ApnaAnaaj aims to solve crop value prediction problem in an efficient way to ensure the guaranteed benefits to the poor farmers. The team decided to use Machine Learning techniques on various data to came out with better solution. This solution uses Decision Tree Regression technique to predict the crop value using the data trained from authenti…

Prediction This repository contains the code and datasets for analyzing and predicting fuel prices, specifically Brent Oil, Crude Oil, Natural Gas, and Heating Oil. The project focuses on creating models to predict the closing prices of these fuels based on their opening, highest, and lowest prices.

This web application halps the user to understand the trends and analysis of Automotive Industry in form of Visualization and demonstrate how the Automotive Industry could harness data to take informed decisions.

This is about a price prediction of cars by considering several factors

Predictive analytics of King County real estate prices using data mining techniques. Insights for home valuation and trend analysis from May 2014 to May 2015 dataset.

A Machine Learning Model to predict the house prices based on the square feet living

Price prediction and appartments recommendation

Python Data Science for Real Estate REIT Amsterdam, North Holland, The Netherlands

Website for rent price prediction using Linear Regression Machine Learning model and Flask as a backend.

AI predictions of house prices (currently Danish only)

A Comprehensive Study of Rice Production and Price in Bangladesh: Geospatial Visualization and Predictive Modeling Techniques.

Using XGboost to predict accommodation listing prices

A data-based approach to analyse and compare prices and characteristics of Airbnbs listings in Montreal and Toronto and identify the key factors affecting their prices.

Price prediction on Large Dataset

A regression problem aiming to predict the price of used vehicles based on factors like the car name, its brand, kilometers driven, fuel type, location of sale and more...

This project utilizes machine learning techniques to predict airline ticket prices based on various factors, aiming to assist travelers in making informed booking decisions.