Employee-Attrition-Prediction-Using-HR-Data

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Employee-Attrition-Prediction-Using-HR-Data

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This project uses synthetic HR data to predict employee attrition through data analysis and machine learning. It explores factors influencing employee turnover and helps HR departments make data-driven retention strategies.

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📂 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

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