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June 2, 2022 11:49
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machine Learning capstone
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| { | |
| "cells": [ | |
| { | |
| "cell_type": "markdown", | |
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| "button": false, | |
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| "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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iGUDyc5d8G0s6U9IcSXP8LW6z/JwnZhsqpEB1BYYAt0TEYODvtKE7LyImRkR9RNT37NmznWGalTfnidmGCilQjUBjRDyXPJ5KrmC9K6kaIPn5XnFCNDOzStRqgYqI5cBbkvoni0YArwC/BcYky8YADxUlQjMzq0iFDhZ7HjBZ0pbA68C3yRW3+yWdASwFTipOiGbpUJeqVOcJU5eq1Noysw0VVKAiogGoz/PUiHTDMSue+GwNB132cGrtPfeTkam1ZWYb8kgSZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSQUXKEldJM2XND15XCvpOUl/lnSfpC2LF6aZmVWatpxBfQ9Y1OLxVcB1EdEP+AtwRpqBmZlZZSuoQEmqAY4Cbk8eCxgOTE1WuQsYXYwAzcysMhV6BnU9cCHwefJ4J+D9iFibPG4EeuXbUNKZkuZImrNixYoOBWtWrpwnZhtqtUBJOhp4LyLmtlycZ9XIt31ETIyI+oio79mzZzvDNCtvzhOzDXUtYJ1DgWMljQK6AduSO6PaXlLX5CyqBnineGGamVmlafUMKiIuiYiaiOgLnAI8ERGnAjOAE5PVxgAPFS1KMzOrOB35HtRFwAWSXiP3mdQd6YRkZmZWWBdfs4iYCcxM7r8OHJh+SGZmZh5JwszMMsoFyszMMskFyszMMskFyszMMskFyszMMskFyszMMskFyszMMskFyszMMskFyszMMskFyszMMskFyszMMskFyszMMskFyszMMskFyszMMskFqpP1qa5GUiq3PtXVpT4cM7OiadN8UNZxS5cvp3H3mlTaqnmnMZV2zMyyyGdQZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSS5QZmaWSa0WKElfkDRD0iJJCyV9L1m+o6RHJf05+blD8cM1M7NKUcgZ1Frg+xGxD/Al4BxJA4GLgccjoh/wePLYzMwsFa0WqIhYFhHzkvt/AxYBvYDjgLuS1e4CRhcrSDMzqzxt+gxKUl9gMPAcsGtELINcEQN22cg2Z0qaI2nOihUrOhatWZlynphtqOACJak78GtgXER8UOh2ETExIuojor5nz57tidGs7DlPzDZUUIGSVEWuOE2OiN8ki9+VVJ08Xw28V5wQzcysEhVyFZ+AO4BFEXFti6d+C4xJ7o8BHko/PDMzq1SFTFh4KHAasEBSQ7LsUuBK4H5JZwBLgZOKE6KZmVWiVgtURMwGtJGnR6QbjpmZlVqf6mqWLl+eSlu9d9uNN5cta9e2nvLdzMzWsXT5chp3r0mlrZp3Gtu9rYc6skzrU12NpFRu5STN30uf6upSH45ZXj6DskzLyju5rPHvxSqBz6DMzCyTyvYM6p8gtW6djnzIZx2jLlV+h29Wocq2QH0C7gIpA/HZGg667OFU2nruJyNTacfMOoe7+MzMLJNcoMzMLJNcoMzMLJNcoMzMLJNcoMzMLJNcoMzMLJNcoMzMLJNcoMzMLJNcoMzMLJNcoMzMLJPKdqgjMzNrnzTHwFSXqnZv6wJlZmbryMoYmO7iM6twTSP/e/JDyxqfQZlVOI/8b1nlMygzM8skFyhL3e41vVPrMjKzyuUuPkvdsrffysQHrGa2ectUgcrKpY1mVnp9qqtZunx5Km313m033ly2LJW2rPNkqkBl5dLGzUXT1VdpcAJb1ixdvtwXb1S4DhUoSSOBG4AuwO0RcWUqUVlBfPWVmZWzdl8kIakLcBPwNWAg8A1JA9MKzMwsLVn9rlef6urU4tq6S9eyuzipI2dQBwKvRcTrAJLuBY4DXkkjMDOztGS1tyHtbswsHmNHKCLat6F0IjAyIr6TPD4NOCgizl1vvTOBM5OH/YE/baLZnYGV7Qpo8+FjLB+tHefKiCjow9A25kkh+y4HPsbyUMgx5s2VjpxB5TsP3KDaRcREYGJBDUpzIqK+AzFlno+xfKR5nG3Jk7T3nVU+xvLQkWPsyBd1G4EvtHhcA7zTgfbMzMyadaRAvQD0k1QraUvgFOC36YRlZmaVrt1dfBGxVtK5wCPkLjO/MyIWdjCegrs4NmM+xvJRyuOshN+xj7E8tPsY232RhJmZWTF5sFgzM8skFygzM8ukzBQoSSMl/UnSa5IuLnU8aZP0BUkzJC2StFDS90odU7FI6iJpvqTppY6lGCRtL2mqpFeTv+fBnbjvss4TqJxcKfc8gY7nSiY+g0qGTVoMfJXc5esvAN+IiLIZlUJSNVAdEfMk9QDmAqPL6RibSLoAqAe2jYijSx1P2iTdBcyKiNuTK1i3joj3O2G/ZZ8nUDm5Uu55Ah3PlaycQTUPmxQRnwJNwyaVjYhYFhHzkvt/AxYBvUobVfok1QBHAbeXOpZikLQtcDhwB0BEfNoZxSlR9nkClZEr5Z4nkE6uZKVA9QLeavG4kTJ7QbYkqS8wGHiutJEUxfXAhcDnpQ6kSPYAVgC/TLpnbpe0TSftu6LyBMo6V8o9TyCFXMlKgSpo2KRyIKk78GtgXER8UOp40iTpaOC9iJhb6liKqCswBLglIgYDfwc667OgiskTKN9cqZA8gRRyJSsFqiKGTZJURS7hJkfEb0odTxEcChwraQm57qfhku4ubUipawQaI6LpHf1UcknYWfsu+zyBss+VSsgTSCFXslKgyn7YJOUmWbkDWBQR15Y6nmKIiEsioiYi+pL7Gz4REd8qcVipiojlwFuS+ieLRtB5U8yUfZ5A+edKJeQJpJMrmZjyvUjDJmXNocBpwAJJDcmySyPiP0sYk7XPecDkpEi8Dny7M3ZaIXkCzpVy0qFcycRl5mZmZuvLShefmZnZOlygzMwsk1ygzMwsk1ygzMwsk1ygzMwsk1ygMkLSjyWNT7G9AZIakiFG9kyr3Rbtz5RUn3a7Zq1xrlQOF6jyNRp4KCIGR8R/lToYswxzrmSUC1QJSfpBMrfPY0D/ZNm/SnpB0ouSfi1pa0k9JL2RDP+CpG0lLZFUJalO0rOSXpL0oKQdJI0CxgHfSebVuVDS+cm210l6Irk/ommIFUlHSnpG0jxJDyTjoCHpAEl/lDRX0iPJVAgtj2ELSXdJ+t+d9ouziuNcqUwuUCUi6QByw5wMBv4F+GLy1G8i4osR8c/kphk4I5lyYCa54flJtvt1RKwB/h24KCIGAQuAHyXfuL8VuC4ijgCeBIYm29YD3ZMEPgyYJWln4IfAVyJiCDAHuCBZ5+fAiRFxAHAn8H9aHEZXYDKwOCJ+mOKvx6yZc6VyZWKoowo1FHgwIj4CkNQ0ptp+yTus7YHu5Ia1gdy8MRcC08gNF/KvkrYDto+IPybr3AU8kGdfc4EDlJv87RNgHrnkGwqcD3wJGAg8lRsGjS2BZ8i9U90PeDRZ3gVY1qLd24D7I6JlIpqlzblSoVygSivfOFOTyM0e+qKkscAwgIh4SlJfSV8GukTEy0nStb6TiDXKjZz8beBp4CXgCGBPcu889wQejYhvtNxO0v7AwojY2DTNTwNHSPq/EbG6kFjM2sm5UoHcxVc6TwLHS9oqebd2TLK8B7As6TI4db1t/h2YAvwSICL+CvxFUlOXxGnAH8nvSWB88nMWcBbQELnBGJ8FDpW0F0DSl7838Cegp6SDk+VVkvZt0eYdwH8CD0jymx0rFudKhXKBKpFkSuv7gAZy897MSp76X+RmD30UeHW9zSYDO5BLvCZjgGskvQTUAT/ZyC5nAdXAMxHxLrC6aZ8RsQIYC0xJ2nkWGJBMK34icJWkF5NYD1nvOK4l1w3yK0l+PVnqnCuVy6OZb0YknQgcFxGnlToWsyxzrpQHn2puJiT9HPgaMKrUsZhlmXOlfPgMyszMMsn9oGZmlkkuUGZmlkkuUGZmlkkuUGZmlkkuUGZmlkn/H82S0fcuZqCmAAAAAElFTkSuQmCC\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": { | |
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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": [ | |
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| " 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", | |
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| ] | |
| }, | |
| "execution_count": 152, | |
| "metadata": {}, | |
| "output_type": "execute_result" | |
| }, | |
| { | |
| "data": { | |
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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 | |
| } |
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