NeatData

Powerful Features

Missing Values

Handle missing data efficiently with the right methods for better model performance.

Data Sampling

Sample your data to improve training results and model accuracy.

Missing Values Smart Analysis

Let NeatData help you choose the most suitable method to handle missing values in your data.

Transform Data

Convert your data between CSV, Excel, JSON, and Parquet formats without losing structure.

Encoding Categorical Variables

Easily encode categorical variables for machine learning models.

Correct Errors

Identify and correct errors in your dataset to improve its quality and reliability.

Handle Outliers

Detect and manage outliers to ensure more accurate data analysis.

Feature Engineering

Reduce the number of input variables and optimize feature selection for better model performance.

Handling Missing Values - Step by Step

Import Your Dataset

Start by uploading your CSV, Excel, or JSON file. NeatData supports all common data formats with automatic format detection.

The system immediately scans your data structure and provides a quick overview of data quality issues.

Simple drag-and-drop interface for data import

Data Overview Dashboard

After import, NeatData presents a comprehensive overview of your dataset. The Data Quality Score gives you an immediate assessment.

Click on the "Show Data Overview" tab to see detailed statistics about null values in your dataset.

Interactive data overview with quality metrics

Visualize Missing Values

NeatData offers multiple ways to visualize missing data patterns. Choose between heatmaps or bar charts to understand the distribution of null values.

The visualization helps identify columns with excessive missing values that might need special treatment.

Heatmap visualization

Bar chart visualization

Processing Missing Values

Choose from multiple handling strategies:

Automatic imputation (mean/median/mode)
Advanced ML-based imputation
Row/column deletion
Custom value replacement

NeatData recommends the optimal approach based on your data characteristics.

Comprehensive processing options

Review and Save Results

After processing, NeatData shows before/after comparisons of your dataset. Verify the changes and see the improvement in your Data Quality Score.

Export your cleaned data in any format, or save the processing pipeline to apply to future datasets.

Before/after comparison

Multiple export formats