Data Cleaning and Preprocessing using Python in Visual Studio Code
Data Cleaning & Preprocessing
Step 1: Import the necessary libraries for data cleaning & preprocessing
Step 2: Read the Data from CSV file in Visual Studio Code (VSC)
Step 3: Remove unwanted features
Step 4: Strip before and after Whitespaces from entire data frame records
Step 5: Replace unwanted values with nan (empty)
Step 6: Convert Specific features from Object to Numeric datatype
Step 7: Convert Binary (Yes/No) feature values to 1 or 0 for ML
Step 8: Convert categorical variables (Gender) using LabelEncoder technique
Step 9: Convert any exceptional cases with proper numeric values
Step 10: Convert Text data feature into categories
Step 11: Convert Categorical variable using OneHotEncode technique. We will merge this later in Step 17.
Step 12: Apply KNNImputer imputation technique to fill missing values (there are many different imputation techniques)
Step 13: Apply rounding function to avoid any decimal values for Yes/No features
Step 14: View/Export the results into new CSV file to check scaled data
Step 15: Split the data based on features (independent and dependent/target variable)
Step 16: Scale the features using the MINMAX Scalar technique (all values between 0 to 1)
Step 17: Merge all the independent variables + dependent (target) variable to export and check.
Step 18: Split the data into training set and the test set.
Step 19: Check if the training data is imbalanced
Step 20: Balance the training data using SMOTE technique
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