Predicting Hepatocellular Carcinoma through Supervised Machine Learning

Evidence synthesis of proportions

Meta-regression of diagnostic accuracy studies in Stata

Course Material for Artificial Intelligence and Machine Learning - Unit 2 @ Computer Science Dept, Sapienza

This project uses machine learning to predict AIDS virus infection with 95% accuracy. By applying logistic regression and random forest algorithms, it involves data preprocessing, feature selection, model training, and evaluation. Comparing these models will identify the most effective method, aiding in early detection and treatment strategies.

Using R Markdown for Data Analysis, Machine Learning

Contains Solutions and Notes for the Machine Learning Specialization By Stanford University and Deeplearning.ai - Coursera (2022) by Prof. Andrew NG

Comparing centralised machine learning and federated learning using flower framework. Building a custom strategy over the base FedAvg called FedCustom which has a higher learning rate and several other hyper parameters to increase the accuracy.

A collection of 8 Applied Data Science projects.

Predicting Baseball Statistics: Classification and Regression Applications in Python Using scikit-learn

An analytical exploration of OSMI MHIT survey data, and the treatment of mental health concerns in the tech industry.

Desenvolvimento de um pipeline de machine learning para o conjunto de dados de pacientes com Carcinoma Hepatocelular (HCC) de modo a prever a sobrevivência dos pacientes 1 ano após o diagnóstico.

Plain python implementations of basic machine learning algorithms

🛡️ Welcome to our Credit Card Fraud Detection project! 💳 Harnessing the formidable prowess machine learning, we're steadfast in our mission to fortify your financial stronghold against deceitful adversaries. Join our crusade for financial resilience,Ensuring every transaction is securely monitored! 🔐💯

EXCELR ALL ASSIGNMENTS

Explore the H1B Visa data from 2014 through a comprehensive multivariate analysis.

To build a classification system to predict whether a customer will churn or not based on the IBM Telecom Data from Kaggle. Technically, it is a binary classifier that divides clients into two groups-those who leave and those who do not. The classifier will be built using bagging algorithms like Random Forest, boosting algorithms & Neural Networks