Easy to follow stock price analysis forecasting techniques on Indian stock data

MSBoost is a gradient boosting algorithm that improves performance by selecting the best model from multiple parallel-trained models for each layer, excelling in small and noisy datasets.

Example machine learning implementation to predict the residual bending moment capacity of corroded reinforced concrete beams tested under monotonic three or four-point bending. Data is collected from 54 experimental programs available in the literature.

This repository enables an engineer to generate predictions for the mechanical bending performance of corroded beams, using a database of 725 corroded beams tested under monotonic bending. Outputs include the maximum bending moment, residual capacity percentage, yield load, yield displacement, and ultimate displacement.

Example machine learning applications for the determination of the residual yield force of corroded steel bars tested under monotonic tensile loading. Data is collected from 26 experimental programs avaialbe in the literature.

This breast cancer diagnosis project evaluates various machine learning models to effectively classify breast masses as benign or malignant. SVM and Logistic Regression excel in identifying positive cases, leveraging their robust performance metrics, while Neural Networks show promising results and offer opportunities for further enhancement!

This repository contains a project I completed for an NTU course titled CB4247 Statistics & Computational Inference to Big Data. In this project, I applied regression and machine learning techniques to predict house prices in India.

U.S.A. house prediction

This project aims to predict taxi fare amounts in New York City using a dataset of historical taxi rides. We employ machine learning techniques to create models that can estimate the total fare amount based on various features of the trips.

Comprehensive analysis and modeling of the Wine Quality dataset, including exploratory data analysis (EDA), data preprocessing, model training, and performance evaluation using MSE and RMSE.

End-to-End Machine Learning project I made as a machine learning intern @ Mentorness

This project employs machine learning to forecast housing prices in California. By scrutinizing location, housing details, and demographics, it constructs various regression models like Linear Regression, KNN, Random Forest, Gradient Boosting, and Neural Networks. These models offer invaluable insights to optimize predictive real estate investment

Leveraging sentiment analysis and data augmentation to recreate recipe scoring algorithm with sparse data. Used MLPs and Gradient Boosting Regressors to compare regression metrics such as RMSE and MSE between raw data and raw data in conjunction with augmented data.

This repository contains a machine learning model aimed at predicting student performance across various metrics. Utilizing a diverse set of Machine Learning Regression algorithms, the model predicts scores based on demographic and academic variables.This project demonstrates robust approach to leveraging machine learning for educational outcomes.

House Sale Prediction Using Gradient Boosting Regressor and AdaBoost Regressor

Predicting house prices using Linear Regression and Gradient Boosting Regressor with the factors like income, schools, hospitals and crime rates.

Insurance Forecasting with EDA, feature engineering, data preprocessing, model building and hyperparameter optimization.

A Machine Learning exploration evaluating various models to predict Airbnb prices, culminating in an optimized Gradient Boosting Regressor.

House Price Prediction (Kaggle)

Predicting hourly bicyclist counts on Coupure Links in Ghent, employing a Histogram Gradient Boosting regressor to forecast July values based on data from January to June, as part of the Machine Learning for Life Sciences course at Ghent University