Credit Approval Prediction: Intro to Data Cleaning, Visualization, and Machine Learning скачать в хорошем качестве

Credit Approval Prediction: Intro to Data Cleaning, Visualization, and Machine Learning

This lesson introduces how to predict credit card application approval using openly available data, covering the full process from loading data to evaluation. Learners work through hands-on steps including data import, cleaning, feature engineering, visualization, and basic modeling. Understand the importance of credit approval for banks and applicants Load the credit approval dataset from OpenML and explore its structure Identify and handle missing values in both numeric and categorical features Visualize distributions of numeric and categorical features by class Perform data cleaning: impute missing values and encode categorical variables Scale numeric features for model readiness Split data into training and test sets, maintaining class balance Train, evaluate, and compare logistic regression and random forest models Implement error handling for common preprocessing and modeling issues Predict outcomes for new credit applications using processed data #machinelearning #datacleaning #creditapproval