This project demonstrates the process of building and comparing simple machine learning models using Python and scikit-learn.
- Developed a basic machine learning project using Python and scikit-learn.
- Loaded and prepared a dataset for model training and testing.
- Split the data into features (X) and target (y) variables.
- Trained two models:
- Linear Regression
- Random Forest Regressor
- Compared both models based on prediction accuracy and performance metrics.
- Visualized results using matplotlib to compare predicted vs. actual values.
- Data preprocessing and splitting
- Model training and evaluation
- Performance comparison
- Data visualization
- Python
- scikit-learn
- NumPy
- matplotlib
- Google Colab