Title:

Predicting AIDS Virus Infection Using Machine Learning: Achieving 95% Accuracy with Logistic Regression and Random Forest Models

Description:

This project aims to leverage advanced machine learning techniques to predict AIDS virus infection with high accuracy. Utilizing logistic regression and random forest algorithms, the study strives to achieve a prediction accuracy of 95%. The project involves data preprocessing, feature selection, model training, and evaluation to create robust predictive models. By comparing the performance of logistic regression and random forest, this project seeks to identify the most effective approach for predicting AIDS virus infection, ultimately contributing to improved early detection and treatment strategies.

Objective:

• To preprocess and prepare a dataset for predicting AIDS virus infection.
• To train logistic regression and random forest models using the prepared dataset.
• To evaluate the performance of both models and achieve a prediction accuracy of at least 95%.
• To compare the effectiveness of logistic regression and random forest in predicting AIDS virus infection.
• To provide insights and recommendations based on the model comparison for practical application in early detection and intervention strategies.