My journey of learning Machine Learning with Python.
titanic/: Titanic survival prediction (EDA, cleaning, Logistic Regression, Random Forest)- (Iris, NumPy vectors, ...)
boston-housing/: Linear Regression from scratch with Gradient Descent, evaluation metrics (MAE, MSE, RMSE, R²), train/test split- titanic/titanic_classification_models.ipynb: Titanic survival prediction using Logistic Regression, KNN, Decision Tree (with pruning) and Random Forest, with model comparison
- model-evaluation/model_evaluation_metrics.ipynb: Confusion matrix, precision, recall, F1 score, precision-recall threshold tradeoff, ROC curve and ROC-AUC, with Logistic Regression vs Random Forest comparison
- kmeans-pca/kmeans_pca_clustering.ipynb: Customer segmentation on Mall Customers dataset using K-Means clustering, Elbow Method to choose optimal K, and PCA
- hierarchical-dbscan/hierarchical_dbscan_clustering.ipynb: Hierarchical Clustering (dendrogram, Ward linkage) and DBSCAN on blob and moon datasets, compared with K-Means using silhouette score
Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn, Google Colab