Analysis and prediction of the sales data during Black Friday sale using some Machine Learning Algorithms.

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.

Build a Machine Learning model to predict the total count of cabs booked in each hour by the new data. Research on Cyclic features

This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.

House price estimation from visual and textual features using both machine learning and deep learning models

This Project deals with determining the product prices based on the historical retail store sales data. After generating the predictions, our model will help the retail store to decide the price of the products to earn more profits.

Evaluate the robustness and performance between ML and DL models in predicting the CPC concentration under various image capturing devices, types of input image datasets, and lighting conditions. The findings in our current study can overcome the bottleneck by eliminating the need for laborious manual extraction processes and reducing the time and

Sales Data Analysis and Forecasting Using Ensemble Methods

The project aims to predict house prices in California based on various features using machine learning techniques. It uses the California housing dataset, comprising 20640 data entries and 8 attributes, with the target being the house price.

Predicting house prices using advanced regression algorithms

A regression model to predict calories burnt using values from multiple sensors.

The goal of this project is to predict the age of abalone using various physical measurements. The dataset used for this task is the Abalone dataset from Kaggle. The age of abalone is determined by the number of rings, which is the dependent variable in this study.

Quick cheatsheet about XGBoost, a Gradient Boosted regularized technique published in 2014

In this project, a regression-based performance prediction model was developed to estimate building energy consumption based on simplified façade attribute information and weather conditions.

Doing Analysis of the sales of video games across the globe and predicting the sales using various Machine Learning Algorithms

Predicting house prices in Boston using the XGBoost regressor model.

Worked on AFLW2000-3D dataset which is a dataset of 2000 images. The regression model of predicting the 3 angles (pitch - yaw - roll) of head pose estimation was XGboost Regressor.

A person’s creditworthiness is often associated (conversely) with the likelihood they may default on loans.

Prediciting the Prices of House using the Boston House Price Dataset by applying the XGBoost Regressor Model