Implementation of a Partial Least Squares Regressor

Fast regression and mediation analysis of vertex or voxel MRI data with TFCE

Simple linear regressor that tries to approximate a simple function deployed in Tensorflow 2.0 without Keras

A working forecasting model to optimize promotions and warehouse stocks of one of the most important European retailers

Used historical usage patterns with weather data in order to forecast hourly bike rental demand.

This repository contains my machine learning models implementation code using streamlit in the Python programming language.

Predict sales prices and practice different machine learning regressors.

My Machine Learning course projects

It is an End to End Data Science project using Linear Regression Machine Learning model.

Solving Industry based and Solution based problems through Neural Networks

Weather prediction with Gaussian Process Regression

Coursework done as part of the Statistical Methods in AI course offered in Monsoon 2023 by Prof. Ravi Kiran Sarvadevabhatla, IIITH. Topics covered include KNNs, Decision Trees, Dimensionality Reduction, Gaussian Mixture Models, Bagging, Boosting, MLP Classifiers and Regressors, Logistic Regression, Kernel Density Estimation and Hidden Markov Models

Experimentation with Neural Networks, as well as recommender systems related to movies.

国内基金数据获取及回归排名

Multiple linear regression model implementation with automated backward elimination (with p-value and adjusted r-squared) in Python and R for showing the relationship among profit and types of expenditures and the states.

Python implementation of Decision Tree Regression and Random Forest Regression. Efficient algorithms for predictive modeling. Ideal for regression tasks in machine learning.

Creates a ML Pipeline leveraging PySpark SQL and PySpark MLib to predict sound level

Applied the Random Forest model in my Regressor model both in Python and R

Neural network library in C++

Gradient Boosting prediction for the profit of 50 american startups