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.
Командный проект по Векторной электрокардиографии
Classifying if a landslide occured or not
Android malware detection using machine learning.
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
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
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.
Expresso Churn Prediction Challenge - dealing with imbalanced dataset
auto avia offer in Aeroclub hackathon
Identify health insurance customers with interest in a vehicle insurance.
This project focuses on predicting house prices using machine learning techniques. The dataset consists of over 1,000,000+ rows and 12 columns containing information about various house attributes. The goal is to build predictive models to estimate house prices based on these attributes.
Web Server Log Analysis
Data Analysis and prediction on Kaggle dataset: Credit Risk Dataset
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