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salary-prediction-using-regressor-models

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A machine learning project for predicting salaries using Decision Tree and Random Forest Regressor models.

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# 💼 Salary Prediction Using Decision Tree & Random Forest Regressors

This project uses supervised machine learning techniques to predict salary based on years of experience. It includes exploratory data analysis, model training using **Decision Tree Regressor** and **Random Forest Regressor**, and performance evaluation. The project demonstrates the practical use of regression algorithms and highlights the importance of model interpretability and accuracy.

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## Dataset

- Size: ~30 entries
- Features:
  - YearsExperience
  - Salary
- Target: Salary
- Source: Kaggle
- Format: CSV file

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## Models Used

- Decision Tree Regressor
- Random Forest Regressor

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## Workflow

1. Data Import & Cleaning
2. Exploratory Data Analysis (EDA)
3. Model Building
4. Model Evaluation (MAE, MSE, RMSE, R² Score)
5. Visualizations & Results Interpretation

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## Evaluation Metrics

| Model              | MAE   | MSE   | RMSE  | R² Score |
|-------------------|-------|-------|-------|----------|
| Decision Tree      | 0.22  | 0.12  | 0.35  | 0.88     |
| Random Forest      | 0.11  | 0.05  | 0.23  | 0.95     |

Random Forest performs better due to ensemble learning, offering more accurate and stable predictions compared to a single Decision Tree.

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## Technologies

- Python 3.10.7
- Jupyter Notebook
- pandas, numpy
- matplotlib, seaborn
- scikit-learn

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## Visual Outputs

- Scatter Plots
- Model Fit Visualization
- Predicted vs Actual Salary Graph
- Error Metrics Plot

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## Future Scope

- Add more features like job title, industry, education
- Use polynomial regression or XGBoost
- Add GUI or Flask API for real-time predictions

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## Report

The final report is included in the /report directory:
- Salary Analysis Report by Akhilesh.pdf

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