Basic machine learning projects focused on breast cancer classification and house price prediction using Python and Scikit-learn. This is a basic machine learning project built to understand and implement the complete ML workflow
Two problem statements covered: Breast Cancer Classification (Classification) Breast Cancer Dataset: Medical diagnostic dataset for tumor classification.
House Price Prediction (Regression) House Price Dataset: Real estate dataset with numerical and categorical features.
The datasets are loaded directly into the notebook for model training. Tools: Python, Pandas, NumPy, Scikit-learn, Matplotlib, Google Colab
Logistic Regression / Random Forest (Cancer) Linear Regression / Random Forest (House Price)
Breast Cancer Prediction achieved high classification accuracy using supervised learning models. House Price Prediction showed better performance with Random Forest compared to Linear Regression.