Kaggle challenge to predict if a customer is satisfied or dissatisfied with their banking experience.
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Updated
May 7, 2016 - HTML
Kaggle challenge to predict if a customer is satisfied or dissatisfied with their banking experience.
Kaggle challenge asking to predict how a supplier will quote a price on a given tube assembly.
Kaggle challenge asking to predict the outcome for each animal of the shelter.
Kaggle challenge asking to predict the final price of each home based on their description/properties.
In this project we will be using the publicly available and Kaggle-popular LendingClub data set to train Linear Regression and Extreme Gradient Descent Boosted Decision Tree models to predict interest rates assigned to loans. First, we will clean and prepare the data. This includes feature removal, feature engineering, and string processing.The…
This repository contains several machine learning projects done in Jupyter Notebooks
Algorithms used to confirm whether a celestial body is a planet or not.
This project compares the different machine learning models on Walmart Weekly Sales Data and predicts the weekly sales for the test data.
Identifying the most influential food groups on COVID-19 recovery rate: exploratory data analysis and statistical modeling
This repo contains the result of my computer science course: An automated tool to classify credit card transactions. Could be used with any dataset
Code for the project "Predicting hospital readmission of diabetic patients using ensemble learning"
Solution for the Ultimate Student Hunt Challenge (1st place).
Detecting Fraudulent Blockchain Accounts on Ethereum with Supervised Machine Learning
Credit Card Fraud Detection using Extreme Gradient Boosting
Using data to help us choice high quality wine
Sports Analytics in R (Gradient Boost approaches for Decision Tree in Regression problems)
Big Mart Sales Prediction is a data-driven project aiming to forecast product sales accurately across Big Mart outlets. Leveraging machine learning and comprehensive datasets, our project empowers retailers to optimize inventory, enhance profitability, and make informed decisions in the dynamic world of retail.
Comparison of ensemble learning methods on diabetes disease classification with various datasets
Applies Machine Learning approach to predict spam.
In this work an application of the Triple-Barrier Method and Meta-Labeling techniques is explored with XGBoost for the creation of a sentiment-based trading signal on the S&P 500 stock market index. The results confirm that sentiment data have predictive power, but a lot of work is to be carried out prior to implementing a strategy.
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