Conformalized Quantile Regression

This repository will contain all the stuffs required for beginners in ML and DL do follow and star this repo for regular updates

Predicting flight ticket prices using a random forest regression model based on scraped data from Kayak. A Kayak scraper is also provided.

Machine Learning Model for Order Demand Prediction based on historical Order data - Built for Swiggy Hackathon 2018

A data-driven tool to predict the reaction order of homogeneous gas-phase reactions. Includes machine learning experiments on the NIST Chemical Kinetics Database.

Using Time Series forecasting and analysis to predict Walmart Sales across 45 stores.

A Machine Learning Project that uses Random Forest Regressor model to predict used cars price based on some attributes such as kilometers driven, age, number of previous owners etc.

Automated ML pipeline with Python, Docker, Luigi, SciKit-Learn and Pandas to predict wine quality ratings

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

Insurance cost predictor

Predicting how much loan will be approved

This repository contains Python functions for predicting time series.

A machine learning project that predicts the price of used cars in the UK

A Flask based Web Application that Predicts the Flight Price using RandomForestRegressor.Its GUI is based on Swagger API. This is hosted on the Heroku platform.

Algerian Forest Fire Prediction

The project would generate report of the result of the students and is also capable of concluding the result analysis report of the students as in tables, figures in Excel Sheet. The project is based on python which will use the web scraping technique used to launch the website from an automated software (as a web browser) to visit the website (…

Machine Learning Software that predicts planets based on their distance from the sun, number of satellites and various properties

A thorough analysis of opensource Fitbit data is performed. The key findings are highlighted and discussed. The analysis provided herein is performed using 940 data points collected from 33 distinct users. Machine Learning Models are used to solve a regression problem using Multiple Linear Regression, Random Forest and Extreme Gradient Booster

Aim of the project is to predict the fare of the flight.

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