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Ce projet a été développé dans le contexte de la dissolution anticipée de l'Assemblée nationale française suivant les élections européennes de 2024. Le but est de prédire les résultats des élections législatives anticipées à partir des données des scrutins précédents, y compris les élections européennes, présidentielles et législatives précédentes
A comprehensive project to predict and analyze diabetes health data using advanced machine learning models, including Logistic Regression, Random Forest, and XGBoost. 📊🔍
The main goal of this project is to build a machine learning model that, given a Covid-19 patient's current symptom, status, and medical history, will predict whether the patient is in high risk or not.
The repository contains notebooks created for collecting and preprocessing the corpus of diary entries and for experiments on creating models for predicting gender, age groups of authors and the time period of text creation.
This repository contains a Jupyter Notebook exploring the adult income dataset. The notebook performs Exploratory Data Analysis (EDA), including visualizations with charts and graphs. Additionally, it implements various classification models to predict income and analyzes their accuracy.
This project utilizes Logistic Regression for breast cancer classification, incorporating data visualization to enhance understanding. It covers the entire workflow, from data import to model evaluation, ensuring a comprehensive analysis of the classification process.
This project explores an IBM telecom dataset, conducting initial EDA and data preprocessing. It examines three genetic algorithm variations for feature selection: one-point, two-point, and uniform crossover. Logistic regression is used to predict customer churn, and performance is evaluated using error bar plots.
Implementation of algorithms such as normal equations, gradient descent, stochastic gradient descent, lasso regularization and ridge regularization from scratch and done linear as well as polynomial regression analysis. Implementation of several classification algorithms from scratch i.e. not used any standard libraries like sklearn or tensorflow.