Simple Data Cleaning Techniques for Improving Machine Learning

High-quality data is essential for building reliable machine learning models. Raw datasets often contain missing values, outliers, duplicates, inconsistent formats, and unstructured categorical variables. These issues reduce model accuracy and lead to biased predictions. This paper presents a simple, reproducible, and universally applicable data cleaning framework using Python, Pandas, and Scikit-Learn. The framework includes structured steps for handling missing values, detecting outliers, removing duplicates, encoding categorical variables, and scaling numerical features. Conceptual figures are provided to illustrate each stage of the cleaning pipeline. The proposed workflow is suitable for beginners, students, and independent researchers, and can be applied to any tabular dataset. This paper serves as a practical reference for improving dataset quality before machine learning model development.