Data Cleaning: The Most Important Step in Machine Learning

We all know that reliable data is crucial for making informed, data-driven decisions and ensuring the quality and integrity of your data management practices.

But what does good, clean data look like? It’s more than just reorganizing some rows and calling it a day. We asked our team of data scientists to tell us what exactly they mean when they tell you to prep, clean, or enrich your data. Here’s what they had to say.

Introduction to Data Cleaning

Data cleaning is a crucial step in the data analysis process that involves identifying and correcting errors, inconsistencies, and inaccuracies in a dataset. The goal of data cleaning is to ensure that the data is accurate, complete, consistent, and reliable, making it suitable for analysis and decision-making. This process is essential to prevent incorrect conclusions and ensure that the insights gained from the data are valuable and meaningful.

During data cleaning, you will encounter various issues such as missing data, duplicate data, and irrelevant data. Handling missing data involves deciding whether to fill in the gaps or remove the incomplete entries. Duplicate data, which often arises from combining multiple data sources, needs to be identified and removed to avoid skewing the analysis. Irrelevant data, which does not contribute to the specific problem you are analyzing, should also be filtered out.

Correcting errors and inconsistencies is another critical aspect of data cleaning. This includes fixing typographical errors, standardizing data formats, and ensuring that all data entries conform to defined business rules. By meticulously identifying and correcting these issues, you can transform messy data into a high-quality dataset that provides accurate and reliable insights.

What is Data Cleaning?

First things first: let’s define data cleaning. 

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Data cleaning is the process of preparing data for analysis by removing or modifying data that is incorrect, incomplete, irrelevant, duplicated, or improperly formatted.

Data cleaning steps are essential for improving data quality and reliability, ensuring robust datasets suitable for analysis. But, as we mentioned above, it isn’t as simple as organizing some rows or erasing information to make space for new data.

Data cleaning is a lot of muscle work. There’s a reason data cleaning is the most important step if you want to create a data-culture, let alone make airtight predictions. It involves:

• Fixing spelling and syntax errors
• Standardizing data sets
• Correcting mistakes such as empty fields
• Identifying duplicate data points

It’s said that the majority of a data scientist’s time is spent on cleaning, rather than machine learning. In fact, 45% of data scientist’s time is spent on preparing data.

And to us, that makes sense—if there’s data that doesn’t belong in your dataset, you aren’t going to get accurate results. Data profiling helps evaluate data accuracy and completeness, ensuring that the data meets specific standards. And with so much data these days, usually combined from multiple sources, and so many critical business decisions to make, you want to be extra sure that your data is clean.

Why is Data Cleaning so Important? 

Businesses have a plethora of data. But not all of it is accurate or organized. When it comes to machine learning, if data is not cleaned thoroughly, the accuracy of your model stands on shaky grounds.

We’ve talked about how no-code simplifies the traditional machine learning process. What is typically a 10-step process instantly becomes a much simpler route with platforms like Zams.

But the traditional and no-code process still require the same important first step: connecting to your data. And all that time you’ve saved by opting for a no-code tool won’t matter if those large stacks of data aren’t properly organized, formatted, or accurate.

Preparing your data helps you maintain quality and makes for more accurate analytics, which increases effective, intelligent decision-making.

Reliable data is crucial for making informed, data-driven decisions and ensuring the success of machine learning and business intelligence initiatives.

These are the kinds of benefits you’ll see:

• Better decision making
• Boost in revenue
• Save time
• Increase productivity
• Streamline business practices‍
  

I've Already Cleaned My Data Myself. Can I Start Predicting?

The short answer is no. Unless you are trained in data science, or already know how to prepare your dataset, we typically advise against jumping right in.

Data validation is crucial in ensuring the accuracy and consistency of data after a cleaning process.

The best way to explain this is with an analogy.

Most people know how to drive a car. But not everyone knows how to drive a race car. Driving a regular car and driving a race car are two very different things. But because they have the same fundamentals (press gas, brake, turn wheel), there are those who think that they’ll be able to successfully drive a race car at the racetrack.

Race cars, however, put out raw power and it is largely up to the driver to filter that force as the car is driven. So, while you could absolutely go out on that racetrack and drive that race car, chances are, you won’t be able to drive it well or get the most out of your experience. You might even crash.

The same thing can be said about data. Everyone knows how to operate an excel spreadsheet. But oftentimes, the dataset in that spreadsheet isn’t set up for building machine learning models.

Let’s say you’re trying to predict housing prices. You have a lot of data on sellers: their demographics, the amount they sold their house for, etc. You might also have data that appears to be irrelevant to what you want to predict. But that outlier may be crucial to your predictions. And machine learning will catch that.

This is why we always advise meeting with our team first before getting started on your first predictions and what we mean when we say that data cleaning is more than just formatting spreadsheets. And typically, we find that most people that go through onboarding have the most success when they see how data needs to be prepped, including how to handle missing data.

How to Clean Your Data

Once you know what to look out for, it’s easier to know what to look for when prepping your data. While the techniques used for data cleaning may vary depending on the type of data you’re working with, the steps to prepare your data are fairly consistent. Effective data cleaning processes are essential for identifying and correcting errors in raw data, ensuring accuracy and consistency.

It is also important to handle null values carefully, using techniques such as imputation, deletion, and substitution to manage missing data.

Here are some steps you can take to properly prepare your data.

1. Remove duplicate observations

Duplicate records most often occur during the data collection process. This typically happens when you combine data from multiple places, or receive data from clients or multiple departments. You want to remove any instances where duplicate data exists.

Redundant data can distort analysis and hinder business processes, particularly in CRM systems, leading to inaccuracies that affect marketing and strategy.

You also want to remove any irrelevant observations from your dataset. This is where you data doesn’t fit into the specific problem you’re trying to analyze. This will help you make your analysis more efficient.

2. Filter unwanted outliers

Outliers are extreme values in your dataset. They’re significantly different from other data points and can distort your analysis and violate assumptions. Removing them is a subjective practice and depends on what you’re trying to analyze. Generally speaking, removing unwanted outliers will help improve the performance of the data you’re working with.

It is crucial to identify outliers as they can negatively affect model performance. Visual tools like box plots can be very effective in detecting these outliers, and various techniques can be employed to manage them effectively.

Remove an outlier if:

• You know that it’s wrong. For example, if you have a really good sense of what range the data should fall in, like people’s ages, you can safely drop values that are outside of that range.
• You have a lot of data. Your sample won’t be hurt by dropping a questionable outlier.
• You can go back and recollect. Or, you can verify the questionable data point.

Remember: just because an outlier exists, doesn’t mean it is incorrect. Sometimes an outlier will help, for instance, prove a theory you’re working on. If that’s the case, keep the outlier.

3. Fix structural errors

Structural errors, including data errors, are things like strange naming conventions, typos, or incorrect capitalization. Anything that is inconsistent will create mislabeled categories.

Inconsistent data can significantly impact the accuracy of your analysis. A thorough review, using both manual inspection and automated tools, is essential to identify and correct these anomalies before proceeding with data analysis or visualization.

A good example of this is when you have both “N/A” and “Not Applicable.” Both are going to appear in separate categories, but they should both be analyzed as the same category.

4. Fix missing data

Make sure that any data that’s missing due to incomplete data collection is filled in.

System failures, along with human errors and incomplete data collection, contribute to gaps in datasets that can negatively impact analysis and model accuracy.

A lot of algorithms won’t accept missing values. You may either drop the observations that have missing values, or you may input the missing value based on other observations.

5. Validate your data

Once you’ve thoroughly prepped your data, you should be able to answer these questions to validate it:

• Does your data make complete sense now?
• Does the data follow the relevant rules for its category or class?
• Does it prove/disprove your working theory?

Ensuring that your data conforms to specific standards or patterns is crucial for effective data cleaning. This process identifies inconsistencies and ensures data accuracy and completeness.

Data Transformation and Formatting

Data transformation and formatting are critical steps in the data cleaning process. Data transformation involves converting data from one format to another, such as changing text data to numerical data, to make it more suitable for analysis. This step is essential for ensuring that the data can be effectively processed and analyzed by various statistical methods and machine learning algorithms.

Data formatting, on the other hand, involves standardizing the format of the data to ensure consistency across the dataset. For example, date formats should be uniform throughout the dataset to avoid confusion and errors during analysis. Consistent data formats make it easier to compare and combine data from multiple sources, enhancing the overall quality of the dataset.

Handling multiple data sets is another important aspect of data transformation and formatting. Often, data is collected from various sources, each with its own format and structure. Combining these data sets into a single, cohesive dataset requires careful attention to detail to ensure that all data points are accurately aligned and standardized. This process not only improves the quality of the data but also makes it more reliable for analysis, leading to more accurate and meaningful insights.

Data Entry and Management

Data entry and management are critical components of the data cleaning process. Data entry errors, such as typographical mistakes or incorrect data entries, can lead to inaccurate data, which can significantly impact the accuracy of the analysis. Ensuring that data is entered correctly from the start is essential for maintaining data quality.

Effective data management involves implementing defined business rules and protocols to prevent errors and inconsistencies. These rules help ensure that data is consistently entered and maintained, reducing the likelihood of errors. Additionally, data management includes using data cleansing tools and techniques to identify and correct errors in the dataset. These tools can automate the process of detecting and fixing issues such as duplicate data, missing data, and incorrect data entries.

Handling missing data and duplicate data is also a crucial part of data management. Missing data can be addressed by either filling in the gaps with appropriate values or removing the incomplete entries. Duplicate data, which often results from combining data from multiple sources, needs to be identified and removed to ensure the accuracy of the analysis.

By focusing on data entry and management, you can ensure that your dataset is accurate, consistent, and reliable. This foundation of high-quality data is essential for making informed decisions and gaining valuable insights from your analysis.

Data Prep Checklist: The Basics

Zams requires a structured dataset to get meaningful prediction outcomes.

We made a quick DIY checklist to ensure your data is well structured and machine learning ready. It was prepared by the data science team at Zams, so you know it’s comprehensive. Identifying and counting unique values in categorical columns is crucial for understanding the diversity and range of data entries within each category.

• Dataset must have at least 1,000 rows
• Dataset must have at least 5 columns
• The first column must be an identifier column, such as a name, customer_id, etc.
• The first row should be column names
• The data should be aggregated in a single file or table
• The data must have as less missing values as possible
• Ensure formulas are applied correctly across multiple columns to maintain data integrity
• No personally identifiable information is required, such as phone numbers, addresses, etc.
• No long text phrases—only use discrete values for text columns

What columns should I bring in my dataset?

A training dataset that’s machine learning ready typically contains several types of columns (features). Understanding the data type of each column, such as categorical or numerical, is crucial for effective analysis. While you don’t need all types of columns, having as many as possible can help make better predictions.

It is also important to assess the data set to identify quality issues, such as inconsistencies and outliers, which can affect the analysis.

Here’s a list of most common column types:

• Identifier column: Anything we use to distinguish a customer from another. Only one is required. (e.g. User ID, Name, Customer ID, etc.)
• Demographic columns: Any columns with demographic data that relates to the user OR the line item in the row. (e.g. Age, Location, Income, etc.)
• Product/Usage columns: Any columns that record activity done by the customer on your product OR details of their account. (e.g. Number of sessions, Account type, etc.)
• Transactional columns: Any columns with details on transactions done by the customer. (e.g. Monthly charges, Payment method, Contract length, etc.)
• Prediction column: Data of historical activity that you would like to predict. (e.g. Churn, Lead status, Sales, Revenue, etc.)

To learn more about the type of columns, check out the following links:

• Sample file outlining each column type
• Sample dataset

Summary

Data cleaning is an extremely vital step for any business that is data-centric, ensuring accurate analysis. Businesses that take proper care of their datasets are rewarded with high-quality predictions and are able to make leaps ahead of their competition. With clean and organized data, you can predict anything—from customer churn to hospital stay to employee attrition.

Failing to validate data can lead to false conclusions, which can negatively impact business strategy and decision-making, potentially causing embarrassment in reporting situations.

Zams has a team of data scientists that become an extension of your team helping you make your datasets machine learning-ready. 

‍Book a demo with us today to learn more about how our dedicated data scientists team can help you get your data machine-learning ready.

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