I'm currently learning data science and machine learning.
I work with Python, scikit-learn, and NumPy, and I enjoy building models from scratch to understand how they work internally.

1. Neural Network From Scratch

   A fully custom implementation of a feed‑forward neural network using only NumPy.

   It includes manual forward propagation, backpropagation, gradient descent, activation functions, and a complete training loop.

   The project demonstrates core ML math concepts and shows how neural networks work internally without any deep learning libraries.

2. Spotify Song Recommendation System

   A content‑based music recommender that uses Spotify audio features to suggest similar songs.

   It performs feature scaling, PCA visualization, K‑Means clustering, and cosine‑similarity ranking to recommend tracks based on musical characteristics.

   The system outputs similar songs given any track in the dataset.

3. House Price Prediction

   A regression project that predicts house prices using scikit‑learn’s RandomForest model.

   It includes data preprocessing, exploratory analysis, feature engineering, and hyperparameter tuning using RandomizedSearchCV.