I worked as part of a team in this project with a goal to predict the points for basketball player for the upcoming season using machine learning techniques.

predicting the risk of heart attack using various machine learning models such as Logistic Regression, Decision Tree, Random Forest, K Nearest Neighbour and SVM

This project showcases a dataset of Amazon Reviews in Hindi, which we created ourselves. We applied various machine learning methods including Naive Bayes, SVM, and Decision Tree, using both Bag-of-Words and TF-IDF. Additionally, we experimented with deep learning techniques such as Feedforward Neural Networks and LSTM with ELMO embeddings.

In this project, we wanna create Credit Risk Management by using Machine Learning, so we dig into the data. what we do for the next steps are Data Preparation, EDA(Exploratory Data Analysis), Data Visualization, Data Preprocessing (Handling Outliers, Missing Value, Feature Encoding, Standardization, and Normalization), Creating Machine Learning …

The goal of this project was to 'Predict Clicked Ads Customer Classification Using Machine Learning' in order to optimize ad costs and increase engagement.

Default_Prediction_for_Loan_Data

Hello there! In this repository I will explain how to predict hand written digits using Spark Machine Learning decision tree classifier algorithm which will produce 88% accurate predictions at the depth of 15.

Aircraft crash prediction using decision tree.

Fake news detector using Svelte for frontend, Robyn for backend and Machine Learning algorithms in order to classify if a piece of news is fake or not.

Code used for the classification of magnetic resonance imaging (MRI) for the detection of Alzheimer's disease using the GLCM technique and classification algorithms: KNN, random forest, decision tree, and logistic regression.

This repository contains PySpark code that implements three machine learning models for predicting diabetes readmission: Decision Forest, Random Forest, and Gradient Boosted models. These models are trained and evaluated using patient information.

This project and exercises were made for the Models in credit and operational risk course at the AGH UST in 2022. All provided methods are a result of my work after hours, when I was solving given tasks (topics).