A model classifying whether a person would survive on Titanic
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Updated
Apr 27, 2021 - Jupyter Notebook
A model classifying whether a person would survive on Titanic
Detección de cardiopatías en pacientes mediante el uso de datos clínicos utilizando técnicas de Machine Learning y Deep Learning.
Android malware detection using machine learning.
A ML model to predict whether the clients will subscribe to insurance or not.
classifying a patient has a heart disease or not
Amazon employee data to predict approval/ denial
This repository contains the project where the goal is to develop a machine learning model that can accurately predict car prices based on various features. We explored multiple models including K-Nearest Neighbor, Decision Tree, Catboost Classifier, and Light Gradient Boosting Classifier.
We all have experienced a time when we have to look up for a new house to buy. But then the journey begins with a lot of frauds, negotiating deals, researching the local areas and so on. So to deal with this kind of issues Today, I prepared a MACHINE LEARNING Based model, trained on the House Price Prediction Dataset.
Address employee attrition effectively with this mini project. Discover a comprehensive solution leveraging data analytics and machine learning techniques. Uncover insights, build predictive models, and implement strategies to mitigate attrition risks, fostering a resilient and productive workforce.
Machine learning to predict which passengers survived the Titanic shipwreck
ML-solution of the case of the District hackathon Leaders of Digital 2023. The task was to predict accidents (accidents, pipe ruptures, fires) based on the weather forecast for each of the urban districts. Gradient boosting (macro f1), cross-validation, shap values.
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 project develops an advanced predictive model to identify thyroid disease recurrence using machine learning algorithms. We used a detailed dataset with demographic, medical, and clinical features, and implemented Logistic Regression, Decision Tree, Random Forest, and CatBoost Classifier. Rigorous preprocessing and EDA were performed.
Jupyter тетрадка с решением Kaggle соревнования Leopard Classification Challenge
Kaggle Playground Series - Season 3, Episode 26 - Multi-Class Cirrhosis | EDA | MI-Score | Feature Engineering
Ad huc solution for anomaly classification of HTTP requests between service and end-user based on limited data / Решение задачи поиска аномальных HTTP запросов (их классификации) к сервису.
Classifying if a landslide occured or not
Data Analysis and prediction on Kaggle dataset: Credit Risk Dataset
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