Employee Attrition Prediction
1. Introduction
Objective: Predict employee attrition (voluntary or involuntary turnover) based
on HR data.
Purpose: Provide insights into employee retention strategies and highlight key
factors contributing to attrition.
2. Project Workflow
1. Problem Definition:
- Predict whether an employee will
leave the organization based on historical data.
- Key questions:
- What factors contribute to
employee attrition?
- How can we predict attrition with
high accuracy?
2. Data Collection:
- Source: Public datasets or
organizational HR databases.
- Example: A dataset containing
attributes like `Age`, `Department`, `Job Satisfaction`, `Years at Company`,
and `Attrition`.
3. Data Preprocessing:
- Clean the dataset, handle missing
values, encode categorical variables, and scale numerical features.
4. Modeling and Evaluation:
- Train classification models and
evaluate their performance.
5. Insights and Recommendations:
- Identify patterns and suggest
strategies to improve retention.
3. Technical Requirements
- Programming Language: Python
- Libraries/Tools:
- Data Handling: Pandas, NumPy
- Visualization: Matplotlib, Seaborn
- Machine Learning: Scikit-learn,
XGBoost
- Model Evaluation: Statsmodels
4. Implementation Steps
Step 1: Setup Environment
Install required libraries:
```
pip install pandas numpy matplotlib seaborn scikit-learn xgboost
```
Step 2: Load and Explore Dataset
Load the HR dataset:
```
import pandas as pd
df = pd.read_csv('employee_data.csv')
```
Explore the dataset:
```
print(df.head())
print(df.info())
```
Step 3: Data Cleaning and Preprocessing
Handle missing values:
```
df.fillna(df.median(), inplace=True)
```
Encode categorical variables:
```
df = pd.get_dummies(df, drop_first=True)
```
Normalize numerical features:
```
from sklearn.preprocessing import MinMaxScaler
scaler = MinMaxScaler()
numerical_features = ['Age', 'YearsAtCompany', 'MonthlyIncome']
df[numerical_features] = scaler.fit_transform(df[numerical_features])
```
Step 4: Train-Test Split
Split the data into training and testing sets:
```
from sklearn.model_selection import train_test_split
X = df.drop('Attrition', axis=1)
y = df['Attrition']
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,
random_state=42)
```
Step 5: Build and Evaluate Models
Train a logistic regression model:
```
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix,
classification_report
model = LogisticRegression()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, predictions))
print(confusion_matrix(y_test, predictions))
print(classification_report(y_test, predictions))
```
Try advanced models (e.g., Random Forests, XGBoost):
```
from xgboost import XGBClassifier
xgb_model = XGBClassifier(random_state=42)
xgb_model.fit(X_train, y_train)
xgb_predictions = xgb_model.predict(X_test)
print("XGBoost Accuracy:", accuracy_score(y_test, xgb_predictions))
```
Step 6: Generate Reports and Insights
Export model performance metrics:
```
import json
results = {
"Logistic Regression
Accuracy": accuracy_score(y_test, predictions),
"XGBoost Accuracy":
accuracy_score(y_test, xgb_predictions)
}
with open('attrition_model_performance.json', 'w') as file:
json.dump(results, file)
```
Save visualizations for feature importance or performance metrics.
5. Expected Outcomes
1. Identification of key factors influencing employee attrition.
2. Trained classification models with performance metrics.
3. Insights to design targeted employee retention strategies.
6. Additional Suggestions
- Advanced Techniques:
- Use hyperparameter tuning for optimal
model performance.
- Implement ensemble methods for better
predictions.
- Explainable AI:
- Use SHAP or LIME to interpret model
predictions.
- Dashboard Integration:
- Build an interactive dashboard using
Streamlit or Power BI for HR teams.