Example machine learning projects.
This project implements a spam filter for SMS messages using the multinomial Naive Bayes algorithm. The filter is trained to classify messages as spam or ham (non-spam) with at least 80% accuracy, using a dataset compiled by Tiago A Almeida and José Gómez Hidalgo.
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This project develops a machine learning model to predict the likelihood of heart disease in patients using anonymized medical data. The dataset includes features such as age, sex, chest pain type, blood pressure, cholesterol, and more. The goal is to build an accurate classifier to assist in early detection of heart disease.
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This project segments a credit card company's customers into distinct groups using the K-means clustering algorithm. The goal is to help the company apply targeted business strategies for each customer segment based on their demographic and behavioral characteristics.
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This project builds predictive models for individual medical insurance costs using the Medical Cost Data Set from Kaggle. The goal is to estimate insurance charges based on patient demographics and personal characteristics, helping hospitals forecast revenue and plan resources.
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This project applies logistic regression modeling in Python to classify the presence of heart disease using the Cleveland Clinic Foundation dataset from the UCI Machine Learning Repository. The analysis explores patient characteristics and evaluates model performance for predicting heart disease.
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