NYC TLC wants to predict the fare prices of the taxicab before ride. A multiple linear regression model is built based on features like ride distance, duration, no. of passengers, Vendor ID, and ride location.

This project uses the Reaction Time Survey dataset to develop a linear regression model for accurately predicting student reaction times based on various predictors. Tech: R (RStudio)

Here the prediction and analysis of student scores using selected features is done entirely by linear regression machine learning algorithm. This project covers all methods of linear regression theory.

This repository contains a Web App Project built with Flask that allows users to upload CSV files, clean those files, and then train a Multiple Linear Regression model to make predictions based on the user's inputs.

A multiple linear regression model using the sklearn library was used to predict the CO2 emissions of a vehicle based on relevant features. The validation check yielded a MSE = 510, variance score = 0.87 and r2 score = 0.86.

This repo is for all my machine-learning mini-projects to showcase my learning as I dive into the world of AI/ML/DL.

This project involves a case study of a real estate company with a dataset containing property prices in the Delhi region. The goal is to optimize the sale prices of properties based on important factors such as area, bedrooms, parking, etc.

Built a multiple linear regression model to predict car MPG using the MPG dataset, achieving 65% accuracy with Python libraries. 🚀

This includes various assignments I worked on learning Data Mining in R Studio

In this project I focused on applying multiple linear regression to analyze and interpret factors influencing diabetes outcomes. The project also evaluates the model's fit using the R-squared (R²) metric.

This project predicts bike demand for BoomBikes post-lockdown using multiple linear regression and feature selection on American market data, aiming to optimize business strategies for post-pandemic recovery.

This project aims to understand which factors in the dataset have the strongest effects of life expectancy using multiple regression.

This repository provides a comprehensive exploration of linear regression techniques using a real-world economic dataset.

I developed a Decision Support System (DSS) for Burger Bounty, to enhance sales and operations. I designed Shiny app interface to enter new orders into the datasets. I designed a second app to create a dashboard to summarize aggregate monthly sales per burger and per town.

We are analyzing how different factors affect students' overall academic performance as measured by the performance index. Correlation Analysis, Predictive Modeling, Statistical Analysis and Visualization.

Toolkit for Data Science & Statistics

Statistical Modelling and Data Visualization of a Climate Change Dataset (January 1984 to December 2008 ) Sourced from Kaggle

A DATA-DRIVEN APPROACH TO ENHANCE CITIZEN SERVICES AND QUALITY OF LIFE

Objective: Analyze historical tsunami data to determine if climatic changes caused by global warming have impacted tsunami intensity or validity before and after 1900.