Developer Documents First Kaggle ML Competition Journey From EDA to Submission

A machine learning learner shared their end-to-end experience participating in a Kaggle Playground competition after weeks of studying statistics, EDA, and classical ML algorithms. The competition required predicting a target class using a mix of numerical and categorical features related to health and lifestyle. The participant followed a structured workflow covering exploratory data analysis, missing value handling, feature engineering, preprocessing pipelines, model training, and final submission. Key decisions included choosing encoding strategies for categorical variables, managing missing data, and comparing ensemble models. The author noted that Kaggle's environment pushed them to think like a practicing ML engineer by evaluating solutions against unseen data under realistic constraints.