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johnnieng/ML0101EN-Proj-Loan-answer-py-v1.ipynb

Created February 17, 2020 04:53
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ML0101EN-Proj-Loan-answer-py-v1.ipynb
{
"cells": [
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "<a href=\"https://www.bigdatauniversity.com\"><img src = \"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width = 400, align = \"center\"></a>\n\n<h1 align=center><font size = 5> Classification with Python</font></h1>"
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "In this notebook we try to practice all the classification algorithms that we learned in this course.\n\nWe load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n\nLets first load required libraries:"
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "import itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import NullFormatter\nimport pandas as pd\nimport numpy as np\nimport matplotlib.ticker as ticker\nfrom sklearn import preprocessing\n%matplotlib inline",
"execution_count": 1,
"outputs": []
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "### About dataset"
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "This dataset is about past loans. The __Loan_train.csv__ data set includes details of 346 customers whose loan are already paid off or defaulted. It includes following fields:\n\n| Field | Description |\n|----------------|---------------------------------------------------------------------------------------|\n| Loan_status | Whether a loan is paid off on in collection |\n| Principal | Basic principal loan amount at the |\n| Terms | Origination terms which can be weekly (7 days), biweekly, and monthly payoff schedule |\n| Effective_date | When the loan got originated and took effects |\n| Due_date | Since it\u2019s one-time payoff schedule, each loan has one single due date |\n| Age | Age of applicant |\n| Education | Education of applicant |\n| Gender | The gender of applicant |"
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "Lets download the dataset"
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv",
"execution_count": 2,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "--2018-06-12 16:16:44-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net... 67.228.254.193\nConnecting to s3-api.us-geo.objectstorage.softlayer.net|67.228.254.193|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 23101 (23K) [text/csv]\nSaving to: \u2018loan_train.csv\u2019\n\nloan_train.csv 100%[===================>] 22.56K 63.3KB/s in 0.4s \n\n2018-06-12 16:16:45 (63.3 KB/s) - \u2018loan_train.csv\u2019 saved [23101/23101]\n\n"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "### Load Data From CSV File "
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "df = pd.read_csv('loan_train.csv')\ndf.head()",
"execution_count": 3,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>Unnamed: 0.1</th>\n <th>loan_status</th>\n <th>Principal</th>\n <th>terms</th>\n <th>effective_date</th>\n <th>due_date</th>\n <th>age</th>\n <th>education</th>\n <th>Gender</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>9/8/2016</td>\n <td>10/7/2016</td>\n <td>45</td>\n <td>High School or Below</td>\n <td>male</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>2</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>9/8/2016</td>\n <td>10/7/2016</td>\n <td>33</td>\n <td>Bechalor</td>\n <td>female</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>3</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>15</td>\n <td>9/8/2016</td>\n <td>9/22/2016</td>\n <td>27</td>\n <td>college</td>\n <td>male</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>4</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>9/9/2016</td>\n <td>10/8/2016</td>\n <td>28</td>\n <td>college</td>\n <td>female</td>\n </tr>\n <tr>\n <th>4</th>\n <td>6</td>\n <td>6</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>9/9/2016</td>\n <td>10/8/2016</td>\n <td>29</td>\n <td>college</td>\n <td>male</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Unnamed: 0 Unnamed: 0.1 loan_status Principal terms effective_date \\\n0 0 0 PAIDOFF 1000 30 9/8/2016 \n1 2 2 PAIDOFF 1000 30 9/8/2016 \n2 3 3 PAIDOFF 1000 15 9/8/2016 \n3 4 4 PAIDOFF 1000 30 9/9/2016 \n4 6 6 PAIDOFF 1000 30 9/9/2016 \n\n due_date age education Gender \n0 10/7/2016 45 High School or Below male \n1 10/7/2016 33 Bechalor female \n2 9/22/2016 27 college male \n3 10/8/2016 28 college female \n4 10/8/2016 29 college male "
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "df.shape",
"execution_count": 4,
"outputs": [
{
"data": {
"text/plain": "(346, 10)"
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "### Convert to date time object "
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "df['due_date'] = pd.to_datetime(df['due_date'])\ndf['effective_date'] = pd.to_datetime(df['effective_date'])\ndf.head()",
"execution_count": 5,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>Unnamed: 0.1</th>\n <th>loan_status</th>\n <th>Principal</th>\n <th>terms</th>\n <th>effective_date</th>\n <th>due_date</th>\n <th>age</th>\n <th>education</th>\n <th>Gender</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-08</td>\n <td>2016-10-07</td>\n <td>45</td>\n <td>High School or Below</td>\n <td>male</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>2</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-08</td>\n <td>2016-10-07</td>\n <td>33</td>\n <td>Bechalor</td>\n <td>female</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>3</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>15</td>\n <td>2016-09-08</td>\n <td>2016-09-22</td>\n <td>27</td>\n <td>college</td>\n <td>male</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>4</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-09</td>\n <td>2016-10-08</td>\n <td>28</td>\n <td>college</td>\n <td>female</td>\n </tr>\n <tr>\n <th>4</th>\n <td>6</td>\n <td>6</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-09</td>\n <td>2016-10-08</td>\n <td>29</td>\n <td>college</td>\n <td>male</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Unnamed: 0 Unnamed: 0.1 loan_status Principal terms effective_date \\\n0 0 0 PAIDOFF 1000 30 2016-09-08 \n1 2 2 PAIDOFF 1000 30 2016-09-08 \n2 3 3 PAIDOFF 1000 15 2016-09-08 \n3 4 4 PAIDOFF 1000 30 2016-09-09 \n4 6 6 PAIDOFF 1000 30 2016-09-09 \n\n due_date age education Gender \n0 2016-10-07 45 High School or Below male \n1 2016-10-07 33 Bechalor female \n2 2016-09-22 27 college male \n3 2016-10-08 28 college female \n4 2016-10-08 29 college male "
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "# Data visualization and pre-processing\n\n"
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "Let\u2019s see how many of each class is in our data set "
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "df['loan_status'].value_counts()",
"execution_count": 6,
"outputs": [
{
"data": {
"text/plain": "PAIDOFF 260\nCOLLECTION 86\nName: loan_status, dtype: int64"
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "260 people have paid off the loan on time while 86 have gone into collection \n"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "Lets plot some columns to underestand data better:"
},
{
"metadata": {},
"cell_type": "code",
"source": "# notice: installing seaborn might takes a few minutes\n!conda install -c anaconda seaborn -y",
"execution_count": 7,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Solving environment: done\n\n# All requested packages already installed.\n\n"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "import seaborn as sns\n\nbins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()",
"execution_count": 8,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 432x216 with 2 Axes>"
},
"metadata": {},
"output_type": "display_data"
}
]
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "bins=np.linspace(df.age.min(), df.age.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'age', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()",
"execution_count": 9,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 432x216 with 2 Axes>"
},
"metadata": {},
"output_type": "display_data"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "# Pre-processing: Feature selection/extraction"
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "### Lets look at the day of the week people get the loan "
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "df['dayofweek'] = df['effective_date'].dt.dayofweek\nbins=np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\ng.axes[-1].legend()\nplt.show()\n",
"execution_count": 10,
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 432x216 with 2 Axes>"
},
"metadata": {},
"output_type": "display_data"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "We see that people who get the loan at the end of the week dont pay it off, so lets use Feature binarization to set a threshold values less then day 4 "
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "df['weekend']= df['dayofweek'].apply(lambda x: 1 if (x>3) else 0)\ndf.head()",
"execution_count": 11,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>Unnamed: 0.1</th>\n <th>loan_status</th>\n <th>Principal</th>\n <th>terms</th>\n <th>effective_date</th>\n <th>due_date</th>\n <th>age</th>\n <th>education</th>\n <th>Gender</th>\n <th>dayofweek</th>\n <th>weekend</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-08</td>\n <td>2016-10-07</td>\n <td>45</td>\n <td>High School or Below</td>\n <td>male</td>\n <td>3</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>2</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-08</td>\n <td>2016-10-07</td>\n <td>33</td>\n <td>Bechalor</td>\n <td>female</td>\n <td>3</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>3</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>15</td>\n <td>2016-09-08</td>\n <td>2016-09-22</td>\n <td>27</td>\n <td>college</td>\n <td>male</td>\n <td>3</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>4</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-09</td>\n <td>2016-10-08</td>\n <td>28</td>\n <td>college</td>\n <td>female</td>\n <td>4</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>6</td>\n <td>6</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-09</td>\n <td>2016-10-08</td>\n <td>29</td>\n <td>college</td>\n <td>male</td>\n <td>4</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Unnamed: 0 Unnamed: 0.1 loan_status Principal terms effective_date \\\n0 0 0 PAIDOFF 1000 30 2016-09-08 \n1 2 2 PAIDOFF 1000 30 2016-09-08 \n2 3 3 PAIDOFF 1000 15 2016-09-08 \n3 4 4 PAIDOFF 1000 30 2016-09-09 \n4 6 6 PAIDOFF 1000 30 2016-09-09 \n\n due_date age education Gender dayofweek weekend \n0 2016-10-07 45 High School or Below male 3 0 \n1 2016-10-07 33 Bechalor female 3 0 \n2 2016-09-22 27 college male 3 0 \n3 2016-10-08 28 college female 4 1 \n4 2016-10-08 29 college male 4 1 "
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "## Convert Categorical features to numerical values"
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "Lets look at gender:"
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)",
"execution_count": 12,
"outputs": [
{
"data": {
"text/plain": "Gender loan_status\nfemale PAIDOFF 0.865385\n COLLECTION 0.134615\nmale PAIDOFF 0.731293\n COLLECTION 0.268707\nName: loan_status, dtype: float64"
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "86 % of female pay there loans while only 73 % of males pay there loan\n"
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "Lets convert male to 0 and female to 1:\n"
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ndf.head()",
"execution_count": 13,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>Unnamed: 0.1</th>\n <th>loan_status</th>\n <th>Principal</th>\n <th>terms</th>\n <th>effective_date</th>\n <th>due_date</th>\n <th>age</th>\n <th>education</th>\n <th>Gender</th>\n <th>dayofweek</th>\n <th>weekend</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-08</td>\n <td>2016-10-07</td>\n <td>45</td>\n <td>High School or Below</td>\n <td>0</td>\n <td>3</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>2</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-08</td>\n <td>2016-10-07</td>\n <td>33</td>\n <td>Bechalor</td>\n <td>1</td>\n <td>3</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>3</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>15</td>\n <td>2016-09-08</td>\n <td>2016-09-22</td>\n <td>27</td>\n <td>college</td>\n <td>0</td>\n <td>3</td>\n <td>0</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>4</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-09</td>\n <td>2016-10-08</td>\n <td>28</td>\n <td>college</td>\n <td>1</td>\n <td>4</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>6</td>\n <td>6</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>2016-09-09</td>\n <td>2016-10-08</td>\n <td>29</td>\n <td>college</td>\n <td>0</td>\n <td>4</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Unnamed: 0 Unnamed: 0.1 loan_status Principal terms effective_date \\\n0 0 0 PAIDOFF 1000 30 2016-09-08 \n1 2 2 PAIDOFF 1000 30 2016-09-08 \n2 3 3 PAIDOFF 1000 15 2016-09-08 \n3 4 4 PAIDOFF 1000 30 2016-09-09 \n4 6 6 PAIDOFF 1000 30 2016-09-09 \n\n due_date age education Gender dayofweek weekend \n0 2016-10-07 45 High School or Below 0 3 0 \n1 2016-10-07 33 Bechalor 1 3 0 \n2 2016-09-22 27 college 0 3 0 \n3 2016-10-08 28 college 1 4 1 \n4 2016-10-08 29 college 0 4 1 "
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "## One Hot Encoding \n#### How about education?"
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "df.groupby(['education'])['loan_status'].value_counts(normalize=True)",
"execution_count": 14,
"outputs": [
{
"data": {
"text/plain": "education loan_status\nBechalor PAIDOFF 0.750000\n COLLECTION 0.250000\nHigh School or Below PAIDOFF 0.741722\n COLLECTION 0.258278\nMaster or Above COLLECTION 0.500000\n PAIDOFF 0.500000\ncollege PAIDOFF 0.765101\n COLLECTION 0.234899\nName: loan_status, dtype: float64"
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "#### Feature befor One Hot Encoding"
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "df[['Principal','terms','age','Gender','education']].head()",
"execution_count": 15,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Principal</th>\n <th>terms</th>\n <th>age</th>\n <th>Gender</th>\n <th>education</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1000</td>\n <td>30</td>\n <td>45</td>\n <td>0</td>\n <td>High School or Below</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1000</td>\n <td>30</td>\n <td>33</td>\n <td>1</td>\n <td>Bechalor</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1000</td>\n <td>15</td>\n <td>27</td>\n <td>0</td>\n <td>college</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1000</td>\n <td>30</td>\n <td>28</td>\n <td>1</td>\n <td>college</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1000</td>\n <td>30</td>\n <td>29</td>\n <td>0</td>\n <td>college</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Principal terms age Gender education\n0 1000 30 45 0 High School or Below\n1 1000 30 33 1 Bechalor\n2 1000 15 27 0 college\n3 1000 30 28 1 college\n4 1000 30 29 0 college"
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame "
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "Feature = df[['Principal','terms','age','Gender','weekend']]\nFeature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\nFeature.drop(['Master or Above'], axis = 1,inplace=True)\nFeature.head()\n",
"execution_count": 16,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Principal</th>\n <th>terms</th>\n <th>age</th>\n <th>Gender</th>\n <th>weekend</th>\n <th>Bechalor</th>\n <th>High School or Below</th>\n <th>college</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1000</td>\n <td>30</td>\n <td>45</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1000</td>\n <td>30</td>\n <td>33</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1000</td>\n <td>15</td>\n <td>27</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1000</td>\n <td>30</td>\n <td>28</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1000</td>\n <td>30</td>\n <td>29</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Principal terms age Gender weekend Bechalor High School or Below \\\n0 1000 30 45 0 0 0 1 \n1 1000 30 33 1 0 1 0 \n2 1000 15 27 0 0 0 0 \n3 1000 30 28 1 1 0 0 \n4 1000 30 29 0 1 0 0 \n\n college \n0 0 \n1 0 \n2 1 \n3 1 \n4 1 "
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "### Feature selection"
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "Lets defind feature sets, X:"
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "X = Feature\nX[0:5]",
"execution_count": 17,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Principal</th>\n <th>terms</th>\n <th>age</th>\n <th>Gender</th>\n <th>weekend</th>\n <th>Bechalor</th>\n <th>High School or Below</th>\n <th>college</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1000</td>\n <td>30</td>\n <td>45</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1000</td>\n <td>30</td>\n <td>33</td>\n <td>1</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n </tr>\n <tr>\n <th>2</th>\n <td>1000</td>\n <td>15</td>\n <td>27</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>1000</td>\n <td>30</td>\n <td>28</td>\n <td>1</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>4</th>\n <td>1000</td>\n <td>30</td>\n <td>29</td>\n <td>0</td>\n <td>1</td>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Principal terms age Gender weekend Bechalor High School or Below \\\n0 1000 30 45 0 0 0 1 \n1 1000 30 33 1 0 1 0 \n2 1000 15 27 0 0 0 0 \n3 1000 30 28 1 1 0 0 \n4 1000 30 29 0 1 0 0 \n\n college \n0 0 \n1 0 \n2 1 \n3 1 \n4 1 "
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "What are our lables?"
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "y = df['loan_status'].values\ny[0:5]",
"execution_count": 18,
"outputs": [
{
"data": {
"text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "## Normalize Data "
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "Data Standardization give data zero mean and unit variance (technically should be done after train test split )"
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "X = preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]",
"execution_count": 19,
"outputs": [
{
"data": {
"text/plain": "array([[ 0.51578458, 0.92071769, 2.33152555, -0.42056004, -1.20577805,\n -0.38170062, 1.13639374, -0.86968108],\n [ 0.51578458, 0.92071769, 0.34170148, 2.37778177, -1.20577805,\n 2.61985426, -0.87997669, -0.86968108],\n [ 0.51578458, -0.95911111, -0.65321055, -0.42056004, -1.20577805,\n -0.38170062, -0.87997669, 1.14984679],\n [ 0.51578458, 0.92071769, -0.48739188, 2.37778177, 0.82934003,\n -0.38170062, -0.87997669, 1.14984679],\n [ 0.51578458, 0.92071769, -0.3215732 , -0.42056004, 0.82934003,\n -0.38170062, -0.87997669, 1.14984679]])"
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "# Classification "
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "Now, it is your turn, use the training set to build an accurate model. Then use the test set to report the accuracy of the model\nYou should use the following algorithm:\n- K Nearest Neighbor(KNN)\n- Decision Tree\n- Support Vector Machine\n- Logistic Regression\n\n\n\n__ Notice:__ \n- You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.\n- You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.\n- You should include the code of the algorithm in the following cells."
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# K Nearest Neighbor(KNN)\nNotice: You should find the best k to build the model with the best accuracy. \n**warning:** You should not use the __loan_test.csv__ for finding the best k, however, you can split your train_loan.csv into train and test to find the best __k__."
},
{
"metadata": {},
"cell_type": "code",
"source": "# We split the X into train and test to find the best k\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=4)\nprint ('Train set:', X_train.shape, y_train.shape)\nprint ('Test set:', X_test.shape, y_test.shape)",
"execution_count": 21,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Train set: (276, 8) (276,)\nTest set: (70, 8) (70,)\n"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "# Modeling\nfrom sklearn.neighbors import KNeighborsClassifier\nk = 3\n#Train Model and Predict \nkNN_model = KNeighborsClassifier(n_neighbors=k).fit(X_train,y_train)\nkNN_model",
"execution_count": 45,
"outputs": [
{
"data": {
"text/plain": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n metric_params=None, n_jobs=1, n_neighbors=3, p=2,\n weights='uniform')"
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "# just for sanity chaeck\nyhat = kNN_model.predict(X_test)\nyhat[0:5]",
"execution_count": 46,
"outputs": [
{
"data": {
"text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "# Best k\nKs=15\nmean_acc=np.zeros((Ks-1))\nstd_acc=np.zeros((Ks-1))\nConfustionMx=[];\nfor n in range(1,Ks):\n \n #Train Model and Predict \n kNN_model = KNeighborsClassifier(n_neighbors=n).fit(X_train,y_train)\n yhat = kNN_model.predict(X_test)\n \n \n mean_acc[n-1]=np.mean(yhat==y_test);\n \n std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\nmean_acc",
"execution_count": 67,
"outputs": [
{
"data": {
"text/plain": "array([ 0.67142857, 0.65714286, 0.71428571, 0.68571429, 0.75714286,\n 0.71428571, 0.78571429, 0.75714286, 0.75714286, 0.67142857,\n 0.7 , 0.72857143, 0.7 , 0.7 ])"
},
"execution_count": 67,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "# Building the model again, using k=7\nfrom sklearn.neighbors import KNeighborsClassifier\nk = 7\n#Train Model and Predict \nkNN_model = KNeighborsClassifier(n_neighbors=k).fit(X_train,y_train)\nkNN_model",
"execution_count": 68,
"outputs": [
{
"data": {
"text/plain": "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n metric_params=None, n_jobs=1, n_neighbors=7, p=2,\n weights='uniform')"
},
"execution_count": 68,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Decision Tree"
},
{
"metadata": {},
"cell_type": "code",
"source": "from sklearn.tree import DecisionTreeClassifier\nDT_model = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\nDT_model.fit(X_train,y_train)\nDT_model",
"execution_count": 84,
"outputs": [
{
"data": {
"text/plain": "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n max_features=None, max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_impurity_split=None,\n min_samples_leaf=1, min_samples_split=2,\n min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n splitter='best')"
},
"execution_count": 84,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "yhat = DT_model.predict(X_test)\nyhat",
"execution_count": 85,
"outputs": [
{
"data": {
"text/plain": "array(['COLLECTION', 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF',\n 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'COLLECTION', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF',\n 'COLLECTION', 'COLLECTION', 'COLLECTION', 'PAIDOFF', 'COLLECTION',\n 'COLLECTION', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'COLLECTION',\n 'COLLECTION', 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'COLLECTION', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'COLLECTION',\n 'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'COLLECTION',\n 'COLLECTION', 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'COLLECTION', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'COLLECTION',\n 'PAIDOFF', 'PAIDOFF'], dtype=object)"
},
"execution_count": 85,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Support Vector Machine"
},
{
"metadata": {},
"cell_type": "code",
"source": "from sklearn import svm\nSVM_model = svm.SVC()\nSVM_model.fit(X_train, y_train) ",
"execution_count": 77,
"outputs": [
{
"data": {
"text/plain": "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n max_iter=-1, probability=False, random_state=None, shrinking=True,\n tol=0.001, verbose=False)"
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "yhat = SVM_model.predict(X_test)\nyhat",
"execution_count": 82,
"outputs": [
{
"data": {
"text/plain": "array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'COLLECTION', 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION',\n 'COLLECTION', 'PAIDOFF', 'COLLECTION', 'COLLECTION', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"
},
"execution_count": 82,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Logistic Regression"
},
{
"metadata": {},
"cell_type": "code",
"source": "from sklearn.linear_model import LogisticRegression\nLR_model = LogisticRegression(C=0.01).fit(X_train,y_train)\nLR_model",
"execution_count": 80,
"outputs": [
{
"data": {
"text/plain": "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n verbose=0, warm_start=False)"
},
"execution_count": 80,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "yhat = LR_model.predict(X_test)\nyhat",
"execution_count": 81,
"outputs": [
{
"data": {
"text/plain": "array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF',\n 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'COLLECTION',\n 'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'COLLECTION', 'PAIDOFF',\n 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'COLLECTION', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF',\n 'PAIDOFF'], dtype=object)"
},
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Model Evaluation using Test set"
},
{
"metadata": {},
"cell_type": "code",
"source": "from sklearn.metrics import jaccard_similarity_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import log_loss",
"execution_count": 93,
"outputs": []
},
{
"metadata": {},
"cell_type": "markdown",
"source": "First, download and load the test set:"
},
{
"metadata": {},
"cell_type": "code",
"source": "!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv",
"execution_count": 94,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "--2018-06-12 17:42:41-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net... 67.228.254.193\nConnecting to s3-api.us-geo.objectstorage.softlayer.net|67.228.254.193|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 3642 (3.6K) [text/csv]\nSaving to: \u2018loan_test.csv\u2019\n\nloan_test.csv 100%[===================>] 3.56K --.-KB/s in 0s \n\n2018-06-12 17:42:42 (105 MB/s) - \u2018loan_test.csv\u2019 saved [3642/3642]\n\n"
}
]
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "### Load Test set for evaluation "
},
{
"metadata": {
"button": false,
"collapsed": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "code",
"source": "test_df = pd.read_csv('loan_test.csv')\ntest_df.head()",
"execution_count": 100,
"outputs": [
{
"data": {
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>Unnamed: 0.1</th>\n <th>loan_status</th>\n <th>Principal</th>\n <th>terms</th>\n <th>effective_date</th>\n <th>due_date</th>\n <th>age</th>\n <th>education</th>\n <th>Gender</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>1</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>9/8/2016</td>\n <td>10/7/2016</td>\n <td>50</td>\n <td>Bechalor</td>\n <td>female</td>\n </tr>\n <tr>\n <th>1</th>\n <td>5</td>\n <td>5</td>\n <td>PAIDOFF</td>\n <td>300</td>\n <td>7</td>\n <td>9/9/2016</td>\n <td>9/15/2016</td>\n <td>35</td>\n <td>Master or Above</td>\n <td>male</td>\n </tr>\n <tr>\n <th>2</th>\n <td>21</td>\n <td>21</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>9/10/2016</td>\n <td>10/9/2016</td>\n <td>43</td>\n <td>High School or Below</td>\n <td>female</td>\n </tr>\n <tr>\n <th>3</th>\n <td>24</td>\n <td>24</td>\n <td>PAIDOFF</td>\n <td>1000</td>\n <td>30</td>\n <td>9/10/2016</td>\n <td>10/9/2016</td>\n <td>26</td>\n <td>college</td>\n <td>male</td>\n </tr>\n <tr>\n <th>4</th>\n <td>35</td>\n <td>35</td>\n <td>PAIDOFF</td>\n <td>800</td>\n <td>15</td>\n <td>9/11/2016</td>\n <td>9/25/2016</td>\n <td>29</td>\n <td>Bechalor</td>\n <td>male</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Unnamed: 0 Unnamed: 0.1 loan_status Principal terms effective_date \\\n0 1 1 PAIDOFF 1000 30 9/8/2016 \n1 5 5 PAIDOFF 300 7 9/9/2016 \n2 21 21 PAIDOFF 1000 30 9/10/2016 \n3 24 24 PAIDOFF 1000 30 9/10/2016 \n4 35 35 PAIDOFF 800 15 9/11/2016 \n\n due_date age education Gender \n0 10/7/2016 50 Bechalor female \n1 9/15/2016 35 Master or Above male \n2 10/9/2016 43 High School or Below female \n3 10/9/2016 26 college male \n4 9/25/2016 29 Bechalor male "
},
"execution_count": 100,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "## Preprocessing\ntest_df['due_date'] = pd.to_datetime(test_df['due_date'])\ntest_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\ntest_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\ntest_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3) else 0)\ntest_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ntest_Feature = test_df[['Principal','terms','age','Gender','weekend']]\ntest_Feature = pd.concat([test_Feature,pd.get_dummies(test_df['education'])], axis=1)\ntest_Feature.drop(['Master or Above'], axis = 1,inplace=True)\ntest_X = preprocessing.StandardScaler().fit(test_Feature).transform(test_Feature)\ntest_X[0:5]",
"execution_count": 101,
"outputs": [
{
"data": {
"text/plain": "array([[ 0.49362588, 0.92844966, 3.05981865, 1.97714211, -1.30384048,\n 2.39791576, -0.79772404, -0.86135677],\n [-3.56269116, -1.70427745, 0.53336288, -0.50578054, 0.76696499,\n -0.41702883, -0.79772404, -0.86135677],\n [ 0.49362588, 0.92844966, 1.88080596, 1.97714211, 0.76696499,\n -0.41702883, 1.25356634, -0.86135677],\n [ 0.49362588, 0.92844966, -0.98251057, -0.50578054, 0.76696499,\n -0.41702883, -0.79772404, 1.16095912],\n [-0.66532184, -0.78854628, -0.47721942, -0.50578054, 0.76696499,\n 2.39791576, -0.79772404, -0.86135677]])"
},
"execution_count": 101,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "test_y = test_df['loan_status'].values\ntest_y[0:5]",
"execution_count": 102,
"outputs": [
{
"data": {
"text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'], dtype=object)"
},
"execution_count": 102,
"metadata": {},
"output_type": "execute_result"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "knn_yhat = kNN_model.predict(test_X)\nprint(\"KNN Jaccard index: %.2f\" % jaccard_similarity_score(test_y, knn_yhat))\nprint(\"KNN F1-score: %.2f\" % f1_score(test_y, knn_yhat, average='weighted') )",
"execution_count": 103,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "KNN Jaccard index: 0.67\nKNN F1-score: 0.63\n"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "DT_yhat = DT_model.predict(test_X)\nprint(\"DT Jaccard index: %.2f\" % jaccard_similarity_score(test_y, DT_yhat))\nprint(\"DT F1-score: %.2f\" % f1_score(test_y, DT_yhat, average='weighted') )",
"execution_count": 104,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "DT Jaccard index: 0.72\nDT F1-score: 0.74\n"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "SVM_yhat = SVM_model.predict(test_X)\nprint(\"SVM Jaccard index: %.2f\" % jaccard_similarity_score(test_y, SVM_yhat))\nprint(\"SVM F1-score: %.2f\" % f1_score(test_y, SVM_yhat, average='weighted') )",
"execution_count": 105,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "SVM Jaccard index: 0.80\nSVM F1-score: 0.76\n"
}
]
},
{
"metadata": {},
"cell_type": "code",
"source": "LR_yhat = LR_model.predict(test_X)\nLR_yhat_prob = LR_model.predict_proba(test_X)\nprint(\"LR Jaccard index: %.2f\" % jaccard_similarity_score(test_y, LR_yhat))\nprint(\"LR F1-score: %.2f\" % f1_score(test_y, LR_yhat, average='weighted') )\nprint(\"LR LogLoss: %.2f\" % log_loss(test_y, LR_yhat_prob))",
"execution_count": 106,
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "LR Jaccard index: 0.74\nLR F1-score: 0.66\nLR LogLoss: 0.57\n"
}
]
},
{
"metadata": {},
"cell_type": "markdown",
"source": "# Report\nYou should be able to report the accuracy of the built model using different evaluation metrics:"
},
{
"metadata": {},
"cell_type": "markdown",
"source": "| Algorithm | Jaccard | F1-score | LogLoss |\n|--------------------|---------|----------|---------|\n| KNN | 0.67 | 0.63 | NA |\n| Decision Tree | 0.72 | 0.74 | NA |\n| SVM | 0.80 | 0.76 | NA |\n| LogisticRegression | 0.74 | 0.66 | 0.57 |"
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "## Want to learn more?\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems \u2013 by your enterprise as a whole. A free trial is available through this course, available here: [SPSS Modeler](http://cocl.us/ML0101EN-SPSSModeler).\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at [Watson Studio](https://cocl.us/ML0101EN_DSX)\n\n\n<hr>\nCopyright &copy; 2018 [Cognitive Class](https://cocl.us/DX0108EN_CC). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).\u200b"
},
{
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"cell_type": "markdown",
"source": "### Thanks for completing this lesson!\n\nNotebook created by: <a href = \"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>"
}
],
"metadata": {
"kernelspec": {
"name": "python3",
"display_name": "Python 3.6",
"language": "python"
},
"language_info": {
"name": "python",
"version": "3.6.9",
"mimetype": "text/x-python",
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"pygments_lexer": "ipython3",
"nbconvert_exporter": "python",
"file_extension": ".py"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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