Skip to content

Instantly share code, notes, and snippets.

@ankitbora31

ankitbora31/ML0101EN-Proj-Loan-py-v1.ipynb

Created June 2, 2022 11:49
Show Gist options
  • Save ankitbora31/8eac7d07a4262a3aa78af0d7ad74a773 to your computer and use it in GitHub Desktop.
Save ankitbora31/8eac7d07a4262a3aa78af0d7ad74a773 to your computer and use it in GitHub Desktop.
Download ZIP
machine Learning capstone
Display the source blob
Display the rendered blob
Raw
ML0101EN-Proj-Loan-py-v1.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<center>\n",
" <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n",
"\n",
"<h1 align=\"center\"><font size=\"5\">Classification with Python</font></h1>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"In this notebook we try to practice all the classification algorithms that we have learned in this course.\n",
"\n",
"We 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",
"\n",
"Let's first load required libraries:\n"
]
},
{
"cell_type": "code",
"execution_count": 128,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"import itertools\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.ticker import NullFormatter\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.ticker as ticker\n",
"from sklearn import preprocessing\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### About dataset\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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’s 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 |\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's download the dataset\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"--2022-06-01 19:21:40-- https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/loan_train.csv\n",
"Resolving cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)... 169.45.118.108\n",
"Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.45.118.108|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 23101 (23K) [text/csv]\n",
"Saving to: 'loan_train.csv'\n",
"\n",
" 0K .......... .......... .. 100% 1.50M=0.01s\n",
"\n",
"2022-06-01 19:21:42 (1.50 MB/s) - 'loan_train.csv' saved [23101/23101]\n",
"\n"
]
}
],
"source": [
"!wget -O loan_train.csv https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/FinalModule_Coursera/data/loan_train.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Load Data From CSV File\n"
]
},
{
"cell_type": "code",
"execution_count": 129,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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 \\\n",
"0 0 0 PAIDOFF 1000 30 9/8/2016 \n",
"1 2 2 PAIDOFF 1000 30 9/8/2016 \n",
"2 3 3 PAIDOFF 1000 15 9/8/2016 \n",
"3 4 4 PAIDOFF 1000 30 9/9/2016 \n",
"4 6 6 PAIDOFF 1000 30 9/9/2016 \n",
"\n",
" due_date age education Gender \n",
"0 10/7/2016 45 High School or Below male \n",
"1 10/7/2016 33 Bechalor female \n",
"2 9/22/2016 27 college male \n",
"3 10/8/2016 28 college female \n",
"4 10/8/2016 29 college male "
]
},
"execution_count": 129,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('loan_train.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 130,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(346, 10)"
]
},
"execution_count": 130,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Convert to date time object\n"
]
},
{
"cell_type": "code",
"execution_count": 131,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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 \\\n",
"0 0 0 PAIDOFF 1000 30 2016-09-08 \n",
"1 2 2 PAIDOFF 1000 30 2016-09-08 \n",
"2 3 3 PAIDOFF 1000 15 2016-09-08 \n",
"3 4 4 PAIDOFF 1000 30 2016-09-09 \n",
"4 6 6 PAIDOFF 1000 30 2016-09-09 \n",
"\n",
" due_date age education Gender \n",
"0 2016-10-07 45 High School or Below male \n",
"1 2016-10-07 33 Bechalor female \n",
"2 2016-09-22 27 college male \n",
"3 2016-10-08 28 college female \n",
"4 2016-10-08 29 college male "
]
},
"execution_count": 131,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['due_date'] = pd.to_datetime(df['due_date'])\n",
"df['effective_date'] = pd.to_datetime(df['effective_date'])\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Data visualization and pre-processing\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let’s see how many of each class is in our data set\n"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"PAIDOFF 260\n",
"COLLECTION 86\n",
"Name: loan_status, dtype: int64"
]
},
"execution_count": 132,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['loan_status'].value_counts()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"260 people have paid off the loan on time while 86 have gone into collection\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's plot some columns to underestand data better:\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Collecting package metadata (current_repodata.json): ...working... done\n",
"Solving environment: ...working... done\n",
"\n",
"## Package Plan ##\n",
"\n",
" environment location: C:\\ProgramData\\Anaconda3\n",
"\n",
" added / updated specs:\n",
" - seaborn\n",
"\n",
"\n",
"The following packages will be UPDATED:\n",
"\n",
" conda pkgs/main::conda-4.7.12-py37_0 --> anaconda::conda-4.12.0-py37haa95532_0\n",
"\n",
"The following packages will be SUPERSEDED by a higher-priority channel:\n",
"\n",
" seaborn pkgs/main --> anaconda\n",
"\n",
"\n",
"Preparing transaction: ...working... done\n",
"Verifying transaction: ...working... failed\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 4.7.12\n",
" latest version: 4.13.0\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base -c defaults conda\n",
"\n",
"\n",
"\n",
"EnvironmentNotWritableError: The current user does not have write permissions to the target environment.\n",
" environment location: C:\\ProgramData\\Anaconda3\n",
"\n",
"\n"
]
}
],
"source": [
"# notice: installing seaborn might takes a few minutes\n",
"!conda install -c anaconda seaborn -y"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x216 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"bins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\n",
"g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
"g.map(plt.hist, 'Principal', bins=bins, ec=\"k\")\n",
"\n",
"g.axes[-1].legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x216 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"bins = np.linspace(df.age.min(), df.age.max(), 10)\n",
"g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
"g.map(plt.hist, 'age', bins=bins, ec=\"k\")\n",
"\n",
"g.axes[-1].legend()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Pre-processing: Feature selection/extraction\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Let's look at the day of the week people get the loan\n"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x216 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"df['dayofweek'] = df['effective_date'].dt.dayofweek\n",
"bins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 10)\n",
"g = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\n",
"g.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\n",
"g.axes[-1].legend()\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"We see that people who get the loan at the end of the week don't pay it off, so let's use Feature binarization to set a threshold value less than day 4\n"
]
},
{
"cell_type": "code",
"execution_count": 136,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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 \\\n",
"0 0 0 PAIDOFF 1000 30 2016-09-08 \n",
"1 2 2 PAIDOFF 1000 30 2016-09-08 \n",
"2 3 3 PAIDOFF 1000 15 2016-09-08 \n",
"3 4 4 PAIDOFF 1000 30 2016-09-09 \n",
"4 6 6 PAIDOFF 1000 30 2016-09-09 \n",
"\n",
" due_date age education Gender dayofweek weekend \n",
"0 2016-10-07 45 High School or Below male 3 0 \n",
"1 2016-10-07 33 Bechalor female 3 0 \n",
"2 2016-09-22 27 college male 3 0 \n",
"3 2016-10-08 28 college female 4 1 \n",
"4 2016-10-08 29 college male 4 1 "
]
},
"execution_count": 136,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3) else 0)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Convert Categorical features to numerical values\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's look at gender:\n"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"Gender loan_status\n",
"female PAIDOFF 0.865385\n",
" COLLECTION 0.134615\n",
"male PAIDOFF 0.731293\n",
" COLLECTION 0.268707\n",
"Name: loan_status, dtype: float64"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"86 % of female pay there loans while only 73 % of males pay there loan\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's convert male to 0 and female to 1:\n"
]
},
{
"cell_type": "code",
"execution_count": 138,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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 \\\n",
"0 0 0 PAIDOFF 1000 30 2016-09-08 \n",
"1 2 2 PAIDOFF 1000 30 2016-09-08 \n",
"2 3 3 PAIDOFF 1000 15 2016-09-08 \n",
"3 4 4 PAIDOFF 1000 30 2016-09-09 \n",
"4 6 6 PAIDOFF 1000 30 2016-09-09 \n",
"\n",
" due_date age education Gender dayofweek weekend \n",
"0 2016-10-07 45 High School or Below 0 3 0 \n",
"1 2016-10-07 33 Bechalor 1 3 0 \n",
"2 2016-09-22 27 college 0 3 0 \n",
"3 2016-10-08 28 college 1 4 1 \n",
"4 2016-10-08 29 college 0 4 1 "
]
},
"execution_count": 138,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## One Hot Encoding\n",
"\n",
"#### How about education?\n"
]
},
{
"cell_type": "code",
"execution_count": 139,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"education loan_status\n",
"Bechalor PAIDOFF 0.750000\n",
" COLLECTION 0.250000\n",
"High School or Below PAIDOFF 0.741722\n",
" COLLECTION 0.258278\n",
"Master or Above COLLECTION 0.500000\n",
" PAIDOFF 0.500000\n",
"college PAIDOFF 0.765101\n",
" COLLECTION 0.234899\n",
"Name: loan_status, dtype: float64"
]
},
"execution_count": 139,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Features before One Hot Encoding\n"
]
},
{
"cell_type": "code",
"execution_count": 140,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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\n",
"0 1000 30 45 0 High School or Below\n",
"1 1000 30 33 1 Bechalor\n",
"2 1000 15 27 0 college\n",
"3 1000 30 28 1 college\n",
"4 1000 30 29 0 college"
]
},
"execution_count": 140,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['Principal','terms','age','Gender','education']].head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"#### Use one hot encoding technique to conver categorical varables to binary variables and append them to the feature Data Frame\n"
]
},
{
"cell_type": "code",
"execution_count": 141,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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 \\\n",
"0 1000 30 45 0 0 0 1 \n",
"1 1000 30 33 1 0 1 0 \n",
"2 1000 15 27 0 0 0 0 \n",
"3 1000 30 28 1 1 0 0 \n",
"4 1000 30 29 0 1 0 0 \n",
"\n",
" college \n",
"0 0 \n",
"1 0 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
]
},
"execution_count": 141,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"Feature = df[['Principal','terms','age','Gender','weekend']]\n",
"Feature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\n",
"Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
"Feature.head()\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Feature Selection\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's define feature sets, X:\n"
]
},
{
"cell_type": "code",
"execution_count": 142,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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 \\\n",
"0 1000 30 45 0 0 0 1 \n",
"1 1000 30 33 1 0 1 0 \n",
"2 1000 15 27 0 0 0 0 \n",
"3 1000 30 28 1 1 0 0 \n",
"4 1000 30 29 0 1 0 0 \n",
"\n",
" college \n",
"0 0 \n",
"1 0 \n",
"2 1 \n",
"3 1 \n",
"4 1 "
]
},
"execution_count": 142,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = Feature\n",
"X[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"What are our lables?\n"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
" dtype=object)"
]
},
"execution_count": 143,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y = df['loan_status'].values\n",
"y[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"## Normalize Data\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Data Standardization give data zero mean and unit variance (technically should be done after train test split)\n"
]
},
{
"cell_type": "code",
"execution_count": 144,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.52, 0.92, 2.33, -0.42, -1.21, -0.38, 1.14, -0.87],\n",
" [ 0.52, 0.92, 0.34, 2.38, -1.21, 2.62, -0.88, -0.87],\n",
" [ 0.52, -0.96, -0.65, -0.42, -1.21, -0.38, -0.88, 1.15],\n",
" [ 0.52, 0.92, -0.49, 2.38, 0.83, -0.38, -0.88, 1.15],\n",
" [ 0.52, 0.92, -0.32, -0.42, 0.83, -0.38, -0.88, 1.15]])"
]
},
"execution_count": 144,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X= preprocessing.StandardScaler().fit(X).transform(X)\n",
"X[0:5]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Classification\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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\n",
"You should use the following algorithm:\n",
"\n",
"* K Nearest Neighbor(KNN)\n",
"* Decision Tree\n",
"* Support Vector Machine\n",
"* Logistic Regression\n",
"\n",
"\\__ Notice:\\__\n",
"\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.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# K Nearest Neighbor(KNN)\n",
"\n",
"Notice: 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**.\n"
]
},
{
"cell_type": "code",
"execution_count": 145,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train set: (276, 8) (276,)\n",
"Test set: (70, 8) (70,)\n"
]
}
],
"source": [
"from sklearn.model_selection import train_test_split\n",
"X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.2, random_state=0)\n",
"print ('Train set:', X_train.shape, y_train.shape)\n",
"print ('Test set:', X_test.shape, y_test.shape)"
]
},
{
"cell_type": "code",
"execution_count": 146,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"The best accuracy was with 0.7571428571428571 with k= 7\n"
]
}
],
"source": [
"from sklearn.neighbors import KNeighborsClassifier\n",
"from sklearn import metrics\n",
"\n",
"Ks = 10\n",
"mean_acc = np.zeros((Ks-1))\n",
"std_acc = np.zeros((Ks-1))\n",
"\n",
"confusion = []\n",
"\n",
"for n in range(1,Ks): \n",
" neighbor = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n",
" yhat=neighbor.predict(X_test)\n",
" mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n",
"\n",
" \n",
" std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n",
"plt.plot(range(1,Ks),mean_acc,'g')\n",
"plt.fill_between(range(1,Ks),mean_acc-1 * std_acc, mean_acc+1*std_acc,alpha=0.10)\n",
"plt.legend(('Accuracy','std'))\n",
"plt.ylabel('Accuracy')\n",
"plt.xlabel('Neighbors (K)')\n",
"plt.tight_layout()\n",
"plt.show()\n",
"print( \"The best accuracy was with\", mean_acc.max(), \"with k=\", mean_acc.argmax()+1) "
]
},
{
"cell_type": "code",
"execution_count": 147,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['COLLECTION', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
" dtype=object)"
]
},
"execution_count": 147,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"neighbor = KNeighborsClassifier(n_neighbors = 7).fit(X_train,y_train)\n",
"yhat = neighbor.predict(X_test)\n",
"yhat[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 148,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Train set Accuracy: 0.8007246376811594\n",
"Test set Accuracy: 0.7571428571428571\n"
]
}
],
"source": [
"# accuracy of model\n",
"from sklearn import metrics\n",
"print(\"Train set Accuracy: \", metrics.accuracy_score(y_train, neighbor.predict(X_train)))\n",
"print(\"Test set Accuracy: \", metrics.accuracy_score(y_test, yhat))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Decision Tree\n"
]
},
{
"cell_type": "code",
"execution_count": 149,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.tree import DecisionTreeClassifier\n",
"import sklearn.tree as tree"
]
},
{
"cell_type": "code",
"execution_count": 150,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['COLLECTION', 'PAIDOFF', 'COLLECTION', 'PAIDOFF', 'PAIDOFF'],\n",
" dtype=object)"
]
},
"execution_count": 150,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dtree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 6).fit(X_train,y_train)\n",
"predTree = dtree.predict(X_test)\n",
"predTree[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 151,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy: 0.7285714285714285\n"
]
}
],
"source": [
"print(\"Accuracy: \", metrics.accuracy_score(y_test, predTree))"
]
},
{
"cell_type": "code",
"execution_count": 152,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[Text(102.4603448275862, 201.90857142857143, 'X[4] <= -0.188\\nentropy = 0.839\\nsamples = 276\\nvalue = [74, 202]'),\n",
" Text(23.089655172413792, 170.84571428571428, 'X[7] <= 0.14\\nentropy = 0.181\\nsamples = 110\\nvalue = [3, 107]'),\n",
" Text(11.544827586206896, 139.78285714285715, 'entropy = 0.0\\nsamples = 67\\nvalue = [0, 67]'),\n",
" Text(34.63448275862069, 139.78285714285715, 'X[2] <= -0.902\\nentropy = 0.365\\nsamples = 43\\nvalue = [3, 40]'),\n",
" Text(23.089655172413792, 108.72, 'entropy = 0.0\\nsamples = 8\\nvalue = [0, 8]'),\n",
" Text(46.179310344827584, 108.72, 'X[2] <= -0.404\\nentropy = 0.422\\nsamples = 35\\nvalue = [3, 32]'),\n",
" Text(23.089655172413792, 77.65714285714284, 'X[1] <= -0.019\\nentropy = 0.619\\nsamples = 13\\nvalue = [2, 11]'),\n",
" Text(11.544827586206896, 46.59428571428572, 'entropy = 0.0\\nsamples = 8\\nvalue = [0, 8]'),\n",
" Text(34.63448275862069, 46.59428571428572, 'X[2] <= -0.653\\nentropy = 0.971\\nsamples = 5\\nvalue = [2, 3]'),\n",
" Text(23.089655172413792, 15.531428571428563, 'entropy = 1.0\\nsamples = 2\\nvalue = [1, 1]'),\n",
" Text(46.179310344827584, 15.531428571428563, 'entropy = 0.918\\nsamples = 3\\nvalue = [1, 2]'),\n",
" Text(69.26896551724138, 77.65714285714284, 'X[2] <= 1.254\\nentropy = 0.267\\nsamples = 22\\nvalue = [1, 21]'),\n",
" Text(57.72413793103448, 46.59428571428572, 'entropy = 0.0\\nsamples = 16\\nvalue = [0, 16]'),\n",
" Text(80.81379310344828, 46.59428571428572, 'X[2] <= 1.42\\nentropy = 0.65\\nsamples = 6\\nvalue = [1, 5]'),\n",
" Text(69.26896551724138, 15.531428571428563, 'entropy = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
" Text(92.35862068965517, 15.531428571428563, 'entropy = 0.0\\nsamples = 5\\nvalue = [0, 5]'),\n",
" Text(181.8310344827586, 170.84571428571428, 'X[1] <= -1.46\\nentropy = 0.985\\nsamples = 166\\nvalue = [71, 95]'),\n",
" Text(170.2862068965517, 139.78285714285715, 'entropy = 0.0\\nsamples = 7\\nvalue = [0, 7]'),\n",
" Text(193.37586206896552, 139.78285714285715, 'X[7] <= 0.14\\nentropy = 0.992\\nsamples = 159\\nvalue = [71, 88]'),\n",
" Text(138.53793103448277, 108.72, 'X[2] <= -1.565\\nentropy = 1.0\\nsamples = 87\\nvalue = [43, 44]'),\n",
" Text(126.99310344827586, 77.65714285714284, 'entropy = 0.0\\nsamples = 3\\nvalue = [0, 3]'),\n",
" Text(150.08275862068965, 77.65714285714284, 'X[2] <= -0.073\\nentropy = 1.0\\nsamples = 84\\nvalue = [43, 41]'),\n",
" Text(126.99310344827586, 46.59428571428572, 'X[5] <= 1.119\\nentropy = 0.979\\nsamples = 41\\nvalue = [24, 17]'),\n",
" Text(115.44827586206895, 15.531428571428563, 'entropy = 0.999\\nsamples = 33\\nvalue = [17, 16]'),\n",
" Text(138.53793103448277, 15.531428571428563, 'entropy = 0.544\\nsamples = 8\\nvalue = [7, 1]'),\n",
" Text(173.17241379310343, 46.59428571428572, 'X[3] <= 0.979\\nentropy = 0.99\\nsamples = 43\\nvalue = [19, 24]'),\n",
" Text(161.62758620689655, 15.531428571428563, 'entropy = 0.999\\nsamples = 37\\nvalue = [19, 18]'),\n",
" Text(184.71724137931034, 15.531428571428563, 'entropy = 0.0\\nsamples = 6\\nvalue = [0, 6]'),\n",
" Text(248.21379310344827, 108.72, 'X[2] <= 0.259\\nentropy = 0.964\\nsamples = 72\\nvalue = [28, 44]'),\n",
" Text(207.80689655172412, 77.65714285714284, 'X[2] <= -1.98\\nentropy = 0.992\\nsamples = 49\\nvalue = [22, 27]'),\n",
" Text(196.26206896551724, 46.59428571428572, 'entropy = 0.0\\nsamples = 1\\nvalue = [1, 0]'),\n",
" Text(219.35172413793103, 46.59428571428572, 'X[2] <= -1.648\\nentropy = 0.989\\nsamples = 48\\nvalue = [21, 27]'),\n",
" Text(207.80689655172412, 15.531428571428563, 'entropy = 0.0\\nsamples = 1\\nvalue = [0, 1]'),\n",
" Text(230.8965517241379, 15.531428571428563, 'entropy = 0.992\\nsamples = 47\\nvalue = [21, 26]'),\n",
" Text(288.6206896551724, 77.65714285714284, 'X[2] <= 0.922\\nentropy = 0.828\\nsamples = 23\\nvalue = [6, 17]'),\n",
" Text(265.5310344827586, 46.59428571428572, 'X[0] <= -0.399\\nentropy = 0.722\\nsamples = 15\\nvalue = [3, 12]'),\n",
" Text(253.98620689655172, 15.531428571428563, 'entropy = 0.0\\nsamples = 4\\nvalue = [0, 4]'),\n",
" Text(277.07586206896553, 15.531428571428563, 'entropy = 0.845\\nsamples = 11\\nvalue = [3, 8]'),\n",
" Text(311.7103448275862, 46.59428571428572, 'X[3] <= 0.979\\nentropy = 0.954\\nsamples = 8\\nvalue = [3, 5]'),\n",
" Text(300.1655172413793, 15.531428571428563, 'entropy = 0.863\\nsamples = 7\\nvalue = [2, 5]'),\n",
" Text(323.2551724137931, 15.531428571428563, 'entropy = 0.0\\nsamples = 1\\nvalue = [1, 0]')]"
]
},
"execution_count": 152,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"tree.plot_tree(dtree)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Support Vector Machine\n"
]
},
{
"cell_type": "code",
"execution_count": 153,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.svm import SVC\n",
"from sklearn.metrics import confusion_matrix, classification_report"
]
},
{
"cell_type": "code",
"execution_count": 154,
"metadata": {},
"outputs": [],
"source": [
"kernels = ['rbf','linear','poly','sigmoid']\n",
"acc = []\n",
"for i in kernels:\n",
" clf = SVC(kernel=i)\n",
" clf.fit(X_train,y_train)\n",
" yhat = clf.predict(X_test)\n",
" acc.append(metrics.accuracy_score(y_test,yhat)*100)\n",
"kernel = acc.index(max(acc))\n"
]
},
{
"cell_type": "code",
"execution_count": 155,
"metadata": {},
"outputs": [],
"source": [
"clf = SVC(kernel = kernels[kernel])\n",
"clf.fit(X_train,y_train)\n",
"yhat = clf.predict(X_test)\n"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" precision recall f1-score support\n",
"\n",
" COLLECTION 0.00 0.00 0.00 12\n",
" PAIDOFF 0.83 1.00 0.91 58\n",
"\n",
" accuracy 0.83 70\n",
" macro avg 0.41 0.50 0.45 70\n",
"weighted avg 0.69 0.83 0.75 70\n",
"\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\ankit\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"C:\\Users\\ankit\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"C:\\Users\\ankit\\AppData\\Roaming\\Python\\Python37\\site-packages\\sklearn\\metrics\\_classification.py:1248: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"data": {
"text/plain": [
"0.8285714285714286"
]
},
"execution_count": 158,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import itertools\n",
"def plot_confusion_matrix(cm, classes,\n",
" normalize=False,\n",
" title='Confusion matrix',\n",
" cmap=plt.cm.Blues):\n",
"\n",
" if normalize:\n",
" cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n",
"\n",
" plt.imshow(cm, interpolation='nearest', cmap=cmap)\n",
" plt.title(title)\n",
" plt.colorbar()\n",
" tick_marks = np.arange(len(classes))\n",
" plt.xticks(tick_marks, classes, rotation=45)\n",
" plt.yticks(tick_marks, classes)\n",
"\n",
" fmt = '.2f' if normalize else 'd'\n",
" thresh = cm.max() / 2.\n",
" for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n",
" plt.text(j, i, format(cm[i, j], fmt),\n",
" horizontalalignment=\"center\",\n",
" color=\"white\" if cm[i, j] > thresh else \"black\")\n",
"\n",
" plt.tight_layout()\n",
" plt.ylabel('True label')\n",
" plt.xlabel('Predicted label')\n",
"cnf_matrix = confusion_matrix(y_test, yhat, labels=['PAIDOFF','COLLECTION'])\n",
"np.set_printoptions(precision=2)\n",
"\n",
"print (classification_report(y_test, yhat))\n",
"\n",
"plt.figure()\n",
"plot_confusion_matrix(cnf_matrix, classes=['PAIDOFF','COLLECTION'],normalize= False, title='Confusion matrix')\n",
"\n",
"f1_score(y_test, yhat, average='weighted')\n",
"\n",
"jaccard_score(y_test, yhat,pos_label='PAIDOFF')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic Regression\n"
]
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"LogisticRegression(C=0.01, solver='liblinear')"
]
},
"execution_count": 159,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from sklearn.linear_model import LogisticRegression\n",
"from sklearn.model_selection import train_test_split\n",
"\n",
"\n",
"LogModel = LogisticRegression(C=0.01,solver='liblinear')\n",
"LogModel.fit(X_train,y_train)\n",
"LogModel"
]
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"0.8"
]
},
"execution_count": 160,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yhat = LogModel.predict(X_test)\n",
"metrics.accuracy_score(y_test,yhat)"
]
},
{
"cell_type": "code",
"execution_count": 161,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[0.5 , 0.5 ],\n",
" [0.49, 0.51],\n",
" [0.49, 0.51],\n",
" [0.33, 0.67],\n",
" [0.49, 0.51],\n",
" [0.3 , 0.7 ],\n",
" [0.32, 0.68],\n",
" [0.46, 0.54],\n",
" [0.34, 0.66],\n",
" [0.42, 0.58],\n",
" [0.45, 0.55],\n",
" [0.3 , 0.7 ],\n",
" [0.46, 0.54],\n",
" [0.44, 0.56],\n",
" [0.35, 0.65],\n",
" [0.35, 0.65],\n",
" [0.46, 0.54],\n",
" [0.45, 0.55],\n",
" [0.27, 0.73],\n",
" [0.5 , 0.5 ],\n",
" [0.46, 0.54],\n",
" [0.47, 0.53],\n",
" [0.27, 0.73],\n",
" [0.34, 0.66],\n",
" [0.5 , 0.5 ],\n",
" [0.31, 0.69],\n",
" [0.5 , 0.5 ],\n",
" [0.47, 0.53],\n",
" [0.46, 0.54],\n",
" [0.46, 0.54],\n",
" [0.5 , 0.5 ],\n",
" [0.35, 0.65],\n",
" [0.35, 0.65],\n",
" [0.32, 0.68],\n",
" [0.46, 0.54],\n",
" [0.49, 0.51],\n",
" [0.49, 0.51],\n",
" [0.29, 0.71],\n",
" [0.31, 0.69],\n",
" [0.35, 0.65],\n",
" [0.49, 0.51],\n",
" [0.47, 0.53],\n",
" [0.3 , 0.7 ],\n",
" [0.45, 0.55],\n",
" [0.5 , 0.5 ],\n",
" [0.34, 0.66],\n",
" [0.5 , 0.5 ],\n",
" [0.44, 0.56],\n",
" [0.5 , 0.5 ],\n",
" [0.35, 0.65],\n",
" [0.34, 0.66],\n",
" [0.33, 0.67],\n",
" [0.3 , 0.7 ],\n",
" [0.5 , 0.5 ],\n",
" [0.31, 0.69],\n",
" [0.44, 0.56],\n",
" [0.34, 0.66],\n",
" [0.49, 0.51],\n",
" [0.5 , 0.5 ],\n",
" [0.26, 0.74],\n",
" [0.34, 0.66],\n",
" [0.45, 0.55],\n",
" [0.33, 0.67],\n",
" [0.49, 0.51],\n",
" [0.5 , 0.5 ],\n",
" [0.33, 0.67],\n",
" [0.33, 0.67],\n",
" [0.46, 0.54],\n",
" [0.5 , 0.5 ],\n",
" [0.31, 0.69]])"
]
},
"execution_count": 161,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"yhat_prob = LogModel.predict_proba(X_test)\n",
"yhat_prob"
]
},
{
"cell_type": "code",
"execution_count": 162,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.5395716058447384\n"
]
}
],
"source": [
"from sklearn.metrics import jaccard_score,log_loss\n",
"print(log_loss(y_test,yhat_prob))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Model Evaluation using Test set\n"
]
},
{
"cell_type": "code",
"execution_count": 194,
"metadata": {},
"outputs": [],
"source": [
"from sklearn.metrics import jaccard_score\n",
"from sklearn.metrics import f1_score\n",
"from sklearn.metrics import log_loss\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"First, download and load the test set:\n"
]
},
{
"cell_type": "code",
"execution_count": 195,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"--2022-06-02 16:54:58-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv\n",
"Resolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\n",
"Connecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\n",
"HTTP request sent, awaiting response... 200 OK\n",
"Length: 3642 (3.6K) [text/csv]\n",
"Saving to: 'loan_test.csv'\n",
"\n",
" 0K ... 100% 1.36G=0s\n",
"\n",
"2022-06-02 16:55:00 (1.36 GB/s) - 'loan_test.csv' saved [3642/3642]\n",
"\n"
]
}
],
"source": [
"!wget -O loan_test.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### Load Test set for evaluation\n"
]
},
{
"cell_type": "code",
"execution_count": 196,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"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 \\\n",
"0 1 1 PAIDOFF 1000 30 9/8/2016 \n",
"1 5 5 PAIDOFF 300 7 9/9/2016 \n",
"2 21 21 PAIDOFF 1000 30 9/10/2016 \n",
"3 24 24 PAIDOFF 1000 30 9/10/2016 \n",
"4 35 35 PAIDOFF 800 15 9/11/2016 \n",
"\n",
" due_date age education Gender \n",
"0 10/7/2016 50 Bechalor female \n",
"1 9/15/2016 35 Master or Above male \n",
"2 10/9/2016 43 High School or Below female \n",
"3 10/9/2016 26 college male \n",
"4 9/25/2016 29 Bechalor male "
]
},
"execution_count": 196,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df = pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_test.csv')\n",
"test_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 197,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[ 0.49, 0.93, 3.06, 1.98, -1.3 , 2.4 , -0.8 , -0.86],\n",
" [-3.56, -1.7 , 0.53, -0.51, 0.77, -0.42, -0.8 , -0.86],\n",
" [ 0.49, 0.93, 1.88, 1.98, 0.77, -0.42, 1.25, -0.86],\n",
" [ 0.49, 0.93, -0.98, -0.51, 0.77, -0.42, -0.8 , 1.16],\n",
" [-0.67, -0.79, -0.48, -0.51, 0.77, 2.4 , -0.8 , -0.86]])"
]
},
"execution_count": 197,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_df['due_date'] = pd.to_datetime(test_df['due_date'])\n",
"test_df['effective_date'] = pd.to_datetime(test_df['effective_date'])\n",
"test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\n",
"test_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3) else 0)\n",
"test_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
"\n",
"#These are the relevant columns\n",
"test_Feature = test_df[['Principal','terms','age','Gender','weekend']]\n",
"test_Feature = pd.concat([test_Feature,pd.get_dummies(test_df['education'])], axis=1)\n",
"test_Feature.drop(['Master or Above'], axis = 1,inplace=True)\n",
"\n",
"#Including variable for these columns\n",
"test_X = preprocessing.StandardScaler().fit(test_Feature).transform(test_Feature)\n",
"test_X[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 198,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n",
" dtype=object)"
]
},
"execution_count": 198,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test_y = test_df['loan_status'].values\n",
"test_y[0:5]"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Jaccard</th>\n",
" <th>F1-score</th>\n",
" <th>LogLoss</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Algorithm</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>KNN</th>\n",
" <td>0.700000</td>\n",
" <td>0.744246</td>\n",
" <td>NA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Decision Tree</th>\n",
" <td>0.717391</td>\n",
" <td>0.756632</td>\n",
" <td>NA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SVM</th>\n",
" <td>0.740741</td>\n",
" <td>0.851064</td>\n",
" <td>NA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Logistic Regresssion</th>\n",
" <td>0.740741</td>\n",
" <td>0.851064</td>\n",
" <td>0.576632</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Jaccard F1-score LogLoss\n",
"Algorithm \n",
"KNN 0.700000 0.744246 NA\n",
"Decision Tree 0.717391 0.756632 NA\n",
"SVM 0.740741 0.851064 NA\n",
"Logistic Regresssion 0.740741 0.851064 0.576632"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"knn = neighbor.predict(test_X)\n",
"decision = dtree.predict(test_X)\n",
"svm = clf.predict(test_X)\n",
"log = LogModel.predict(test_X)\n",
"\n",
"Algorithm = ['KNN','Decision Tree','SVM','Logistic Regresssion']\n",
"\n",
"Jaccard = [jaccard_score(knn,test_y,pos_label='PAIDOFF'),\n",
" jaccard_score(decision,test_y,pos_label='PAIDOFF'),\n",
" jaccard_score(svm,test_y,pos_label='PAIDOFF'),\n",
" jaccard_score(log,test_y,pos_label='PAIDOFF')]\n",
"\n",
"F1_score = [f1_score(knn,test_y,average='weighted'),\n",
" f1_score(decision,test_y,average='weighted'),\n",
" f1_score(svm,test_y,average='weighted'),\n",
" f1_score(log,test_y,average='weighted')]\n",
"\n",
"dict = {'Algorithm':Algorithm,\n",
" 'Jaccard':Jaccard,\n",
" 'F1-score':F1_score}\n",
"\n",
"logProba = LogModel.predict_proba(test_X)\n",
"logLoss = log_loss(test_y,logProba)\n",
"\n",
"df = pd.DataFrame(dict)\n",
"df['LogLoss'] = ['NA','NA','NA',logLoss]\n",
"df.set_index('Algorithm',inplace=True)\n",
"df"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Report\n",
"\n",
"You should be able to report the accuracy of the built model using different evaluation metrics:\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"| Algorithm | Jaccard | F1-score | LogLoss |\n",
"| ------------------ | ------- | -------- | ------- |\n",
"| KNN | ? | ? | NA |\n",
"| Decision Tree | ? | ? | NA |\n",
"| SVM | ? | ? | NA |\n",
"| LogisticRegression | ? | ? | ? |\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<h2>Want to learn more?</h2>\n",
"\n",
"IBM 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 – by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">SPSS Modeler</a>\n",
"\n",
"Also, 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 <a href=\"https://cocl.us/ML0101EN_DSX?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Watson Studio</a>\n",
"\n",
"<h3>Thanks for completing this lesson!</h3>\n",
"\n",
"<h4>Author: <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkML0101ENSkillsNetwork20718538-2022-01-01\">Saeed Aghabozorgi</a></h4>\n",
"<p><a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">Saeed Aghabozorgi</a>, PhD is a Data Scientist in IBM with a track record of developing enterprise level applications that substantially increases clients’ ability to turn data into actionable knowledge. He is a researcher in data mining field and expert in developing advanced analytic methods like machine learning and statistical modelling on large datasets.</p>\n",
"\n",
"<hr>\n",
"\n",
"## Change Log\n",
"\n",
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"| ----------------- | ------- | ------------- | ------------------------------------------------------------------------------ |\n",
"| 2020-10-27 | 2.1 | Lakshmi Holla | Made changes in import statement due to updates in version of sklearn library |\n",
"| 2020-08-27 | 2.0 | Malika Singla | Added lab to GitLab |\n",
"\n",
"<hr>\n",
"\n",
"## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\n",
"\n",
"<p>\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment