An Image classifier model and builder for binary image classification.

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.

a library so simple you will learn Within An Hour

Simple ML system for the iris problem

Successfully established a Seq2Seq with attention model which can perform English to Spanish language translation up to an accuracy of almost 97%.

Successfully developed a fine-tuned DistilBERT transformer model which can accurately predict the overall sentiment of a piece of financial news up to an accuracy of nearly 81.5%.

Successfully developed a fine-tuned BERT transformer model which can accurately classify symptoms to their corresponding diseases upto an accuracy of 89%.

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.

Extensive Collection of Jupyter Notebooks focused on Machine Learning covering different techniques includes Feature Engineering, Feature Selection, Feature Extraction, Model Training & Testing.

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

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

Successfully established a machine learning model that can accurately predict the sales of a superstore based on various features such as quantity, profit, discount, postal code, etc. The features are mainly associated with order details and customer demographics.

CNN Based Approach for Audio File Classification. Contains Notebooks Illustrating Data Preprocessing, Feature Extraction, Model Training, & Model Inference Workflows & Overall Pipeline

using YOLOv7 for shoe detection and ResNet for shoe classification

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.

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

Deployment ready machine learning model to predict the math scores of students based on various features related to their demographics, background, and academic engagement.

This Machine Learning repository encompasses theory, hands-on labs, and two projects. Project 1 analyzes customer segmentation for marketing using clustering, while Project 2 applies supervised classification in marketing and sales.

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.