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MAR.assignment
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "5e877900",
"metadata": {},
"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": "code",
"execution_count": 2,
"id": "424793a7",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"--2022-09-24 23:04:47-- 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)... 169.63.118.104\n",
"Connecting to cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud (cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud)|169.63.118.104|: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% 298K=0,08s\n",
"\n",
"2022-09-24 23:04:49 (298 KB/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\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "163ff4ea",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</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",
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" <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",
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" </tr>\n",
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" <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": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv('loan_train.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "ed978675",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(346, 10)"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "29e95961",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></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": 5,
"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": "code",
"execution_count": 6,
"id": "59a7c15a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"PAIDOFF 260\n",
"COLLECTION 86\n",
"Name: loan_status, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['loan_status'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "73e9d28f",
"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"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n",
"EnvironmentNotWritableError: The current user does not have write permissions to the target environment.\n",
" environment location: C:\\ProgramData\\Anaconda3\n",
"\n",
"\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"\n",
" added / updated specs:\n",
" - seaborn\n",
"\n",
"\n",
"The following packages will be SUPERSEDED by a higher-priority channel:\n",
"\n",
" certifi pkgs/main --> anaconda\n",
" seaborn pkgs/main --> anaconda\n",
"\n",
"\n",
"Preparing transaction: ...working... done\n",
"Verifying transaction: ...working... failed\n"
]
}
],
"source": [
"# notice: installing seaborn might takes a few minutes\n",
"!conda install -c anaconda seaborn -y"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "bdb53730",
"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": 9,
"id": "48851b1b",
"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": [
"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": "code",
"execution_count": 10,
"id": "49ea31be",
"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": [
"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()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "5ffea4c4",
"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>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": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['weekend'] = df['dayofweek'].apply(lambda x: 1 if (x>3) else 0)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "8523486f",
"metadata": {},
"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": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(['Gender'])['loan_status'].value_counts(normalize=True)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "8f99736e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <td>2016-10-07</td>\n",
" <td>45</td>\n",
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" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
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" <td>2</td>\n",
" <td>PAIDOFF</td>\n",
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" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
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" <td>1000</td>\n",
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" <td>2016-09-08</td>\n",
" <td>2016-09-22</td>\n",
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" <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": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Gender'].replace(to_replace=['male','female'], value=[0,1],inplace=True)\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "0a94d5c0",
"metadata": {},
"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": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.groupby(['education'])['loan_status'].value_counts(normalize=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "c735b624",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" <td>0</td>\n",
" <td>college</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1000</td>\n",
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" <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": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[['Principal','terms','age','Gender','education']].head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "f5c63c41",
"metadata": {},
"outputs": [
{
"data": {
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