💼 Salary Prediction using Machine Learning

This project predicts employee salaries based on features such as education level, gender, age, years of experience, and job title. It demonstrates a complete end-to-end machine learning pipeline using Python and scikit-learn.

---

📁 Dataset Description

The dataset contains the following columns:

• Gender
• Education Level
• Job Title
• Years of Experience
• Age
• Salary (Target)

---

🔧 Workflow Summary

1. Data Cleaning

• Checked for missing values
• Dropped rows with <1% missing data
• Standardized inconsistent categorical values (e.g., “PhD” vs “phD”)

2. Exploratory Data Analysis (EDA)

• Statistical summaries
• Distribution plots for numerical & categorical variables
• Identified outliers and data imbalance

3. Feature Engineering & Preprocessing

• OneHotEncoder for categorical columns
• StandardScaler for numeric features (when required)
• Combined using ColumnTransformer and Pipeline

4. Model Training & Evaluation

Tested multiple models:

• Linear Regression
• Random Forest Regressor
• Decision Tree Regressor
• ElasticNet
• KNN
• SVR
• XGBoost

Metrics used:

• MAE
• MSE
• RMSE
• R² Score

Cross-validation applied for robustness.

5. Hyperparameter Tuning

• Used GridSearchCV

6. Export

• Best model saved using joblib
• Model simulation prediction