Home battery prediction and charging automation for Home Assistant, supporting many inverter types

This project aims to predict heart failure outcomes by applying statistical learning algorithms. The goal is to improve the prediction accuracy through the SuperLearner algorithm.

This repo contains various Regression Models

Extended Joint Models for Longitudinal and Survival Data

Student performance predicting along with gender, race/ethnicity, parent level of education, lunch, test preparation course status, and past reading & math scores

This repository contains all the assignments and related files for excelR data science and machine learning course.

Laptop Price Prediction

Machine Learning | Deep Learning

FES is the last version of the FES (Finite Element Solution) tide model.

Group Project using Supervised Learning and Neural Network Models

🧰 A package to help create and deploy Translator Reasoner APIs (TRAPI) from any prediction model exposed as a regular python function.

Weather-Driven Renewable Energy Analysis

A Streamlit application for predicting handwritten digits using a trained machine learning model. This project includes the necessary code to run the Streamlit app and train the model using a Jupyter notebook.

This project uses the Multinomial Naive Bayes classifier to enhance movie genre classification based on metadata such as descriptions and ratings. Utilizing a dataset from Kaggle, it aims to improve content recommendation systems through accurate genre prediction.

Se realiza un Análisis de Discriminate a la base de datos SP500 que contiene el porcentaje de retorno de este índice, se realiza un modelo de predicción sobre el porcentaje de retorno.

Se realiza una serie de tiempo de acciones de CEMEX 2018-2019 con el objetivo de predecir su valor futuro y de esta forma conocer si es viable incluir dicha acción en un portafolio de inversión.

This project, created during a UNSW work placement with Ellipsis, predicts sales lift for merchants using statistical and machine learning models. It includes data preparation, modeling, and visualization in R, leveraging techniques like OLS, Ridge, Lasso, Random Forest, and Neural Networks.

Predicting University Admission Chances, where we explore the likelihood of admission for prospective students based on various factors. Leveraging machine learning, we have employed two powerful algorithms, Decision Tree and Random Forest, to predict the chances of admission.

IoT and ML to assuage the uncertainty in city bus schedules. Track live running status and avail tentative schedule of buses. Minimal IoT setup with a central ML-driven web-backend.

The objective of this project is to predict insurance charges based on various factors.