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04 – Impute Missing Values

📘 RIDE User Manual – Panel 4: Impute Missing Values

🧠 Purpose of the Panel

This panel allows users to handle missing values in their dataset using nine distinct strategies ranging from simple deletion to statistical and ML-inspired methods. The goal is to maintain dataset integrity while preparing for modeling or analysis.

Recommended Reading

🧭 User Workflow

  1. Upload Dataset
    Automatically initializes df_processed from the uploaded dataset.

  2. Review Current Data
    The current dataset (with missing values) is displayed.

  3. Missing Values Summary
    A visual summary highlights all features with missing values.

  4. Imputation Settings

    • Select a column with missing data.
    • Choose an imputation method.
    • If applicable, input a specific replacement value.

  5. Apply & View Results

    • Press the “Apply Imputation” button.
    • View/download the processed data.

💻 Features Breakdown

Feature Description
Column Selection Dropdown to choose a column with missing values.
Imputation Method Picker Dropdown with 9 methods to handle missing values.
Value Input (Conditional) User enters value when using “Replace with Specific Value.”
Imputation Logic Execution Custom logic executed depending on the chosen method.
Feedback & Result Viewer Instant success/error message, optional data preview, and downloadable result.

🧪 Imputation Methods & Significance

Method Explanation When to Use
1. Drop Missing Values Removes all rows where the selected column has a missing value. ✅ Simple but risky if data loss is significant. Use only when missing rows are few and ignorable.
2. Replace with Specific Value Replaces missing values with a fixed value entered by the user. ✅ Good for categorical defaults or domain-specific values. Use when you have contextual knowledge (e.g., 0 = No Response).
3. Forward Fill (ffill) Fills each missing value with the last known value above it. ✅ Useful for time-series or ordered data. Use when data has a logical time flow.
4. Backward Fill (bfill) Fills missing values with the next known value below it. ✅ Similar to ffill but forward-looking. Use in reverse-time data or last-to-first ordering.
5. Distribution Sampling Randomly samples values from the column's distribution (normal approximation). ✅ Keeps variability and prevents overfitting. Use for continuous, numeric columns with normal-like distributions.
6. Mean Imputation Fills missing values with the mean of the column. ✅ Easy, but may reduce variance. Use with normally distributed, symmetric numeric data.
7. Median Imputation Fills with the median of the column. ✅ Better than mean for skewed data. Use with skewed or outlier-heavy numeric features.
8. Nearest Neighbors Estimates missing values based on similarity to nearest rows (distance-based). ✅ Sophisticated but computationally heavier. Use when strong similarity exists among rows.
9. Interpolation Fills values by interpolating between surrounding known values. ✅ Natural fit for numeric, ordered data. Best for numeric sequences or time-series data.