What's included

• Data Cleaning and Preprocessing

  1️⃣ Data Quality Assessment Identifying missing values, duplicates, inconsistencies, and outliers. 2️⃣ Handling Missing Data Imputation using mean, median, mode, forward/backward fill, or advanced ML techniques. 3️⃣ Duplicate & Outlier Removal Detecting and removing redundant data entries and extreme values affecting analysis. 4️⃣ Data Standardization & Normalization Scaling numerical data using MinMaxScaler, StandardScaler, or log transformation for consistency. 5️⃣ Categorical Data Encoding Converting categorical variables into numeric format using One-Hot Encoding, Label Encoding, or Target Encoding. 6️⃣ Feature Engineering & Selection Creating new features, transforming existing ones, and selecting the most relevant attributes for analysis. 7️⃣ Data Formatting & Structuring Converting data into a structured format (CSV, Excel, SQL, JSON) for further analysis or modeling. 8️⃣ Final Cleaned Dataset & Report Providing the processed dataset along with a summary report detailing all transformations and justifications.