This is a collection of some of the important machine learning algorithms which are implemented with out using any libraries. Libraries such as numpy and pandas are used to improve computational complexity of algorithms

Machine Learning Concepts with Concepts

This Repository contains Solutions to the Quizes & Lab Assignments of the Machine Learning Specialization (2022) from Deeplearning.AI on Coursera taught by Andrew Ng, Eddy Shyu, Aarti Bagul, Geoff Ladwig.

A simple python program that implements a very basic Multiple Linear Regression model

This project analyzes and visualizes the Used Car Prices from the Automobile dataset in order to predict the most probable car price

In this project we are comparing various regression models to find which model works better for predicting the AQI (Air Quality Index).

Machine Learning in R

Calibration of an air pollution sensor monitoring network in uncontrolled environments with multiple machine learning algorithms

Building a Machine Learning Model to Predict the Price of the Car By Comparing Performance of Different Regression Techniques (Simple Linear Regression, Multiple Linear Regression, Polynomial Regression)

Linear Regression model that helps to identify the demand of bike sharing(eg-Yulu bikes) depends on which factors.

Estimating the rent in Paris using multiple linear regression with dummies variables.

The respository is for Machine learning basiscs.

This is an assignment wherein a multiple linear regression model is built to predict demand for shared bikes depending on the current trend.

Computational Intelligence Packages (CIP) for Mathematica

Implementation of backward elimination algorithm used for dimensionality reduction for improving the performance of risk calculation in life insurance industry.

Functions to analyse compositional data and produce confidence intervals for relative increases and decreases in the compositional components

All my Machine Learning Projects from A to Z in (Python & R)

Statistical model on NBA basketball players' performance using multiple linear regression and stepwise search.

Project using multiple linear regression to model prices of houses in Ames, IA.

Data: Boston Housing Dataset (HousingData.csv) Programming language(s): R Tool(s): RStudio Business problem: To understand the drivers behind the value of houses in Boston and provide data-driven recommendation to the client on how they can increase the value of housing.The Boston housing dataset consisted of 506 observations and 14 variables. P…