https://github.com/sayande01/employee_churn_prediction_machine_learning

Leveraging ColumnTransformer, pipelines, standardization, and encoding, we'll preprocess data. Using Logistic Regression, Decision Trees, Random Forest, and XGBoost, we'll analyze factors like job satisfaction, promotion, and salary to predict churn. This helps companies improve satisfaction, reduce turnover, and enhance stability.
https://github.com/sayande01/employee_churn_prediction_machine_learning

decison-trees logistic-regression random-forest xgboost-classifier

Last synced: about 1 month ago
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Leveraging ColumnTransformer, pipelines, standardization, and encoding, we'll preprocess data. Using Logistic Regression, Decision Trees, Random Forest, and XGBoost, we'll analyze factors like job satisfaction, promotion, and salary to predict churn. This helps companies improve satisfaction, reduce turnover, and enhance stability.

• Host: GitHub
• URL: https://github.com/sayande01/employee_churn_prediction_machine_learning
• Owner: sayande01
• Created: 2024-05-02T17:11:33.000Z (about 1 year ago)
• Default Branch: main
• Last Pushed: 2024-05-02T17:16:31.000Z (about 1 year ago)
• Last Synced: 2025-02-13T02:39:19.687Z (3 months ago)
• Topics: decison-trees, logistic-regression, random-forest, xgboost-classifier
• Language: Jupyter Notebook
• Homepage:
• Size: 1.77 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
Title: Predicting Employee Churn: A Comprehensive Machine Learning Approach

Description:

In this project, we aim to predict employee churn using a comprehensive machine learning approach. By leveraging techniques such as ColumnTransformer, data pipelines, data standardization, and data encoding, we will preprocess the data efficiently. We'll utilize popular machine learning models like Logistic Regression, Decision Trees, Random Forest, and XGBoost to analyze various factors such as job satisfaction, promotion, salary, and performance evaluation, determining their impact on employee retention. By understanding the likelihood of an employee leaving the organization, companies can proactively take steps to improve employee satisfaction and reduce turnover rates, ultimately enhancing organizational stability and productivity.

Objective:

The primary objective of this project is to develop a predictive model that accurately identifies employees who are likely to leave the organization based on diverse factors such as job satisfaction, promotion history, salary, performance evaluations, and other relevant features. Specifically, our goals include:

1. Data Preparation:

   - Utilize ColumnTransformer to preprocess heterogeneous data types efficiently.

   - Construct a data pipeline to automate the preprocessing steps, ensuring reproducibility and scalability.

   - Standardize numerical features to ensure uniformity and compatibility across different scales.

   - Encode categorical features to transform them into a numerical representation suitable for machine learning algorithms.

2. Model Selection and Evaluation:

   - Implement various machine learning models including Logistic Regression, Decision Trees, Random Forest, and XGBoost.

   - Train and evaluate each model using appropriate evaluation metrics such as accuracy, precision, recall, and F1-score.

   - Perform hyperparameter tuning to optimize the performance of each model, ensuring robustness and generalization.

3. Interpretability and Insights:

   - Analyze the feature importance provided by the trained models to understand the factors driving employee churn.

   - Extract actionable insights from the model predictions to help management make informed decisions and implement targeted retention strategies.

4. Deployment and Integration:

   - Deploy the final predictive model into a production environment, allowing real-time predictions on new employee data.

   - Integrate the model into existing HR systems or dashboards for seamless integration and utilization by relevant stakeholders.

   - Provide documentation and guidelines for ongoing model maintenance and updates to ensure long-term usability and effectiveness.

By accomplishing these objectives, we aim to empower organizations with actionable insights to mitigate employee churn, foster employee satisfaction, and enhance overall organizational performance and stability.