عمل preprocessing لdata

Data Preprocessing Project In this project, I focused on preparing raw data for machine learning model development by applying comprehensive data preprocessing techniques. The dataset initially contained missing values, inconsistencies, outliers, and unstructured features that required careful handling. The project included: Cleaning the dataset by handling missing values and removing duplicates Detecting and treating outliers Encoding categorical variables Feature scaling and normalization Feature engineering to improve model performance Splitting the dataset into training and testing sets The main objective was to transform raw data into a structured, high-quality dataset suitable for accurate and efficient model building. Proper preprocessing significantly improved data reliability and overall model performance.