From 4cbc79a6d16b3ca07834516c152737f171b508bd Mon Sep 17 00:00:00 2001
From: Priyanshu Yashwant Deshmukh
<69320370+priyansh4320@users.noreply.github.com>
Date: Sun, 13 Aug 2023 10:16:39 +0000
Subject: [PATCH 1/2] made changes in learner.ipynb import, x_train variable
---
.../Learner_Notebook3.ipynb | 5320 +++++++
.../README.md | 40 +
.../employee_data.csv | 11583 ++++++++++++++++
.../partition-feature-space.png | Bin 0 -> 60217 bytes
.../showcase _on_streamlit/app.py | 0
5 files changed, 16943 insertions(+)
create mode 100644 Predict_Employee_Turnover_with_scikitlearn/Learner_Notebook3.ipynb
create mode 100644 Predict_Employee_Turnover_with_scikitlearn/README.md
create mode 100644 Predict_Employee_Turnover_with_scikitlearn/employee_data.csv
create mode 100644 Predict_Employee_Turnover_with_scikitlearn/partition-feature-space.png
create mode 100644 Predict_Employee_Turnover_with_scikitlearn/showcase _on_streamlit/app.py
diff --git a/Predict_Employee_Turnover_with_scikitlearn/Learner_Notebook3.ipynb b/Predict_Employee_Turnover_with_scikitlearn/Learner_Notebook3.ipynb
new file mode 100644
index 0000000..a54d8da
--- /dev/null
+++ b/Predict_Employee_Turnover_with_scikitlearn/Learner_Notebook3.ipynb
@@ -0,0 +1,5320 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "\n",
+ "
Predict Employee Churn with Decision Trees and Random Forests
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Task 1: Import Libraries\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "!pip install pandas_profiling"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from __future__ import print_function\n",
+ "%matplotlib inline\n",
+ "import os\n",
+ "import warnings\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.image as image\n",
+ "import pandas as pd\n",
+ "import pandas_profiling\n",
+ "plt.style.use(\"ggplot\")\n",
+ "warnings.simplefilter(\"ignore\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "plt.rcParams['figure.figsize'] = (12,8)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Task 2: Exploratory Data Analysis\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " satisfaction_level | \n",
+ " last_evaluation | \n",
+ " number_project | \n",
+ " average_montly_hours | \n",
+ " time_spend_company | \n",
+ " Work_accident | \n",
+ " quit | \n",
+ " promotion_last_5years | \n",
+ " department | \n",
+ " salary | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 0.38 | \n",
+ " 0.53 | \n",
+ " 2 | \n",
+ " 157 | \n",
+ " 3 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " sales | \n",
+ " low | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 0.80 | \n",
+ " 0.86 | \n",
+ " 5 | \n",
+ " 262 | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " sales | \n",
+ " medium | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 0.11 | \n",
+ " 0.88 | \n",
+ " 7 | \n",
+ " 272 | \n",
+ " 4 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " sales | \n",
+ " medium | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 0.72 | \n",
+ " 0.87 | \n",
+ " 5 | \n",
+ " 223 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " sales | \n",
+ " low | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 0.37 | \n",
+ " 0.52 | \n",
+ " 2 | \n",
+ " 159 | \n",
+ " 3 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " sales | \n",
+ " low | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " satisfaction_level last_evaluation number_project average_montly_hours \\\n",
+ "0 0.38 0.53 2 157 \n",
+ "1 0.80 0.86 5 262 \n",
+ "2 0.11 0.88 7 272 \n",
+ "3 0.72 0.87 5 223 \n",
+ "4 0.37 0.52 2 159 \n",
+ "\n",
+ " time_spend_company Work_accident quit promotion_last_5years department \\\n",
+ "0 3 0 1 0 sales \n",
+ "1 6 0 1 0 sales \n",
+ "2 4 0 1 0 sales \n",
+ "3 5 0 1 0 sales \n",
+ "4 3 0 1 0 sales \n",
+ "\n",
+ " salary \n",
+ "0 low \n",
+ "1 medium \n",
+ "2 medium \n",
+ "3 low \n",
+ "4 low "
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "hr=pd.read_csv('data/employee_data.csv')\n",
+ "hr.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ " \n",
+ " "
+ ],
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": []
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "hr.profile_report(title=\"DATA REPORT\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pd.crosstab(hr.salary,hr.quit).plot(kind='bar')\n",
+ "plt.title(\"Turnover Frequency on salary Bracket\")\n",
+ "plt.xlabel('Salary')\n",
+ "plt.ylabel('Frequency of turnover')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "pd.crosstab(hr.department,hr.quit).plot(kind='bar')\n",
+ "plt.title(\"Turnover Frequency on department\")\n",
+ "plt.xlabel('Department')\n",
+ "plt.ylabel('Frequency of turnover')\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Task 3: Encode Categorical Features\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "cat_vars=['department','salary']\n",
+ "for i in cat_vars:\n",
+ " cat_list=pd.get_dummies(hr[i], prefix=i)\n",
+ " hr=hr.join(cat_list)\n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " satisfaction_level | \n",
+ " last_evaluation | \n",
+ " number_project | \n",
+ " average_montly_hours | \n",
+ " time_spend_company | \n",
+ " Work_accident | \n",
+ " quit | \n",
+ " promotion_last_5years | \n",
+ " department | \n",
+ " salary | \n",
+ " ... | \n",
+ " department_hr | \n",
+ " department_management | \n",
+ " department_marketing | \n",
+ " department_product_mng | \n",
+ " department_sales | \n",
+ " department_support | \n",
+ " department_technical | \n",
+ " salary_high | \n",
+ " salary_low | \n",
+ " salary_medium | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 0.38 | \n",
+ " 0.53 | \n",
+ " 2 | \n",
+ " 157 | \n",
+ " 3 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " sales | \n",
+ " low | \n",
+ " ... | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 0.80 | \n",
+ " 0.86 | \n",
+ " 5 | \n",
+ " 262 | \n",
+ " 6 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " sales | \n",
+ " medium | \n",
+ " ... | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 0.11 | \n",
+ " 0.88 | \n",
+ " 7 | \n",
+ " 272 | \n",
+ " 4 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " sales | \n",
+ " medium | \n",
+ " ... | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 0.72 | \n",
+ " 0.87 | \n",
+ " 5 | \n",
+ " 223 | \n",
+ " 5 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " sales | \n",
+ " low | \n",
+ " ... | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 0.37 | \n",
+ " 0.52 | \n",
+ " 2 | \n",
+ " 159 | \n",
+ " 3 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " sales | \n",
+ " low | \n",
+ " ... | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 23 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " satisfaction_level last_evaluation number_project average_montly_hours \\\n",
+ "0 0.38 0.53 2 157 \n",
+ "1 0.80 0.86 5 262 \n",
+ "2 0.11 0.88 7 272 \n",
+ "3 0.72 0.87 5 223 \n",
+ "4 0.37 0.52 2 159 \n",
+ "\n",
+ " time_spend_company Work_accident quit promotion_last_5years department \\\n",
+ "0 3 0 1 0 sales \n",
+ "1 6 0 1 0 sales \n",
+ "2 4 0 1 0 sales \n",
+ "3 5 0 1 0 sales \n",
+ "4 3 0 1 0 sales \n",
+ "\n",
+ " salary ... department_hr department_management department_marketing \\\n",
+ "0 low ... 0 0 0 \n",
+ "1 medium ... 0 0 0 \n",
+ "2 medium ... 0 0 0 \n",
+ "3 low ... 0 0 0 \n",
+ "4 low ... 0 0 0 \n",
+ "\n",
+ " department_product_mng department_sales department_support \\\n",
+ "0 0 1 0 \n",
+ "1 0 1 0 \n",
+ "2 0 1 0 \n",
+ "3 0 1 0 \n",
+ "4 0 1 0 \n",
+ "\n",
+ " department_technical salary_high salary_low salary_medium \n",
+ "0 0 0 1 0 \n",
+ "1 0 0 0 1 \n",
+ "2 0 0 0 1 \n",
+ "3 0 0 1 0 \n",
+ "4 0 0 1 0 \n",
+ "\n",
+ "[5 rows x 23 columns]"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "hr.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "hr.drop(columns=['department','salary'],axis=1,inplace=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Task 4: Visualize Class Imbalance\n",
+ "---"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from yellowbrick.target import ClassBalance\n",
+ "plt.style.use(\"ggplot\")\n",
+ "plt.rcParams['figure.figsize'] = (12,8)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "