You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
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.
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
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 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.