Dash App for visualizing function approximations by polynomials.

A predictive software that would help both the police department and the “general public” to get an idea about the likelihood of bicycle theft and the recovery.

Comparaison de différentes approches d'apprentissage supervisé pour la prédiction de la consommation d'énergie et des émissions de CO2 de bâtiments de la ville de Seattle.

Q-learning algorithm from scratch in python for optimal path finding

A model designed to predict the teams with the highest point value for a given week in fantasy football based on the parameters of the DraftKings website. Utilizing a web scraper and a SQL database, build a ridge regression to predict the fantasy football point value for all competing players in a given week. Using convex optimization find combi…

Models bank loan applications to classify and predict approval decisions using customer demographic, financial, and loan data. Applies machine learning algorithms like logistic regression and random forest for enhanced automation.

An intelligent grid-traversal agent

Building classification and regression models to predict quality and type of the wine.

DEPTs: Parameter tuning for software fault prediction with different variants of differential evolution *** Parameter tuners for software analytics problems ***

This is a maching learning lecture project about artificial neural network for classification problem with parameter tunning

Predict credit card approval status based on past customer details such as `Gender`, `Age`, `Debt`, `Married`, `BankCustomer`, `EducationLevel` etc.

Classification Machine Learning models predict repayment loan

Finding linear seperability on a dataset of accents.

Iris flower predictor based on Grid Search Algorithm. **Requires IPython Notebooks to be run ;)

Telecom customer churn example with h2o

This repository contains my work on Machine Learning with Python using scikit-learn library

Increasing your Reddit karma. Help Reddit Moderators improve karma by autosubmitting posts to the correct subreddit. Reddiquette: Cross-post if it belongs to either or both subs?

In this project, I aim to predict raisin variety using machine learning. The dataset includes raisin features and two classes: 'Kecimen' and 'Besni.' We employ Decision Tree and Logistic Regression models, optimizing them with Grid Search and Randomized Search for improved performance.