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.

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

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

C# Console Application: Asks for two files containing historical financial data in the same format as files from Yahoo Finance. Performs the two-step Engel-Granger Test for Cointegration and simulates profits of applying the Pairs Trading Strategy to these stocks. To Project further Includes code to conduct statistical inference and a Function t…

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

A Comprehensive Software for Data Mining and Multivariate Analysis

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

The current repository is able to assess the relationship between EEG components and HDDM parameters of top-down attention in perceptual decision-making using a multiple regression model

The respository is for Machine learning basiscs.

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

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

This is a repository that contains Python implementations of Machine Learning (ML) models from scratch.

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

Computational Intelligence Packages (CIP) for Mathematica

Master Degree Coursework: Econometrics I