MediaEval challenge 2019 - to predict the memorability of the Videos

A collection of machine learning models for predicting laptop prices

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

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

👩‍💻Artificial Intelligence Course Projects, University of Tehran

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

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.

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

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…

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 🚴

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

Predict the university admission using machine learning

Predict sales prices and practice feature engineering and advanced regression techniques.

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.