The Employee Data Analysis project focuses on exploring and analyzing employee data to gain insights into factors influencing employee churn. It includes exploratory data analysis (EDA) techniques and utilizes various machine learning algorithms for customer churn prediction.
- Customer Churn Prediction: Utilizes univariate and bivariate analysis techniques to predict customer churn.
- Machine Learning Algorithms: Implements different machine learning algorithms including Support Vector Machine (SVM), Gaussian Naïve Bayes, and Decision Tree for customer churn prediction.
- Published Notebook: The project notebook containing the EDA and prediction models is published on Kaggle, making it accessible to the community for learning and further analysis.
- Data Exploration: Begin by exploring the employee data to understand its structure, distribution, and relationships between variables.
- Customer Churn Prediction: Use univariate and bivariate analysis techniques to identify key factors influencing customer churn.
- Model Training: Train machine learning models using the identified factors and algorithms such as SVM, Gaussian Naïve Bayes, and Decision Tree.
- Evaluation: Evaluate the performance of trained models using appropriate metrics to assess their predictive capabilities.
- Kaggle Publication: Publish the project notebook on Kaggle to share insights and findings with the community and encourage collaboration and feedback.
- Python (3.x recommended)
- Jupyter Notebook
- Python Libraries (e.g., Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn)
- Kaggle Account (for publishing the notebook)
Contributions to this project are welcome. Feel free to provide feedback, suggestions, or improvements to the notebook. Collaborate with the community on Kaggle to further enhance the analysis and prediction models.