🧠 Employee Salary Prediction using Machine Learning

Welcome to the Employee Salary Prediction project!
This machine learning model predicts whether an employee's salary is above or below $50K annually, based on their personal and professional attributes.

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📌 Project Highlights

• 🔍 Data Source: UCI Adult Income Dataset
• ⚙ Model Type: Supervised Classification
• 📈 Algorithms: Logistic Regression, Random Forest, Gradient Boosting, SVM, KNN
• 🌐 Deployment Option: Streamlit / Flask
• 🧩 Output: Predicts if salary is >50K or <=50K

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🗂 Folder Structure

├── data/                  # Raw dataset
├── notebooks/             # EDA & model building notebooks
├── requirements.txt
└── README.md

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🧰 Technologies Used

Tool/Library	Purpose
Python	Programming Language
Pandas	Data Manipulation
NumPy	Numerical Computation
Scikit-learn	ML Algorithms & Preprocessing
Matplotlib	Data Visualization
Joblib	Model Serialization
Streamlit	Web App Deployment (Optional)

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📊 Features Used

• Age
• Education Level
• Occupation
• Hours per Week
• Work Experience
• Native Country
• Marital Status
• Salary Label (>50K or <=50K)

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🚀 How to Run the Project

1. Clone the repository
   bash git clone https://github.com/Naveen-Yerrannagari/Employee-Salary-Prediction-.git cd Employee-Salary-Prediction

2. Install the dependencies

   bash pip install -r requirements.txt

3. Train the model

   bash python src/train_model.py

4. Launch the Streamlit app (Optional)

   bash streamlit run app.py

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📈 Model Performance

• ✅ Accuracy: 85%+ (varies by algorithm)
• 📉 Precision, Recall, F1-score: Reported in terminal output
• 📊 Confusion Matrix & Feature Importance: Visualized in notebook

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🔍 Example Prediction

Feature	Value
Age	34
Education	Bachelors
Occupation	Tech-support
Hours/Week	40
Experience	5
Prediction	💰 <=50K

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🧠 Future Improvements

• 📦 Add advanced models like XGBoost or LightGBM
• 🧮 Include regression mode to predict actual salary amount
• 🌍 Add country-wise salary normalization
• 🧾 Improve explainability using SHAP / LIME

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📚 References

• 📊 UCI Machine Learning Repository – Adult Income Dataset
• 📘 Scikit-learn Documentation
• 📕 Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
• 💬 Kaggle Discussions

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🤝 Contributing

Contributions, bug reports, and feature requests are welcome! Please open an issue or submit a pull request.

---# Employee-Salary-Prediction- The goal is to predict whether an employee's salary exceeds a specific threshold (e.g., $50,000) based on various personal and professional attributes such as age, education level, occupation, hours worked per week, and years of experience.