Shrinkage methods: Ridge Regression and Lasso

Linear Regression, Logistic Regression, Neural Networks, Convolutional Neural networks, Auto Encoders

Predicted World Cup winner of 2014 through Linear Regression on FIFA World Cup data

Classification multiclasse, kNN, SVM

This repository contains some classification algorithms which predicts the movement of the mid-price for a pair of currencies namely INR and USD.

Python scripts that build 3K time series correction models to predict individual department sales in 45 stores.

Polynomial Regression using personalized datasets and sci-kit learn library. Particularly focusing on Ridge and Lasso regularization

Notebook that predicts the rate of the book from using different machine learning models.

Predict the quality of red wines using linear models.

In this R project, we use variable selection, regularization (Lasso & Ridge), PCR, and PLS to find the best model for this dataset.

Predicting house prices using Ridge, SVR, GBR, XGBoost, LightGBM, Random Forest and Stacked CV

Predict the products price for hosts using Linear Regression

This Repo Explores Regression Analysis, using an example based on a real study in which data on different attribrutes of used/refurbished mobile phones were collected, the objective being to analyze the data provided and build a linear regression model to predict the price of a used phone and identify factors that significantly influence it

Simple regression to predict the house sales price in King County, USA

This repository has been published for the Advanced Regression assignment to predict house prices in the Australia market

Multivariate time series forecasting(MLTS) has been a mainstream tool for forecasting in economics, traffic modelling, economics, future shipments, temperature forecasts(temperature forecast solely on previous year data(as shown in "Lugano temperature forecast", it requires weather modelling too). The basic assumption in multivariate time series…

Linear regression project looking at what variables improve a nation's 'Environmental Performance Index', a 'Green' score developed by Yale

Classification of fetal cardiotocography to determine whether pattern is normal, suspect or pathalogical.

I executed this assignment for a US-based housing company named Surprise Housing, wherein a regression model with regularisation was used to predict the actual value of the prospective properties and decide whether to invest in them or not