A collection of end-to-end ML projects showcasing classic algorithms and real-world datasets.
- Decision Trees – Predict outcomes using tree-based models.
- KNN – K-Nearest Neighbors classifier examples.
- SVC – Support Vector Classification examples.
- Naive Bayes – Probabilistic classification projects.
- Linear Regression – Predict continuous outcomes.
- Regularized Regression – Ridge and Lasso implementations.
- Forest Fire Prediction – Regression model for predicting forest fire areas.
- Clean, well-documented Jupyter notebooks
- Example datasets included
- Step-by-step explanations of algorithms
- Visualizations for better understanding
- Python 3.11
- NumPy, Pandas, Matplotlib, Seaborn
- scikit-learn
git clone https://github.com/sailaxmitumu2000/ML_Projects.git
cd ML_Projects
pip install -r requirements.txt