I leveraged an algorithmic approach to predict the price and carat of the diamond using Machine Learning. Various regression models have been trained and their performance has been evaluated using the R Squared Score followed by tuning of the hyperparameters of top models. I have also carried out a trade-off based on the R Squared Score and the …

An npm package to make it easier to deal with a handful of values, and try to model them in one of the most used mathematical models, with an R/Numpy-like accuracy algorithm

GDP Forcasting

Hosts R content associated with Linear Regression series on YouTube

This repository contains all the Machine Learning projects I did using different Machine Learning methods. Python being the main software used.

Using multiple linear regression model to predict customer demand in order to make business decision

project to predict smartphone sales based on the marketing budget spent on advertising using three platforms involves collecting data on marketing spending and smartphone sales, and using statistical and machine learning techniques to build a model that can predict future smartphone sales based on changes in marketing budget.

Predicting annual highest of sneakers on StockX

Statistical analysis to predict the importance of various manufacturing parameters on fuel economy of a prototype car.

The comparison of multiple Machine Learning models refers to training, evaluating, and analyzing the performance of different algorithms on the same dataset to identify which model performs best for a specific predictive task.

This project calculates the equation of the line of best fit of a given correlation

This project aims to enhance the accuracy and efficiency of stock market predictions by employing a sophisticated machine learning methodology. This project leverages the power of PySpark, a robust framework for distributed data processing, to handle large datasets and perform complex computations.

Functional specification to calculate per country's happiness score

Gold Price Prediction

Learn how to use R and statistics in order to analyze vehicle data

Heart Risk Level Predicting Regression Model 💔

Probability and Statistics for Machine Learning

Business Goal: To model the demand for shared bikes with the available independent variables. It will be used by the management to understand how exactly the demands vary with different features. They can accordingly manipulate the business strategy to meet the demand levels and meet the customer's expectations.

Hosts Python content associated with Linear Regression series on YouTube