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Created June 19, 2020 10:00
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Machine learning assignment for classification algorithms part of IBM data science week6 project
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Machine learning assignment.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"button": false,
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"run_control": {
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},
"source": "<a href=\"https://www.bigdatauniversity.com\"><img src=\"https://ibm.box.com/shared/static/cw2c7r3o20w9zn8gkecaeyjhgw3xdgbj.png\" width=\"400\" align=\"center\"></a>\n\n<h1 align=\"center\"><font size=\"5\">Classification with Python</font></h1>"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
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}
},
"source": "In this notebook we try to practice all the classification algorithms that we learned in this course.\n\nWe load a dataset using Pandas library, and apply the following algorithms, and find the best one for this specific dataset by accuracy evaluation methods.\n\nLets first load required libraries:"
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": "import itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import NullFormatter\nimport pandas as pd\nimport numpy as np\nimport matplotlib.ticker as ticker\nfrom sklearn import preprocessing\n%matplotlib inline"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "### About dataset"
},
{
"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\u2019s one-time payoff schedule, each loan has one single due date |\n| Age | Age of applicant |\n| Education | Education of applicant |\n| Gender | The gender of applicant |"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "Lets download the dataset"
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "--2020-06-19 06:37:28-- https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv\nResolving s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)... 67.228.254.196\nConnecting to s3-api.us-geo.objectstorage.softlayer.net (s3-api.us-geo.objectstorage.softlayer.net)|67.228.254.196|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 23101 (23K) [text/csv]\nSaving to: \u2018loan_train.csv\u2019\n\n100%[======================================>] 23,101 --.-K/s in 0.001s \n\n2020-06-19 06:37:28 (15.9 MB/s) - \u2018loan_train.csv\u2019 saved [23101/23101]\n\n"
}
],
"source": "!wget -O loan_train.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/ML0101ENv3/labs/loan_train.csv"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "### Load Data From CSV File "
},
{
"cell_type": "code",
"execution_count": 4,
"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 \\\n0 0 0 PAIDOFF 1000 30 9/8/2016 \n1 2 2 PAIDOFF 1000 30 9/8/2016 \n2 3 3 PAIDOFF 1000 15 9/8/2016 \n3 4 4 PAIDOFF 1000 30 9/9/2016 \n4 6 6 PAIDOFF 1000 30 9/9/2016 \n\n due_date age education Gender \n0 10/7/2016 45 High School or Below male \n1 10/7/2016 33 Bechalor female \n2 9/22/2016 27 college male \n3 10/8/2016 28 college female \n4 10/8/2016 29 college male "
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "df = pd.read_csv('loan_train.csv')\ndf.head()"
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "(346, 10)"
},
"execution_count": 5,
"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 "
},
{
"cell_type": "code",
"execution_count": 6,
"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 \\\n0 0 0 PAIDOFF 1000 30 2016-09-08 \n1 2 2 PAIDOFF 1000 30 2016-09-08 \n2 3 3 PAIDOFF 1000 15 2016-09-08 \n3 4 4 PAIDOFF 1000 30 2016-09-09 \n4 6 6 PAIDOFF 1000 30 2016-09-09 \n\n due_date age education Gender \n0 2016-10-07 45 High School or Below male \n1 2016-10-07 33 Bechalor female \n2 2016-09-22 27 college male \n3 2016-10-08 28 college female \n4 2016-10-08 29 college male "
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "df['due_date'] = pd.to_datetime(df['due_date'])\ndf['effective_date'] = pd.to_datetime(df['effective_date'])\ndf.head()"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "# Data visualization and pre-processing\n\n"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "Let\u2019s see how many of each class is in our data set "
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": "PAIDOFF 260\nCOLLECTION 86\nName: loan_status, dtype: int64"
},
"execution_count": 7,
"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": "Lets plot some columns to underestand data better:"
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Solving environment: done\n\n## Package Plan ##\n\n environment location: /opt/conda/envs/Python36\n\n added / updated specs: \n - seaborn\n\n\nThe following packages will be downloaded:\n\n package | build\n ---------------------------|-----------------\n certifi-2020.4.5.2 | py36_0 160 KB anaconda\n openssl-1.1.1g | h7b6447c_0 3.8 MB anaconda\n ca-certificates-2020.1.1 | 0 132 KB anaconda\n seaborn-0.10.1 | py_0 160 KB anaconda\n ------------------------------------------------------------\n Total: 4.2 MB\n\nThe following packages will be UPDATED:\n\n ca-certificates: 2020.1.1-0 --> 2020.1.1-0 anaconda\n certifi: 2020.4.5.1-py36_0 --> 2020.4.5.2-py36_0 anaconda\n openssl: 1.1.1g-h7b6447c_0 --> 1.1.1g-h7b6447c_0 anaconda\n seaborn: 0.9.0-pyh91ea838_1 --> 0.10.1-py_0 anaconda\n\n\nDownloading and Extracting Packages\ncertifi-2020.4.5.2 | 160 KB | ##################################### | 100% \nopenssl-1.1.1g | 3.8 MB | ##################################### | 100% \nca-certificates-2020 | 132 KB | ##################################### | 100% \nseaborn-0.10.1 | 160 KB | ##################################### | 100% \nPreparing transaction: done\nVerifying transaction: done\nExecuting transaction: done\n"
}
],
"source": "# notice: installing seaborn might takes a few minutes\n!conda install -c anaconda seaborn -y"
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": "count, binedges = np.histogram(df.Principal)"
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "[ 3 0 2 0 0 0 0 81 2 258] [ 300. 370. 440. 510. 580. 650. 720. 790. 860. 930. 1000.]\n"
}
],
"source": "print(count,binedges)"
},
{
"cell_type": "code",
"execution_count": 11,
"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\nbins = np.linspace(df.Principal.min(), df.Principal.max(), 10)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'Principal', bins=binedges, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()"
},
{
"cell_type": "code",
"execution_count": 12,
"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)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'age', bins=bins, ec=\"k\")\n\ng.axes[-1].legend()\nplt.show()"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "# Pre-processing: Feature selection/extraction"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "### Lets look at the day of the week people get the loan "
},
{
"cell_type": "code",
"execution_count": 13,
"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\nbins = np.linspace(df.dayofweek.min(), df.dayofweek.max(), 7)\ng = sns.FacetGrid(df, col=\"Gender\", hue=\"loan_status\", palette=\"Set1\", col_wrap=2)\ng.map(plt.hist, 'dayofweek', bins=bins, ec=\"k\")\ng.axes[-1].legend()\nplt.show()\n"
},
{
"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 dont pay it off, so lets use Feature binarization to set a threshold values less than day 4 "
},
{
"cell_type": "code",
"execution_count": 14,
"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 \\\n0 0 0 PAIDOFF 1000 30 2016-09-08 \n1 2 2 PAIDOFF 1000 30 2016-09-08 \n2 3 3 PAIDOFF 1000 15 2016-09-08 \n3 4 4 PAIDOFF 1000 30 2016-09-09 \n4 6 6 PAIDOFF 1000 30 2016-09-09 \n\n due_date age education Gender dayofweek weekend \n0 2016-10-07 45 High School or Below male 3 0 \n1 2016-10-07 33 Bechalor female 3 0 \n2 2016-09-22 27 college male 3 0 \n3 2016-10-08 28 college female 4 1 \n4 2016-10-08 29 college male 4 1 "
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3) else 0)\ndf.head()"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "## Convert Categorical features to numerical values"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "Lets look at gender:"
},
{
"cell_type": "code",
"execution_count": 105,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": "Gender loan_status\n0 PAIDOFF 0.731293\n COLLECTION 0.268707\n1 PAIDOFF 0.865385\n COLLECTION 0.134615\nName: loan_status, dtype: float64"
},
"execution_count": 105,
"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": "Lets convert male to 0 and female to 1:\n"
},
{
"cell_type": "code",
"execution_count": 16,
"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 \\\n0 0 0 PAIDOFF 1000 30 2016-09-08 \n1 2 2 PAIDOFF 1000 30 2016-09-08 \n2 3 3 PAIDOFF 1000 15 2016-09-08 \n3 4 4 PAIDOFF 1000 30 2016-09-09 \n4 6 6 PAIDOFF 1000 30 2016-09-09 \n\n due_date age education Gender dayofweek weekend \n0 2016-10-07 45 High School or Below 0 3 0 \n1 2016-10-07 33 Bechalor 1 3 0 \n2 2016-09-22 27 college 0 3 0 \n3 2016-10-08 28 college 1 4 1 \n4 2016-10-08 29 college 0 4 1 "
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ndf.head()"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "## One Hot Encoding \n#### How about education?"
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": "education loan_status\nBechalor PAIDOFF 0.750000\n COLLECTION 0.250000\nHigh School or Below PAIDOFF 0.741722\n COLLECTION 0.258278\nMaster or Above COLLECTION 0.500000\n PAIDOFF 0.500000\ncollege PAIDOFF 0.765101\n COLLECTION 0.234899\nName: loan_status, dtype: float64"
},
"execution_count": 17,
"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": "#### Feature before One Hot Encoding"
},
{
"cell_type": "code",
"execution_count": 18,
"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\n0 1000 30 45 0 High School or Below\n1 1000 30 33 1 Bechalor\n2 1000 15 27 0 college\n3 1000 30 28 1 college\n4 1000 30 29 0 college"
},
"execution_count": 18,
"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 "
},
{
"cell_type": "code",
"execution_count": 19,
"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 \\\n0 1000 30 45 0 0 0 1 \n1 1000 30 33 1 0 1 0 \n2 1000 15 27 0 0 0 0 \n3 1000 30 28 1 1 0 0 \n4 1000 30 29 0 1 0 0 \n\n college \n0 0 \n1 0 \n2 1 \n3 1 \n4 1 "
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "Feature = df[['Principal','terms','age','Gender','weekend']]\nFeature = pd.concat([Feature,pd.get_dummies(df['education'])], axis=1)\nFeature.drop(['Master or Above'], axis = 1,inplace=True)\nFeature.head()\n"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "### Feature selection"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "Lets defind feature sets, X:"
},
{
"cell_type": "code",
"execution_count": 20,
"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 \\\n0 1000 30 45 0 0 0 1 \n1 1000 30 33 1 0 1 0 \n2 1000 15 27 0 0 0 0 \n3 1000 30 28 1 1 0 0 \n4 1000 30 29 0 1 0 0 \n\n college \n0 0 \n1 0 \n2 1 \n3 1 \n4 1 "
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "X = Feature\nX[0:5]"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "What are our lables?"
},
{
"cell_type": "code",
"execution_count": 21,
"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": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "y = df['loan_status'].values\ny[0:5]"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "## Normalize Data "
},
{
"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 )"
},
{
"cell_type": "code",
"execution_count": 107,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[ 0.52, 0.92, 2.33, -0.42, -1.21],\n [ 0.52, 0.92, 0.34, 2.38, -1.21],\n [ 0.52, -0.96, -0.65, -0.42, -1.21],\n [ 0.52, 0.92, -0.49, 2.38, 0.83],\n [ 0.52, 0.92, -0.32, -0.42, 0.83]])"
},
"execution_count": 107,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "X= preprocessing.StandardScaler().fit(X).transform(X)\nX[0:5]"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "# Classification "
},
{
"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\nYou should use the following algorithm:\n- K Nearest Neighbor(KNN)\n- Decision Tree\n- Support Vector Machine\n- Logistic Regression\n\n\n\n__ Notice:__ \n- You can go above and change the pre-processing, feature selection, feature-extraction, and so on, to make a better model.\n- You should use either scikit-learn, Scipy or Numpy libraries for developing the classification algorithms.\n- You should include the code of the algorithm in the following cells."
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# K Nearest Neighbor(KNN)\nNotice: You should find the best k to build the model with the best accuracy. \n**warning:** You should not use the __loan_test.csv__ for finding the best k, however, you can split your train_loan.csv into train and test to find the best __k__."
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([0.71153846, 0.625 , 0.72115385, 0.72115385, 0.73076923,\n 0.71153846, 0.72115385, 0.72115385, 0.75 ])"
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "from sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\n\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=4)\n\nKs = 10\nmean_acc = np.zeros((Ks-1))\nstd_acc = np.zeros((Ks-1))\nConfustionMx = [];\nfor n in range(1,Ks):\n \n #Train Model and Predict \n neigh = KNeighborsClassifier(n_neighbors = n).fit(X_train,y_train)\n yhat=neigh.predict(X_test)\n mean_acc[n-1] = metrics.accuracy_score(y_test, yhat)\n std_acc[n-1]=np.std(yhat==y_test)/np.sqrt(yhat.shape[0])\n\nmean_acc"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Accuracy is high when K=9"
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [],
"source": "neigh_high = KNeighborsClassifier(n_neighbors = 9).fit(X,y)\n"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# Decision Tree"
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0.7403846153846154"
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "from sklearn.tree import DecisionTreeClassifier\n\nloanTree = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=4)\nloanTree.fit(X_train,y_train)\n\npredTree = loanTree.predict(X_test)\nmetrics.accuracy_score(y_test, predTree)"
},
{
"cell_type": "code",
"execution_count": 45,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=4,\n max_features=None, max_leaf_nodes=None,\n min_impurity_decrease=0.0, min_impurity_split=None,\n min_samples_leaf=1, min_samples_split=2,\n min_weight_fraction_leaf=0.0, presort=False, random_state=None,\n splitter='best')"
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "loanTree_high = DecisionTreeClassifier(criterion=\"entropy\", max_depth = 4)\nloanTree_high.fit(X,y)"
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [],
"source": "#!conda install -c conda-forge pydotplus -y\n#!conda install -c conda-forge python-graphviz -y"
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [],
"source": "from sklearn.externals.six import StringIO\nimport pydotplus\nimport matplotlib.image as mpimg\nfrom sklearn import tree\n%matplotlib inline "
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "Index(['Principal', 'terms', 'age', 'Gender', 'weekend', 'Bechalor',\n 'High School or Below'],\n dtype='object')"
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "Feature.columns[0:7]\n"
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "['PAIDOFF', 'COLLECTION']"
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "df[\"loan_status\"].unique().tolist()"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": "dot_data = StringIO()\nfilename = \"drugtree.png\"\nfeatureNames = Feature.columns[0:8]\ntargetNames = df[\"loan_status\"].unique().tolist()\nout=tree.export_graphviz(loanTree_high,feature_names=featureNames, out_file=dot_data, class_names= np.unique(y), filled=True, special_characters=True,rotate=False) \ngraph = pydotplus.graph_from_dot_data(dot_data.getvalue()) \ngraph.write_png(filename)\nimg = mpimg.imread(filename)\nplt.figure(figsize=(100, 200))\nplt.imshow(img,interpolation='nearest')"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# Support Vector Machine"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "1.Linear\n2.Polynomial\n3.Radial basis function (RBF)\n4.Sigmoid"
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/svm/base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n \"avoid this warning.\", FutureWarning)\n"
},
{
"data": {
"text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n dtype=object)"
},
"execution_count": 72,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "from sklearn import svm\nclf = svm.SVC(kernel='poly')\n\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=4)\nclf.fit(X_train, y_train) \nyhat = clf.predict(X_test)\nyhat [0:5]"
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [],
"source": "def plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\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')"
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " precision recall f1-score support\n\n COLLECTION 0.67 0.07 0.13 27\n PAIDOFF 0.75 0.99 0.85 77\n\n micro avg 0.75 0.75 0.75 104\n macro avg 0.71 0.53 0.49 104\nweighted avg 0.73 0.75 0.67 104\n\nConfusion matrix, without normalization\n[[76 1]\n [25 2]]\n"
},
{
"data": {
"image/png": 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\n",
"text/plain": "<Figure size 720x360 with 2 Axes>"
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": "# Compute confusion matrix\nfrom sklearn.metrics import classification_report, confusion_matrix\n\ncnf_matrix = confusion_matrix(y_test, yhat, labels=['PAIDOFF','COLLECTION'])\nnp.set_printoptions(precision=2)\n\nprint (classification_report(y_test, yhat))\n\n# Plot non-normalized confusion matrix\nplt.figure(figsize=(10,5))\nplot_confusion_matrix(cnf_matrix, classes=['PAIDOFF','COLLECTION'],normalize= False, title='Confusion matrix')"
},
{
"cell_type": "code",
"execution_count": 75,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0.6668539325842696"
},
"execution_count": 75,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "from sklearn.metrics import f1_score\nf1_score(y_test, yhat, average='weighted') "
},
{
"cell_type": "code",
"execution_count": 76,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0.75"
},
"execution_count": 76,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "from sklearn.metrics import jaccard_similarity_score\njaccard_similarity_score(y_test, yhat)"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "Model for entire data using poly as it gave more f1_score and similarity score than others"
},
{
"cell_type": "code",
"execution_count": 77,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n kernel='poly', max_iter=-1, probability=False, random_state=None,\n shrinking=True, tol=0.001, verbose=False)"
},
"execution_count": 77,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "from sklearn import svm\nclf_high = svm.SVC(kernel='poly')\nclf_high.fit(X, y) "
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# Logistic Regression"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "can use any of these'newton-cg\u2019, \u2018lbfgs\u2019, \u2018liblinear\u2019, \u2018sag\u2019, \u2018saga\u2019"
},
{
"cell_type": "code",
"execution_count": 87,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n intercept_scaling=1, max_iter=100, multi_class='warn',\n n_jobs=None, penalty='l2', random_state=None, solver='sag',\n tol=0.0001, verbose=0, warm_start=False)"
},
"execution_count": 87,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "from sklearn.linear_model import LogisticRegression\nX_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.3, random_state=4)\n\nLR = LogisticRegression(C=0.01, solver='sag').fit(X_train,y_train)\nLR"
},
{
"cell_type": "code",
"execution_count": 88,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n dtype=object)"
},
"execution_count": 88,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "yhat = LR.predict(X_test)\nyhat[0:5]"
},
{
"cell_type": "code",
"execution_count": 89,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array([[0.31, 0.69],\n [0.28, 0.72],\n [0.17, 0.83],\n [0.2 , 0.8 ],\n [0.18, 0.82]])"
},
"execution_count": 89,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "yhat_prob = LR.predict_proba(X_test)\nyhat_prob[0:5]"
},
{
"cell_type": "code",
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0.7403846153846154"
},
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "from sklearn.metrics import jaccard_similarity_score\njaccard_similarity_score(y_test, yhat)"
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "Confusion matrix, without normalization\n[[73 4]\n [25 2]]\n"
},
{
"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": "# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, yhat, labels=['PAIDOFF','COLLECTION'])\nnp.set_printoptions(precision=2)\n\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=['PAIDOFF','COLLECTION'],normalize= False, title='Confusion matrix')"
},
{
"cell_type": "code",
"execution_count": 91,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": " precision recall f1-score support\n\n COLLECTION 0.00 0.00 0.00 27\n PAIDOFF 0.74 1.00 0.85 77\n\n micro avg 0.74 0.74 0.74 104\n macro avg 0.37 0.50 0.43 104\nweighted avg 0.55 0.74 0.63 104\n\n"
},
{
"name": "stderr",
"output_type": "stream",
"text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n 'precision', 'predicted', average, warn_for)\n/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n 'precision', 'predicted', average, warn_for)\n/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n 'precision', 'predicted', average, warn_for)\n"
}
],
"source": "print (classification_report(y_test, yhat))"
},
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "0.5225909174948803"
},
"execution_count": 92,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "from sklearn.metrics import log_loss\nlog_loss(y_test, yhat_prob)"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "sag gave better accuracy , so lets build model using sag for entire dataset"
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "LogisticRegression(C=0.01, class_weight=None, dual=False, fit_intercept=True,\n intercept_scaling=1, max_iter=100, multi_class='warn',\n n_jobs=None, penalty='l2', random_state=None, solver='sag',\n tol=0.0001, verbose=0, warm_start=False)"
},
"execution_count": 96,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "from sklearn.linear_model import LogisticRegression\n\nLR_high = LogisticRegression(C=0.01, solver='sag').fit(X,y)\nLR_high"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# Model Evaluation using Test set"
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": "from sklearn.metrics import jaccard_similarity_score\nfrom sklearn.metrics import f1_score\nfrom sklearn.metrics import log_loss"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "First, download and load the test set:"
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"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 "
},
{
"cell_type": "code",
"execution_count": 95,
"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 \\\n0 1 1 PAIDOFF 1000 30 9/8/2016 \n1 5 5 PAIDOFF 300 7 9/9/2016 \n2 21 21 PAIDOFF 1000 30 9/10/2016 \n3 24 24 PAIDOFF 1000 30 9/10/2016 \n4 35 35 PAIDOFF 800 15 9/11/2016 \n\n due_date age education Gender \n0 10/7/2016 50 Bechalor female \n1 9/15/2016 35 Master or Above male \n2 10/9/2016 43 High School or Below female \n3 10/9/2016 26 college male \n4 9/25/2016 29 Bechalor male "
},
"execution_count": 95,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "test_df = pd.read_csv('loan_test.csv')\ntest_df.head()"
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {},
"outputs": [],
"source": "test_df['due_date'] = pd.to_datetime(test_df['due_date'])\ntest_df['effective_date'] = pd.to_datetime(test_df['effective_date'])"
},
{
"cell_type": "code",
"execution_count": 99,
"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>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>1</td>\n <td>1</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>50</td>\n <td>Bechalor</td>\n <td>1</td>\n <td>3</td>\n <td>0</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>2016-09-09</td>\n <td>2016-09-15</td>\n <td>35</td>\n <td>Master or Above</td>\n <td>0</td>\n <td>4</td>\n <td>1</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>2016-09-10</td>\n <td>2016-10-09</td>\n <td>43</td>\n <td>High School or Below</td>\n <td>1</td>\n <td>5</td>\n <td>1</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>2016-09-10</td>\n <td>2016-10-09</td>\n <td>26</td>\n <td>college</td>\n <td>0</td>\n <td>5</td>\n <td>1</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>2016-09-11</td>\n <td>2016-09-25</td>\n <td>29</td>\n <td>Bechalor</td>\n <td>0</td>\n <td>6</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Unnamed: 0 Unnamed: 0.1 loan_status Principal terms effective_date \\\n0 1 1 PAIDOFF 1000 30 2016-09-08 \n1 5 5 PAIDOFF 300 7 2016-09-09 \n2 21 21 PAIDOFF 1000 30 2016-09-10 \n3 24 24 PAIDOFF 1000 30 2016-09-10 \n4 35 35 PAIDOFF 800 15 2016-09-11 \n\n due_date age education Gender dayofweek weekend \n0 2016-10-07 50 Bechalor 1 3 0 \n1 2016-09-15 35 Master or Above 0 4 1 \n2 2016-10-09 43 High School or Below 1 5 1 \n3 2016-10-09 26 college 0 5 1 \n4 2016-09-25 29 Bechalor 0 6 1 "
},
"execution_count": 99,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "test_df['dayofweek'] = test_df['effective_date'].dt.dayofweek\ntest_df['weekend'] = test_df['dayofweek'].apply(lambda x: 1 if (x>3) else 0)\ntest_df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\ntest_df.head()"
},
{
"cell_type": "code",
"execution_count": 109,
"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>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>1.0</td>\n <td>0.0</td>\n <td>0.0</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>0.0</td>\n <td>0.0</td>\n <td>0.0</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>0.0</td>\n <td>1.0</td>\n <td>0.0</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>0.0</td>\n <td>0.0</td>\n <td>1.0</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>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Principal terms age Gender weekend Bechalor High School or Below \\\n0 1000 30 45 0 0 1.0 0.0 \n1 1000 30 33 1 0 0.0 0.0 \n2 1000 15 27 0 0 0.0 1.0 \n3 1000 30 28 1 1 0.0 0.0 \n4 1000 30 29 0 1 1.0 0.0 \n\n college \n0 0.0 \n1 0.0 \n2 0.0 \n3 1.0 \n4 0.0 "
},
"execution_count": 109,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "Feature_test = test_df[['Principal','terms','age','Gender','weekend']]\nFeature_test = pd.concat([Feature,pd.get_dummies(test_df['education'])], axis=1)\nFeature_test.drop(['Master or Above'], axis = 1,inplace=True)\nFeature_test.head()\n"
},
{
"cell_type": "code",
"execution_count": 111,
"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>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>1.0</td>\n <td>0.0</td>\n <td>0.0</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>0.0</td>\n <td>0.0</td>\n <td>0.0</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>0.0</td>\n <td>1.0</td>\n <td>0.0</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>0.0</td>\n <td>0.0</td>\n <td>1.0</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>1.0</td>\n <td>0.0</td>\n <td>0.0</td>\n </tr>\n </tbody>\n</table>\n</div>",
"text/plain": " Principal terms age Gender weekend Bechalor High School or Below \\\n0 1000 30 45 0 0 1.0 0.0 \n1 1000 30 33 1 0 0.0 0.0 \n2 1000 15 27 0 0 0.0 1.0 \n3 1000 30 28 1 1 0.0 0.0 \n4 1000 30 29 0 1 1.0 0.0 \n\n college \n0 0.0 \n1 0.0 \n2 0.0 \n3 1.0 \n4 0.0 "
},
"execution_count": 111,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "X_test_final = Feature_test\nX_test_final[0:5]"
},
{
"cell_type": "code",
"execution_count": 112,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": "array(['PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF', 'PAIDOFF'],\n dtype=object)"
},
"execution_count": 112,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "y_test_final = df['loan_status'].values\ny_test_final[0:5]"
},
{
"cell_type": "code",
"execution_count": 113,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/preprocessing/data.py:645: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n return self.partial_fit(X, y)\n/opt/conda/envs/Python36/lib/python3.6/site-packages/ipykernel/__main__.py:1: DataConversionWarning: Data with input dtype int64, float64 were all converted to float64 by StandardScaler.\n if __name__ == '__main__':\n"
},
{
"data": {
"text/plain": "array([[ 0.52, 0.92, 2.33, -0.42, -1.21, 2.4 , -0.8 , -0.86],\n [ 0.52, 0.92, 0.34, 2.38, -1.21, -0.42, -0.8 , -0.86],\n [ 0.52, -0.96, -0.65, -0.42, -1.21, -0.42, 1.25, -0.86],\n [ 0.52, 0.92, -0.49, 2.38, 0.83, -0.42, -0.8 , 1.16],\n [ 0.52, 0.92, -0.32, -0.42, 0.83, 2.4 , -0.8 , -0.86]])"
},
"execution_count": 113,
"metadata": {},
"output_type": "execute_result"
}
],
"source": "X_test_final= preprocessing.StandardScaler().fit(X_test_final).transform(X_test_final)\nX_test_final[0:5]"
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "total null values: 876\ntotal values: 2768\ntotal non null values: 1892\n"
}
],
"source": "#Before:\nprint('total null values: ',np. isnan(X_test_final).sum())\nprint('total values: ',X_test_final.size)\nprint('total non null values: ',np.count_nonzero(~np.isnan(X_test_final)))"
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "[ 3.80e-16 8.21e-17 -2.05e-16 -4.88e-17 8.21e-17 2.47e-17 1.64e-17\n -1.64e-17]\n"
}
],
"source": "col_mean = np.nanmean(X_test_final, axis=0)\nprint(col_mean)\n#Find indices that you need to replace\ninds = np.where(np.isnan(X_test_final))\n#Place column means in the indices. Align the arrays using take\nX_test_final[inds] = np.take(col_mean, inds[1])"
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "total null values: 0\ntotal values: 2768\ntotal non null values: 2768\n"
}
],
"source": "#After:\nprint('total null values: ',np. isnan(X_test_final).sum())\nprint('total values: ',X_test_final.size)\nprint('total non null values: ',np.count_nonzero(~np.isnan(X_test_final)))"
},
{
"cell_type": "code",
"execution_count": 159,
"metadata": {},
"outputs": [],
"source": "yhat1_knn = neigh_high.predict(X_test_final)\nyhat2_Dtree = loanTree_high.predict(X_test_final)\nyhat3_svm = clf_high.predict(X_test_final)\nyhat4_LR = LR_high.predict(X_test_final)"
},
{
"cell_type": "code",
"execution_count": 160,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "jaccard KNN: 0.7283236994219653\njaccard decision tree: 0.7514450867052023\njaccard SVM : 0.7427745664739884\njaccard LR: 0.7514450867052023\n"
}
],
"source": "print('jaccard KNN: ',jaccard_similarity_score(y_test_final, yhat1_knn))\nprint('jaccard decision tree:',jaccard_similarity_score(y_test_final,yhat2_Dtree))\nprint('jaccard SVM :',jaccard_similarity_score(y_test_final, yhat3_svm))\nprint('jaccard LR: ',jaccard_similarity_score(y_test_final, yhat4_LR))"
},
{
"cell_type": "code",
"execution_count": 168,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "f1 score knn: 0.7169912838520613\nf1 score Dtree: 0.673085302317342\nf1 score svm: 0.6405352812047661\nf1 score LR: 0.6448043648295465\n"
},
{
"name": "stderr",
"output_type": "stream",
"text": "/opt/conda/envs/Python36/lib/python3.6/site-packages/sklearn/metrics/classification.py:1143: UndefinedMetricWarning: F-score is ill-defined and being set to 0.0 in labels with no predicted samples.\n 'precision', 'predicted', average, warn_for)\n"
}
],
"source": "print('f1 score knn: ',f1_score(y_test_final, yhat1_knn, average='weighted')) \nprint('f1 score Dtree:',f1_score(y_test_final, yhat2_Dtree,average='weighted'))\nprint('f1 score svm: ',f1_score(y_test_final, yhat3_svm, average='weighted'))\nprint('f1 score LR: ',f1_score(y_test_final, yhat4_LR, average='weighted'))"
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {},
"outputs": [],
"source": "#yhat1_prob_knn = neighTree_high.predict_proba(X_test_final)\nyhat2_prob_dtree = loanTree_high.predict_proba(X_test_final)\n#yhat3_prob_svm = clf_high.predict_proba(X_test_final)\nyhat4_prob_LR= LR_high.predict_proba(X_test_final)\n"
},
{
"cell_type": "code",
"execution_count": 170,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": "dtree log loss: 0.6704484261331349\nLR log loss: 0.4933433171847397\n"
}
],
"source": "print('dtree log loss: ',log_loss(y_test_final, yhat2_prob_dtree))\nprint('LR log loss: ',log_loss(y_test_final, yhat4_prob_LR))"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "# Report\nYou should be able to report the accuracy of the built model using different evaluation metrics:"
},
{
"cell_type": "markdown",
"metadata": {},
"source": "| Algorithm | Jaccard | F1-score | LogLoss |\n|--------------------|---------|----------|---------|\n| KNN | 0.73 | 0.72 | NA |\n| Decision Tree | 0.75 | 0.67 | NA |\n| SVM | 0.74 | 0.64 | NA |\n| LogisticRegression | 0.75 | 0.64 | 0.49 |"
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": "<h2>Want to learn more?</h2>\n\nIBM SPSS Modeler is a comprehensive analytics platform that has many machine learning algorithms. It has been designed to bring predictive intelligence to decisions made by individuals, by groups, by systems \u2013 by your enterprise as a whole. A free trial is available through this course, available here: <a href=\"http://cocl.us/ML0101EN-SPSSModeler\">SPSS Modeler</a>\n\nAlso, you can use Watson Studio to run these notebooks faster with bigger datasets. Watson Studio is IBM's leading cloud solution for data scientists, built by data scientists. With Jupyter notebooks, RStudio, Apache Spark and popular libraries pre-packaged in the cloud, Watson Studio enables data scientists to collaborate on their projects without having to install anything. Join the fast-growing community of Watson Studio users today with a free account at <a href=\"https://cocl.us/ML0101EN_DSX\">Watson Studio</a>\n\n<h3>Thanks for completing this lesson!</h3>\n\n<h4>Author: <a href=\"https://ca.linkedin.com/in/saeedaghabozorgi\">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\u2019 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<p>Copyright &copy; 2018 <a href=\"https://cocl.us/DX0108EN_CC\">Cognitive Class</a>. This notebook and its source code are released under the terms of the <a href=\"https://bigdatauniversity.com/mit-license/\">MIT License</a>.</p>"
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3.6",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.9"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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