This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD).
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Updated
Nov 6, 2022 - Jupyter Notebook
This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD).
This is an optional model development project on a real dataset related to predicting the different progressive levels of Alzheimer’s disease (AD) with MRI data.
Uses letter frequency and catboost classifier model in synchronous for guessing letters in hangman game instance. The model performance is evaluated on both seen words in the dictionary and words out of the dictionary.
ML-bot that detects toxicity in russian texts.
Android malware detection using machine learning.
Classifying if a landslide occured or not
Командный проект по Векторной электрокардиографии
Multimodal Sentiment Analysis using Text and Image Data on twitter dataset
A model on the streamlit framework predicts disease and makes a treatment recommendation
A Domestic violence support system for the victims, that enables users to share their thought and provides knowledge about the particular type of abuse they are going through.
This is my final solution to the Mars-spectrometry challenge by NASA hosted on @drivendataorg
A model classifying whether a person would survive on Titanic
Global plastic waste is a pressing environmental issue, with massive production, limited recycling, and high risks to ecosystems and human health
Disaster Tweets Classifications by Machine Learning, which is a currently Kaggle Competition.
Machine learning project to predict obesity risk levels based on lifestyle and demographic data. This project utilizes advanced algorithms like CatBoost, LightGBM, and more to classify individuals into different obesity categories
Repository for the thesis study "Evaluation and Comparison of Boosted ML Models in Behavior-Based Malware Detection".
This is an end-to-end ML project, which aims at developing a classification model for the problem of classifying a given customer profile into either of the risk category (safe or not safe). The final classifier used for this project is CatBoost classifier. Deployed in AWS.
Data Analysis and prediction on Kaggle dataset: Credit Risk Dataset
Machine Learning aplicado al mantenimiento predictivo. Se realizaron 2 modelos: 1 por medio de clasificación binaria que predice si una máquina fresadora estará en riesgo de fallar o no, y el 2 modelo a través de clasificación multiclase que predecirá el modo de falla
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