github.com/Okes2024/Employee-Attrition-Prediction-Using-HR-Data @main

📂 Features Synthetic HR dataset with 100+ data points

Data cleaning and preprocessing

Exploratory Data Analysis (EDA) with visual insights

Machine Learning model for attrition prediction

Downloadable dataset and Jupyter Notebook for reproducibility

📊 Dataset The dataset includes key HR attributes like age, department, education, monthly income, years at company, and attrition status. Synthetic data ensures privacy while maintaining realistic patterns.

🛠️ Technologies Used Python (pandas, numpy, matplotlib, seaborn, scikit-learn)

Jupyter Notebook for interactive analysis and visualization

Machine Learning Models (Logistic Regression, Decision Tree, Random Forest, etc.)

🚀 How to Run Clone this repository:

bash Copy code git clone https://github.com/Okes2024/Employee-Attrition-Prediction-Using-HR-Data.git Navigate into the project folder.

Open the Jupyter Notebook:

bash Copy code jupyter notebook Employee_Attrition_Prediction.ipynb 📈 Visuals The notebook contains various visualizations showing patterns in attrition, including:

Attrition by Age Group

Attrition by Department

Monthly Income vs Attrition

👤 Author Okes Imoni GitHub: Okes2024