💼 Salary Prediction Model

Project File: https://colab.research.google.com/drive/1zZhyFMuXrXDgkCO20zYk9zwvYontkQ7S?usp=sharing Note: The dataset has been acquired from kaggle.com and has been uploaded to this repo.

This project implements a prediction model using Linear Regression to estimate an employee's salary.
The model uses multiple features to capture different aspects of an employee's profile:

• Experience
• Tenure
• Session Index
• Gender Index
• Location
• Tech Stack

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📂 Project Structure

• Employe_Performance_dataset.csv → Dataset containing employee features and salary information
• README.md → Project documentation and link to Jupyter Notebook with complete ML workflow (data preprocessing, model training, evaluation)

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🚀 Roadmap of Machine Learning Project

This project follows an end-to-end machine learning pipeline for regression tasks:

1. Data Collection

• Load the dataset (Employe_Performance_dataset.csv).
• Inspect data structure, features, and target (Salary).

2. Data Preprocessing & Cleaning

• Handle missing values (imputation or removal).
• Remove duplicates and fix inconsistencies.
• Encode categorical variables (GenderIdx, Location, Tech Stack).
• Standardize/normalize numerical features (Experience, Tenure, Session_Idx).

3. Exploratory Data Analysis (EDA)

• Visualize salary distribution.
• Study correlations between features and salary.
• Detect outliers in salary or experience data.

4. Feature Engineering

• Create derived features if beneficial (e.g., interaction terms).
• Scale numerical features for regression stability.
• Apply encoding methods (Label/One-Hot Encoding) for categorical data.

5. Splitting the Dataset

• Train-Test split (commonly 80:20).
• Ensure randomization for unbiased evaluation.

6. Model Building (Linear Regression)

• Train Linear Regression on preprocessed features.
• Fit the model using Ordinary Least Squares (OLS).
• Interpret regression coefficients to understand feature importance.

7. Model Evaluation

Evaluate predictions with regression performance metrics:

• Mean Absolute Error (MAE) – average absolute errors.
• Mean Squared Error (MSE) – penalizes larger errors.
• Root Mean Squared Error (RMSE) – interpretable in salary units.
• R² Score (Coefficient of Determination) – variance explained by the model.

8. Model Validation

• Apply k-fold cross-validation to check robustness.
• Compare Linear Regression with baseline models (Ridge, Lasso, Decision Tree Regressor).

9. Deployment (Future Scope)

• Save the trained model using joblib or pickle.
• Deploy as an API using Flask/Django.
• Build a simple frontend for interactive salary prediction.

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🛠️ Tech Stack

• Programming Language: Python
• Libraries: pandas, numpy, matplotlib, seaborn, scikit-learn
• Environment: Jupyter Notebook

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📊 Results

• Successfully trained a Linear Regression model to predict employee salary.
• Evaluated performance with multiple regression metrics.
• Established a reliable baseline for future experimentation with advanced models.

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🔮 Future Enhancements

• Add more advanced models (Random Forest, Gradient Boosting, XGBoost).
• Perform hyperparameter tuning for improved accuracy.
• Incorporate additional employee attributes for better prediction.
• Deploy as a full-stack application for HR analytics.

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📌 Key Takeaway

This project demonstrates the end-to-end regression pipeline — from raw data collection to model evaluation — for predicting employee salaries. It provides a solid foundation for building advanced salary prediction systems in real-world HR applications.

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