The aim of this data science project is to predict crop yield using the dataset provided from Crop Yield Prediction.

In this project, we tried to predict the prices of other houses according to the values ​​of these features with the model we obtained by training a data set containing some features and prices of real houses with linear regression and decision tree regression methods

Implementation of an intelligence system to detect the fraud cases on the basis of classification.

This repository presents a comprehensive exploration of customer churn using various machine learning algorithms, including linear regression, logistic regression, decision tree, and random forest. Through this project, we aim to understand and predict customer churn, providing valuable insights for proactive customer retention strategies.

I was just applying things that i learnt.(End-to-End).Well it does basically what the name says with 97% accuracy ig.

A beginner-friendly machine learning project that predicts house prices based on various features using a Decision Tree Regressor.

🥚📊 Smart poultry egg-production prediction with React Native + GraphQL + Flask + ML (Decision Tree Regressor).

The comparison of multiple Machine Learning models refers to training, evaluating, and analyzing the performance of different algorithms on the same dataset to identify which model performs best for a specific predictive task.

Comparing different ML models for regression to predict the house prices using the California dataset provided by scikit learn.sc