Employee Salary Prediction System - Copilot Instructions

This is a comprehensive Machine Learning project for predicting employee salaries. When working on this project, please follow these guidelines:

Project Context

This is an AIML internship project demonstrating professional data science workflow
Focus on creating production-quality, well-documented code
Use modern Python data science best practices
Ensure all code is suitable for portfolio presentation

Code Style Guidelines

Follow PEP 8 Python style guidelines
Use type hints for function parameters and return values
Include comprehensive docstrings for all functions and classes
Write modular, reusable code with proper error handling
Use meaningful variable and function names

Data Science Workflow

Implement complete ML pipeline: data collection → preprocessing → EDA → modeling → evaluation → deployment
Use pandas for data manipulation, scikit-learn for ML, and matplotlib/seaborn/plotly for visualization
Include proper train-test-validation splits and cross-validation
Implement multiple ML algorithms and compare performance
Use feature engineering and selection techniques

Project Structure

Keep notebooks organized and well-documented with markdown explanations
Separate utility functions into src/ modules for reusability
Save trained models and artifacts in models/ directory
Create professional Streamlit web application in webapp/
Maintain clean data organization in data/ folders

Key Requirements

Handle missing values, outliers, and data inconsistencies professionally
Create comprehensive EDA with statistical insights and visualizations
Implement multiple ML algorithms (Linear Regression, Random Forest, XGBoost, etc.)
Include model interpretability using SHAP or similar tools
Build an attractive, user-friendly web interface with Streamlit
Ensure salary predictions are in Indian Rupees (INR)

Quality Standards

Write production-ready code with proper exception handling
Include unit tests where appropriate
Optimize for performance and memory efficiency
Ensure reproducibility with random seeds
Create comprehensive documentation and comments

Visualization Guidelines

Create professional, publication-quality plots
Use consistent color schemes and styling
Include proper titles, labels, and legends
Make interactive visualizations where beneficial
Export high-resolution plots for presentations

Data Science Workflow

Implement complete ML pipeline: data collection → preprocessing → EDA → modeling → evaluation → deployment

Use pandas for data manipulation, scikit-learn for ML, and matplotlib/seaborn/plotly for visualization

Include proper train-test-validation splits and cross-validation

Implement multiple ML algorithms and compare performance

Use feature engineering and selection techniques

Key Requirements

Handle missing values, outliers, and data inconsistencies professionally

Create comprehensive EDA with statistical insights and visualizations

Implement multiple ML algorithms (Linear Regression, Random Forest, XGBoost, etc.)

Include model interpretability using SHAP or similar tools

Build an attractive, user-friendly web interface with Streamlit

Ensure salary predictions are in Indian Rupees (INR)

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Employee Salary Prediction System - Copilot Instructions

Project Context

Code Style Guidelines

Data Science Workflow

Project Structure

Key Requirements

Quality Standards

Visualization Guidelines

Related Skills

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Employee Salary Prediction System - Copilot Instructions

Project Context

Code Style Guidelines

Data Science Workflow

Project Structure

Key Requirements

Quality Standards

Visualization Guidelines