In this regression project, We will make use of different features like age, BMI, region, sex, smoker, etc to predict the medical insurance cost for an individual.

Airline Fare Prediction using Regression

Pong with Pygame and k-NN Prediction Algorithm (KNeighborsRegressor) for Opponent

Explore and predict the used car price by building the machine learning model based on the existing data. Then examining the model between the actual price and the predicted price.

Ensamble Voting for Financial Time Series

Wind turbine power long term prediction/Forecast using XGBOOST, Prophet and other Time series models

This repo hosts an end-to-end machine learning project designed to cover the full lifecycle of a data science initiative. The project encompasses a comprehensive approach including data Ingestion, preprocessing, exploratory data analysis (EDA), feature engineering, model training and evaluation, hyperparameter tuning, and cloud deployment.

This repository contains introductory notebooks for KNN algorithm

A powerful stacked ensemble model for income prediction, combining GradientBoosting, AdaBoost, Bagging, Linear Regression, and Decision Trees. Achieves an impressive R² of 0.8761 on the RoS_sample_submission dataset.

Developed student performance predicting model, showing strong understanding of predictive modeling techniques.

Predicting diamond prices helps buyers and sellers make informed decisions by understanding market trends and potential future values.

Predicting monthly sales using Machine Learning, kneighborsregressor

Projecte amb quatre classificadors diferents per predir l'espècie de pingüí a partir de les mesures dels individus: regressió logística, màquines de suport vectorial, arbres de decisió i K veïns més propers.

What I taught myself about Machine learning

A three-part project that does as the title suggests with different machine learning models

Progetto: Machine Learning

Diamond Price Predictor - Web Application: Predict diamond prices using various regression models: Linear Regression, Lasso, Ridge, ElasticNet, Decision Tree Regressor, Random Forest Regressor, and KNeighbors Regressor. The chosen Random Forest Regressor, with a remarkable accuracy of 97%, is deployed in a user-friendly Flask app

Predicting Compressive Strength of Concrete

Store Sales Time Series Forecast & Visualization