A fun repository for code creativity, machine learning, AI, and various projects! This is where I document my coding journey, focusing on ML, AI, data structures in Python, and exciting projects.
Welcome to TheCodeChronicles! This repository is all about exploring and experimenting with code in a way that's engaging and fun. While daily logging is ambitious, the goal is to consistently contribute and share exciting projects related to ML, AI, and Python.
- Simple Neural Network Without Machine Learning Libraries
- Implemented a basic neural network from scratch using only Python's built-in modules.
- SHAP Explanation for XGBoost on California Housing Data
- SHAP visualizations for XGBoost on the California Housing dataset reveal how features like MedInc, Latitude, and Longitude influence house price predictions.
- Gap Statistic Method for Finding Optimal Clusters
- A statistical approach to determine the optimal number of clusters by comparing actual clustering performance with a random baseline.
- Linear & Logistic Regression from Scratch
- Linear Regression predicts continuous values using a straight-line equation optimized by gradient descent, while Logistic Regression performs binary classification by applying the sigmoid function to a linear model.
- Support Vector Machine (SVM) from Scratch
- Implements a Support Vector Machine (SVM) classifier using Stochastic Gradient Descent (SGD)for binary classification. The model is trained using the hinge loss function and L2 regularization.
- Regression Analysis with Scikit-Learn
- Implements various regression techniques using Scikit-Learn and other relevant Python libraries.
- Deep Neural Network from Scratchn
- This project implements two versions of a Deep Neural Network (DNN) from scratch using NumPy
More projects coming soon! Stay tuned. 🚀
