFksycPpDh3ft+QFYBmrFrwsr6obW+SUNLslWRvYm6Hg4AnASXRDm0uq6qqG8SRpFEk2BQ5kWAvvoVsHFwGnVtUtDeNJmiP6Yqk/BV4OHFpV5zeOJEmj6/e7/o2uwPg3fM6StDolWQv4M+DFdM9bFzWOJEmjS7IF8CXgGuAVVXVb40iS5oAk6wL7MOyhbwucSLeHfkJVXdMwniRJkiTNaBa/SJIkSZIkSZI0CyTZiO6G4/sWvGzOquUuKz/Xli8BJD0ESbaku6FvCjgY+BHdYOZi4BtVdVfDeJK0xvXlC7vTrYNTwNOBbzKshRf4vCVpdUqyAfBJ4PHA86vq2saRJGl0SR5P97y1EHibt8NLWlOS/Bbwv4EXV9XXWueRpLElWR/4B7qi9yP8DSppdUuyLd27xgXAc4GL6fbWFwNn+3tPkiRJkgYWv0iSJEmSJEmSNIMkWQ/YmekFL9sAFzK94OWKqlrRJq2kuSTJ2sDTGG7h2wk4he7A3ZKqurJhPEkaRZJN6AbQV66F0A2hLwJOqaqft8omaW5L8kjgWOBq4FXeti5pEiV5JvBF4L1V9bHWeSTNfUkOAv4J+IOq+mzrPJI0tr74+N3AK4DDquq8tokkzVX9HMQzGfbeHwMspdt/X1JV1zWMJ0mSJEnNWfwiSZIkSZIkSVIDSdYCdmB6wcuOwOVML3i5pKrubpNW0lyV5FHAfLohy4PpDhqvvGnv61V1Z8N4krTG9YdbdqUbNF8A7A2czbAWnlcOVkhaw5LsRFcwdQzwDss9JU2iJC8B/hp4dVUtap1H0uRIsgewEPgU8Kf+BpQ0iZK8EvhL4L9V1Smt80ia+5I8lm5ffoqujP1Cuv2xxcA5VXVPw3iSJEmSNDqLXyRJkiRJkiRJWoP6w8RbM73gZTfgeqYXvFxQVbe3SStprkuyNrAXwyDlrsCpdIOUS6rqiobxJGkUSTamGyRfWfayNsNA+clVdXPDeJImTJJnAV8A3lVVn2idR5LG1u+d/THw28DhVfW9xpEkTaAkjwaOAy4AXldVdzSOJEmjS7I/8C/A/6iqTzWOI2mCJFkP2I9hz35L4AS6PfsTqur6hvEkSZIkaRQWv0iSJEmSJEmStJok2YLpBS97AncwveDlvKq6qVFUSRMkySOBg+kGJecD19INSi4CzqiqOxvGk6Q1rj9MvDPD0Pg+wDkMa+Fyb3SX1EKSlwMfBl5eVSe2ziNJY0uyLvD/gKcCh1XVjxpHkjTBkmwEfBZ4BPCiqvpJ40iSNLokuwILgc/RFZS6ZyZpdEm2Z7jEYn/gfIby9m9X1Yp26SRJkiRpzbD4RZIkSZIkSZKkByjJJsAeTC942YjpBS/nVtV1jaJKmkBJ1qI7NLdyIHIP4FS6YcjFVXV5w3iSNIr+wN4BDGUv6zMMhp9kAZ+klvpCqncCr6UrOji3cSRJGl2SzYFj6AqTj6qqnzeOJEkkWRv4AHAYcGhV/UfjSJI0uiSPAo4DLgNeU1W3N44kaYIlWR94FsNe/xbAErq9/qVVdUPDeJIkSZK02lj8IkmSJEmSJEnSL9EPEe3C9IKXrYALmF7ycqU330lqIckWwMF0Q4/zgZ/QDTwuAk6vqjsaxpOkUSTZiW7wewrYF/gOw1q4zOc0STNBkvWATwC7A0dU1dWNI0nS6JI8DlgInAL8flXd3TSQJN1HkjcC/ws4sqq+0TqPJI0tyYbAp4GtgRdU1fWNI0kSAEl2YLj84jl0cxor3wP8e1WtaBhPkiRJkh40i18kSZIkSZIkSROvv8XzCUwveNkBuJTpBS+XVtU9bdJKEiRZC3gyw+12ewJfpRtsXFxVlzWMJ0mj6A+g7M+wFm5Evw4CJ1XVje3SSdJ0SR4O/BtwI/AbVXVL40iSNLokTwO+BHygqj7aOo8k/TJ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