Welcome to Machine Learning Models, a curated collection of machine learning classification and regression projects. Each folder contains a self-contained project with dataset handling, model training, evaluation, and results visualization.
| Project | Category | Algorithms / Techniques |
|---|---|---|
| Heart Disease Prediction | Classification | Logistic Regression, Decision Tree, Random Forest, Voting Classifier |
| House Price Prediction (California) | Regression | Linear Regression, Ridge, Lasso |
| Iris Flower Classification | Multi-class Classification | Support Vector Machine (SVM), GridSearchCV |
| Mushroom Classification | Classification | KNN, Logistic Regression, Random Forest |
| SMS Spam Classifier | NLP Classification | TF-IDF, Multinomial Naive Bayes |
| Student Habits & Exam Performance | Exploratory Data Analysis | Statistical analysis, visualization |
| Student Social Media Addiction Analysis | Classification & Regression | Logistic Regression, Linear Regression |
| Titanic Survival Analysis | Exploratory Data Analysis | Data cleaning, visualization, feature analysis |
- Python
- scikit-learn
- pandas, numpy
- matplotlib, seaborn
- Google Colab
- Practice real-world ML pipelines
- Compare model performance
- Learn core concepts hands-on (EDA, preprocessing, metrics, model selection)
- Learn different ML Algorithms through hands-on-learning.