This requires basic machine learning literacy — what kinds of problems can machine learning solve, and how to talk about those problems with data scientists. Linear regression and feature ...

A collection of hands-on experiments and assignments designed to reinforce core concepts in machine learning. This repo covers the full ML pipeline—from data preprocessing to model training and ...

Based on my experience, the three most important parameters to explore and modify are n_estimators, min_data_in_leaf and learning_rate. A LightGBM regression model is made up of n_estimators (default ...

A project using linear regression to predict Bitcoin prices based on historical data. Includes data preprocessing, feature selection, and model training to forecast future price trends. Visualizes ...

Data cleaning and Exploratory Data Analysis (EDA) might not seem glamorous, but the process is vital for guiding your real-world data projects. The chances are that you have heard of linear regression ...

Then, the overall framework and algorithm flow of the model are introduced, which are divided into four stages: data preprocessing, model training, cost prediction and result analysis. Finally, the ...