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Data Preparation

Updated May 12, 2023 ·

Data Preparation​

Data preparation involves applying EDA insights to identify and perform data-cleaning steps. This ensures that the data is well-structured and suitable for training an accurate model.

Handling Missing Values​

Missing values can affect model performance and need to be addressed. There are two main ways to handle missing values:

Dropping Missing Data​

If some rows or columns have too many missing values, they should be removed to avoid negatively impacting the model's performance.

  • Use dropna() to remove rows with missing data.
  • Use drop() to remove specific columns.

Example:

df = df.dropna(axis=0)                      # Drop rows with missing values
df = df.dropna(how='all')                   # Drop all empty or sparse rows
df = df.drop(columns=["unneeded_column"])   # Drop specific columns

Imputing Missing Values​

When columns cannot be dropped entirely, imputing missing values ensures that no crucial data is lost.

  • Fill missing values with the mean, median, or mode.
  • For time series, use forward-fill (previous value).
  • For complex cases, apply ML-based imputation techniques.

Example:

df["age"].fillna(df["age"].median(), inplace=True)      # Fill with median
df["cholesterol"].fillna(method="ffill", inplace=True)  # Forward fill

Advanced Imputation​

In cases where basic imputation doesn't work, advanced techniques like K-nearest neighbors or SMOTE can be used to predict missing values based on other features in the dataset.

  • Use KNN imputer for more accurate imputation.
  • Apply fit_transform() to impute missing values.

Example:

from sklearn.impute import KNNImputer

imputer = KNNImputer(n_neighbors=2, weights="uniform")
df_imputed = imputer.fit_transform(df)  # Impute missing values using KNN

Removing Duplicates​

Duplicates can distort model accuracy and should be removed to ensure the data is clean and reliable. .

Identifying Duplicates​

Check for duplicate rows in the dataset to identify any redundancy.

  • Compare across all columns or a subset.
  • For time series data, check for uniqueness based on time.

Example:

duplicates = df.duplicated()
df[duplicates]    # View duplicate rows

Dropping Duplicates​

Removing duplicates ensures that each row in the dataset is unique and doesn't introduce bias.

  • Use drop_duplicates() to remove identical rows.
  • Specify columns to check for duplicates.
  • Keep the first or last occurrence as needed.

Example:

df = df.drop_duplicates()  # Remove duplicate rows
df = df.drop_duplicates(subset=["id"], keep="first")  # Drop by ID

Iterative Data Cleaning​

Data cleaning may need multiple iterations for best results.

  • Adjust cleaning steps as data evolves.
  • Reassess missing values after imputation.
  • Validate processed data before model training.