🧠 Machine Learning Practice Repository

A curated collection of Jupyter notebooks documenting my Coursera learning journey and personal ML experiments.
This repo showcases hands-on practice with core machine learning algorithms, applied examples, and visualization techniques.

---

📁 Repository Structure

📂 Regression

• Simple Linear Regression – One feature, basic predictions & plots
• Multiple Linear Regression – Multiple features, extended modeling
• Logistic Regression – Intro to classification, OvA vs OvO strategies

📂 Classification

• Decision Trees – Splitting criteria & visualization
• Random Forests & XGBoost – Ensemble learning for better predictions
• SVM (Credit Card Fraud) – Support Vector Machines in practice
• Multi-class Classification – OvA & OvO strategies

📂 Clustering & Dimensionality Reduction

• K-Means (Customer Segmentation) – Grouping similar customers
• DBSCAN vs HDBSCAN – Density-based clustering comparison
• PCA – Principal Component Analysis for dimensionality reduction
• t-SNE & UMAP – Visualization of high-dimensional data

📂 Applied Case Studies

• Regression Trees (Taxi Tip Prediction) – Real-world regression task

---

🚀 Upcoming

• Add: KNN, Gradient Boosting
• Expand: More case studies with real-world datasets
• Explore: Deep Learning (PyTorch/TensorFlow)

---

💡 About

Built with ❤️ by Nidhi Kulkarni

---