Apache Spark machine learning project using pyspark

A Portuguese hotel group seeks to understand reasons for its excessive cancellation rates.

The Office of Foreign Labor Certification is facing a dramatic increase in work visa applications, but is hampered by a sluggish review system. It needs to improve the process by developing a way to quickly, accurately identify applications likely to be accepted or rejected so their processing may be prioritized.

Kaggle Teaches Data Scientists How to Grow and Some Important Things to Note

Aircraft Engine Run-to-Failure Simulation

This project explores the working of various Boosting algorithms and analyzes the results across different algorithms. Algorithms Used are: Random Forest, Ada Boost, Gradient Boost and XG Boost

Most machine learning algorithms require data to be formatted in a very specific way, so datasets generally require preparation before they can yield useful insights. This repository is to document my study notes as I work through steps that I have personally found most challenging.

Predicting effectiveness of marketing campaign. Effective financial marketing strategies can help you to focus on efforts so that you can better reach targets and goals.

using A/B testing to test if the ads that the advertising company ran resulted in a significant lift in brand awareness

Real-Fake-Job-Post

Titanic Dataset Exploration, Visualization and Modelling with Logistic Regression.

The primary objective of this study is to develop a dependable and precise prediction model to forecast alterations in Bitcoin's hash rate.

This repository includes Machine Learning model on second hand car price prediction fron cardekho.com I have used RandomForest Regressor as it is best one performing on this dataset . This repository include model file which have all the implementation of model and other file is MODALUSAGE in which I have used the model I did by giving the featu…

ReneWind operates wind farms. Unexpected turbine failures are presenting operational and financial problems. This project uses machine learning to develop a model that accurately predict component failure, which will give the firm more control over maintenance scheduling, costs and power generation.