Website Prediksi Penyakit jantung dengan 5 fitur menggunakan metode KNN,bagging classifier,random forest

This project predicts wind turbine failure using numerous sensor data by applying classification based ML models that improves prediction by tuning model hyperparameters and addressing class imbalance through over and under sampling data. Final model is productionized using a data pipeline

A Tkinter based ML tool that classifies astronomical entities as Galaxy, Star, and Quasar

Customer Churn Prediction using Machine Learning and Deep learning. With Integration of MLFlow

The project is to analyze the flight booking dataset obtained from a platform which is used to book flight tickets.

R | Classification Project

Bagging is the term from "Bootstrap Aggregation Algorithm", That is for Low Bias & Low Variance

Drug consumption prediction models are like crystal balls for public health. By analyzing vast amounts of data, these models can identify individuals or communities at higher risk of drug use. They consider factors like demographics, social media activity, prescription history, and even economic indicators.

Analyze the data of Visa applicants, build a predictive model to facilitate the process of visa approvals, and based on important factors that significantly influence the Visa status recommend a suitable profile for the applicants for whom the visa should be certified or denied.

Data Mining Machine Learning | Assignments | JAN-APR 2023 | CMI

Diyabet Tespiti Projesi 💉

This project aims to predict the success of crowdfunding campaigns using machine learning models: Ensemble Learning, Naive Bayes, and Support Vector Machine (SVM).

The sinking of the RMS Titanic is one of the most infamous shipwrecks in world history. In this model, need to analyse what sorts of people were likely to survive. We also need to apply the tools of machine learning to predict which passengers survived in this tragedy.

A machine learning application, deployed using Flask, is designed to identify the presence of heart disease in patients by analyzing various medical features.

This mini-project involves experimenting with a variety of classification and regression models, exploring different techniques to understand their behaviors and applications in predictive analytics.

Machine Learning

Project for the "Computational and Statistical Learning" course.

I applied the bagging and boosting methods using the decision tree as the base predictor on the sklearn’s breast cancer data set. I experiment with different parameters and report the results obtained.