Mastering Data Cleaning & Data Preprocessing

Data quality is crucial for machine learning models' performance. Data cleaning and preprocessing are vital steps in the data science pipeline that involve identifying and correcting errors, removing duplicates, handling missing values, outliers, and transforming raw data into a suitable format for machine learning algorithms. Techniques such as imputation, deletion, encoding categorical variables, data splitting, feature selection, and scaling are commonly used in data preprocessing. Tools like Pandas, DataHeroes, and FuzzyWuzzy can aid in these processes. Effective data cleaning and preprocessing lead to more accurate predictions and better decision-making across various industries such as retail, manufacturing, and finance.