Successfully established a supervised machine learning model which can accurately forecast the total weekly sales amount obtained at Walmart stores, based on a certain set of features provided as input.

The nonprofit foundation Alphabet Soup wants a tool that can help it select the applicants for funding with the best chance of success in their ventures

Successfully developed a machine learning model which can accurately predict up to 100% accuracy whether a credit card application of a given applicant would be approved or not, based on several demographic features such as applicant age, total income, marital status, total years of work experience, etc.

Comment classifier model trainer using keras tensorflow, stanza tokenizer and transformers.

The enhancement of Intelligent Transport Systems (ITS) involves the precise prediction of bike-trip durations, incorporating a comprehensive consideration of Seoul's weather conditions.

Simple ML system for the iris problem

Successfully developed a machine learning model which can accurately predict the strength of cement based on various features such as blast furnace slag, water, coarse aggregate, etc.

Successfully established a machine learning model to accurately predict the price of a flight in India based on several features such as duration, days left, arrival time, departure time and so on.

Aditya Marketing is facing low response rates to their marketing campaigns. The objective of this project is to conduct thorough Exploratory Data Analysis, extracting insights through univariate and bivariate analysis. And Recommended strategic customer targeting tactics.

This project implements a movie recommendation service with Apache Spark using collaborative filtering.

University Admission Predictor is a sophisticated Flask-based web application designed to predict the likelihood of admission to graduate programs based on student profiles. It leverages a range of regression techniques to evaluate admission chances.This project showcases the practical application of machine learning in educational forecasting.

This repository contains a machine learning project aimed at predicting housing prices in Boston. This project showcases the end-to-end process of building and deploying a machine learning model, from data preprocessing and model training to serialization and deployment.

The Employee Attrition Control project uses data analysis and predictive modeling to understand and address employee turnover. It provides insights and recommendations to reduce attrition and improve employee satisfaction and retention.

Zomato delivery time prediction

This repository contains a project showcasing Federated Learning using the EMNIST dataset. Federated Learning is a privacy-preserving machine learning approach that allows a model to be trained across multiple decentralized devices or servers holding local data samples, without exchanging them.

Successfully established a machine learning model which can accurately predict the expected life duration of a human being based on several demographic features such as alcohol consumption per capita, average BMI of entire population, etc.

Successfully developed a machine learning model which can accurately classify the weather based on various features pertaining to weather-related data and atmospheric conditions.

Successfully created a machine learning model which can accurately predict the fare of a taxi trip based on several features such as trip duration, tip amount, etc.

Successfully established a machine learning model that can accurately classify an e-commerce product into one of four categories, namely "Books", "Clothing & Accessories", "Household" and "Electronics", based on the product's description.

This repository contains code for predicting house sales prices using machine learning models. It includes data preprocessing, model training, evaluation, and prediction on test data.