HSC Result Predictor using Random Forest Regression to forecast student exam scores based on historical data and performance indicators.

A collection of machine learning models for predicting laptop prices

MediaEval challenge 2019 - to predict the memorability of the Videos

Repository showcasing a collection of diverse regression analysis projects including salary prediction and more.

BUDGET : VotingRegressor(XGBoost+LightGBM) * (5 Fold CV) — This model, built for a Kaggle insurance regression competition, preprocesses data by imputing missing values (KNN), cleaning, and engineering new features. Statistical analysis reduces features before encoding and scaling for machine learning

This Repository contains the implementation of various Classification Algorithms on different different datasets.

A hybrid machine learning framework for river discharge forecasting that combines ensemble regression models with the Arithmetic Optimization Algorithm (AOA) for hyperparameter tuning and next-day flow prediction.

This project investigates ensemble learning techniques, combining multiple models to enhance accuracy and robustness. It covers both basic methods (Max Voting, Averaging, Weighted Averaging) and advanced techniques (Stacking, Blending, Bagging, Boosting), aiming to improve predictive performance by addressing model weaknesses.

Time series modeling to predict fares for Sweet Lift Taxi Company. Predictions will be used to allocate drivers for peak hours.

This project will focus on creating models to predict NBA salaries based on advanced statistics

This project aims to predict flight arrival delays using various machine learning algorithms. It involves EDA, feature engineering, and model tuning with XGBoost, LightGBM, CatBoost, SVM, Lasso, Ridge, Decision Tree, and Random Forest Regressors. The goal is to identify the best model for accurate predictions.

Key Stroke Based Essay Examination System

Predict the university admission using machine learning

Machine learning project predicting electricity consumption based on ASHRAE dataset.

started by analysing and determining the aspects required for tuning of the final ml model. Performed Eda and feature engineering inorder to determine the import parameters of the dataset and to derive more useful features, finally creating a ml model by using various different basic and advanced regression techniques.

Problem Moving from traditional energy plans powered by fossils fuels to unlimited renewable energy subscriptions allows for instant access to clean energy without heavy investment in infrastructure like solar panels, for example. One clean energy source that has been gaining popularity around the world is wind turbines. Turbines are massive str…

🏡House Price Prediction, Artificial Intelligence course, University of Tehran

Video transition time estimation with different regression techniques

Creates a model used to forecast use of a city bike-share system at any given hour depending on environmental conditions with machine learning 🚴