Employee Attrition Prediction

 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.