This project presents an Intelligent Energy Management System designed to optimize energy usage by predicting electricity tariffs, forecasting energy consumption, and estimating solar energy production. Leveraging advanced machine learning models like LSTM for tariff prediction, Random Forest for consumption forecasting, and ARIMA for solar energy.

Subject from my studies, it's pretty interesting so I'm putting it here

MSBoost is a gradient boosting algorithm that improves performance by selecting the best model from multiple parallel-trained models for each layer, excelling in small and noisy datasets.

Data Science Portfolio

Machine Learning Models trained on Scikit-learn datasets. This repository contains the code files and saved models trained on Toy datasets (Classification & Regression), and Real World dataset.

This repository is a related to all about Machine Learning - an A-Z guide to the world of Data Science. This supplement contains the implementation of algorithms, statistical methods and techniques (in Python), Feature Selection technique in python etc. Follow Coursesteach for more content

A small project automates the classification of customer ratings into Low or High categories using machine learning, enabling hotels to enhance customer satisfaction and retention based on insights derived from sentiment analysis.

Get prediction intervals, confidence intervals, and parameter uncertainties for various machine learning models

Heart Disease Prediction by Python

EDA and Decision Tree Classifier for the Different Species of the Iris Flower

Challenge #10: Analyse historical IPL data and provide insights on IPL 2024 for a Sports Magazine (Codebasics)

Python libraries for data science enable efficient data manipulation, analysis, and modeling. Key libraries include NumPy for numerical computing, pandas for data handling, Matplotlib for visualization, Scikit-learn for machine learning, TensorFlow for deep learning, and BeautifulSoup/requests for web scraping. These libraries simplify complex data

This repository contains the code and projects developed during the 2023-2024 Machine Learning course. It covers various algorithms, models, and techniques explored throughout the year.

This is a machine learning model deployment project of Iris classification model in a minimal UI using flask web framework and deployed it in Azure cloud using Azure app service. We initially made this project as a requirement for an internship at Indian Servers. We are now making it open to contribution.

This project implements a Decision Tree Classifier to predict the type of drug a patient should take based on their characteristics (age, sex, blood pressure, cholesterol levels, and sodium-potassium ratio). The model is trained using a dataset and evaluated based on its accuracy.

Machine Learning Practice

Image Classification using CNN and SVM (Keras & Scikit-Learn)

This project demonstrates how to implement the K-Nearest Neighbors (KNN) algorithm for classification on a customer dataset. The program iterates through different values of k (number of neighbors) and plots the accuracy against k. The goal is to identify the optimal number of neighbors that yield the highest accuracy.

This repository features a machine learning model using scikit-learn and Random Forest to predict the nth prime number. Trained on prime number data, the model effectively forecasts the nth prime for a given n, showcasing machine learning's capability in addressing mathematical challenges.

Movement Steps Detecting Full Stack Mobile Application