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Data_ScienceUse_Cases/Classification/HRD_Survey.ipynb
Sang Putu Sandhyana Yogi 67f194acfa Add files via upload
2024-04-29 18:29:32 +07:00

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{
"cells": [
{
"cell_type": "markdown",
"id": "b1913dbb",
"metadata": {},
"source": [
"# Table of Contents <a id=\"top\"></a>\n",
"- [Reading The Data](#readData)\n",
"- [First Takeaway](#firstTake)\n",
"- [Second Takeaway](#secondTake)\n",
"- [Training The Data](#trainData)\n",
" - [Third Takeaway](#thirdTake)\n",
" - [Class Weights](#weightCtrl)\n",
" - [Oversampling](#trainOver)\n",
" - [Undersampling](#trainUnder)\n",
"- [Feature Importance](#dissect)"
]
},
{
"cell_type": "markdown",
"id": "71195f0a",
"metadata": {},
"source": [
"## Reading The Data <a id=\"readData\"></a>\n",
"For starters, lets read the data and its properties"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "411169cb",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "c95cf542",
"metadata": {},
"outputs": [
{
"data": {
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"<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>Role Satisfaction</th>\n",
" <th>Skill Utilization</th>\n",
" <th>Career Growth Opportunity</th>\n",
" <th>Supervisor Support</th>\n",
" <th>Work-Life Balance</th>\n",
" <th>Recognition &amp; Appreciation</th>\n",
" <th>Company Culture</th>\n",
" <th>Training &amp; Development</th>\n",
" <th>Communication Effectiveness</th>\n",
" <th>Diversity &amp; Inclusion</th>\n",
" <th>Work Environment</th>\n",
" <th>Compensation</th>\n",
" <th>Staff_Id</th>\n",
" <th>Month_Of_Service</th>\n",
" <th>Years_Of_Service</th>\n",
" <th>Residence</th>\n",
" <th>Residence_Code</th>\n",
" <th>Net_Salary</th>\n",
" <th>Resigned</th>\n",
" </tr>\n",
" </thead>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
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" <td>5582218</td>\n",
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" <tr>\n",
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" <td>SP10211</td>\n",
" <td>43</td>\n",
" <td>3</td>\n",
" <td>Jakarta</td>\n",
" <td>1</td>\n",
" <td>9213443</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>3</td>\n",
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" <td>2</td>\n",
" <td>2</td>\n",
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" <td>4</td>\n",
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" <td>10</td>\n",
" <td>0</td>\n",
" <td>Bekasi</td>\n",
" <td>3</td>\n",
" <td>5836455</td>\n",
" <td>0</td>\n",
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" <td>4</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>SA02310</td>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" <td>Depok</td>\n",
" <td>4</td>\n",
" <td>6035466</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>SA98565</td>\n",
" <td>17</td>\n",
" <td>1</td>\n",
" <td>Jakarta</td>\n",
" <td>1</td>\n",
" <td>5568101</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Role Satisfaction Skill Utilization Career Growth Opportunity \\\n",
"0 3 4 5 \n",
"1 2 3 1 \n",
"2 3 3 2 \n",
"3 3 3 4 \n",
"4 3 2 4 \n",
"\n",
" Supervisor Support Work-Life Balance Recognition & Appreciation \\\n",
"0 2 2 3 \n",
"1 2 4 3 \n",
"2 2 2 5 \n",
"3 4 3 1 \n",
"4 3 3 2 \n",
"\n",
" Company Culture Training & Development Communication Effectiveness \\\n",
"0 3 3 2 \n",
"1 4 3 2 \n",
"2 4 4 3 \n",
"3 4 4 4 \n",
"4 3 4 2 \n",
"\n",
" Diversity & Inclusion Work Environment Compensation Staff_Id \\\n",
"0 3 4 3 SA63171 \n",
"1 2 2 3 SP10211 \n",
"2 2 4 4 SA79627 \n",
"3 5 3 4 SA02310 \n",
"4 3 2 2 SA98565 \n",
"\n",
" Month_Of_Service Years_Of_Service Residence Residence_Code Net_Salary \\\n",
"0 1 0 Depok 4 5582218 \n",
"1 43 3 Jakarta 1 9213443 \n",
"2 10 0 Bekasi 3 5836455 \n",
"3 17 1 Depok 4 6035466 \n",
"4 17 1 Jakarta 1 5568101 \n",
"\n",
" Resigned \n",
"0 0 \n",
"1 0 \n",
"2 0 \n",
"3 0 \n",
"4 0 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Grab Data\n",
"df = pd.read_excel(\"https://raw.githubusercontent.com/youronlydimwit/Data_ScienceUse_Cases/main/Classification/Data/HRD_Survey_50.xlsx\")\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "7c5d95f1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Role Satisfaction int64\n",
"Skill Utilization int64\n",
"Career Growth Opportunity int64\n",
"Supervisor Support int64\n",
"Work-Life Balance int64\n",
"Recognition & Appreciation int64\n",
"Company Culture int64\n",
"Training & Development int64\n",
"Communication Effectiveness int64\n",
"Diversity & Inclusion int64\n",
"Work Environment int64\n",
"Compensation int64\n",
"Staff_Id object\n",
"Month_Of_Service int64\n",
"Years_Of_Service int64\n",
"Residence object\n",
"Residence_Code int64\n",
"Net_Salary int64\n",
"Resigned int64\n",
"dtype: object"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Check data types\n",
"df.dtypes"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "c386bd3a",
"metadata": {},
"outputs": [
{
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" <td>0</td>\n",
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],
"text/plain": [
" Role Satisfaction Skill Utilization Career Growth Opportunity \\\n",
"0 3 4 5 \n",
"1 2 3 1 \n",
"2 3 3 2 \n",
"3 3 3 4 \n",
"4 3 2 4 \n",
"\n",
" Supervisor Support Work-Life Balance Recognition & Appreciation \\\n",
"0 2 2 3 \n",
"1 2 4 3 \n",
"2 2 2 5 \n",
"3 4 3 1 \n",
"4 3 3 2 \n",
"\n",
" Company Culture Training & Development Communication Effectiveness \\\n",
"0 3 3 2 \n",
"1 4 3 2 \n",
"2 4 4 3 \n",
"3 4 4 4 \n",
"4 3 4 2 \n",
"\n",
" Diversity & Inclusion Work Environment Compensation Month_Of_Service \\\n",
"0 3 4 3 1 \n",
"1 2 2 3 43 \n",
"2 2 4 4 10 \n",
"3 5 3 4 17 \n",
"4 3 2 2 17 \n",
"\n",
" Years_Of_Service Residence_Code Net_Salary Resigned \n",
"0 0 4 5582218 0 \n",
"1 3 1 9213443 0 \n",
"2 0 3 5836455 0 \n",
"3 1 4 6035466 0 \n",
"4 1 1 5568101 0 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Making a copy of df, but with only numerical information\n",
"# Removing unnecessary columns\n",
"pred_df = df.drop(columns=['Staff_Id','Residence'])\n",
"pred_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "2489f593",
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Role Satisfaction</th>\n",
" <th>Skill Utilization</th>\n",
" <th>Career Growth Opportunity</th>\n",
" <th>Supervisor Support</th>\n",
" <th>Work-Life Balance</th>\n",
" <th>Recognition &amp; Appreciation</th>\n",
" <th>Company Culture</th>\n",
" <th>Training &amp; Development</th>\n",
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" <th>Diversity &amp; Inclusion</th>\n",
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" <th>Years_Of_Service</th>\n",
" <th>Residence_Code</th>\n",
" <th>Net_Salary</th>\n",
" <th>Resigned</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>500.000000</td>\n",
" <td>500.000000</td>\n",
" <td>500.000000</td>\n",
" <td>500.000000</td>\n",
" <td>500.000000</td>\n",
" <td>500.00000</td>\n",
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" <td>500.000000</td>\n",
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" <td>500.000000</td>\n",
" <td>500.000000</td>\n",
" <td>500.000000</td>\n",
" <td>500.00000</td>\n",
" <td>500.000000</td>\n",
" <td>5.000000e+02</td>\n",
" <td>500.0000</td>\n",
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" <th>mean</th>\n",
" <td>3.006000</td>\n",
" <td>3.038000</td>\n",
" <td>3.004000</td>\n",
" <td>2.948000</td>\n",
" <td>2.950000</td>\n",
" <td>3.01600</td>\n",
" <td>2.946000</td>\n",
" <td>3.016000</td>\n",
" <td>3.024000</td>\n",
" <td>3.014000</td>\n",
" <td>2.990000</td>\n",
" <td>3.022000</td>\n",
" <td>34.510000</td>\n",
" <td>2.30400</td>\n",
" <td>3.018000</td>\n",
" <td>7.908836e+06</td>\n",
" <td>0.1000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>0.989911</td>\n",
" <td>1.046301</td>\n",
" <td>1.020817</td>\n",
" <td>0.991596</td>\n",
" <td>0.980522</td>\n",
" <td>1.00487</td>\n",
" <td>1.000543</td>\n",
" <td>1.042073</td>\n",
" <td>1.016608</td>\n",
" <td>1.048811</td>\n",
" <td>0.977656</td>\n",
" <td>1.021569</td>\n",
" <td>19.267391</td>\n",
" <td>1.43935</td>\n",
" <td>1.444246</td>\n",
" <td>4.383144e+06</td>\n",
" <td>0.3003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.00000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>1.000000</td>\n",
" <td>5.070036e+06</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.00000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>2.000000</td>\n",
" <td>18.750000</td>\n",
" <td>1.00000</td>\n",
" <td>2.000000</td>\n",
" <td>5.688296e+06</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.00000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>3.000000</td>\n",
" <td>35.000000</td>\n",
" <td>2.00000</td>\n",
" <td>3.000000</td>\n",
" <td>6.341903e+06</td>\n",
" <td>0.0000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.00000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>4.000000</td>\n",
" <td>50.000000</td>\n",
" <td>4.00000</td>\n",
" <td>4.000000</td>\n",
" <td>8.342422e+06</td>\n",
" <td>0.0000</td>\n",
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" <tr>\n",
" <th>max</th>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>5.00000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>5.000000</td>\n",
" <td>66.000000</td>\n",
" <td>4.00000</td>\n",
" <td>5.000000</td>\n",
" <td>5.443630e+07</td>\n",
" <td>1.0000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Role Satisfaction Skill Utilization Career Growth Opportunity \\\n",
"count 500.000000 500.000000 500.000000 \n",
"mean 3.006000 3.038000 3.004000 \n",
"std 0.989911 1.046301 1.020817 \n",
"min 1.000000 1.000000 1.000000 \n",
"25% 2.000000 2.000000 2.000000 \n",
"50% 3.000000 3.000000 3.000000 \n",
"75% 4.000000 4.000000 4.000000 \n",
"max 5.000000 5.000000 5.000000 \n",
"\n",
" Supervisor Support Work-Life Balance Recognition & Appreciation \\\n",
"count 500.000000 500.000000 500.00000 \n",
"mean 2.948000 2.950000 3.01600 \n",
"std 0.991596 0.980522 1.00487 \n",
"min 1.000000 1.000000 1.00000 \n",
"25% 2.000000 2.000000 2.00000 \n",
"50% 3.000000 3.000000 3.00000 \n",
"75% 4.000000 4.000000 4.00000 \n",
"max 5.000000 5.000000 5.00000 \n",
"\n",
" Company Culture Training & Development Communication Effectiveness \\\n",
"count 500.000000 500.000000 500.000000 \n",
"mean 2.946000 3.016000 3.024000 \n",
"std 1.000543 1.042073 1.016608 \n",
"min 1.000000 1.000000 1.000000 \n",
"25% 2.000000 2.000000 2.000000 \n",
"50% 3.000000 3.000000 3.000000 \n",
"75% 4.000000 4.000000 4.000000 \n",
"max 5.000000 5.000000 5.000000 \n",
"\n",
" Diversity & Inclusion Work Environment Compensation \\\n",
"count 500.000000 500.000000 500.000000 \n",
"mean 3.014000 2.990000 3.022000 \n",
"std 1.048811 0.977656 1.021569 \n",
"min 1.000000 1.000000 1.000000 \n",
"25% 2.000000 2.000000 2.000000 \n",
"50% 3.000000 3.000000 3.000000 \n",
"75% 4.000000 4.000000 4.000000 \n",
"max 5.000000 5.000000 5.000000 \n",
"\n",
" Month_Of_Service Years_Of_Service Residence_Code Net_Salary \\\n",
"count 500.000000 500.00000 500.000000 5.000000e+02 \n",
"mean 34.510000 2.30400 3.018000 7.908836e+06 \n",
"std 19.267391 1.43935 1.444246 4.383144e+06 \n",
"min 0.000000 0.00000 1.000000 5.070036e+06 \n",
"25% 18.750000 1.00000 2.000000 5.688296e+06 \n",
"50% 35.000000 2.00000 3.000000 6.341903e+06 \n",
"75% 50.000000 4.00000 4.000000 8.342422e+06 \n",
"max 66.000000 4.00000 5.000000 5.443630e+07 \n",
"\n",
" Resigned \n",
"count 500.0000 \n",
"mean 0.1000 \n",
"std 0.3003 \n",
"min 0.0000 \n",
"25% 0.0000 \n",
"50% 0.0000 \n",
"75% 0.0000 \n",
"max 1.0000 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Describe the new df\n",
"pred_df.describe()"
]
},
{
"cell_type": "markdown",
"id": "ee3b8be1",
"metadata": {},
"source": [
"## First Takeaway <a id=\"firstTake\"></a>\n",
"[Back to Top](#top)<br>\n",
"- Most features have a minimum value of 1 and a maximum value of 5, except for `Month_of_service` to `Resigned` features.\n",
"- The mean value from `Role Satisfaction` to `Compensation` consistently hovers around 3, suggesting normality in the data distribution, which aligns with the observed minimum and maximum values."
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "fccbbca9",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1080x720 with 18 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Making BOXPLOTS to see if there's outlier\n",
"# Create a figure and axes for subplots\n",
"fig, axes = plt.subplots(nrows=3, ncols=len(pred_df.columns) // 3 + (len(pred_df.columns) % 3 > 0), figsize=(15, 10))\n",
"\n",
"# Flatten the axes array to easily iterate over\n",
"axes = axes.flatten()\n",
"\n",
"# Loop through each column and create a boxplot\n",
"for i, col in enumerate(pred_df.columns):\n",
" pred_df.boxplot(column=col, ax=axes[i])\n",
" axes[i].set_title(col)\n",
"\n",
"# Adjust layout\n",
"plt.tight_layout()\n",
"\n",
"# Show the plot\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "45fb2a76",
"metadata": {},
"outputs": [],
"source": [
"import seaborn as sns\n",
"from scipy.stats import norm"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "178492fc",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x720 with 12 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Making HISTOGRAM and comparing with normal distirbution's bell curve\n",
"# Create a figure and axes for subplots\n",
"fig, axes = plt.subplots(nrows=2, ncols=6, figsize=(15, 10))\n",
"\n",
"# Flatten the axes array to easily iterate over\n",
"axes = axes.flatten()\n",
"\n",
"# Loop through each column and create a histogram\n",
"for i, col in enumerate(pred_df.columns[:12]):\n",
" # Plot histogram\n",
" pred_df[col].hist(ax=axes[i], bins=[1, 2, 3, 4, 5, 6], alpha=0.5, edgecolor='black', density=True) # Adjust the number of bins as needed\n",
" axes[i].set_title(col)\n",
" axes[i].set_xlabel('Value')\n",
" axes[i].set_ylabel('Frequency')\n",
"\n",
" # Add normal distribution curve\n",
" mu, std = norm.fit(pred_df[col])\n",
" xmin, xmax = axes[i].get_xlim()\n",
" x = np.linspace(xmin, xmax, 100)\n",
" p = norm.pdf(x, mu, std)\n",
" axes[i].plot(x, p, 'k', linewidth=2, alpha=0.3)\n",
"\n",
"# Adjust layout\n",
"plt.tight_layout()\n",
"\n",
"# Show the plot\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "6496f677",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x360 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Making HISTOGRAM and comparing with normal distirbution's bell curve,\n",
"# But this time for the remaining columns\n",
"# Create a figure and axes for subplots\n",
"fig, axes = plt.subplots(nrows=1, ncols=5, figsize=(15, 5))\n",
"\n",
"# Loop through each column and create a histogram\n",
"for i, col in enumerate(['Month_Of_Service', 'Years_Of_Service', 'Residence_Code', 'Net_Salary', 'Resigned']):\n",
" # Plot histogram\n",
" pred_df[col].hist(ax=axes[i], bins=5, alpha=0.5, edgecolor='black', density=True) # Adjust the number of bins as needed\n",
" axes[i].set_title(col)\n",
" axes[i].set_xlabel('Value')\n",
" axes[i].set_ylabel('Frequency')\n",
"\n",
" # Add normal distribution curve\n",
" mu, std = norm.fit(pred_df[col])\n",
" xmin, xmax = axes[i].get_xlim()\n",
" x = np.linspace(xmin, xmax, 100)\n",
" p = norm.pdf(x, mu, std)\n",
" axes[i].plot(x, p, 'k', linewidth=2, alpha=0.5) # Adjust opacity here\n",
"\n",
"# Adjust layout\n",
"plt.tight_layout()\n",
"\n",
"# Show the plot\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "6d937b21",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x720 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Calculate the correlation matrix\n",
"correlation_matrix = pred_df.corr()\n",
"\n",
"# Create the heatmap\n",
"plt.figure(figsize=(12, 10))\n",
"sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=\".2f\", linewidths=0.5)\n",
"plt.title('Correlation Heatmap of All Columns')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "db3aa707",
"metadata": {},
"source": [
"## Second takeaway <a id=\"secondTake\"></a>\n",
"[Back to Top](#top)<br>\n",
"- Identified autocorrelation between the columns `Years_Of_Service` and `Month_Of_Service`. It's necessary to remove one of them. In this scenario, `Years_Of_Service` is chosen for removal.\n",
"- After analyzing the histogram of the `Resigned` column, it's evident that the dataset is imbalanced, with a significantly smaller number of instances labeled as 1 compared to 0. This requires a specialized approach to train the data effectively."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "134bbf58",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Confirm the Resigned Column's Labels\n",
"plt.figure(figsize=(8, 4))\n",
"sns.countplot(x='Resigned', data=pred_df)\n",
"plt.title('Resigned Column Countplot')\n",
"plt.xlabel('Resigned')\n",
"plt.ylabel('Count')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "4dfd0a80",
"metadata": {},
"source": [
"An **\"Imbalanced\"** Dataset, where in a feature, the disparity between minority and majority class is very evident."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ba06ed3a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Role Satisfaction int64\n",
"Skill Utilization int64\n",
"Career Growth Opportunity int64\n",
"Supervisor Support int64\n",
"Work-Life Balance int64\n",
"Recognition & Appreciation int64\n",
"Company Culture int64\n",
"Training & Development int64\n",
"Communication Effectiveness int64\n",
"Diversity & Inclusion int64\n",
"Work Environment int64\n",
"Compensation int64\n",
"Month_Of_Service int64\n",
"Residence_Code int64\n",
"Net_Salary int64\n",
"Resigned int64\n",
"dtype: object"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drop the column Years_Of_Service\n",
"pred_df = pred_df.drop(columns=[\"Years_Of_Service\"])\n",
"pred_df.dtypes"
]
},
{
"cell_type": "markdown",
"id": "8ffccfd1",
"metadata": {},
"source": [
"## Training the data <a id=\"trainData\"></a>\n",
"[Back to Top](#top)<br>\n",
"Now, we will attempt to train the dataset. Firstly, with normal settings / no special technique."
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "88eead04",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.model_selection import train_test_split\n",
"from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "90beb683",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.naive_bayes import GaussianNB\n",
"from sklearn.tree import DecisionTreeClassifier"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "7d6b6698",
"metadata": {},
"outputs": [],
"source": [
"# Split the data into features (X) and labels (y)\n",
"X = pred_df.drop(columns=['Resigned'])\n",
"y = pred_df['Resigned']\n",
"\n",
"# Split the data into training and testing sets\n",
"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
"\n",
"# Define a dictionary to store results\n",
"results = {'Model': [], 'F1_score': [], 'Accuracy': [], 'Precision': [], 'Recall': []}"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "17ff7433",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Asus\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"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>Random Forest</th>\n",
" <th>Decision Tree</th>\n",
" <th>Naive Bayes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>0.87</td>\n",
" <td>0.820000</td>\n",
" <td>0.85</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Precision</th>\n",
" <td>0.00</td>\n",
" <td>0.272727</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>0.00</td>\n",
" <td>0.230769</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1 Score</th>\n",
" <td>0.00</td>\n",
" <td>0.250000</td>\n",
" <td>0.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Random Forest Decision Tree Naive Bayes\n",
"Accuracy 0.87 0.820000 0.85\n",
"Precision 0.00 0.272727 0.00\n",
"Recall 0.00 0.230769 0.00\n",
"F1 Score 0.00 0.250000 0.00"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Train Random Forest model\n",
"rf_model = RandomForestClassifier(random_state=42)\n",
"rf_model.fit(X_train, y_train)\n",
"\n",
"# Train Decision Tree model\n",
"dt_model = DecisionTreeClassifier(random_state=42)\n",
"dt_model.fit(X_train, y_train)\n",
"\n",
"# Train Naive Bayes model\n",
"nb_model = GaussianNB()\n",
"nb_model.fit(X_train, y_train)\n",
"\n",
"# Evaluate models\n",
"models = {\"Random Forest\": rf_model, \"Decision Tree\": dt_model, \"Naive Bayes\": nb_model}\n",
"metrics = {\"Accuracy\": accuracy_score, \"Precision\": precision_score, \"Recall\": recall_score, \"F1 Score\": f1_score}\n",
"results = {}\n",
"\n",
"for name, model in models.items():\n",
" y_pred = model.predict(X_test)\n",
" result = {}\n",
" for metric_name, metric_func in metrics.items():\n",
" result[metric_name] = metric_func(y_test, y_pred)\n",
" results[name] = result\n",
"\n",
"# Convert results to DataFrame for easier plotting\n",
"results_df = pd.DataFrame(results)\n",
"\n",
"# print results\n",
"results_df"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "745713e6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Calculate class frequencies for y_train and y_test\n",
"unique_train, counts_train = np.unique(y_train, return_counts=True)\n",
"unique_test, counts_test = np.unique(y_test, return_counts=True)\n",
"\n",
"# Plot y_train and y_test\n",
"plt.figure(figsize=(8, 4))\n",
"\n",
"# Plot for y_train\n",
"plt.subplot(1, 2, 1)\n",
"plt.bar(unique_train, counts_train, color='red')\n",
"plt.title('Distribution of y_train')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Frequency')\n",
"plt.xticks(unique_train)\n",
"plt.ylim(0, max(max(counts_train), max(counts_train)) + 10)\n",
"\n",
"# Plot for y_train\n",
"plt.subplot(1, 2, 2)\n",
"plt.bar(unique_test, counts_test, color='blue')\n",
"plt.title('Distribution of y_test')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Frequency')\n",
"plt.xticks(unique_test)\n",
"plt.ylim(0, max(max(counts_test), max(counts_test)) + 10)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "3ce6e7f8",
"metadata": {},
"source": [
"## Third Takeaway <a id=\"thirdTake\"></a>\n",
"[Back to Top](#top)<br>\n",
"- Despite achieving relatively high accuracy, the Random Forest and Naive Bayes models exhibit a precision of 0, suggesting poor performance specifically on the minority class (Resigned = 1).\n",
"- To enhance model performance, we will explore additional data transformation techniques. These will include strategies such as adjusting class weights and implementing over/undersampling methods."
]
},
{
"cell_type": "markdown",
"id": "3e11c086",
"metadata": {},
"source": [
"## Class Weights <a id=\"weightCtrl\"></a>\n",
"[Back to Top](#top)<br>\n",
"We will start by adjusting the class weights of the `Resigned` Feature."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "635dee3e",
"metadata": {},
"outputs": [],
"source": [
"from sklearn.utils.class_weight import compute_class_weight"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "5d8c44c2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Class Weights: {0: 0.5555555555555556, 1: 5.0}\n"
]
}
],
"source": [
"# Firstly, we want to apply stratified splitting method, and containing the value on a new variable.\n",
"# X and y are already instantiated in previous code above.\n",
"\n",
"# Run Stratified train-test split\n",
"X_train_strat, X_test_strat, y_train_strat, y_test_strat = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n",
"\n",
"# Then, compute class weights\n",
"class_labels = np.unique(y_train_strat)\n",
"class_weights = compute_class_weight(class_weight='balanced', classes=class_labels, y=y_train_strat)\n",
"\n",
"# Convert class weights to dictionary format\n",
"class_weights_dict = dict(zip(class_labels, class_weights))\n",
"\n",
"# Print class weights\n",
"print(\"Class Weights:\", class_weights_dict)"
]
},
{
"cell_type": "markdown",
"id": "ffefd4b5",
"metadata": {},
"source": [
"- Class 0 has a weight of approximately **0.5.** This suggests that misclassifications of class 0 will be penalized less heavily during model training compared to class 1. Since this weight is less than 1, it implies that **class 0 is relatively well-represented in the training data.**\n",
"- Class 1 has a weight of **5.0.** This indicates that misclassifications of class 1 will be penalized more heavily during model training compared to class 0. Since this weight is greater than 1, it suggests that **class 1 is underrepresented in the training data.**"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "2c8cf18b",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Asus\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n",
"C:\\Users\\Asus\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"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>Random Forest</th>\n",
" <th>Decision Tree</th>\n",
" <th>Naive Bayes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>0.9</td>\n",
" <td>0.810000</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Precision</th>\n",
" <td>0.0</td>\n",
" <td>0.153846</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>0.0</td>\n",
" <td>0.200000</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1 Score</th>\n",
" <td>0.0</td>\n",
" <td>0.173913</td>\n",
" <td>0.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Random Forest Decision Tree Naive Bayes\n",
"Accuracy 0.9 0.810000 0.9\n",
"Precision 0.0 0.153846 0.0\n",
"Recall 0.0 0.200000 0.0\n",
"F1 Score 0.0 0.173913 0.0"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Force the new class weights\n",
"# Play around with this value\n",
"class_weights_new = {0: 1, 1: 1.5}\n",
"\n",
"# Train Random Forest model\n",
"rf_model = RandomForestClassifier(random_state=42)\n",
"rf_model.fit(X_train_strat, y_train_strat)\n",
"\n",
"# Train Decision Tree model\n",
"dt_model = DecisionTreeClassifier(random_state=42)\n",
"dt_model.fit(X_train_strat, y_train_strat)\n",
"\n",
"# Train Naive Bayes model\n",
"nb_model = GaussianNB()\n",
"nb_model.fit(X_train_strat, y_train_strat)\n",
"\n",
"# Evaluate models\n",
"models = {\"Random Forest\": rf_model, \"Decision Tree\": dt_model, \"Naive Bayes\": nb_model}\n",
"metrics = {\"Accuracy\": accuracy_score, \"Precision\": precision_score, \"Recall\": recall_score, \"F1 Score\": f1_score}\n",
"results = {}\n",
"\n",
"for name, model in models.items():\n",
" y_pred = model.predict(X_test_strat)\n",
" result = {}\n",
" for metric_name, metric_func in metrics.items():\n",
" result[metric_name] = metric_func(y_test_strat, y_pred)\n",
" results[name] = result\n",
"\n",
"# Convert results to DataFrame for easier plotting\n",
"results_df = pd.DataFrame(results)\n",
"\n",
"# print results\n",
"results_df"
]
},
{
"cell_type": "markdown",
"id": "65dbd45b",
"metadata": {},
"source": [
"Just like before, the `Random Forest` and `Naive Bayes` model did not pick up the minority class. We will move on to the next technique."
]
},
{
"cell_type": "markdown",
"id": "7a56b0c5",
"metadata": {},
"source": [
"# Oversampling <a id=\"trainOver\"></a>\n",
"[Back to Top](#top)<br>\n",
"The goal of oversampling is to balance the class distribution by artificially increasing the number of instances in the minority class."
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "a242b66f",
"metadata": {},
"outputs": [],
"source": [
"from imblearn.over_sampling import RandomOverSampler"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "1f8756c5",
"metadata": {},
"outputs": [],
"source": [
"# Instantiate oversampler\n",
"oversampler = RandomOverSampler(random_state=42)\n",
"\n",
"# Apply Random Oversampling to balance the training data\n",
"X_train_resampled, y_train_resampled = oversampler.fit_resample(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "ff610390",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Calculate class frequencies for y_train_resampled and y_train\n",
"unique_train_resampled, counts_train_resampled = np.unique(y_train_resampled, return_counts=True)\n",
"unique_train, counts_train = np.unique(y_train, return_counts=True)\n",
"\n",
"# Plot bar plots\n",
"plt.figure(figsize=(8, 4))\n",
"\n",
"# Plot for y_train_resampled\n",
"plt.subplot(1, 2, 1)\n",
"plt.bar(unique_train_resampled, counts_train_resampled, color='red')\n",
"plt.title('Distribution of y_train_resampled')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Frequency')\n",
"plt.xticks(unique_train_resampled)\n",
"plt.ylim(0, max(max(counts_train_resampled), max(counts_train)) + 10)\n",
"\n",
"# Plot for y_train\n",
"plt.subplot(1, 2, 2)\n",
"plt.bar(unique_train, counts_train, color='blue')\n",
"plt.title('Distribution of y_train')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Frequency')\n",
"plt.xticks(unique_train)\n",
"plt.ylim(0, max(max(counts_train_resampled), max(counts_train)) + 10)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "eac0bdd9",
"metadata": {},
"source": [
"We can see the oversampling algorithm makes synthetic samples to the label 1, to match the frequency of label 0."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "05f6901e",
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Asus\\anaconda3\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1318: UndefinedMetricWarning: Precision is ill-defined and being set to 0.0 due to no predicted samples. Use `zero_division` parameter to control this behavior.\n",
" _warn_prf(average, modifier, msg_start, len(result))\n"
]
},
{
"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>Random Forest</th>\n",
" <th>Decision Tree</th>\n",
" <th>Naive Bayes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>0.87</td>\n",
" <td>0.78</td>\n",
" <td>0.780000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Precision</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.090909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.076923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1 Score</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.083333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Random Forest Decision Tree Naive Bayes\n",
"Accuracy 0.87 0.78 0.780000\n",
"Precision 0.00 0.00 0.090909\n",
"Recall 0.00 0.00 0.076923\n",
"F1 Score 0.00 0.00 0.083333"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Train Random Forest model\n",
"rf_model = RandomForestClassifier(random_state=42)\n",
"rf_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Train Decision Tree model\n",
"dt_model = DecisionTreeClassifier(random_state=42)\n",
"dt_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Train Naive Bayes model\n",
"nb_model = GaussianNB()\n",
"nb_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Evaluate models\n",
"models = {\"Random Forest\": rf_model, \"Decision Tree\": dt_model, \"Naive Bayes\": nb_model}\n",
"metrics = {\"Accuracy\": accuracy_score, \"Precision\": precision_score, \"Recall\": recall_score, \"F1 Score\": f1_score}\n",
"results = {}\n",
"\n",
"for name, model in models.items():\n",
" y_pred = model.predict(X_test)\n",
" result = {}\n",
" for metric_name, metric_func in metrics.items():\n",
" result[metric_name] = metric_func(y_test, y_pred)\n",
" results[name] = result\n",
"\n",
"# Convert results to DataFrame for easier plotting\n",
"results_df_resampled = pd.DataFrame(results)\n",
"\n",
"results_df_resampled"
]
},
{
"cell_type": "markdown",
"id": "5833dc09",
"metadata": {},
"source": [
"Using Oversampling, the `Naive Bayes` did pick up the minority class. But, the others are still very poorly performed. We will try the next technique."
]
},
{
"cell_type": "markdown",
"id": "6bba635d",
"metadata": {},
"source": [
"## Undersampling <a id=\"trainUnder\"></a>\n",
"[Back to Top](#top)<br>\n",
"Instead of using the entire dataset, undersampling involves reducing the size of the majority class to balance it with the minority class."
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "b4d95ba7",
"metadata": {},
"outputs": [],
"source": [
"from imblearn.under_sampling import RandomUnderSampler"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "8843cc81",
"metadata": {},
"outputs": [],
"source": [
"# Instantiate undersampler\n",
"undersampler = RandomUnderSampler(random_state=42)\n",
"\n",
"# Apply Random Undersampling to balance the training data\n",
"X_train_resampled, y_train_resampled = undersampler.fit_resample(X_train, y_train)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "987956b5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Calculate class frequencies for y_train_resampled and y_train\n",
"unique_train_resampled, counts_train_resampled = np.unique(y_train_resampled, return_counts=True)\n",
"unique_train, counts_train = np.unique(y_train, return_counts=True)\n",
"\n",
"# Plot bar plots\n",
"plt.figure(figsize=(8, 4))\n",
"\n",
"# Plot for y_train_resampled\n",
"plt.subplot(1, 2, 1)\n",
"plt.bar(unique_train_resampled, counts_train_resampled, color='red')\n",
"plt.title('Distribution of y_train_resampled')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Frequency')\n",
"plt.xticks(unique_train_resampled)\n",
"plt.ylim(0, max(max(counts_train_resampled), max(counts_train)) + 10)\n",
"\n",
"# Plot for y_train\n",
"plt.subplot(1, 2, 2)\n",
"plt.bar(unique_train, counts_train, color='blue')\n",
"plt.title('Distribution of y_train')\n",
"plt.xlabel('Class')\n",
"plt.ylabel('Frequency')\n",
"plt.xticks(unique_train)\n",
"plt.ylim(0, max(max(counts_train_resampled), max(counts_train)) + 10)\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "3c6f27ab",
"metadata": {},
"source": [
"We can see the undersampling algorithm makes \"shrinks\" the data of label 0, to match the frequency of label 1."
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "d0fb7411",
"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>Random Forest</th>\n",
" <th>Decision Tree</th>\n",
" <th>Naive Bayes</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Accuracy</th>\n",
" <td>0.560000</td>\n",
" <td>0.490000</td>\n",
" <td>0.780000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Precision</th>\n",
" <td>0.155556</td>\n",
" <td>0.183333</td>\n",
" <td>0.090909</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Recall</th>\n",
" <td>0.538462</td>\n",
" <td>0.846154</td>\n",
" <td>0.076923</td>\n",
" </tr>\n",
" <tr>\n",
" <th>F1 Score</th>\n",
" <td>0.241379</td>\n",
" <td>0.301370</td>\n",
" <td>0.083333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Random Forest Decision Tree Naive Bayes\n",
"Accuracy 0.560000 0.490000 0.780000\n",
"Precision 0.155556 0.183333 0.090909\n",
"Recall 0.538462 0.846154 0.076923\n",
"F1 Score 0.241379 0.301370 0.083333"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Train Random Forest model\n",
"rf_model = RandomForestClassifier(random_state=42)\n",
"rf_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Train Decision Tree model\n",
"dt_model = DecisionTreeClassifier(random_state=42)\n",
"dt_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Train Naive Bayes model\n",
"nb_model = GaussianNB()\n",
"nb_model.fit(X_train_resampled, y_train_resampled)\n",
"\n",
"# Evaluate models\n",
"models = {\"Random Forest\": rf_model, \"Decision Tree\": dt_model, \"Naive Bayes\": nb_model}\n",
"metrics = {\"Accuracy\": accuracy_score, \"Precision\": precision_score, \"Recall\": recall_score, \"F1 Score\": f1_score}\n",
"results = {}\n",
"\n",
"for name, model in models.items():\n",
" y_pred = model.predict(X_test)\n",
" result = {}\n",
" for metric_name, metric_func in metrics.items():\n",
" result[metric_name] = metric_func(y_test, y_pred)\n",
" results[name] = result\n",
"\n",
"# Convert results to DataFrame for easier plotting\n",
"results_df_resampled = pd.DataFrame(results)\n",
"\n",
"results_df_resampled"
]
},
{
"cell_type": "markdown",
"id": "1e574b3c",
"metadata": {},
"source": [
"While sacrificing some accuracy, our adjustments have notably improved the recall and F1 Score of the models. This indicates that the model is now capable of effectively capturing instances belonging to both label 1 and label 0, enhancing its overall performance."
]
},
{
"cell_type": "markdown",
"id": "dc20a26b",
"metadata": {},
"source": [
"## Feature Importance <a id=\"dissect\"></a>\n",
"[Back to Top](#top)<br>\n",
"From the results of the undersampling method, it produces the best result so far. And we can attempt to dissect the feature importances from each model."
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "dffda208",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Get feature importances for Decision Tree model\n",
"feature_importances = dt_model.feature_importances_\n",
"\n",
"# Create a DataFrame to store feature names and their importance scores\n",
"feature_importance_df = pd.DataFrame({'Feature': X_train_resampled.columns, 'Importance': feature_importances})\n",
"\n",
"# Sort the DataFrame by importance scores in descending order\n",
"feature_importance_df = feature_importance_df.sort_values(by='Importance', ascending=False)\n",
"\n",
"# Plot feature importance with decimal value labels\n",
"plt.figure(figsize=(8, 4))\n",
"bars = plt.barh(feature_importance_df['Feature'], feature_importance_df['Importance'], color='lightblue')\n",
"\n",
"plt.xlabel('Importance Score')\n",
"plt.ylabel('Feature')\n",
"plt.title('Decision Tree Feature Importance')\n",
"plt.gca().invert_yaxis() # Invert y-axis to display the most important features at the top\n",
"\n",
"# Add decimal value labels to bars\n",
"for bar, importance in zip(bars, feature_importance_df['Importance']):\n",
" plt.text(importance, bar.get_y() + bar.get_height()/2, f'{importance:.2f}', ha='left', va='center')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "2cd6c72b",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 576x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# Get feature importances for Random Forest model\n",
"feature_importances_rf = rf_model.feature_importances_\n",
"\n",
"# Create a DataFrame to store feature names and their importance scores\n",
"feature_importance_df_rf = pd.DataFrame({'Feature': X_train_resampled.columns, 'Importance': feature_importances_rf})\n",
"\n",
"# Sort the DataFrame by importance scores in descending order\n",
"feature_importance_df_rf = feature_importance_df_rf.sort_values(by='Importance', ascending=False)\n",
"\n",
"# Plot feature importance with decimal value labels\n",
"plt.figure(figsize=(8, 4))\n",
"bars_rf = plt.barh(feature_importance_df_rf['Feature'], feature_importance_df_rf['Importance'], color='lightgreen')\n",
"\n",
"plt.xlabel('Importance Score')\n",
"plt.ylabel('Feature')\n",
"plt.title('Random Forest Feature Importance')\n",
"plt.gca().invert_yaxis() # Invert y-axis to display the most important features at the top\n",
"\n",
"# Add decimal value labels to bars\n",
"for bar, importance in zip(bars_rf, feature_importance_df_rf['Importance']):\n",
" plt.text(importance, bar.get_y() + bar.get_height()/2, f'{importance:.2f}', ha='left', va='center')\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "markdown",
"id": "dddadd89",
"metadata": {},
"source": [
"In traditional Naive Bayes models, such as Gaussian Naive Bayes, there isn't a direct concept of \"feature importance\" as there is in models like Random Forest or Logistic Regression.\n",
"\n",
"Naive Bayes models make predictions based on the probability distribution of features given the class and the prior probability of each class. These models assume that features are conditionally independent given the class, which means that the presence of one feature does not directly influence the presence of another feature.\n",
"\n",
"As a result, Naive Bayes models do not provide feature importance values in the same way as decision tree-based or linear models. Instead, they focus on probability calculations based on the input features and class labels."
]
},
{
"cell_type": "markdown",
"id": "e4495be5",
"metadata": {},
"source": [
"## Let's Test it Out!\n",
"Let's first make a dummy dataframe with 3 rows."
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "a961eeef",
"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>Role Satisfaction</th>\n",
" <th>Skill Utilization</th>\n",
" <th>Career Growth Opportunity</th>\n",
" <th>Supervisor Support</th>\n",
" <th>Work-Life Balance</th>\n",
" <th>Recognition &amp; Appreciation</th>\n",
" <th>Company Culture</th>\n",
" <th>Training &amp; Development</th>\n",
" <th>Communication Effectiveness</th>\n",
" <th>Diversity &amp; Inclusion</th>\n",
" <th>Work Environment</th>\n",
" <th>Compensation</th>\n",
" <th>Staff_Id</th>\n",
" <th>Month_Of_Service</th>\n",
" <th>Residence_Code</th>\n",
" <th>Net_Salary</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>SA63171</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>5582218</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>SA63172</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>6200012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>5</td>\n",
" <td>4</td>\n",
" <td>4</td>\n",
" <td>SA63173</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>7305551</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Role Satisfaction Skill Utilization Career Growth Opportunity \\\n",
"0 3 2 3 \n",
"1 4 3 4 \n",
"2 5 4 4 \n",
"\n",
" Supervisor Support Work-Life Balance Recognition & Appreciation \\\n",
"0 3 4 2 \n",
"1 3 3 4 \n",
"2 3 2 4 \n",
"\n",
" Company Culture Training & Development Communication Effectiveness \\\n",
"0 3 3 3 \n",
"1 4 3 3 \n",
"2 5 4 4 \n",
"\n",
" Diversity & Inclusion Work Environment Compensation Staff_Id \\\n",
"0 5 4 3 SA63171 \n",
"1 5 3 3 SA63172 \n",
"2 5 4 4 SA63173 \n",
"\n",
" Month_Of_Service Residence_Code Net_Salary \n",
"0 1 4 5582218 \n",
"1 2 1 6200012 \n",
"2 3 2 7305551 "
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a dictionary with custom values for each row\n",
"data = {\n",
" 'Role Satisfaction': [3, 4, 5],\n",
" 'Skill Utilization': [2, 3, 4],\n",
" 'Career Growth Opportunity': [3, 4, 4],\n",
" 'Supervisor Support': [3, 3, 3],\n",
" 'Work-Life Balance': [4, 3, 2],\n",
" 'Recognition & Appreciation': [2, 4, 4],\n",
" 'Company Culture': [3, 4, 5],\n",
" 'Training & Development': [3, 3, 4],\n",
" 'Communication Effectiveness': [3, 3, 4],\n",
" 'Diversity & Inclusion': [5, 5, 5],\n",
" 'Work Environment': [4, 3, 4],\n",
" 'Compensation': [3, 3, 4],\n",
" 'Staff_Id': ['SA63171', 'SA63172', 'SA63173'],\n",
" 'Month_Of_Service': [1, 2, 3],\n",
" 'Residence_Code': [4, 1, 2],\n",
" 'Net_Salary': [5582218, 6200012, 7305551]\n",
"}\n",
"\n",
"# Create the DataFrame\n",
"test_df = pd.DataFrame(data)\n",
"\n",
"# Display the DataFrame\n",
"test_df"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "ea84b693",
"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>Staff_Id</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>SA63171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>SA63172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>SA63173</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Staff_Id\n",
"0 SA63171\n",
"1 SA63172\n",
"2 SA63173"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Create a DataFrame with only the 'Staff_Id' column for later \n",
"staff_id_df = test_df[['Staff_Id']].copy()\n",
"\n",
"# Display the DataFrame\n",
"staff_id_df"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "b1b66185",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Role Satisfaction</th>\n",
" <th>Skill Utilization</th>\n",
" <th>Career Growth Opportunity</th>\n",
" <th>Supervisor Support</th>\n",
" <th>Work-Life Balance</th>\n",
" <th>Recognition &amp; Appreciation</th>\n",
" <th>Company Culture</th>\n",
" <th>Training &amp; Development</th>\n",
" <th>Communication Effectiveness</th>\n",
" <th>Diversity &amp; Inclusion</th>\n",
" <th>Work Environment</th>\n",
" <th>Compensation</th>\n",
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" Role Satisfaction Skill Utilization Career Growth Opportunity \\\n",
"0 3 2 3 \n",
"1 4 3 4 \n",
"2 5 4 4 \n",
"\n",
" Supervisor Support Work-Life Balance Recognition & Appreciation \\\n",
"0 3 4 2 \n",
"1 3 3 4 \n",
"2 3 2 4 \n",
"\n",
" Company Culture Training & Development Communication Effectiveness \\\n",
"0 3 3 3 \n",
"1 4 3 3 \n",
"2 5 4 4 \n",
"\n",
" Diversity & Inclusion Work Environment Compensation Month_Of_Service \\\n",
"0 5 4 3 1 \n",
"1 5 3 3 2 \n",
"2 5 4 4 3 \n",
"\n",
" Residence_Code Net_Salary \n",
"0 4 5582218 \n",
"1 1 6200012 \n",
"2 2 7305551 "
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Drop the Staff_Id\n",
"test_df = test_df.drop('Staff_Id', axis=1)\n",
"test_df"
]
},
{
"cell_type": "markdown",
"id": "95545726",
"metadata": {},
"source": [
"Since we trained 3 models, we will use all of them."
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "d66c5fc4",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Predictions using Decision Tree:\n",
" Staff_Id Prediction\n",
"0 SA63171 0\n",
"1 SA63172 0\n",
"2 SA63173 0\n",
"\n",
"\n",
"Predictions using Random Forest:\n",
" Staff_Id Prediction\n",
"0 SA63171 0\n",
"1 SA63172 0\n",
"2 SA63173 1\n",
"\n",
"\n",
"Predictions using Naive Bayes:\n",
" Staff_Id Prediction\n",
"0 SA63171 0\n",
"1 SA63172 0\n",
"2 SA63173 0\n",
"\n",
"\n"
]
}
],
"source": [
"# Initialize the models\n",
"model_to_test = {\n",
" \"Decision Tree\": dt_model,\n",
" \"Random Forest\": rf_model,\n",
" \"Naive Bayes\": nb_model\n",
"}\n",
"\n",
"# Iterate over the models\n",
"for model_name, model in model_to_test.items():\n",
" # Make predictions\n",
" predictions = model.predict(test_df)\n",
" \n",
" # Create a DataFrame to store predictions\n",
" results_df = pd.DataFrame({\n",
" \"Prediction\": predictions\n",
" })\n",
" \n",
" # Merge predictions with Staff_Id\n",
" results_with_id_df = pd.concat([staff_id_df, results_df], axis=1)\n",
" \n",
" # Print the title\n",
" print(f\"Predictions using {model_name}:\")\n",
" \n",
" # Print the resulting DataFrame\n",
" print(results_with_id_df)\n",
" print(\"\\n\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6793d27b",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
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"pygments_lexer": "ipython3",
"version": "3.9.12"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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