Data scientists all over the world agree that better data is more important than having optimized algorithms, to gain optimal results from ML models. Data cleaning and organizing form the foundation of accurate ML models.
Data cleaning is a process that entails all the tasks that fall under prepping your data for ML training. It is an essential step in data analysis that defines the efficiency of insights that will drive your business decisions.
Introducing Data Validator
Do you ever wonder, if data cleaning is so important for optimal performance of ML models, then why are today’s technologies not improving on it? If you have been waiting for technology to come to your aid for data cleaning, we have good news for you!
Allow us to introduce you to our industry’s first of its kind- Obviously AI’s Data Validator. Yes, you read that right! We are launching a game-changing solution to your data validation needs. Our Data Validator platform is going to be the first ever to aid data scientists in automated examination of their data to check how good or bad their raw data is.
Data Validator labels a dataset in the form of a percentage score that determines the readiness of the data for ML training. For example, a dataset with a 50% score is expected to need more cleaning than a dataset with an 85% score.
The motivation behind building this tool was to help users understand the current status of their dataset. Users who do not have an ML background are generally unaware of how decentralized and messy their data is. The entire data validation process is automated to present such users with a quantitative measure of their raw data status. As a result, our users are guided in the right direction to make their data ML ready.
Based on the initial impression of your data, you can proceed directly to preparing your dataset for ML training. So brace yourselves for our Data Validator, which sets the stage for your data to be ML-ready.
What does Data Validation entail?
Obviously AI’s Data Validator runs multiple standard checks on your data to examine how ready it is for ML and recommends fixes as needed. You can upload your dataset (A CSV file) on the data validator, input some basic information on your data, such as ID column, data column, etc. and that’s it. Our platform performs automated data validation and emails your report to you in seconds.
How does Data Validator Work?
Now that you know our Data Validator let us dive into the details of behind-the-scenes validation tests. Data Validator performs a wide range of tests on your data. These tests are divided into the following six categories. We will review each of these categories and elaborate on their importance in training machine learning models.
Column Standardization refers to the category of tests that ensure that all data variables equally contribute to the results of ML model training. In terms of machine learning, this is an important process for bringing column values to a common scale.
Large differences in the range of column values can distort model training results, making column standardization an essential category of tests.
Class Balancing includes all data validation tests that determine whether your dataset is unbiased. A balanced dataset only applies to classification use cases, where the expectation is to have equal or near to equal representation of each class. Unbalanced class results in the dataset are divided into majority and minority classes.
A biased dataset can negatively impact model training as the model fails to generalize well for all classes, resulting in a skewed outcome.
Data sensitivity tests are performed to check the sensitivity of your dataset to external changes. External changes refer to changes in input values, data quality, data structure, noise tolerance, and more. These tests are insightful for studying the effect of input variables on outputs.
A highly sensitive dataset can result in varying results when passed through an ML model.
ML models are no strangers to outliers. Outlier recognition tests your dataset for the presence of outliers. Outliers are few data values significantly different from the rest of the dataset. Outliers can be caused by sampling errors, data manipulation, or processing errors.
Outliers are a common occurrence, especially in large datasets. They can skew the accuracy and effectiveness of ML models.
Statistics is an integral part of machine learning for exposing patterns and changes in real-world data. Statistical checks refer to the category of tests performed to extract useful data insights. The most common statistical checks include p-tests, data sparseness, data variance, and more.
Datasets often carry hidden meaning in the form of patterns that can easily be detected using statistical checks.
Validation tests in this category test your data to ensure that it is aligned with the KPIs of your business. Business logic refers to decisions that govern your business strategies. To drive your business decisions, it is important to derive correct insights from the right data.
Business logic changes rapidly with the evolving market. Businesses need relevant insights from their data that correctly represent their goals.
Data Validator in Action
Enough background information, it’s time for you to see Data Validator scoring in action.
Required steps for generating a validation report:
- Step 1: Take a churn dataset and upload it to the validator platform.
- Step 2: Fill in the required columns: ID and prediction columns.
- Step 3: Provide your email where you want to receive the report. Your data validation report will be generated in seconds and immediately sent to your provided email.
Results of the Report
Overall Data Readiness Score:
At the top of your report, you will see your data readiness score which depicts your dataset’s ML readiness in the form of a percentage. A readiness score between 75%-100% is considered optimal for ML model training.
The summary of your data validation report provides the details of all the parameters that contributed to your ML readiness score.
On the right hand side of your summary, you will see an ML Ready notification if your data score lies in the readiness range. On the left hand side, you will find an elaborate list of all the tests performed on your data, under the headings, Prediction Value Tests and Feature Checks.
Prediction Value Tests include all the tests conducted on the prediction column and their results.
Feature Checks include the tests conducted on the feature columns present in your data, sorted into categorical and numerical columns. The results are depicted in front of the tests as a check or cross corresponding to passing or failing the tests.
The following is an overview of the feature checks performed on your data divided into three categories:
- No columns with a Single Record Category: A single record category is insufficient to represent a category, and therefore does not contribute to patterns present in your data. In case of failing this check, identify and omit such columns.
- Excludes Comma-Separated Values and JSON values: These checks test your dataset for different types of formats like having multiple values in one cell or a JSON formatted value in a cell. You can perform data parsing to standardize the formats of your data so a single cell has just one value and the data is easily analyzable.
- Number of Categories per Column: A large number of categories per column can increase the dataset dimensions that can impact model performance and accuracy. Group the categories based on similarities to reduce the number of categories per column.
- Category Occurrences per Column: Checks your dataset for the number of times a category is repeated in a column. Sufficient representation of each category is required in order to learn useful patterns from the data.
- Percentage of Empty Values per Column: In order for your dataset to be machine learning ready, it must have as few missing values as possible. This check tests the percentage of missing values in each column of your dataset. Remove gaps in your data to pass this check.
- Correlated Features: If the features in your dataset are highly correlated, then there is redundant information in your data. Consequently, the ML algorithm will not be able to find useful patterns from those features. Remove data inconsistencies and ensure your dataset does not contain highly correlated features.
- Valid Character Length: Structural errors in data can affect data analysis and ML models do not recognize them as mistakes and incorporate these errors to deliver skewed results. Make sure your data values are within a valid character limit to pass this test.
- Correlated Features: Correlation tests are the same for numerical values. You will need correlated numerical values for ML models to interpret them.
- No Large Decimal Values: Large decimal values in your data can result in significant differences in mathematical computations. Identify and round off such values in your dataset.
- No Large Numbers: Large number values are considered bad data for an ML model.
- High Number of Unique Values: Unique values can result in a class imbalance in your dataset. Resample your dataset to reduce the number of unique values.
- No outliers: Outliers can mess with your ML model outcomes and deliver inaccurate results. Identify outliers through computations like standard deviation, and omit these outlying values from your dataset.
Set the stage for your data to be ML-ready!
To sum it up, a wide range of tests and checks determine the readiness of your data for machine learning. Manual data cleaning, as little as possible, leads to saving valuable time on your end.
Obviously AI is the only no-code AI product in the market right now offering an automated data validation solution. Our unique solution is here to help you make your data ML ready at the fastest possible rate for performing efficient data analysis. Give our Data Validator a try and refocus your resources on data analysis to get the most out of your data in a short time.