Salary Prediction

• An end-to-end data science project I made as a machine learning intern @ Mentorness.
• This project predicts employee salaries using advanced regression techniques and thorough data preprocessing. We compare multiple machine learning models to select the best-performing one, which is then integrated into a pipeline. This pipeline includes all necessary preprocessing steps, ensuring accurate predictions and easy deployment.
• You can find the web app here.

Steps performed:

1. Exploratory Data Analysis

• Gender Distribution: More females than males.
• Unit Distribution: Most employees are in the IT department.
• Designation Distribution: A significantly large population of analysts.
• Age Trend: Older employees tend to have higher salaries.
• Experience Trend: Higher experience correlates with higher salaries.
• Notebook link here

2. Data Preprocessing

• Encoding: Ordinal Encoding for ordered categories and Label Encoding for unordered categories.
• Dropped duplicates.
• Checked for null values.
• Imputed DOJ, AGE, and RATINGS columns with mode.
• Imputed LEAVES USED, LEAVES REMAINING columns with median.
• Dropped FIRST NAME, LAST NAME columns.
• Notebook link here

3. Feature Engineering

• Converted DOJ and CURRENT DATE columns to datetime datatype.
• Created a new feature: years_experience.
• Dropped date columns.

4. Feature Selection:

• Used SelectKBest to select best features.
• Extracted selected feature names: SEX, DESIGNATION, AGE, UNIT, PAST EXP, years_experience.
• Reassigned selected features to training and test datasets.
• Conducted correlation study and dropped columns with correlation exceeding [-0.8, 0.8].

5. Model Training and Evaluation

• Trained models: Linear Regression, Ridge, Lasso, Gradient Boosting, XGBoost, SVR, and KNN Regression.
• Metrics: Evaluated using MAE, MSE, RMSE, and R² score.
• Visualization: Plotted metrics for comparison.

6. Performance Comparison

• Used a custom scorer and train test comparison function for consistent evaluation.
• Analyzed results to determine the best model.

7. Model Selection

• Selected Gradient Boosting Regression as the best-performing model with an R2 score of 0.9495 for final pipeline integration.
• Notebook link here

7. Hyperparameter Tuning

• Conducted hyperparameter tuning using RandomizedSearchCV to optimize the Gradient Boosting Regressor.
• Tuned hyperparameters such as n_estimators, learning_rate, max_depth, min_samples_split, min_samples_leaf, and subsample.

8. Pipeline Building

• Built a preprocessing pipeline using ColumnTransformer to handle different types of categorical encoding (ordinal and nominal).
• Integrated SelectKBest for feature selection within the pipeline to automatically choose the most relevant features.
• Incorporated the best-performing model, Gradient Boosting Regressor, into the pipeline.
• Configured the final pipeline to include all preprocessing steps, feature selection, and the regression model for seamless integration and deployment.
• Ensured that the entire pipeline, including preprocessing and model training, could be saved and reused efficiently.
• Notebook link here

9. Web Application Deployment

• Developed a web application using Streamlit for easy and interactive salary prediction.
• Created a user-friendly interface for inputting employee information and obtaining salary predictions.
• Deployed the application using Streamlit's built-in sharing platform.
• Included a requirements.txt file to ensure easy replication of the development environment for deployment.
• App file link

Thank you for taking the time to check this project out!