Search is not available for this dataset
text
stringlengths 1.33k
102M
| id
stringlengths 22
24
| file_path
stringclasses 62
values |
|---|---|---|
{
"cells": [
{
"cell_type": "markdown",
"id": "11f18672",
"metadata": {
"papermill": {
"duration": 0.004011,
"end_time": "2022-08-31T13:03:04.197896",
"exception": false,
"start_time": "2022-08-31T13:03:04.193885",
"status": "completed"
},
"tags": []
},
"source": [
"# <h1 style='background:red; border:0; border-radius: 10px; color:black'><center> INTRODUCTION </center></h1>\n",
"## **What is a Time Series??**👀\n",
"\n",
"- Time-series is series of obeservations that are recorded over a period of time. these observations are dependent of time component which can not be neglected thus we have have to analysis the this data keeping time component in mind.\n",
"\n",
"<img src = \"https://miro.medium.com/max/1400/0*j8LjgYr1r1xPrJkr.gif\" width = 900 height = 400/>\n",
"\n",
"### **Time Series Forecasting**\n",
"\n",
"- Time series forecasting is parhaps one of the most common type of machine learning techniques used in real-world scenarios. `time-sereis forecsting refers to predicting future values from historical data by statical analysis of trends and patterns from certain time-series data.` it falls under unsupervised learning category but called as a self-supervised learning or supervised learning technique. time-series data can be much complex to find patterns out of it, this is because irregular component of time series. \n",
"\n",
"## **Use-cases and applications:**\n",
"- Forecast product demand\n",
"- Economic growth and population forecasting\n",
"- Weather forecasting\n",
"- Stock price forecasting\n",
"- Sales/Revenue forecasting\n",
"- Web-traffic forecasting\n",
"\n",
"\n",
"## **Problem-statement**\n",
"\n",
"- In this notebook, our problem-statement is to analyse S&P500 stock prices (We will analyze 10 popular stocks and forecast the future prices) and build forecasting models that beat the market.\n",
"\n",
"roadmap: 1)explore the data 2) analysis of the data 3) interpretation 4) forecasting models (why some models and some don't, experiments and exaplanation with useful tips)\n"
]
},
{
"cell_type": "markdown",
"id": "474cb740",
"metadata": {
"papermill": {
"duration": 0.002799,
"end_time": "2022-08-31T13:03:04.204347",
"exception": false,
"start_time": "2022-08-31T13:03:04.201548",
"status": "completed"
},
"tags": []
},
"source": [
"*this notebook is under progress!⚠*"
]
},
{
"cell_type": "markdown",
"id": "55b8300e",
"metadata": {
"papermill": {
"duration": 0.002668,
"end_time": "2022-08-31T13:03:04.210078",
"exception": false,
"start_time": "2022-08-31T13:03:04.207410",
"status": "completed"
},
"tags": []
},
"source": [
"# <h1 style='background:green; border:0; border-radius: 10px; color:black'><center> Importing Libraries</center></h1>"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "2f0f82fd",
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2022-08-31T13:03:04.219105Z",
"iopub.status.busy": "2022-08-31T13:03:04.218190Z",
"iopub.status.idle": "2022-08-31T13:03:05.632695Z",
"shell.execute_reply": "2022-08-31T13:03:05.631677Z"
},
"papermill": {
"duration": 1.422706,
"end_time": "2022-08-31T13:03:05.635801",
"exception": false,
"start_time": "2022-08-31T13:03:04.213095",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import datetime\n",
"\n",
"%matplotlib inline\n",
"\n",
"# matplotlib defaults\n",
"plt.style.use(\"fivethirtyeight\")\n",
"plt.rc(\"figure\", autolayout=True)\n",
"plt.rc(\n",
" \"axes\",\n",
" labelweight=\"bold\",\n",
" labelsize=\"large\",\n",
" titleweight=\"bold\",\n",
" titlesize=14,\n",
" titlepad=10,\n",
")\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "4f63be26",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:05.644067Z",
"iopub.status.busy": "2022-08-31T13:03:05.643506Z",
"iopub.status.idle": "2022-08-31T13:03:06.489583Z",
"shell.execute_reply": "2022-08-31T13:03:06.488622Z"
},
"papermill": {
"duration": 0.852542,
"end_time": "2022-08-31T13:03:06.491769",
"exception": false,
"start_time": "2022-08-31T13:03:05.639227",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<style type=\"text/css\">\n",
"#T_10a74_row0_col0, #T_10a74_row0_col1, #T_10a74_row0_col2, #T_10a74_row0_col3, #T_10a74_row0_col4, #T_10a74_row0_col5, #T_10a74_row0_col6, #T_10a74_row1_col0, #T_10a74_row1_col1, #T_10a74_row1_col2, #T_10a74_row1_col3, #T_10a74_row1_col4, #T_10a74_row1_col5, #T_10a74_row1_col6, #T_10a74_row2_col0, #T_10a74_row2_col1, #T_10a74_row2_col2, #T_10a74_row2_col3, #T_10a74_row2_col4, #T_10a74_row2_col5, #T_10a74_row2_col6, #T_10a74_row3_col0, #T_10a74_row3_col1, #T_10a74_row3_col2, #T_10a74_row3_col3, #T_10a74_row3_col4, #T_10a74_row3_col5, #T_10a74_row3_col6, #T_10a74_row4_col0, #T_10a74_row4_col1, #T_10a74_row4_col2, #T_10a74_row4_col3, #T_10a74_row4_col4, #T_10a74_row4_col5, #T_10a74_row4_col6 {\n",
" background-color: black;\n",
" color: lawngreen;\n",
" border: 1.5px white;\n",
"}\n",
"</style>\n",
"<table id=\"T_10a74_\">\n",
" <thead>\n",
" <tr>\n",
" <th class=\"blank level0\" > </th>\n",
" <th class=\"col_heading level0 col0\" >date</th>\n",
" <th class=\"col_heading level0 col1\" >open</th>\n",
" <th class=\"col_heading level0 col2\" >high</th>\n",
" <th class=\"col_heading level0 col3\" >low</th>\n",
" <th class=\"col_heading level0 col4\" >close</th>\n",
" <th class=\"col_heading level0 col5\" >volume</th>\n",
" <th class=\"col_heading level0 col6\" >Name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th id=\"T_10a74_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
" <td id=\"T_10a74_row0_col0\" class=\"data row0 col0\" >2013-02-08</td>\n",
" <td id=\"T_10a74_row0_col1\" class=\"data row0 col1\" >15.070000</td>\n",
" <td id=\"T_10a74_row0_col2\" class=\"data row0 col2\" >15.120000</td>\n",
" <td id=\"T_10a74_row0_col3\" class=\"data row0 col3\" >14.630000</td>\n",
" <td id=\"T_10a74_row0_col4\" class=\"data row0 col4\" >14.750000</td>\n",
" <td id=\"T_10a74_row0_col5\" class=\"data row0 col5\" >8407500</td>\n",
" <td id=\"T_10a74_row0_col6\" class=\"data row0 col6\" >AAL</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_10a74_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
" <td id=\"T_10a74_row1_col0\" class=\"data row1 col0\" >2013-02-11</td>\n",
" <td id=\"T_10a74_row1_col1\" class=\"data row1 col1\" >14.890000</td>\n",
" <td id=\"T_10a74_row1_col2\" class=\"data row1 col2\" >15.010000</td>\n",
" <td id=\"T_10a74_row1_col3\" class=\"data row1 col3\" >14.260000</td>\n",
" <td id=\"T_10a74_row1_col4\" class=\"data row1 col4\" >14.460000</td>\n",
" <td id=\"T_10a74_row1_col5\" class=\"data row1 col5\" >8882000</td>\n",
" <td id=\"T_10a74_row1_col6\" class=\"data row1 col6\" >AAL</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_10a74_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
" <td id=\"T_10a74_row2_col0\" class=\"data row2 col0\" >2013-02-12</td>\n",
" <td id=\"T_10a74_row2_col1\" class=\"data row2 col1\" >14.450000</td>\n",
" <td id=\"T_10a74_row2_col2\" class=\"data row2 col2\" >14.510000</td>\n",
" <td id=\"T_10a74_row2_col3\" class=\"data row2 col3\" >14.100000</td>\n",
" <td id=\"T_10a74_row2_col4\" class=\"data row2 col4\" >14.270000</td>\n",
" <td id=\"T_10a74_row2_col5\" class=\"data row2 col5\" >8126000</td>\n",
" <td id=\"T_10a74_row2_col6\" class=\"data row2 col6\" >AAL</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_10a74_level0_row3\" class=\"row_heading level0 row3\" >3</th>\n",
" <td id=\"T_10a74_row3_col0\" class=\"data row3 col0\" >2013-02-13</td>\n",
" <td id=\"T_10a74_row3_col1\" class=\"data row3 col1\" >14.300000</td>\n",
" <td id=\"T_10a74_row3_col2\" class=\"data row3 col2\" >14.940000</td>\n",
" <td id=\"T_10a74_row3_col3\" class=\"data row3 col3\" >14.250000</td>\n",
" <td id=\"T_10a74_row3_col4\" class=\"data row3 col4\" >14.660000</td>\n",
" <td id=\"T_10a74_row3_col5\" class=\"data row3 col5\" >10259500</td>\n",
" <td id=\"T_10a74_row3_col6\" class=\"data row3 col6\" >AAL</td>\n",
" </tr>\n",
" <tr>\n",
" <th id=\"T_10a74_level0_row4\" class=\"row_heading level0 row4\" >4</th>\n",
" <td id=\"T_10a74_row4_col0\" class=\"data row4 col0\" >2013-02-14</td>\n",
" <td id=\"T_10a74_row4_col1\" class=\"data row4 col1\" >14.940000</td>\n",
" <td id=\"T_10a74_row4_col2\" class=\"data row4 col2\" >14.960000</td>\n",
" <td id=\"T_10a74_row4_col3\" class=\"data row4 col3\" >13.160000</td>\n",
" <td id=\"T_10a74_row4_col4\" class=\"data row4 col4\" >13.990000</td>\n",
" <td id=\"T_10a74_row4_col5\" class=\"data row4 col5\" >31879900</td>\n",
" <td id=\"T_10a74_row4_col6\" class=\"data row4 col6\" >AAL</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n"
],
"text/plain": [
"<pandas.io.formats.style.Styler at 0x7f1b99ab2710>"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# read the dataframe\n",
"df = pd.read_csv(\"../input/sandp500/all_stocks_5yr.csv\")\n",
"df.head().style.set_properties(**{'background-color': 'black',\n",
" 'color': 'lawngreen',\n",
" 'border': '1.5px white'})"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "983ab22a",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:06.503127Z",
"iopub.status.busy": "2022-08-31T13:03:06.502428Z",
"iopub.status.idle": "2022-08-31T13:03:06.509392Z",
"shell.execute_reply": "2022-08-31T13:03:06.507987Z"
},
"papermill": {
"duration": 0.01425,
"end_time": "2022-08-31T13:03:06.511901",
"exception": false,
"start_time": "2022-08-31T13:03:06.497651",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"def information_func(df):\n",
" \n",
" # unique stocks\n",
" print(\"Uniques stocks available in dataset:\", df['Name'].nunique())\n",
" print(\"----\"*20)\n",
" \n",
" # metadata of dataset\n",
" print(\"Metadata of the dataset:\\n\")\n",
" df.info()\n",
" print(\"----\"*20)\n",
" \n",
" # missing values\n",
" null = df.isnull().sum()\n",
" print(null)\n",
" print(\"----\"*20)\n",
" \n",
" # max range of stocks dataset\n",
" delta = (pd.to_datetime(df['date']).max() - pd.to_datetime(df['date']).min())\n",
" print(\"Time range of stocks dataset:\\n\", delta)\n",
" print(\"----\"*20) "
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c2c5267e",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:06.520432Z",
"iopub.status.busy": "2022-08-31T13:03:06.520075Z",
"iopub.status.idle": "2022-08-31T13:03:06.879161Z",
"shell.execute_reply": "2022-08-31T13:03:06.878229Z"
},
"papermill": {
"duration": 0.366398,
"end_time": "2022-08-31T13:03:06.881978",
"exception": false,
"start_time": "2022-08-31T13:03:06.515580",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Uniques stocks available in dataset: 505\n",
"--------------------------------------------------------------------------------\n",
"Metadata of the dataset:\n",
"\n",
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 619040 entries, 0 to 619039\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 date 619040 non-null object \n",
" 1 open 619029 non-null float64\n",
" 2 high 619032 non-null float64\n",
" 3 low 619032 non-null float64\n",
" 4 close 619040 non-null float64\n",
" 5 volume 619040 non-null int64 \n",
" 6 Name 619040 non-null object \n",
"dtypes: float64(4), int64(1), object(2)\n",
"memory usage: 33.1+ MB\n",
"--------------------------------------------------------------------------------\n",
"date 0\n",
"open 11\n",
"high 8\n",
"low 8\n",
"close 0\n",
"volume 0\n",
"Name 0\n",
"dtype: int64\n",
"--------------------------------------------------------------------------------\n",
"Time range of stocks dataset:\n",
" 1825 days 00:00:00\n",
"--------------------------------------------------------------------------------\n"
]
}
],
"source": [
"information_func(df)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "721da1d6",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:06.891690Z",
"iopub.status.busy": "2022-08-31T13:03:06.890996Z",
"iopub.status.idle": "2022-08-31T13:03:07.142882Z",
"shell.execute_reply": "2022-08-31T13:03:07.141161Z"
},
"papermill": {
"duration": 0.259717,
"end_time": "2022-08-31T13:03:07.145860",
"exception": false,
"start_time": "2022-08-31T13:03:06.886143",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"Int64Index: 619029 entries, 0 to 619039\n",
"Data columns (total 7 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 date 619029 non-null datetime64[ns]\n",
" 1 open 619029 non-null float64 \n",
" 2 high 619029 non-null float64 \n",
" 3 low 619029 non-null float64 \n",
" 4 close 619029 non-null float64 \n",
" 5 volume 619029 non-null int64 \n",
" 6 ticks 619029 non-null object \n",
"dtypes: datetime64[ns](1), float64(4), int64(1), object(1)\n",
"memory usage: 37.8+ MB\n"
]
}
],
"source": [
"# rename Name to ticks\n",
"rdf = df.rename(columns={'Name':'ticks'})\n",
"\n",
"# drop the null as they a few values and time-series won't be affected by such values\n",
"rdf.dropna(inplace=True)\n",
"\n",
"# change the dtype of date column\n",
"new_df = rdf.copy()\n",
"new_df.loc[:, 'date'] = pd.to_datetime(rdf.loc[:, 'date'], format='%Y/%m/%d')\n",
"\n",
"# new dataframe info\n",
"new_df.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "97054a47",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:07.155069Z",
"iopub.status.busy": "2022-08-31T13:03:07.154698Z",
"iopub.status.idle": "2022-08-31T13:03:07.161277Z",
"shell.execute_reply": "2022-08-31T13:03:07.159958Z"
},
"papermill": {
"duration": 0.014603,
"end_time": "2022-08-31T13:03:07.164281",
"exception": false,
"start_time": "2022-08-31T13:03:07.149678",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# Find top 10 most traded stocks for analysis\n",
"# high and low charts\n",
"# time-duration of total data available"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "4fa3786f",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:07.173350Z",
"iopub.status.busy": "2022-08-31T13:03:07.172986Z",
"iopub.status.idle": "2022-08-31T13:03:07.216812Z",
"shell.execute_reply": "2022-08-31T13:03:07.215335Z"
},
"papermill": {
"duration": 0.051544,
"end_time": "2022-08-31T13:03:07.219224",
"exception": false,
"start_time": "2022-08-31T13:03:07.167680",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"2457501.4948371723"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# (NEXT STEP)find the average volume of each stocks using function and sort top 10 most traded stocks for further analysis \n",
"new_df[new_df['ticks'] == 'GOOGL']['volume'].mean()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "60aa8da3",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:07.228699Z",
"iopub.status.busy": "2022-08-31T13:03:07.228167Z",
"iopub.status.idle": "2022-08-31T13:03:07.233347Z",
"shell.execute_reply": "2022-08-31T13:03:07.231850Z"
},
"papermill": {
"duration": 0.012527,
"end_time": "2022-08-31T13:03:07.235534",
"exception": false,
"start_time": "2022-08-31T13:03:07.223007",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"# question to answer, does daily change in stock price is normally distributed and is mean of that\n",
"# distribution is zero??"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.12"
},
"papermill": {
"default_parameters": {},
"duration": 15.074175,
"end_time": "2022-08-31T13:03:07.960323",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2022-08-31T13:02:52.886148",
"version": "2.3.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| 0104/601/104601398.ipynb | s3://data-agents/kaggle-outputs/sharded/025_00104.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "c6acee78",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:48.683350Z",
"iopub.status.busy": "2022-08-31T13:03:48.682883Z",
"iopub.status.idle": "2022-08-31T13:03:50.338057Z",
"shell.execute_reply": "2022-08-31T13:03:50.336969Z"
},
"papermill": {
"duration": 1.663554,
"end_time": "2022-08-31T13:03:50.340568",
"exception": false,
"start_time": "2022-08-31T13:03:48.677014",
"status": "completed"
},
"tags": []
},
"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>manufacturer</th>\n",
" <th>year</th>\n",
" <th>units_sold_m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2003</td>\n",
" <td>250.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2005</td>\n",
" <td>247.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>2014</td>\n",
" <td>224.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>2013</td>\n",
" <td>200.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>11</td>\n",
" <td>2015</td>\n",
" <td>174.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" manufacturer year units_sold_m\n",
"0 1 2003 250.0\n",
"1 1 2005 247.5\n",
"2 1 2014 224.0\n",
"3 1 2013 200.0\n",
"4 11 2015 174.1"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas as pd\n",
"from matplotlib import pyplot as plt\n",
"import seaborn as sns\n",
"from sklearn.tree import DecisionTreeRegressor\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score\n",
"df=pd.read_csv('../input/tigerk/parrot.csv')\n",
"df.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "e63f0f09",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:50.348878Z",
"iopub.status.busy": "2022-08-31T13:03:50.348468Z",
"iopub.status.idle": "2022-08-31T13:03:50.381768Z",
"shell.execute_reply": "2022-08-31T13:03:50.380751Z"
},
"papermill": {
"duration": 0.039803,
"end_time": "2022-08-31T13:03:50.383987",
"exception": false,
"start_time": "2022-08-31T13:03:50.344184",
"status": "completed"
},
"tags": []
},
"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>manufacturer</th>\n",
" <th>year</th>\n",
" <th>units_sold_m</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>59.000000</td>\n",
" <td>59.000000</td>\n",
" <td>59.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>3.677966</td>\n",
" <td>2010.576271</td>\n",
" <td>85.369492</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>6.257246</td>\n",
" <td>6.286832</td>\n",
" <td>62.983532</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1996.000000</td>\n",
" <td>21.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>1.000000</td>\n",
" <td>2005.000000</td>\n",
" <td>33.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2.000000</td>\n",
" <td>2010.000000</td>\n",
" <td>60.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2.000000</td>\n",
" <td>2016.500000</td>\n",
" <td>140.600000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>33.000000</td>\n",
" <td>2020.000000</td>\n",
" <td>250.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" manufacturer year units_sold_m\n",
"count 59.000000 59.000000 59.000000\n",
"mean 3.677966 2010.576271 85.369492\n",
"std 6.257246 6.286832 62.983532\n",
"min 1.000000 1996.000000 21.000000\n",
"25% 1.000000 2005.000000 33.500000\n",
"50% 2.000000 2010.000000 60.000000\n",
"75% 2.000000 2016.500000 140.600000\n",
"max 33.000000 2020.000000 250.000000"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "eaa75e45",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:50.392511Z",
"iopub.status.busy": "2022-08-31T13:03:50.392119Z",
"iopub.status.idle": "2022-08-31T13:03:50.688203Z",
"shell.execute_reply": "2022-08-31T13:03:50.687250Z"
},
"papermill": {
"duration": 0.303005,
"end_time": "2022-08-31T13:03:50.690485",
"exception": false,
"start_time": "2022-08-31T13:03:50.387480",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(df['units_sold_m'])\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "6bd19d67",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:50.700740Z",
"iopub.status.busy": "2022-08-31T13:03:50.700049Z",
"iopub.status.idle": "2022-08-31T13:03:50.929580Z",
"shell.execute_reply": "2022-08-31T13:03:50.928874Z"
},
"papermill": {
"duration": 0.237108,
"end_time": "2022-08-31T13:03:50.931555",
"exception": false,
"start_time": "2022-08-31T13:03:50.694447",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
" warnings.warn(msg, FutureWarning)\n"
]
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(df['year'])\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "aac13435",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:50.941323Z",
"iopub.status.busy": "2022-08-31T13:03:50.940871Z",
"iopub.status.idle": "2022-08-31T13:03:50.950986Z",
"shell.execute_reply": "2022-08-31T13:03:50.950005Z"
},
"papermill": {
"duration": 0.017463,
"end_time": "2022-08-31T13:03:50.953231",
"exception": false,
"start_time": "2022-08-31T13:03:50.935768",
"status": "completed"
},
"tags": []
},
"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>manufacturer</th>\n",
" <th>year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>2005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>2014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>2013</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>11</td>\n",
" <td>2015</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" manufacturer year\n",
"0 1 2003\n",
"1 1 2005\n",
"2 1 2014\n",
"3 1 2013\n",
"4 11 2015"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X=df.drop(labels='units_sold_m',axis=1)\n",
"X\n",
"X.head()\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "84d90d4b",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:50.964026Z",
"iopub.status.busy": "2022-08-31T13:03:50.963598Z",
"iopub.status.idle": "2022-08-31T13:03:50.972161Z",
"shell.execute_reply": "2022-08-31T13:03:50.971267Z"
},
"papermill": {
"duration": 0.01685,
"end_time": "2022-08-31T13:03:50.974478",
"exception": false,
"start_time": "2022-08-31T13:03:50.957628",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"y=df['units_sold_m']\n",
"y.head()\n",
"X_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.2)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "2b714dd2",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:03:50.985760Z",
"iopub.status.busy": "2022-08-31T13:03:50.985207Z",
"iopub.status.idle": "2022-08-31T13:03:51.000329Z",
"shell.execute_reply": "2022-08-31T13:03:50.999351Z"
},
"papermill": {
"duration": 0.023854,
"end_time": "2022-08-31T13:03:51.003132",
"exception": false,
"start_time": "2022-08-31T13:03:50.979278",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/sklearn/base.py:451: UserWarning: X does not have valid feature names, but DecisionTreeRegressor was fitted with feature names\n",
" \"X does not have valid feature names, but\"\n"
]
},
{
"data": {
"text/plain": [
"array([224.])"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model=DecisionTreeRegressor(random_state=8)\n",
"\n",
"model.fit(X,y)\n",
"predictions=model.predict([[1 ,2015]])\n",
"predictions"
]
},
{
"cell_type": "markdown",
"id": "a170bf0a",
"metadata": {
"papermill": {
"duration": 0.004706,
"end_time": "2022-08-31T13:03:51.012551",
"exception": false,
"start_time": "2022-08-31T13:03:51.007845",
"status": "completed"
},
"tags": []
},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "71d13e38",
"metadata": {
"papermill": {
"duration": 0.00431,
"end_time": "2022-08-31T13:03:51.021723",
"exception": false,
"start_time": "2022-08-31T13:03:51.017413",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"id": "b048fbe4",
"metadata": {
"papermill": {
"duration": 0.004194,
"end_time": "2022-08-31T13:03:51.030427",
"exception": false,
"start_time": "2022-08-31T13:03:51.026233",
"status": "completed"
},
"tags": []
},
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.12"
},
"papermill": {
"default_parameters": {},
"duration": 13.237128,
"end_time": "2022-08-31T13:03:51.756072",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2022-08-31T13:03:38.518944",
"version": "2.3.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| 0104/601/104601444.ipynb | s3://data-agents/kaggle-outputs/sharded/025_00104.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "889be2d2",
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2022-08-31T13:06:52.566256Z",
"iopub.status.busy": "2022-08-31T13:06:52.565762Z",
"iopub.status.idle": "2022-08-31T13:06:52.596335Z",
"shell.execute_reply": "2022-08-31T13:06:52.595435Z"
},
"papermill": {
"duration": 0.043594,
"end_time": "2022-08-31T13:06:52.599822",
"exception": false,
"start_time": "2022-08-31T13:06:52.556228",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/lending-club/rejected_2007_to_2018Q4.csv.gz\n",
"/kaggle/input/lending-club/accepted_2007_to_2018Q4.csv.gz\n",
"/kaggle/input/lending-club/accepted_2007_to_2018q4.csv/accepted_2007_to_2018Q4.csv\n",
"/kaggle/input/lending-club/rejected_2007_to_2018q4.csv/rejected_2007_to_2018Q4.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
"cell_type": "markdown",
"id": "14fa9e1b",
"metadata": {
"papermill": {
"duration": 0.006527,
"end_time": "2022-08-31T13:06:52.614518",
"exception": false,
"start_time": "2022-08-31T13:06:52.607991",
"status": "completed"
},
"tags": []
},
"source": [
"This series of articles walks you through end to end process to build a data science project based on a real business problem. The business problem along with its scientific solution is focused around Fintech Industry. You could find all articles on my medium here - https://medium.com/@data.science.enthusiast"
]
},
{
"cell_type": "markdown",
"id": "b3cea6d3",
"metadata": {
"papermill": {
"duration": 0.006314,
"end_time": "2022-08-31T13:06:52.627474",
"exception": false,
"start_time": "2022-08-31T13:06:52.621160",
"status": "completed"
},
"tags": []
},
"source": [
"# Dataset preparation"
]
},
{
"cell_type": "markdown",
"id": "2e4a6881",
"metadata": {
"papermill": {
"duration": 0.006312,
"end_time": "2022-08-31T13:06:52.640489",
"exception": false,
"start_time": "2022-08-31T13:06:52.634177",
"status": "completed"
},
"tags": []
},
"source": [
"Let's load the dataset and import all the required packages:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "6a5c158e",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:06:52.655806Z",
"iopub.status.busy": "2022-08-31T13:06:52.654844Z",
"iopub.status.idle": "2022-08-31T13:07:58.083736Z",
"shell.execute_reply": "2022-08-31T13:07:58.082452Z"
},
"papermill": {
"duration": 65.43977,
"end_time": "2022-08-31T13:07:58.086867",
"exception": false,
"start_time": "2022-08-31T13:06:52.647097",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/opt/conda/lib/python3.7/site-packages/IPython/core/interactiveshell.py:3552: DtypeWarning: Columns (0,19,49,59,118,129,130,131,134,135,136,139,145,146,147) have mixed types.Specify dtype option on import or set low_memory=False.\n",
" exec(code_obj, self.user_global_ns, self.user_ns)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 2260701 entries, 0 to 2260700\n",
"Columns: 151 entries, id to settlement_term\n",
"dtypes: float64(113), object(38)\n",
"memory usage: 2.5+ GB\n"
]
}
],
"source": [
"#load packages and set pd formats\n",
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"#pd.set_option('display.float_format', lambda x: '%.0f' % x)\n",
"from scipy import stats\n",
"\n",
"#load dataset\n",
"loan = pd.read_csv('../input/lending-club/accepted_2007_to_2018Q4.csv.gz', compression='gzip', low_memory=True)\n",
"loan.info()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a55b5f97",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:07:58.102754Z",
"iopub.status.busy": "2022-08-31T13:07:58.101804Z",
"iopub.status.idle": "2022-08-31T13:07:58.294963Z",
"shell.execute_reply": "2022-08-31T13:07:58.293688Z"
},
"papermill": {
"duration": 0.204104,
"end_time": "2022-08-31T13:07:58.297775",
"exception": false,
"start_time": "2022-08-31T13:07:58.093671",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#consider only business critical features\n",
"loans = loan[['id', 'loan_amnt', 'term','int_rate', 'sub_grade','home_ownership', 'addr_state','emp_length','grade', 'annual_inc', 'loan_status', 'dti',\n",
"'mths_since_recent_inq', 'revol_util', 'bc_open_to_buy', 'bc_util', 'num_op_rev_tl']]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e4d84f3f",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:07:58.318034Z",
"iopub.status.busy": "2022-08-31T13:07:58.317298Z",
"iopub.status.idle": "2022-08-31T13:08:04.206549Z",
"shell.execute_reply": "2022-08-31T13:08:04.205255Z"
},
"papermill": {
"duration": 5.902938,
"end_time": "2022-08-31T13:08:04.209296",
"exception": false,
"start_time": "2022-08-31T13:07:58.306358",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"#preprocessing\n",
"\n",
"#remove missing values\n",
"loans = loans.dropna()\n",
"\n",
"#remove outliers\n",
"q_low = loans[\"annual_inc\"].quantile(0.08)\n",
"q_hi = loans[\"annual_inc\"].quantile(0.92)\n",
"loans = loans[(loans[\"annual_inc\"] < q_hi) & (loans[\"annual_inc\"] > q_low)]\n",
"loans = loans[(loans['dti'] <=45)]\n",
"q_hi = loans['bc_open_to_buy'].quantile(0.95)\n",
"loans = loans[(loans['bc_open_to_buy'] < q_hi)]\n",
"loans = loans[(loans['bc_util'] <=160)]\n",
"loans = loans[(loans['revol_util'] <=150)]\n",
"loans = loans[(loans['num_op_rev_tl'] <=35)]\n",
"\n",
"#categorical features processing\n",
"cleaner_app_type = {\"term\": {\" 36 months\": 1.0, \" 60 months\": 2.0},\n",
" \"sub_grade\": {\"A1\": 1.0, \"A2\": 2.0, \"A3\": 3.0, \"A4\": 4.0, \"A5\": 5.0,\n",
" \"B1\": 11.0, \"B2\": 12.0, \"B3\": 13.0, \"B4\": 14.0, \"B5\": 15.0,\n",
" \"C1\": 21.0, \"C2\": 22.0, \"C3\": 23.0, \"C4\": 24.0, \"C5\": 25.0,\n",
" \"D1\": 31.0, \"D2\": 32.0, \"D3\": 33.0, \"D4\": 34.0, \"D5\": 35.0,\n",
" \"E1\": 41.0, \"E2\": 42.0, \"E3\": 43.0, \"E4\": 44.0, \"E5\": 45.0,\n",
" \"F1\": 51.0, \"F2\": 52.0, \"F3\": 53.0, \"F4\": 54.0, \"F5\": 55.0,\n",
" \"G1\": 61.0, \"G2\": 62.0, \"G3\": 63.0, \"G4\": 64.0, \"G5\": 65.0,\n",
" },\n",
" \"emp_length\": {\"< 1 year\": 0.0, '1 year': 1.0, '2 years': 2.0, '3 years': 3.0, '4 years': 4.0, \n",
" '5 years': 5.0, '6 years': 6.0, '7 years': 7.0, '8 years': 8.0, '9 years': 9.0,\n",
" '10+ years': 10.0 }\n",
" }\n",
"loans = loans.replace(cleaner_app_type)"
]
},
{
"cell_type": "markdown",
"id": "84340642",
"metadata": {
"papermill": {
"duration": 0.006657,
"end_time": "2022-08-31T13:08:04.222821",
"exception": false,
"start_time": "2022-08-31T13:08:04.216164",
"status": "completed"
},
"tags": []
},
"source": [
"# Label grooming"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "8877f727",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:04.238258Z",
"iopub.status.busy": "2022-08-31T13:08:04.237855Z",
"iopub.status.idle": "2022-08-31T13:08:04.341027Z",
"shell.execute_reply": "2022-08-31T13:08:04.339786Z"
},
"papermill": {
"duration": 0.113678,
"end_time": "2022-08-31T13:08:04.343407",
"exception": false,
"start_time": "2022-08-31T13:08:04.229729",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Fully Paid 677985\n",
"Current 529702\n",
"Charged Off 177323\n",
"Late (31-120 days) 14088\n",
"In Grace Period 5583\n",
"Late (16-30 days) 2743\n",
"Default 23\n",
"Name: loan_status, dtype: int64"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"loans['loan_status'].value_counts()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4d863929",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:04.359254Z",
"iopub.status.busy": "2022-08-31T13:08:04.358842Z",
"iopub.status.idle": "2022-08-31T13:08:04.648233Z",
"shell.execute_reply": "2022-08-31T13:08:04.646837Z"
},
"papermill": {
"duration": 0.300438,
"end_time": "2022-08-31T13:08:04.650993",
"exception": false,
"start_time": "2022-08-31T13:08:04.350555",
"status": "completed"
},
"tags": []
},
"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>id</th>\n",
" <th>loan_amnt</th>\n",
" <th>term</th>\n",
" <th>int_rate</th>\n",
" <th>sub_grade</th>\n",
" <th>home_ownership</th>\n",
" <th>addr_state</th>\n",
" <th>emp_length</th>\n",
" <th>grade</th>\n",
" <th>annual_inc</th>\n",
" <th>loan_status</th>\n",
" <th>dti</th>\n",
" <th>mths_since_recent_inq</th>\n",
" <th>revol_util</th>\n",
" <th>bc_open_to_buy</th>\n",
" <th>bc_util</th>\n",
" <th>num_op_rev_tl</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>68407277</td>\n",
" <td>3600.0</td>\n",
" <td>1.0</td>\n",
" <td>13.99</td>\n",
" <td>24.0</td>\n",
" <td>MORTGAGE</td>\n",
" <td>PA</td>\n",
" <td>10.0</td>\n",
" <td>C</td>\n",
" <td>55000.0</td>\n",
" <td>Fully Paid</td>\n",
" <td>5.91</td>\n",
" <td>4.0</td>\n",
" <td>29.7</td>\n",
" <td>1506.0</td>\n",
" <td>37.2</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>68341763</td>\n",
" <td>20000.0</td>\n",
" <td>2.0</td>\n",
" <td>10.78</td>\n",
" <td>14.0</td>\n",
" <td>MORTGAGE</td>\n",
" <td>IL</td>\n",
" <td>10.0</td>\n",
" <td>B</td>\n",
" <td>63000.0</td>\n",
" <td>Fully Paid</td>\n",
" <td>10.78</td>\n",
" <td>10.0</td>\n",
" <td>56.2</td>\n",
" <td>2737.0</td>\n",
" <td>55.9</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>68476807</td>\n",
" <td>10400.0</td>\n",
" <td>2.0</td>\n",
" <td>22.45</td>\n",
" <td>51.0</td>\n",
" <td>MORTGAGE</td>\n",
" <td>PA</td>\n",
" <td>3.0</td>\n",
" <td>F</td>\n",
" <td>104433.0</td>\n",
" <td>Fully Paid</td>\n",
" <td>25.37</td>\n",
" <td>1.0</td>\n",
" <td>64.5</td>\n",
" <td>4567.0</td>\n",
" <td>77.5</td>\n",
" <td>7.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>67275481</td>\n",
" <td>20000.0</td>\n",
" <td>1.0</td>\n",
" <td>8.49</td>\n",
" <td>11.0</td>\n",
" <td>MORTGAGE</td>\n",
" <td>SC</td>\n",
" <td>10.0</td>\n",
" <td>B</td>\n",
" <td>85000.0</td>\n",
" <td>Fully Paid</td>\n",
" <td>17.61</td>\n",
" <td>8.0</td>\n",
" <td>5.7</td>\n",
" <td>13674.0</td>\n",
" <td>5.7</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>68466926</td>\n",
" <td>10000.0</td>\n",
" <td>1.0</td>\n",
" <td>6.49</td>\n",
" <td>2.0</td>\n",
" <td>RENT</td>\n",
" <td>PA</td>\n",
" <td>6.0</td>\n",
" <td>A</td>\n",
" <td>85000.0</td>\n",
" <td>Fully Paid</td>\n",
" <td>13.07</td>\n",
" <td>1.0</td>\n",
" <td>34.5</td>\n",
" <td>8182.0</td>\n",
" <td>50.1</td>\n",
" <td>13.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" id loan_amnt term int_rate sub_grade home_ownership addr_state \\\n",
"0 68407277 3600.0 1.0 13.99 24.0 MORTGAGE PA \n",
"2 68341763 20000.0 2.0 10.78 14.0 MORTGAGE IL \n",
"4 68476807 10400.0 2.0 22.45 51.0 MORTGAGE PA \n",
"7 67275481 20000.0 1.0 8.49 11.0 MORTGAGE SC \n",
"8 68466926 10000.0 1.0 6.49 2.0 RENT PA \n",
"\n",
" emp_length grade annual_inc loan_status dti mths_since_recent_inq \\\n",
"0 10.0 C 55000.0 Fully Paid 5.91 4.0 \n",
"2 10.0 B 63000.0 Fully Paid 10.78 10.0 \n",
"4 3.0 F 104433.0 Fully Paid 25.37 1.0 \n",
"7 10.0 B 85000.0 Fully Paid 17.61 8.0 \n",
"8 6.0 A 85000.0 Fully Paid 13.07 1.0 \n",
"\n",
" revol_util bc_open_to_buy bc_util num_op_rev_tl \n",
"0 29.7 1506.0 37.2 4.0 \n",
"2 56.2 2737.0 55.9 4.0 \n",
"4 64.5 4567.0 77.5 7.0 \n",
"7 5.7 13674.0 5.7 3.0 \n",
"8 34.5 8182.0 50.1 13.0 "
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"array = ['Charged Off', 'Fully Paid']\n",
"loans = loans.loc[loans['loan_status'].isin(array)]\n",
"loans.head()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "eb90f10f",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:04.667823Z",
"iopub.status.busy": "2022-08-31T13:08:04.667427Z",
"iopub.status.idle": "2022-08-31T13:08:04.925032Z",
"shell.execute_reply": "2022-08-31T13:08:04.923947Z"
},
"papermill": {
"duration": 0.269276,
"end_time": "2022-08-31T13:08:04.927816",
"exception": false,
"start_time": "2022-08-31T13:08:04.658540",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"cleaner_app_type1 = {\"loan_status\": { \"Fully Paid\": 1.0, \"Charged Off\": 0.0}}\n",
"loans = loans.replace(cleaner_app_type1)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "8c92a121",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:04.945634Z",
"iopub.status.busy": "2022-08-31T13:08:04.944723Z",
"iopub.status.idle": "2022-08-31T13:08:05.091358Z",
"shell.execute_reply": "2022-08-31T13:08:05.090168Z"
},
"papermill": {
"duration": 0.158433,
"end_time": "2022-08-31T13:08:05.094148",
"exception": false,
"start_time": "2022-08-31T13:08:04.935715",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"loans = loans.drop('grade', axis=1)\n",
"loans = loans.drop('id', axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "dfeb7db5",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:05.110450Z",
"iopub.status.busy": "2022-08-31T13:08:05.109984Z",
"iopub.status.idle": "2022-08-31T13:08:05.585545Z",
"shell.execute_reply": "2022-08-31T13:08:05.584303Z"
},
"papermill": {
"duration": 0.486501,
"end_time": "2022-08-31T13:08:05.588039",
"exception": false,
"start_time": "2022-08-31T13:08:05.101538",
"status": "completed"
},
"tags": []
},
"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>loan_amnt</th>\n",
" <th>term</th>\n",
" <th>int_rate</th>\n",
" <th>sub_grade</th>\n",
" <th>emp_length</th>\n",
" <th>annual_inc</th>\n",
" <th>loan_status</th>\n",
" <th>dti</th>\n",
" <th>mths_since_recent_inq</th>\n",
" <th>revol_util</th>\n",
" <th>...</th>\n",
" <th>addr_state__SD</th>\n",
" <th>addr_state__TN</th>\n",
" <th>addr_state__TX</th>\n",
" <th>addr_state__UT</th>\n",
" <th>addr_state__VA</th>\n",
" <th>addr_state__VT</th>\n",
" <th>addr_state__WA</th>\n",
" <th>addr_state__WI</th>\n",
" <th>addr_state__WV</th>\n",
" <th>addr_state__WY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3600.0</td>\n",
" <td>1.0</td>\n",
" <td>13.99</td>\n",
" <td>24.0</td>\n",
" <td>10.0</td>\n",
" <td>55000.0</td>\n",
" <td>1.0</td>\n",
" <td>5.91</td>\n",
" <td>4.0</td>\n",
" <td>29.7</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>20000.0</td>\n",
" <td>2.0</td>\n",
" <td>10.78</td>\n",
" <td>14.0</td>\n",
" <td>10.0</td>\n",
" <td>63000.0</td>\n",
" <td>1.0</td>\n",
" <td>10.78</td>\n",
" <td>10.0</td>\n",
" <td>56.2</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>10400.0</td>\n",
" <td>2.0</td>\n",
" <td>22.45</td>\n",
" <td>51.0</td>\n",
" <td>3.0</td>\n",
" <td>104433.0</td>\n",
" <td>1.0</td>\n",
" <td>25.37</td>\n",
" <td>1.0</td>\n",
" <td>64.5</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>20000.0</td>\n",
" <td>1.0</td>\n",
" <td>8.49</td>\n",
" <td>11.0</td>\n",
" <td>10.0</td>\n",
" <td>85000.0</td>\n",
" <td>1.0</td>\n",
" <td>17.61</td>\n",
" <td>8.0</td>\n",
" <td>5.7</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>10000.0</td>\n",
" <td>1.0</td>\n",
" <td>6.49</td>\n",
" <td>2.0</td>\n",
" <td>6.0</td>\n",
" <td>85000.0</td>\n",
" <td>1.0</td>\n",
" <td>13.07</td>\n",
" <td>1.0</td>\n",
" <td>34.5</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 69 columns</p>\n",
"</div>"
],
"text/plain": [
" loan_amnt term int_rate sub_grade emp_length annual_inc loan_status \\\n",
"0 3600.0 1.0 13.99 24.0 10.0 55000.0 1.0 \n",
"2 20000.0 2.0 10.78 14.0 10.0 63000.0 1.0 \n",
"4 10400.0 2.0 22.45 51.0 3.0 104433.0 1.0 \n",
"7 20000.0 1.0 8.49 11.0 10.0 85000.0 1.0 \n",
"8 10000.0 1.0 6.49 2.0 6.0 85000.0 1.0 \n",
"\n",
" dti mths_since_recent_inq revol_util ... addr_state__SD \\\n",
"0 5.91 4.0 29.7 ... 0 \n",
"2 10.78 10.0 56.2 ... 0 \n",
"4 25.37 1.0 64.5 ... 0 \n",
"7 17.61 8.0 5.7 ... 0 \n",
"8 13.07 1.0 34.5 ... 0 \n",
"\n",
" addr_state__TN addr_state__TX addr_state__UT addr_state__VA \\\n",
"0 0 0 0 0 \n",
"2 0 0 0 0 \n",
"4 0 0 0 0 \n",
"7 0 0 0 0 \n",
"8 0 0 0 0 \n",
"\n",
" addr_state__VT addr_state__WA addr_state__WI addr_state__WV \\\n",
"0 0 0 0 0 \n",
"2 0 0 0 0 \n",
"4 0 0 0 0 \n",
"7 0 0 0 0 \n",
"8 0 0 0 0 \n",
"\n",
" addr_state__WY \n",
"0 0 \n",
"2 0 \n",
"4 0 \n",
"7 0 \n",
"8 0 \n",
"\n",
"[5 rows x 69 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"#define the list of categorical features want to process\n",
"cat_columns = [\"home_ownership\", \"addr_state\"]\n",
"#create a new DataFrame for our processed data\n",
"df_processed = pd.get_dummies(loans, prefix_sep=\"__\",\n",
" columns=cat_columns)\n",
"df_processed.head()"
]
},
{
"cell_type": "markdown",
"id": "f3c7f7af",
"metadata": {
"papermill": {
"duration": 0.007407,
"end_time": "2022-08-31T13:08:05.604231",
"exception": false,
"start_time": "2022-08-31T13:08:05.596824",
"status": "completed"
},
"tags": []
},
"source": [
"# Income. "
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c562cc63",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:05.621697Z",
"iopub.status.busy": "2022-08-31T13:08:05.621221Z",
"iopub.status.idle": "2022-08-31T13:08:05.709734Z",
"shell.execute_reply": "2022-08-31T13:08:05.708570Z"
},
"papermill": {
"duration": 0.100327,
"end_time": "2022-08-31T13:08:05.712360",
"exception": false,
"start_time": "2022-08-31T13:08:05.612033",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"binned loan_status\n",
"30-50k 0.0 47381\n",
" 1.0 157817\n",
"50-70k 0.0 57348\n",
" 1.0 207750\n",
"70-90k 0.0 37606\n",
" 1.0 151492\n",
"90-110k 0.0 20389\n",
" 1.0 91159\n",
"110-130k 0.0 10830\n",
" 1.0 50611\n",
"130-150k 0.0 3769\n",
" 1.0 19156\n",
"Name: loan_status, dtype: int64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"bins = [30000, 50000, 70000, 90000, 110000, 130000, 150000]\n",
"labels = ['30-50k', '50-70k', '70-90k', '90-110k','110-130k','130-150k']\n",
"loans['binned'] = pd.cut(loans['annual_inc'], bins=bins, labels=labels)\n",
"analyse_income = loans.groupby(['binned','loan_status'])['loan_status'].count()\n",
"analyse_income"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "d3bc22e4",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:05.729995Z",
"iopub.status.busy": "2022-08-31T13:08:05.729603Z",
"iopub.status.idle": "2022-08-31T13:08:05.758437Z",
"shell.execute_reply": "2022-08-31T13:08:05.757197Z"
},
"papermill": {
"duration": 0.041359,
"end_time": "2022-08-31T13:08:05.761661",
"exception": false,
"start_time": "2022-08-31T13:08:05.720302",
"status": "completed"
},
"tags": []
},
"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>loan_status</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>binned</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>30-50k</th>\n",
" <td>23.090381</td>\n",
" <td>76.909619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50-70k</th>\n",
" <td>21.632755</td>\n",
" <td>78.367245</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70-90k</th>\n",
" <td>19.887043</td>\n",
" <td>80.112957</td>\n",
" </tr>\n",
" <tr>\n",
" <th>90-110k</th>\n",
" <td>18.278230</td>\n",
" <td>81.721770</td>\n",
" </tr>\n",
" <tr>\n",
" <th>110-130k</th>\n",
" <td>17.626666</td>\n",
" <td>82.373334</td>\n",
" </tr>\n",
" <tr>\n",
" <th>130-150k</th>\n",
" <td>16.440567</td>\n",
" <td>83.559433</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"loan_status 0.0 1.0\n",
"binned \n",
"30-50k 23.090381 76.909619\n",
"50-70k 21.632755 78.367245\n",
"70-90k 19.887043 80.112957\n",
"90-110k 18.278230 81.721770\n",
"110-130k 17.626666 82.373334\n",
"130-150k 16.440567 83.559433"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analyse_income = analyse_income.groupby(level=0).apply(lambda x:\n",
" 100 * x / float(x.sum()))\n",
"analyse_income = analyse_income.unstack()\n",
"analyse_income"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "0a9dbb7f",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:05.779580Z",
"iopub.status.busy": "2022-08-31T13:08:05.779144Z",
"iopub.status.idle": "2022-08-31T13:08:06.005857Z",
"shell.execute_reply": "2022-08-31T13:08:06.004314Z"
},
"papermill": {
"duration": 0.23903,
"end_time": "2022-08-31T13:08:06.008795",
"exception": false,
"start_time": "2022-08-31T13:08:05.769765",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='binned'>"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"analyse_income.plot.area()"
]
},
{
"cell_type": "markdown",
"id": "6cae7383",
"metadata": {
"papermill": {
"duration": 0.008186,
"end_time": "2022-08-31T13:08:06.025595",
"exception": false,
"start_time": "2022-08-31T13:08:06.017409",
"status": "completed"
},
"tags": []
},
"source": [
"# Employment Length."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "93281d1a",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:06.045293Z",
"iopub.status.busy": "2022-08-31T13:08:06.044052Z",
"iopub.status.idle": "2022-08-31T13:08:06.124596Z",
"shell.execute_reply": "2022-08-31T13:08:06.123172Z"
},
"papermill": {
"duration": 0.09333,
"end_time": "2022-08-31T13:08:06.127394",
"exception": false,
"start_time": "2022-08-31T13:08:06.034064",
"status": "completed"
},
"tags": []
},
"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>loan_status</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>emp_length</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0.0</th>\n",
" <td>21.525221</td>\n",
" <td>78.474779</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1.0</th>\n",
" <td>21.608556</td>\n",
" <td>78.391444</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2.0</th>\n",
" <td>20.861115</td>\n",
" <td>79.138885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3.0</th>\n",
" <td>21.185496</td>\n",
" <td>78.814504</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4.0</th>\n",
" <td>20.992975</td>\n",
" <td>79.007025</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5.0</th>\n",
" <td>20.788144</td>\n",
" <td>79.211856</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6.0</th>\n",
" <td>20.656953</td>\n",
" <td>79.343047</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7.0</th>\n",
" <td>20.817159</td>\n",
" <td>79.182841</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8.0</th>\n",
" <td>21.114681</td>\n",
" <td>78.885319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9.0</th>\n",
" <td>21.085912</td>\n",
" <td>78.914088</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10.0</th>\n",
" <td>20.129304</td>\n",
" <td>79.870696</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"loan_status 0.0 1.0\n",
"emp_length \n",
"0.0 21.525221 78.474779\n",
"1.0 21.608556 78.391444\n",
"2.0 20.861115 79.138885\n",
"3.0 21.185496 78.814504\n",
"4.0 20.992975 79.007025\n",
"5.0 20.788144 79.211856\n",
"6.0 20.656953 79.343047\n",
"7.0 20.817159 79.182841\n",
"8.0 21.114681 78.885319\n",
"9.0 21.085912 78.914088\n",
"10.0 20.129304 79.870696"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analyse_emp_length = loans.groupby(['emp_length','loan_status'])['loan_status'].count()\n",
"analyse_emp_length = analyse_emp_length.groupby(level=0).apply(lambda x:\n",
" 100 * x / float(x.sum()))\n",
"analyse_emp_length = analyse_emp_length.unstack()\n",
"analyse_emp_length\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "5c0ee9f6",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:06.146637Z",
"iopub.status.busy": "2022-08-31T13:08:06.146202Z",
"iopub.status.idle": "2022-08-31T13:08:06.374607Z",
"shell.execute_reply": "2022-08-31T13:08:06.373141Z"
},
"papermill": {
"duration": 0.240977,
"end_time": "2022-08-31T13:08:06.377186",
"exception": false,
"start_time": "2022-08-31T13:08:06.136209",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='emp_length'>"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"analyse_emp_length.plot.area()"
]
},
{
"cell_type": "markdown",
"id": "5f885e57",
"metadata": {
"papermill": {
"duration": 0.008845,
"end_time": "2022-08-31T13:08:06.395141",
"exception": false,
"start_time": "2022-08-31T13:08:06.386296",
"status": "completed"
},
"tags": []
},
"source": [
"# Home Ownership."
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "0a86ba2e",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:06.415586Z",
"iopub.status.busy": "2022-08-31T13:08:06.415136Z",
"iopub.status.idle": "2022-08-31T13:08:06.542788Z",
"shell.execute_reply": "2022-08-31T13:08:06.541590Z"
},
"papermill": {
"duration": 0.141664,
"end_time": "2022-08-31T13:08:06.545918",
"exception": false,
"start_time": "2022-08-31T13:08:06.404254",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"home_ownership loan_status\n",
"ANY 0.0 22.651934\n",
" 1.0 77.348066\n",
"MORTGAGE 0.0 18.092473\n",
" 1.0 81.907527\n",
"NONE 0.0 14.285714\n",
" 1.0 85.714286\n",
"OTHER 0.0 21.875000\n",
" 1.0 78.125000\n",
"OWN 0.0 21.205417\n",
" 1.0 78.794583\n",
"RENT 0.0 24.250178\n",
" 1.0 75.749822\n",
"Name: loan_status, dtype: float64"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"analyse_home_ownership = loans.groupby(['home_ownership','loan_status'])['loan_status'].count()\n",
"analyse_home_ownership = analyse_home_ownership.groupby(level=0).apply(lambda x:\n",
" 100 * x / float(x.sum()))\n",
"analyse_home_ownership"
]
},
{
"cell_type": "markdown",
"id": "ed597223",
"metadata": {
"papermill": {
"duration": 0.008777,
"end_time": "2022-08-31T13:08:06.563756",
"exception": false,
"start_time": "2022-08-31T13:08:06.554979",
"status": "completed"
},
"tags": []
},
"source": [
"# DTI."
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "c3ac44e3",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:06.584273Z",
"iopub.status.busy": "2022-08-31T13:08:06.583484Z",
"iopub.status.idle": "2022-08-31T13:08:06.677224Z",
"shell.execute_reply": "2022-08-31T13:08:06.676096Z"
},
"papermill": {
"duration": 0.107075,
"end_time": "2022-08-31T13:08:06.679863",
"exception": false,
"start_time": "2022-08-31T13:08:06.572788",
"status": "completed"
},
"tags": []
},
"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>loan_status</th>\n",
" <th>0.0</th>\n",
" <th>1.0</th>\n",
" </tr>\n",
" <tr>\n",
" <th>binneddti</th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1-10</th>\n",
" <td>15.538925</td>\n",
" <td>84.461075</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10-20</th>\n",
" <td>18.431276</td>\n",
" <td>81.568724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20-30</th>\n",
" <td>23.678031</td>\n",
" <td>76.321969</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30-40</th>\n",
" <td>29.672536</td>\n",
" <td>70.327464</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40-50</th>\n",
" <td>31.049251</td>\n",
" <td>68.950749</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
"loan_status 0.0 1.0\n",
"binneddti \n",
"1-10 15.538925 84.461075\n",
"10-20 18.431276 81.568724\n",
"20-30 23.678031 76.321969\n",
"30-40 29.672536 70.327464\n",
"40-50 31.049251 68.950749"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"binsdti = [1, 10, 20, 30, 40, 50]\n",
"labelsdti = ['1-10', '10-20', '20-30','30-40','40-50']\n",
"loans['binneddti'] = pd.cut(loans['dti'], bins=binsdti, labels=labelsdti)\n",
"analyse_dti = loans.groupby(['binneddti','loan_status'])['loan_status'].count()\n",
"analyse_dti = analyse_dti.groupby(level=0).apply(lambda x:\n",
" 100 * x / float(x.sum()))\n",
"analyse_dtianalyse_dti = analyse_dti.unstack()\n",
"analyse_dtianalyse_dti"
]
},
{
"cell_type": "markdown",
"id": "21552f1a",
"metadata": {
"papermill": {
"duration": 0.009055,
"end_time": "2022-08-31T13:08:06.698042",
"exception": false,
"start_time": "2022-08-31T13:08:06.688987",
"status": "completed"
},
"tags": []
},
"source": [
"# Subgrade. "
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "ac9983ac",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:06.718756Z",
"iopub.status.busy": "2022-08-31T13:08:06.718040Z",
"iopub.status.idle": "2022-08-31T13:08:07.029830Z",
"shell.execute_reply": "2022-08-31T13:08:07.028703Z"
},
"papermill": {
"duration": 0.325323,
"end_time": "2022-08-31T13:08:07.032771",
"exception": false,
"start_time": "2022-08-31T13:08:06.707448",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='sub_grade'>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAEHCAYAAABV4gY/AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAl3ElEQVR4nO3dd5wU9f3H8ddn9+44+tHuBA4EFbGLiKJiC1iwYlQ0FkQBSYwaTdSImmJs0RSjJtFfTBQ1tigWLCSKhdiVoygqgljovffbnfn8/tiFHHAncHvH3s69n4/HPm5n5jsznz2WN1++O/sdc3dERCRaYtkuQEREap7CXUQkghTuIiIRpHAXEYkghbuISATlZbsAgNatW3unTp2yXYaISE4ZN27cIndvU9m2OhHunTp1oqysLNtliIjkFDObXtU2DcuIiESQwl1EJIIU7iIiEaRwFxGJIIW7iEgEKdxFRCJoq+FuZg+a2QIz+7TCupZmNtrMvkz/bJFeb2Z2j5lNM7NPzKx7bRYvIiKV25ae+0NA383WDQNed/cuwOvpZYATgC7px1DgvpopU0REtsdWv8Tk7m+ZWafNVvcDjk4/fxgYA1ybXv+IpyaJ/8DMisysrbvPrbGKK/r3MJg9DoLyWjm8iEita3cAnHJXjR+2ut9QLakQ2POAkvTz9sDMCu1mpddtEe5mNpRU756OHTtWswxg0RRYvxKw6h9DRCQb3GHd8lo5dMbTD7i7m9l2387J3e8H7gfo0aNH9W4HdcLtMGc8zPywWruLiGRdq91q5bDVvVpmvpm1BUj/XJBePxvoUKFdaXqdiIjsQNUN9xeAgennA4GRFdZfkL5q5hBgea2Nt4uISJW2OixjZk+Q+vC0tZnNAn4N3A48ZWaDgenAWenmo4ATgWnAGuCiWqhZRES2Yluuljmnik19KmnrwKWZFiUiIpnRN1RFRCJI4S4iEkEKdxGRCFK4i4hEkMJdRCSCFO4iIhGkcBcRiSCFu4hIBCncRUQiSOEuIhJBCncRkQhSuIuIRJDCXUQkghTuIiIRpHAXEYkghbuISAQp3EVEIkjhLiISQQp3EZEIUriLiESQwl1EJIIU7iIiEaRwFxGJIIW7iEgEKdxFRCJI4S4iEkEKdxGRCFK4i4hEkMJdRCSCFO4iIhGkcBcRiSCFu4hIBGUU7mb2UzP7zMw+NbMnzKzQzDqb2YdmNs3M/mVmBTVVrIiIbJtqh7uZtQd+AvRw932AOPAD4A7gT+6+G7AUGFwThYqIyLbLdFgmD2hoZnlAI2Au0BsYkd7+MHBahucQEZHtVO1wd/fZwB+AGaRCfTkwDljm7sl0s1lA+8r2N7OhZlZmZmULFy6sbhkiIlKJTIZlWgD9gM5AO6Ax0Hdb93f3+929h7v3aNOmTXXLEBGRSmQyLHMM8I27L3T3BPAs0AsoSg/TAJQCszOsUUREtlMm4T4DOMTMGpmZAX2Az4E3gTPTbQYCIzMrUUREtlcmY+4fkvrgdDwwKX2s+4FrgZ+Z2TSgFfBADdQpIiLbIW/rTarm7r8Gfr3Z6q+BgzM5roiIZEbfUBURiSCFu4hIBCncRUQiSOEuIhJBCncRkQhSuIuIRJDCXUQkghTuIiIRpHAXEYkghbuISAQp3EVEIkjhLiISQQp3EZEIUriLiESQwl1EJIIU7iIiEaRwFxGJIIW7iEgEKdxFRCJI4S4iEkEKdxGRCFK4i4hEkMJdRCSCFO4iIhGkcBcRiSCFu4hIBCncRUQiSOEuIhJBCncRkQhSuIuIRJDCXUQkghTuIiIRlFG4m1mRmY0wsy/MbLKZHWpmLc1stJl9mf7ZoqaKFRGRbZNpz/1u4D/uvgewPzAZGAa87u5dgNfTyyIisgNVO9zNrDlwJPAAgLuXu/syoB/wcLrZw8BpmZUoIiLbK5Oee2dgITDczCaY2T/MrDFQ4u5z023mASWZFikiItsnk3DPA7oD97n7AcBqNhuCcXcHvLKdzWyomZWZWdnChQszKENERDaXSbjPAma5+4fp5RGkwn6+mbUFSP9cUNnO7n6/u/dw9x5t2rTJoAwREdlctcPd3ecBM82sa3pVH+Bz4AVgYHrdQGBkRhWKiMh2y8tw/8uBx8ysAPgauIjUPxhPmdlgYDpwVobnEBGR7ZRRuLv7RKBHJZv6ZHJcERHJjL6hKiISQQp3EZEIUriLiGTJlLAUr/Ri8cwp3EVEsmBS2JlTym/lvgV71crxFe4iIjvYSm/IZYnLaclKWtiqWjmHwl1EZAdyh2GJIczyNvws7ylGF/WvlfMo3EVEdqDHg968HB7KZfHnuDPZn9DitXIehbuIyA4yOezAb5IX0Cs2iY/DXZlHq1o7l8JdRKSGLfam/DfYj2lhO9Z5PgCrvQGXJq6gOas5LPY5Y/yAWq0h0+kHRESkgsCNQeXX8LHvtnFdMUsptHJmeRtuz/871ycG13odCncRkRr0cHA8H/tuXBZ/jtm0Jgas8QKWelMG5f2be5Knk9wB0atwFxGpITPD1vw+eRZHxj7mg2BPythjk+0fJvfAd9BouMbcRURqgDvckBxMDOeI+KdbBDuww4IdFO4iIjXiufBw3gr35/K857kn8f1sl6NwFxHJ1CJvxk2JAXSzaYwNdmcljbJdksbcRSQ3zfZW3JvsxyfhLhSQoIElKCBJA8ppUMlyAUkaWHrbFsvpdrZhW6LSY+RZWGktNycGsJqG9I+/xQ3JQTv4N1E5hbuI5JS53pJ7k6fyZNAbcHraZEJilHseK2lEOXmso4D1YT4J8lhPPuvJJ0F+xueOE1BAgjghhhNLP5bSlEvjz3NX8vTMX2ANUbiLSE5Y4Q25M9mfx4PeODHOiL1FaWwRDyWPZxHNASefYJOeelNbQ+t0LzzfExRYkjwC8gmIExC3gDghcZwYITFC8i0kTkgMxwzAcTfcjMCNgBgGOIY7hBitbQVfhyUspCirv6OKFO4iUueVe5yhiZ8xNtyD78feYdfYXIYnj2dB2KJCKyNBHgny2DjP4uZzpW9tOUIU7iJSp224xPCDcG9uznuQvwWnMCJ5VLbLqvN0tYyIZF3oVuW2/wtO4engaC6Jj+Th4DhmeZsdWFnuUriLSFat8Qb0Lv8jfdf/lpHBoST9f7E0KjiYO5LncFLsfT4Jd2Gal2ax0tyicBeRrPpz8jS+9Z1YRwFXJC6nd/kfeSzZm4/Crvw08WO62TSasYZ3fd9sl5pTNOYuIlnzVdiWfwQn0S/2Dp+GndjPprGeAm5IDgGg1BZwfHwsdyTPyXKluUfhLiJZ4Q43JgfSgHK6xOYwMjw8ffWKswfTaR9bTJ/4RH6RuDDLleYmhbuIZMUrYQ/eDvfjmviT3JvsV2GL8QU780W4M6+H3bNWX67TmLuI7HBrvYCbEwPoYrP4POzIahpmu6TIUbiLyA53b7Ifs2nDoLz/8LIfmu1yIknDMiKyQ7jDevL51nfib8HJnBx7j+HJ44Gqr3GX6lO4iwiQuvfnWhqwlgLWeiFrKWANDVjrDTauX+OFrKOAtTRgjTdg3YY2G9ttvk8D1nrBxudherCgMWvZKzaDl5KHZflVR5fCXaSeCtx4LDiGe5OnsoSmlFOw3ccoZD0NKaeQchraehqynkasp4hVlNjS1AReliQ/PWFX3FKTdJWwlD9v8iGq1DSFu0g9NCnszA2JQXziu3JwbDJ9bEI6eEMMMJyQGIEb5cRJeGruxARxkh6jnHzKyWctDVhHA9ake/KLaMY6GlBOHnzHlAJS+xTuIvXIhmlzHwmOoxUr+E3ecEYFPXksPCbbpUkNyzjczSwOlAGz3f1kM+sMPAm0AsYBA9y9PNPziEj1BW6MCI7iD8n+LKI558TeoJmt5ZbkABLq40VSTVwKeQUwucLyHcCf3H03YCkwuAbOISLV9HawDyeV38a1yaG0t8Xckj+ct31f/i84RcEeYRmFu5mVAicB/0gvG9AbGJFu8jBwWibnEJHq+SzcmQvLf86AxPWsohE35Q2nMWu5ITGYmV6S7fKklmX6z/ZdwM+BpunlVsAyd0+ml2cB7Svb0cyGAkMBOnbsmGEZIgKp6XNfCg7h8aA3E70LTVnDz+JPM93bcGNy4MZLESX6qh3uZnYysMDdx5nZ0du7v7vfD9wP0KNHjwjf7Eqk9s3xltyb7MfzQS9W0YhdbTY/iz/FMprwf8EprKEw2yXKDpZJz70XcKqZnQgUAs2Au4EiM8tL995LgdmZlykiVXk32JvLE5ezmkJOiH3E3vEZvJHcjz8FZ+Lqqddb1f6Td/fr3L3U3TsBPwDecPfzgDeBM9PNBgIjM65SRLbgDvclT2FA4jpa2EpuzH+Et8N9uTVxLu/7Pgr2eq42Piq/FnjSzG4BJgAP1MI5ROq1VV7INYkf8u+wJ31jH1Fqi7guMRjN0yIb1Ei4u/sYYEz6+dfAwTVxXBH5n3Wez0TflbKwK88GRzDdS7gq/hTvhnvzn1B/5WRTushVpI5a4k0pC3enLOzK2LArn3rnjdel724zuT3/H/wxcSbzaJXlSqUuUriL1AHuMNOLGetdKQu78lHYla88dRVxPgn2tW84Pz6aIlvDnLAFk3wXrk1crEsbpUoKd5EsCNyY7B0ZG+6xsWe+gBYANGUN3WNTOTY2jnwL+TZowwR2Z3jQF42py7ZSuIvsAGu9gAnhbpR5KsgnhLuxikYAtGUxPWOT6RybxzoK+DJox/hwd/5Lt+wWLTlN4S5SCxZ704098rFhVz7zTiTJwwjpYrM5JfY+O8WWsdibMDncmVfDHqwPt38+dZGqKNxFMuQO072EsWHXjT3zr70dAAUk2M++5oL4qzSztcwJW/Kx78qT4ffwUOPlUnsU7iLVtMSbMiI4kseDPnzrOwHQjNX0iE2hb2wsMXO+CYqZ4LtTFnTNcrVS3yjcRbaDO5R5Vx5L9mFU2JNy8jnQpnB63tusopCpQSll4e68Qfdslyr1nMJdZBslPM7ViR8xMuxFE9ZwVvxN2tkSXg56cmeyf7bLE9mEwl1kG6zzfH6cuII3wu5cEh/JUm/C88HhG694EalrFO4iW7HKCxmSuIoPwz25Ie9R/hUczTQvzXZZIt9J4S7yHZZ6Ey4sv5ZPvRM35T3E/cFJuouR5ASFu0glQjfeDffmluT5fOM7cVveA9yZPJP5tMx2aSLbROEuUsHMsDUjgqMYERzJbNpQxEpuy3+QWxPnspRm2S5PZJsp3EWARd6MaxNDeSPsBsBh9hkXxEczJSzlF4mLWEeD7BYosp0U7lLvTQvbcVHi5yz05vwo/iL5FvBScAi/TZ6b7dJEqk3hLvXae8Fe/DDxUxqQ5Nb84dyUOJ/lNMl2WSIZU7hLvfVU8iiuTw6mk83jgrzRDEsM2XgzDJFcp3eyRJ47LKYZM7yYmV7MdC/hi7Ajo8KeHBb7lANsGr9KXIjmSpcoUbhLJKzzfGZ5G2Z6MTMqPDYsr6Fwk/bFLOWC+CssCZvy1/C07BQtUosU7pIT3GERzZjhJZsGeJj6ufl9RBuynlJbSEebT/fYVJraWsBYHhay3Jsym9Y8HxzOChpn5wWJ1DKFu9QZG3rfM6rofa/drPddwhI62AIOiX1OS1tFoSVY6wUs90Ys8CJmUsx/w/1J6m0u9ZDe9ZJVS70Jw5N9GREcyRxab7KtYu/7wAq97xVhIcvSve8vvZQy74rGy0U2pXCXrFjgRfw9eSKPBcewhkKOjk2gr41N977zWe6NWeBFzKBEvW+hWYMYl/dswc5F+VjE/iEvzI8xefLk725TWEhpaSn5+fnbfFz9jZEdqtzj/DZ5Lo8FfUiSx0mxD9g/9g2PB99jTHhAtsuTOuryni3ovms78ho1xSxa4d60MJ/Orav+7MfdWbx4MbNmzaJz587bfFyFu+wwSY9xZeIyRoU9OSP2X7rE5/Josg8vJg/LdmlSx+1clB/JYN8WZkarVq1YuHDhdu2ncJcdInTj54kfMirsyTXxf/Fc2ItnEkdluyzJEYbVy2DfoDqvXbdfl1rnDr9IXsSz4RFcHn+Wl8JDdLMLkVqmnrvUKne4JXk+jwfHMCT+Mm8F+zGZnbNdlkjkqecutcYdfp88mweCEzk/PpqPg134mN2yXZZExCFds/e/v7Hvv8PEsg9rrF1tULhLrUh6jOuTQ7g36Ef/2Bi+DUsYy57ZLkukRpS9/w4fl31UY+1qg4ZlpMat9QIuT1zGa2EPhsZfZFLYmfd9n2yXJRHl7vzp1l/xzpuvYWZc/JOr6Xvq6axZvYorBp/HiuXLSCYSXHbNL/je8Scye+YMLr2gPwccdAgTx31EcUlb7n7gMQobNqz0+I89+DdGPDqceDzOLl26csV1N/L0o8OJxeK8/NxTDLvpDlasWM7f7/kjiUQ5RS1a8tt77mfdunVbtHvuyUc58pjjOfakfkDqfx+fTZ/P3LlzOfvss1mxYgXJZJL77ruPI444IqPfS7XD3cw6AI8AJYAD97v73WbWEvgX0An4FjjL3ZdmVKXkjGXemMHlVzPeuzAs73FeDA7jM++U7bIkwl7/94tM+fxTnn71HZYtWcy5J/fmwJ6H0aJVa/7093/SpGkzli5ZzIBTj+Xo404AYMY3X3H7X/7Br393N9dcchGv/fsFTj797EqPP/yvdzHqvYkUNGjAiuXLada8Of3Pv4hGjRoz8EeXA7Bi2TIefWE0ZsazTzzC8Pvu4epf3bJFu+eefLTSczz++OMcf/zx3HDDDQRBwJo1azL+vWTSc08CV7n7eDNrCowzs9HAhcDr7n67mQ0DhgHXZlyp1HmzvDUXlv+c6V7CrXkPcm9wKrO8ONtlScRN+OgD+p56BvF4nFZtijnwkF589vF4en3vWO6542bGf/gesViMBfPmsnjhAgDad9iZPfbeF4A9992fOTNnVnn8LnvuzXU/Gcr3jj+R3sefVGmb+XNnc82PB7FowTwSiQTtO2zfRQMHHXQQgwYNIpFIcNppp9GtW7ft2r8y1R5zd/e57j4+/XwlMBloD/QDHk43exg4LcMaJQeMDrpz8vpbme8t+W3+A/wheZaCXbJq1HNPs3TxYp4YNYanXnmbVm3asH79egDyCwo2tovH4gRBssrj/OXhf3H2wCFMnvQJ557ch2Ryy7a3/+pazrlwCM+89h6/vP1PlK9fV+mx4nl5hGEIQBiGJBLlABx55JG89dZbtG/fngsvvJBHHnmk2q97gxr5QNXMOgEHAB8CJe4+N71pHqlhm8r2GWpmZWZWtr3fvJK6o9zj3JQ4n4sTV9PWlnBt/pP8MnEhS2iW7dKknjig56G88uJzBEHAksWLGP/he+zT7UBWrVxBy9atyc/P56P33mbOrKp751UJw5B5c2Zz8GFHcOX1N7JqxQrWrF5No8ZNWL161cZ2K1euoHindgC88PQTG9dv3q5daUc+nzQRgDGv/ptkIgHA9OnTKSkp4eKLL2bIkCGMHz++Or+KTWT8gaqZNQGeAa509xUVv0nl7m5mXtl+7n4/cD9Ajx49Km0jddv0sJjLE5fzie/K+fHRrPc8fpG4CM3QKDtSn74n88m4j+h/3OGYGVde/xtaF5dw4vf785OLzuGMYw5jr/0OoPNuu2/3sYMg4PorhrJqxQrcnXMGDaVZ8+YcdWxfrv7hQMa8OophN93BJT8dxtWXXEiz5kUcfNgRzJk5HWCLdmecewFXDD6P/scdzmFH96Fho9ScMmPGjOH3v/89+fn5NGnSpEZ67uZe/Vw1s3zgJeAVd78zvW4KcLS7zzWztsAYd+/6Xcfp0aOHl5WVVa+IB46Dmdm5jrQ+SXqMb30npnopX4QdmOodeCfchxjOsLwneCg4nqneIdtlSkT9/dS2lHTcJdtl1IqtTRy2weTJk9lzz00vJzazce7eo7L2mVwtY8ADwOQNwZ72AjAQuD39c2R1zyE7njvMoRVTwg5M8Q5MDUv5wjvwlbennNR0ozFCOtoCvhebyAHxr7gpMYB1NMhy5SJSUSbDMr2AAcAkM5uYXnc9qVB/yswGA9OBszKqUGrNYm/KlLADU72UKd6RKWEpU72UVTTa2KaEJXS1mRwUn0ozW8MaL2BBWMRXtOeV8CBeDDWjo0TDbTdcvcW3Sc8d9CNOO/u8LFWUmWqHu7u/Q9WDq32qe1ypeau8kC+9PVPDDnzhqSGVKWEpiyja2KYZq+lqMzkl9j4tY6tIeJyF3pxvfSfGexfeCvbP3gsQ2QGuv/UP2S6hRukbqhFS7nG+9nZM8Q4be+RfeIdNLklsyHp2s9kcGfuEYluOGSwOmzDDi/nSSxnre0CYxRchIjVC4R4Bc7wlP0/8kA/CPTfeji6PJJ1sHvvb1/SNjaXQkiwJGzHXWzHVO/CsH4GuahGJLoV7jhsT7MdPE5dSTh4XxF+lkZWz0guZH7bgS0p5Jeyh+4+K1EP6W5+jAjfuSp7JX4J+7G6zGJg3mtsS52zyYahIfXPBAx+xbG2ixo5X1DCfRwYfvNV27775GnfceB1hEPD9cwYw+NKfbrK9fP16brjyEiZPmkjzFi353b0P0r5DxxqrszKa8jcHLfRmnJ+4nj8H3+f02NscZF9wfWKQgl3qvZoM9m09XhAE3PaLa7j3kad57o0P+M/IZ/hq6hebtHnuyX/SrKg5L70znvOHXMJdt91Yo3VWRuGeYz4PO9Jv/S1MCHfjxryH+NJLeTQ8Do2fi2THpxPH0aHTLpTu3In8ggL6nno6Y14dtUmbN1/9N6eeeQ4Ax57Uj4/e/S+ZfIF0Wyjcc8gbQTf6l/+agBi35A/nzuSZfOK7ZrsskXptwby57NSu/cbl4rbtmD9v7mZt5mxsk5eXR5OmzVi2dEmt1qUx9xzgDsODvtySPJ89bTqn573LtYmLCYhnuzQRqaMU7nVc0mPcmBzIo8GxHBMbR6kt4ubEeWgYRqRuKN6pLfPmzN64vGDuHEp2artZm3bMmzObkrbtSSaTrFq5gqIWLWu1Lg3L1GEzwmL6l/+aR4NjGRwfReDGQ8HxKNhF6o699+/OjG+/YtaM6STKy/nPC89y1LEnbNLm6GP78sKI1FTAo18eycG9jqTiDLq1QT33Ouq5oBe/TFyEATfnPcjjQR8m+/bd3UWkvilqmF/jl0JuTV5eHtfd/DsuOf8MwiDgtLPPY7eue/LXP9zG3vt14+jjTuT7PxjADVf+iJMP706zohb87q8P1FiNVcloyt+aoil//2eFN+SXiUGMDHtxoE3llLwP+F3iLNZQmO3SRLJGU/7uwCl/pWYt98aMCI7gweQJzKMll8WfY2rYnhsTF2S7NBHJQQr3LJsUduafwTG8EBzGOhqwv03jJ3nPc1fidObSKtvliUiOUrjvAGu9gJnehhlewgwvZoYXM8vb8LW35WtvR0PWc2rsPXaLzWVUcDDXJoagD01FJBMK9xoQujGfFqngDouZmQ7wVKAXs5AWm7RvxDo62AJ2sTn0i7/HUm/M88HhPBU2zdIrEJGoUbhvo5XecGNYbwjvDc9neZuNt6CD1G3odmIJHW0BR8Ym0cJWUmABa7yA5d6Yed6CmRTzZngAr4WVfhYiIpIRhXuaO8ymNTPCDcFdsrH3PdOLWUKzTdo3ZQ0dbAG720x6xT6lia0nwFgWNmKpN2U2bfjMO/GB75WlVyQi9ZnCHVjkzRiWGLJJLzqPJG1tMR1ZQJ/YeIpiq8kjZJUXsMIbM89bMcOL+cI7Euq7YCJ1wp6PHUj+2kU1drxEw9ZMPm/cVtv96qrLeOv1V2jZqjXPvv7+FtvdnTt+PYx33hhNYcOG3Hznvey5b+3eurLeh/vooDvDEhezkob8OP485eSz3Buz2Jsyi2LGexfe9X116zmRHFCTwb49x+vX/xzOufBibrjyR5Vuf+fN0cz45itefHsckyaUccv1V/HYi6/VZKlbqLfhvtobcHNyAE8GvdnDZnBV3tP8IXHWFsMvIiJbc+AhvZg9c0aV2998dRSnnPEDzIz9uh/EyhXLWTh/Hm1Kdqq1mupluL8d7MMNycHM9DYMib/MUm/C9YnB6PJDEakNC+bNpaTCtMAlbduxYN5chXtNme9F3JwYwEvhoXS0+dyR/w/uSZ7GLC/OdmkiIjWqXoR70mM8EhzHnckzKSePS+PPsdCLuDYxBNeHoSJSy4p3asv8CtMCz587h+LNpgWuaZEO91nemheDQ3kmOIJpXsrhsUn0iU/gz4nTNLYuIjvM0ceewJMP/Z2+/c5g0oQymjRtVqtDMhDBcF/qTXgxOJSRwWGM864A7G/TuDHvYV5K9uQ3oSbiEomqRMPWNX4p5La49tLBlH3wLsuWLObYg/bmkquGkUykph4+a8Agjuh9HO+8MZqTD+9OYcOG3PTHv9ZYjVWJTLjP8xbcnzyJJ4LerKWQLjaLy+LPY+a8HhzAjckL0AemItG2Ldek14Y7tjI/u5lx/a1/2EHVpOR8uH9b3py/JYYwIjiSEOPk2PvsGZvJ6KA7fw1O1Zi6iNRLOR3uD7zzDbdOv4A8kpwZ+y8dYov4Z/IYRoaHZ7s0EZGsyulw77FzC45q+A37JSbyaPIYFofNs12SiNQCx3H3Wr/vaF1VnTvm5XS479+hiBXFPbh7+q7ZLkVEatH0ZQlatVpBXqNm9S7g3Z3FixdTWLh9t9rM6XAXkfrhzx8u5XJg56JFWMQujFieH2Pdwgbf2aawsJDS0tLtOq7CXUTqvBXrQ259a3G2y6gVR3dtw0MXdavx49bKpSRm1tfMppjZNDMbVhvnEBGRqtV4uJtZHPgrcAKwF3COmemOFSIiO1Bt9NwPBqa5+9fuXg48CfSrhfOIiEgVamPMvT0ws8LyLKDn5o3MbCgwNL24ysymbOPxWwMbv1+c17ykozVo3LKate5Q4doVFmvYbPuvaaojcrn+XK4dcrv+XK4dar/+R9etXPTwoIWzqrn7zlVtyNoHqu5+P3D/9u5nZmXunpN3lTazsuSKhTlZO+R2/blcO+R2/blcO+Ru/bUxLDMb6FBhuTS9TkREdpDaCPexQBcz62xmBcAPgBdq4TwiIlKFGh+WcfekmV0GvALEgQfd/bMaPMV2D+XUIblcO+R2/blcO+R2/blcO+Ro/VadOQtERKRu03y4IiIRpHAXEYmgnAn3XJvSwMweNLMFZvZphXUtzWy0mX2Z/tkimzVWxcw6mNmbZva5mX1mZlek1+dK/YVm9pGZfZyu/zfp9Z3N7MP0e+hf6Q/86yQzi5vZBDN7Kb2cS7V/a2aTzGyimZWl1+XKe6fIzEaY2RdmNtnMDs2V2jeXE+Geo1MaPAT03WzdMOB1d+8CvJ5erouSwFXuvhdwCHBp+vedK/WvB3q7+/5AN6CvmR0C3AH8yd13A5YCg7NX4lZdAUyusJxLtQN8z927VfhOSq68d+4G/uPuewD7k/ozyJXaN+Xudf4BHAq8UmH5OuC6bNe1DXV3Aj6tsDwFaJt+3haYku0at/F1jASOzcX6gUbAeFLfkl4E5FX2nqpLD1LfDXkd6A28ROrmvzlRe7q+b4HWm62r8+8doDnwDekLTXKp9soeOdFzp/IpDdpnqZZMlLj73PTzeUBJNovZFmbWCTgA+JAcqj89rDERWACMBr4Clrl7Mt2kLr+H7gJ+DoTp5VbkTu0ADrxqZuPS04xAbrx3OgMLgeHpIbF/mFljcqP2LeRKuEeOp7oBdfo6VDNrAjwDXOnuKypuq+v1u3vg7t1I9YIPBvbIbkXbxsxOBha4+7hs15KBw929O6lh1EvN7MiKG+vweycP6A7c5+4HAKvZbAimDte+hVwJ96hMaTDfzNoCpH8uyHI9VTKzfFLB/pi7P5tenTP1b+Duy4A3SQ1lFJnZhi/u1dX3UC/gVDP7ltSMqr1JjQPnQu0AuPvs9M8FwHOk/nHNhffOLGCWu3+YXh5BKuxzofYt5Eq4R2VKgxeAgennA0mNZdc5lrpJ5QPAZHe/s8KmXKm/jZkVpZ83JPV5wWRSIX9mulmdrN/dr3P3UnfvROp9/oa7n0cO1A5gZo3NrOmG58BxwKfkwHvH3ecBM82sa3pVH+BzcqD2SmV70H87Puw4EZhKauz0hmzXsw31PgHMBRKkegSDSY2dvg58CbwGtMx2nVXUfjip/3p+AkxMP07Mofr3Ayak6/8U+FV6/S7AR8A04GmgQbZr3crrOBp4KZdqT9f5cfrx2Ya/qzn03ukGlKXfO88DLXKl9s0fmn5ARCSCcmVYRkREtoPCXUQkghTuIiIRpHAXEYkghbuISAQp3EVEIkjhLvWCmd1oZldn8fyrsnVuqZ8U7iLVlJ6KWqROUrhLzkp/1f3l9E05PjWzs9M3imid3t7DzMZU2GV/M3s/fdOFi7/juDEzuzd9w4bRZjbKzM5Mb/vWzO4ws/FAfzO72MzGpmt4xswapdt1Tp9rkpndstnxr0nv88mGG4mI1DSFu+SyvsAcd9/f3fcB/rOV9vuRmojrUOBXZtauinank5qLfy9gQLp9RYvdvbu7Pwk86+4HeerGIJP530007iY1u+C+pKahAMDMjgO6kJpMqxtw4OazJorUBIW75LJJwLHpnvQR7r58K+1Huvtad19EaiKug6todzjwtLuHnppM6s3Ntv+rwvN9zOxtM5sEnAfsnV7fi9T8QgD/rND+uPRjAqmbiOxBKuxFalTe1puI1E3uPtXMupOa1OwWM3ud1C0CN3RaCjffZSvL22p1hecPAae5+8dmdiGpyb6+6/gG/Nbd/1bNc4tsE/XcJWelh1XWuPujwO9Jzb39LXBguskZm+3Sz1I3z25FKoTHVnHod4Ez0mPvJWwa2JtrCsxNz39/3mbH+EH6ecX1rwCD0jdCwczam1nxdxxfpFrUc5dcti/wezMLSU2tfAnQEHjAzG4GxmzW/hNSQyytgZvdfU4Vx32G/83lPZPU8ElVQz6/JHULwoXpn03T668AHjeza6kw/7e7v2pmewLvp6bNZxVwPjlyAwjJHZryV6QSZtbE3Vele/kfAb3S4+8iOUE9d5HKvZS+m1MBqV6+gl1yinruUm+Z2b5seiULwHp375mNekRqksJdRCSCdLWMiEgEKdxFRCJI4S4iEkEKdxGRCPp/zhbgoERlDpMAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"analyse_sub_grade = loans.groupby(['sub_grade','loan_status'])['loan_status'].count()\n",
"analyse_sub_grade = analyse_sub_grade.groupby(level=0).apply(lambda x:\n",
" 100 * x / float(x.sum()))\n",
"analyse_sub_grade = analyse_sub_grade.unstack()\n",
"analyse_sub_grade.plot.area()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "34ecf9b6",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:07.054203Z",
"iopub.status.busy": "2022-08-31T13:08:07.053479Z",
"iopub.status.idle": "2022-08-31T13:08:07.291976Z",
"shell.execute_reply": "2022-08-31T13:08:07.290783Z"
},
"papermill": {
"duration": 0.252937,
"end_time": "2022-08-31T13:08:07.295137",
"exception": false,
"start_time": "2022-08-31T13:08:07.042200",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"loans = loan[['id', 'loan_amnt', 'term','int_rate', 'sub_grade','home_ownership', 'addr_state','emp_length','grade', 'annual_inc', 'loan_status', 'dti',\n",
"'mths_since_recent_inq', 'revol_util', 'bc_open_to_buy', 'bc_util', 'num_op_rev_tl', 'emp_title']]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "7004b515",
"metadata": {
"execution": {
"iopub.execute_input": "2022-08-31T13:08:07.316594Z",
"iopub.status.busy": "2022-08-31T13:08:07.315514Z",
"iopub.status.idle": "2022-08-31T13:08:09.520231Z",
"shell.execute_reply": "2022-08-31T13:08:09.518018Z"
},
"papermill": {
"duration": 2.218369,
"end_time": "2022-08-31T13:08:09.523090",
"exception": false,
"start_time": "2022-08-31T13:08:07.304721",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1080x864 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(15, 12))\n",
"plt.subplot(2, 2, 2)\n",
"plt.barh(loans.emp_title.value_counts()[:10].index, loans.emp_title.value_counts()[:10])\n",
"plt.title(\"The most 10 jobs title applied for a loan\")\n",
"plt.tight_layout()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.7.12"
},
"papermill": {
"default_parameters": {},
"duration": 88.550861,
"end_time": "2022-08-31T13:08:11.161590",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2022-08-31T13:06:42.610729",
"version": "2.3.4"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
| 0104/601/104601676.ipynb | s3://data-agents/kaggle-outputs/sharded/025_00104.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"057316c2\",\n \"metadata\": (...TRUNCATED) | 0104/601/104601876.ipynb | s3://data-agents/kaggle-outputs/sharded/025_00104.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"309e0c01\",\n \"metadata\": (...TRUNCATED) | 0104/601/104601897.ipynb | s3://data-agents/kaggle-outputs/sharded/025_00104.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"3b0f40(...TRUNCATED) | 0104/602/104602028.ipynb | s3://data-agents/kaggle-outputs/sharded/025_00104.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"80676a91\",\n \"metadata\": (...TRUNCATED) | 0104/602/104602033.ipynb | s3://data-agents/kaggle-outputs/sharded/025_00104.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"dda7c26e\",\n \"metadata\": (...TRUNCATED) | 0104/602/104602048.ipynb | s3://data-agents/kaggle-outputs/sharded/025_00104.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"c0fa9e35\",\n \"metadata\": (...TRUNCATED) | 0104/602/104602172.ipynb | s3://data-agents/kaggle-outputs/sharded/025_00104.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"5ec106(...TRUNCATED) | 0104/603/104603752.ipynb | s3://data-agents/kaggle-outputs/sharded/025_00104.jsonl.gz |
End of preview. Expand
in Dataset Viewer.
README.md exists but content is empty.
- Downloads last month
- 38