Credit Risk Analysis utilizing imbalanced classification machine learning models
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Updated
Aug 16, 2022 - Jupyter Notebook
Credit Risk Analysis utilizing imbalanced classification machine learning models
This project is an end-to-end machine learning solution to predict student performance using key features like study time and test scores. It includes exploratory data analysis, model training, and a Flask-based web app for real-time predictions, all built with modular programming for clean and maintainable code.
Applied numerous algorithm models to solve a binary classification problem of predicting if any given prospective customer converts to a sale, through the company’s online sales channel.
Application of Machine and Deep Learning techniques on images and texts.
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a classification problem using ensemble methods on the Titanic dataset.
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Predicting potential donors using various machine learning models for Charity
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Machine Learning assignments, Machine Learning (IE500618) course, fall 2022.
We analyze a stroke dataset and formulate advanced statistical models for predicting whether a person has had a stroke based on measurable predictors.
In this analysis we build and evaluate several machine learning algorithms by resampling models to predict credit risk.
This project aims to build a regression model that predicts the number of views for TED Talks videos on the TED website.
Frame Level Driver Drowsiness Prediction
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