💼 Employee Salary Prediction System

An AI-powered web application built with Streamlit and Scikit-learn that predicts employee salaries in the Indian job market based on demographic and professional attributes. This project is part of the IBM Capstone Project.

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🧠 Problem Statement

Accurate salary prediction is essential for HR departments and job seekers. This system uses machine learning to estimate salaries based on factors such as:

• Age, Gender, Education
• Occupation, Experience, Performance
• Work hours, Certifications, Bonus expectations, etc.

It helps users make informed decisions regarding compensation, hiring, and job offers.

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🧱 Tech Stack

Layer	Technology
🧮 ML Model	Scikit-learn (RandomForestRegressor)
📊 Frontend	Streamlit
🗃️ Data	Cleaned & preprocessed CSV (Kaggle)
📦 Deployment	Localhost / Streamlit Sharing

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🧪 Features

• 🔢 Real-time salary prediction with 85%+ accuracy
• 📊 Interactive charts using Plotly (scatter, bar, histogram)
• 📄 Generate downloadable PDF salary reports
• ⚙️ Fully customizable inputs for 12+ features
• 🌐 Responsive UI with modern styling

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🏗️ System Architecture

1. Data Preprocessing: Clean, encode, and scale input data.
2. Model Training: RandomForestRegressor inside a pipeline.
3. Serialization: Save the trained model using joblib.
4. UI Layer: Streamlit-based frontend with form inputs.
5. Prediction + PDF Generation: Predict and export reports.

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🔁 Project Structure

salary-predictor-app/ │ ├── app.py # Main Streamlit app ├── model/ │ └── salary_predictor_corrected.pkl ├── data/ │ └── salary_dataset.csv ├── assets/ │ └── screenshots, logo, etc. ├── requirements.txt ├── README.md └── ...

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📸 Screenshots

Add screenshots in assets/ folder and update the paths below

• 🏠 Home Dashboard
• 💰 Salary Prediction Input Form
• 📈 Model Analytics
• 📄 PDF Report

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🛠️ How to Run Locally

# Clone the repository
git clone https://github.com/shyamchouhan/salary-predictor-app.git
cd salary-predictor-app

# Create virtual environment & activate
python -m venv venv
venv\Scripts\activate    # On Windows

# Install dependencies
pip install -r requirements.txt

# Run the Streamlit app
streamlit run app.py
📂 Dependencies
text
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streamlit
pandas
numpy
scikit-learn
matplotlib
plotly
joblib
fpdf
Install them via:

bash
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pip install -r requirements.txt
✅ Future Enhancements
Deploy to cloud (Heroku/AWS)

Add user authentication

Fetch live job market salary data

Extend model to global regions

🧠 Author
Shyam Chouhan
IBM Capstone – AI/ML
🔗 LinkedIn
🔗 GitHub

📚 References
Scikit-learn Docs

Streamlit Docs

Kaggle Salary Dataset

Python Official Docs

GitHub ML Deployment Repos

📄 License
This project is licensed under the MIT License - see the LICENSE file for details.

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Would you like me to export this as a `README.md` file and attach it for download?