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A detailed Linear Regression implementation from scratch with only NumPy - fully documented covering all the theory, math and code

A quantum-inspired linear regression implementation in Java, leveraging superposition and tunneling to enhance optimization. Includes modular components and a detailed PDF explanation.

Detailed Analysis is done on Linear models and Regression. Problem Statement is uploaded in the repo with implementation.

Hands-on implementation of machine learning algorithms including Decision Trees, KMeans, PCA, Linear Regression, Logistic Regression, Naive Bayes, and Support Vector Machines. Features both custom and library-based implementations with real-world datasets and detailed evaluations.

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implementation of Linear Regression with Gradient Descent

A detailed "from scratch" linear regression implementation.

Implementation of Linear Regression from scratch in Python with detailed notes

Comprehensive Simple Linear Regression implementation with datasets, notebooks, and detailed explanations. Covers univariate linear regression concepts and practical examples.

Multiple Linear Regression from scratch — includes mathematical formulation, Python implementation, regression metrics, and detailed study notes.

This repository showcases a detailed implementation of Linear and Logistic Regression models from scratch for air quality prediction.

A detailed bootcamp in Python focused on the mathematical implementation of regression algorithms (linear, multivariate, polynomial and logistic).

A detailed Linear Regression implementation from scratch with only NumPy - fully documented covering all the theory, math and code

This Jupyter notebook demonstrates the implementation of simple linear regression from scratch using NumPy and Matplotlib. It provides a detailed example of estimating regression coefficients and plotting the regression line.

Implementation of linear regression on CPU and GPU. Uses C++ and CUDA for parallelized compuatations. A detailed latex report is provided

Implementation of linear regression on CPU and GPU. Uses C++ and CUDA for parallelized compuatations. A detailed latex report is provided

Implementation of Multivariate Linear Regression models for the Statistical Analysis course at HUST. This project focuses on model fit, includes practical coding with Kaggle datasets, and provides detailed discussion on multiple linear regression.

A from-scratch implementation of linear regression optimized via gradient descent, applied to salary prediction with detailed analysis of convergence and model performance.

Detailed analysis of bicycle rental demand using regression models. Includes data cleaning, EDA, and the implementation of Linear Regression, Random Forest, Gradient Boosting, and XGBoost to predict rentals based on weather and seasonal factors.

This repository provides a detailed implementation of Simple Linear Regression using Python. The code goes step by step through the mathematical aspects of simple linear regression, making it an excellent learning tool for those who want a thorough grasp of the algorithm.

This repository provides a comprehensive implementation of Multiple Linear Regression (MLR) for predictive modeling. It includes detailed steps for preprocessing data, handling categorical variables, and visualizing relationships between features.

Final project developed for the Predictive Modeling class at Insper. The work consists of a detailed explanation and implementation of a few artificial intelligence algorithms (Regression Trees, Decision Tree's for binary classification, Multiple Linear Regression, K-NN, Logistical Regression, Boosting, Random Forest etc).

"Implementation of ML models from scratch. Contains pure Python/NumPy implementations of Linear Regression, Logistic Regression, Hierarchical Clustering, Single & Multi-Layer perceptron, Decision Tree, Random Forest, SVM, KNN, and K-Means with detailed comments explaining the math behind cost functions and optimization."

Comprehensive AI & ML Portfolio exploring end-to-end data pipelines. Features implementation of Linear/Logistic Regression, Decision Trees, SVM, & K-Means Clustering using Python, Pandas, & Scikit-Learn. Includes detailed data preprocessing, visualization, and model evaluation workflows developed at college.

This package provides an R implementation of linear regression using QR decomposition for enhanced numerical stability. It includes detailed vignettes with tutorials and comparative examples. This package was developed as an assignment for the course 732A94 Advanced R Programming at Linköping University.

This project covers a detailed implementation of Linear Regression, including model training, evaluation, and error metrics. It focuses on predicting outcomes based on input features using Python, NumPy, and scikit-learn. The notebook explores the entire workflow, from data preprocessing to model tuning.

Comprehensive R implementation and detailed analysis of Chapter 5.3 (Resampling Methods) and 6.5 (Linear Models and Regularization) Labs from An Introduction to Statistical Learning (ISLR, James et al., 2021). Features implementation of Cross-Validation (LOOCV, k-Fold), Bootstrap, Subset Selection, Ridge Regression, the Lasso, PCR, and PLS models.

In this course, I learnt the fundamental concepts of Machine Learning. It includes a detailed mathematical implementation of various ML algorithms. As part of the coursework, I implemented Linear Regression, Logistic Regression, Neural Networks, Support Vector Machines, K-Mean Clustering, Anomaly Detection and Collaborative Filtering Recommendation Systems without using any existing libraries in Matlab. This helped me to get a clear understanding of the working of these algorithms and how the algorithms are efficiently implemented using vectorisation.