The project aims to perform various visualizations and provide various insights from the considered Indian automobile dataset by performing data analysis that utilizing machine learning algorithms in R programming language.

This repository is created by Team 08 for the course Data Preparation and Workflow Management, taught at Tilburg University for the MSc Marketing Analytics. This project will research the price mark-up on the prices of Airbnb's in New Orleans during festivals.

Estimating the effect of Hawaiian AirBnB listing characteristics, the time until the booking starts (in days) and the season on the price per night.

R code to predict the AirBnB property price using Linear Regression model . This would help AirBnB firm to predict prices of the property customer want to rent out based on the amenities present and show it to the customer while booking a property. This helps company promote better customer centric experience.

This is a Supervised Learning Model which predicts the Price of Real Estate in Madrid, Spain.

The goal is to develop a model that can accurately forecast airline prices based on a range of factors, such as departure and arrival locations, travel dates, airline carriers, seat class, and number of stops. To address this problem, several approaches have been proposed in the past, including regression-based models, machine learning algorithms

This project was undertaken as the culmination of our statistical learning course. Its primary objective was to utilize data from the 1990 U.S. Census to predict median house values, employing multiple regression models and advanced statistical analysis to attain precise predictions and gain valuable insights.

Insurance Price Prediction given the Individual Info

Price determinants and superhost prediction on Airbnb Hawaii 2019 dataset

Predictive Analysis of Price on Amsterdam Airbnb Listings Using Ordinary Least Squares.

Analysis and ​ forecasting​ of weekly prices of vegetables in ​ India​ during ​ 1st​, ​ 2nd​ and ​ 3rd​ ​ lockdown ​ due to COVID’19