Essential data cleaning techniques for machine learning models

In a 2019 survey of more than 23,000 data professionals, it was discovered that they dedicate around 40% of their time to just collecting and cleaning data, 20% to developing and choosing models, and only 11% to identifying insights and sharing them with stakeholders. With everything riding on data, it’s worth knowing why data cleaning techniques are important and how you can maintain your datasets in their best condition.

If data is unstructured or inconsistent, even the most advanced models can resort to making prejudiced predictions or drawing tenuous conclusions. 

In this blog, we will examine six crucial techniques that can take raw data and turn it into a useful building block for AI—starting with why data cleaning is essential in the first place.

The role of data cleaning techniques in AI implementation

In 2021, Zillow Offers, the real estate marketplace firm’s AI-powered home-flipping program, collapsed after its pricing algorithm overestimated property values repeatedly. 

As Inside AI News reports, the model had been trained on noisy, incomplete, and overly optimistic housing data that did not accurately reflect real-time changes in market conditions. This caused Zillow to purchase thousands of properties at above-market prices, which it then had to sell at a loss.

The outcome was a loss of $500 million, mass layoffs, and shutdown of the operation. The failure highlights a fundamental reality of machine learning. Without clean, representative, and timely data, even the most well-designed models can yield fundamentally faulty results.

Machine learning algorithms acquire their predictive powers from the data they are trained on. Good data allows the algorithms to pick up strong patterns, leading to sound decision-making and actionable insights. Conversely, raw data adds noise and decreases overall reliability, essentially sabotaging the model’s performance. 

As highlighted by the renowned AI leader Andrew Ng, quality-checked and consistent data is key to obtaining reliable, unbiased, and equitable results. Ultimately, the whole value chain of model development is about providing an algorithm with reliable input.

Some of the most critical reasons why data cleaning techniques should be given precedence in AI workflows are as follows:

1. Improving model accuracy

Clean data guarantees that a model can differentiate legitimate trends and not get distracted by errors or non-representative outliers.

• Example: Eliminating duplicate or incorrectly labeled product entries in an e-commerce recommendation engine allows the model to concentrate on genuine customer preferences. This can result in improved product suggestions and higher conversion rates.

2. Enhancing reliability

Models developed on well-maintained data are more likely to produce reliable results when cross-checked with unseen data.

• Example: A model developed for predicting customer churn based on complete and consistent CRM data is more reliable on the test set with new users. This enables proactive retention.

3. Lessening bias

Models can produce biased or unfair results when they learn from data filled with errors or missing values. This problem can be partially mitigated by correcting outliers or incomplete entries before training.

• Example: In a loan approval model, imputation of missing income data using valid inputs decreases bias against certain demographic groups and results in more equitable and inclusive decisions.

4. Improving resource productivity

By reducing anomalies, well-refined datasets reduce redundant processing and debugging time, freeing teams to concentrate on optimizing model structures.

• Example: In a predictive maintenance application, anomaly cleaning eliminates false positives. This can save hours of unnecessary diagnostic work for engineering teams and boosts uptime.

5. Improved interpretability

With cleaner data, outcomes are simpler to comprehend, making better diagnostics and business-relevant insights possible.

• Example: In sales forecasting dashboards, clean and standardized transaction histories enable more clearer trend analysis such that leadership has an easier time drawing actionable insight.

6. Regulatory compliance

Most sectors work under strict laws that require adequate use of data, and data cleaning helps in adhering to such compliance requirements.

• Example: In healthcare analytics, data cleaning and validation of patient records assures HIPAA compliance. This minimizes legal risk and allows for safe application of AI in clinical decision-making.

Due to these far-reaching implications, data cleaning techniques have become absolutely critical in various AI applications, ranging from fraud prevention, customer analytics, health diagnostics, to supply chain optimization.

Read more: Why the data cleansing process is critical for business success

1. Handling missing values with care

Missing data is perhaps the most frequent challenge faced by data scientists. Missing values can occur due to a number of causes: data entry errors, faulty sensor readings, non-response to surveys, or system crashes. Unless these gaps are addressed, they can negatively bias model training and result in below-par or deceptive outcomes.

The following steps detail how data cleaning techniques can reduce problems relating to missing data:

1. Deletion: Incomplete records are deleted from the dataset. Although easy to implement, this method may lose important information if missing values are common. It must therefore be applied when missing data is low and appears to be random.

2. Simple imputation: Missing numerical data are substituted with quantities such as mean or median. For categorical data, the mode could be applied. This serves to leave more records intact but may skew the distribution if the missing data has particular patterns. The decision between using mean or median must be adjudicated by both a data scientist and a business analyst. They must know the background and field to capture the nuanced scope of the information or dataset.

3. Predictive imputation: Other machine learning models or regression models predict missing values from relationships with other features. While this method tends to preserve statistical relationships, careful validation is important to prevent overfitting and the introduction of new biases.

4. K-nearest neighbors (KNN) imputation: Similar points are employed to find a likely replacement for any missing entry. The approach is based on the assumption that “neighbors” in feature space are similar, but it can be computationally intensive for very large datasets.

5. Separate ‘missing’ category: You can create a separate category for categorical variables to represent missing or incomplete data, allowing the model to explicitly learn how to handle missingness.

6. Domain knowledge: Subject matter consultation may provide direction on how best to estimate missing values. This enables more accurate results than general imputation processes.

A hypothetical example

Consider a fictional company named FreshFare, which operates a grocery delivery application. While examining customer data to forecast weekly order frequency, the team discovers that numerous user profiles lack age and income information. This is particularly for users who registered via social logins.

Removing these rows would eliminate a high percentage of recent users, biasing the dataset towards older, more conservative sign-ups. Instead, the team applies predictive imputation based on location, average order value, and browsing history to make educated guesses about the missing values. After thorough validation, the new dataset generates a more balanced and accurate model for targeting personalized offers.

Key takeaway

Completing missing records by applying data cleaning techniques ensures that the final dataset utilized for model training is coherent and fair. And once those gaps are filled, another latent problem comes into play—outliers.

2. Addressing outliers to maintain robustness

Outliers—instances that are vastly different from most—can impede analysis, bias parameter estimation, and hamper overall model performance. Efficiently identifying and treating these aberrations is an intrinsic part of data cleaning techniques. 

Unchecked outliers have the capacity to misdirect the model and make anomalous predictions. Even one outlier can produce deceptive averages or standard deviations. Outlier identification can be carried out by:- 

Statistical methods:

• Z-score: Values that deviate significantly from the mean in terms of standard deviations are identified. This method, however, assumes a roughly normal distribution and is therefore restricted in use to specific types of data.
• IQR (Interquartile Range) method: Data points that fall outside the interval of (Q1 – 1.5 * IQR) to (Q3 + 1.5 * IQR) are defined as outliers. This method provides greater robustness against extreme values than a regular z-score.
• Modified z-score: Utilizes median absolute deviation (MAD) rather than standard deviation, providing improved outlier detection for skewed or heavy-tailed distributions.

Visualization techniques

• Box plots: Compact representation of data’s quartiles and also highlight outliers if beyond the whiskers.
• Scatter plots: Used to note outliers in two or more dimensions on plotting with concerned features.
• Histograms: Observe aberrations in the distribution of one variable.

Machine learning algorithms:

• Isolation forest: An unsupervised partitioning-based algorithm which identifies likely anomalies rapidly.
• One-class SVM: Applies support vector machines learned from “normal” data only, marking outliers as points outside this acquired norm.

Once outliers are identified, the following data cleaning techniques can manage them:

• Elimination: In the event of obvious error, the removal of outliers may make the dataset easier to work with, but such removal without solid reason for doing so may mean discarding valuable information.
• Transformation: Log or square-root transformations can squeeze high-end values, reducing their undue impact on the model.
• Capping (winsorization): Truncates extreme values at specific quantiles to reduce their statistical effect.
• Separate analysis: Sometimes the outliers are the most important data points (e.g., fraud analysis). In this case, processing them separately saves valuable insights instead of eliminating them.

A hypothetical example

At DataFlux One, a fictional fintech company providing credit risk modeling, the data team spotted abnormally large loan values biasing the average in a risk scoring model. These outliers were from some corporate customers embedded in a dataset for individual borrowers.

Once they detected the anomaly through IQR and box plots, they partitioned the data and retrained the model. This diminishes false risk notifications by 25%, supporting more precise and equitable credit rating for personal loan applicants.

Key takeaway

Choosing what to do with outliers finally rests on knowledge, the potential effect of such extreme points, and the amount of distortions they cause. With those values taken care of, the next step is to ensure the data is usable reasonably well across types of models. This is particularly true for those which make use of distance or gradient calculations.

3. Normalizing and standardizing for fair comparisons

Distance-based metrics algorithms (such as k-nearest neighbors, support vector machines, or neural networks) can bias those features with larger numeric ranges disproportionately, skewing the model unintentionally. 

Therefore, uniform scaling of feature values is an essential component of data cleaning techniques. Applying standardization or normalization can improve stability as well as model accuracy.

Popular scaling methods are:

• Standardization: Scales features so that their mean is zero and standard deviation is one, without removing outliers but simplifying the relative importance of various variables.
• Min-max normalization: Bins all values within a defined range (usually 0 to 1), allowing for direct comparisons among features.
• Robust scaling: Applies robust statistics such as median and interquartile range, which are less sensitive to outliers.
• Unit vector normalization: Normalizes feature vectors separately such that they have a magnitude of one, which can particularly be useful for applications such as text processing.

A hypothetical example

At Florentix, an imaginary startup that creates customized fitness programs, the data team creates a model to suggest exercise intensity from user information—such as age, weight, activity time per week, and heart rate.

In early experimentation, the model overestimates weight as a predictor consistently. Why? Because weight is in the 100s (in pounds), whereas heart rate and age are in much smaller ranges. Without scaling, algorithms such as k-nearest neighbors regard weight as more significant merely because its numeric range is larger.

Once standardization is implemented on all features, the model begins treating all variables with proper weight. Recommendations then become more well-rounded and accurate, more meaningfully representing real user profiles rather than biased correlations.

Key takeaway

Using these transformations guarantees that one large-scale feature does not overpower others, reducing unintended bias in model results. However, data scientists need to choose the strategy that best corresponds to their dataset’s distribution and the sensitivities of the learning algorithm.

Scaling data enables more efficient model training. But another, less obvious pitfall arises when you don’t structure values uniformly to begin with.

4. Adhering to consistent formats and values

A machine learning model’s pattern recognition ability relies significantly on consistent, clearly defined input fields. Unpredictable text casing, inconsistent units of currency, or more than one date format can make a model’s ability to understand relevant features difficult. Therefore, consistent formats are one of the key data cleaning techniques to obtain first-rate data quality.

Best practices for guaranteeing uniform formats are:

• Standard date and time formats: Standardized date patterns (such as YYYY-MM-DD) reduce uncertainty across datasets from different regions.
• Text normalization: Normalizing to all uppercase or all lowercase prevents unintentional duplication (for example, “APPLE” vs. “apple”).
• Measurement units: Converting varied units (imperial vs. metric) into one standard prevents any accidental scale difference from inhibiting analysis.
• Categorical values: Translating synonyms or alternative spellings into one “official” form (“Male” vs. “M” vs. “MALE”). This frequently requires domain knowledge to verify that different but similar words actually map to the same category.

A hypothetical example

Imagine you’re the lead analytics engineer at a hypothetical company called Novalume, which offers AI-powered inventory optimization for global retail chains. Novalume integrates data from client warehouses, sales systems, and third-party logistics providers.

A sample data inconsistency would be when South American warehouse teams weigh products in kilograms, while their U.S. counterparts enter the same values in pounds, and some European locations append units as text (e.g., “kg”, “lbs”) inside the numeric field. While date formats differ between YYYY-MM-DD and MM/DD/YYYY across sources.

When such unsteady data enters Novalume’s forecasting model for demand, it confuses the model’s comprehension of product turnover rates, creating situations of overstock or stockout. Once all incoming feeds were standardized for units and formats, forecast accuracy increased and fulfillment delays fell significantly.

Key takeaway

Even seemingly minor inconsistencies—such as units of measurement or date formats—can greatly derail the performance of AI models. Normalizing data inputs is not merely about cleanliness. It’s essential to producing accurate, actionable insights.

After the data has been formatted and made consistent, the next crucial step is figuring out which variables truly impact the model’s performance. Not every feature is of equal worth—some are redundant, others unnecessary, and some might even create noise or bias. 

 5. Choosing the most appropriate features

Not every variable recorded provides useful information, and some may even reduce the overall performance of the model. Feature selection, a group of data cleaning techniques, finds and retains only those columns that are useful for training. Redundant or noisy features can create overfitting and reduce generalizability.

Feature section methods include:

• Filter approaches: Rapidly assess feature significance through statistical indicators such as correlation or mutual information, eliminating or retaining features prior to model training.
• Wrapper approaches: Measure subsets of features by repeatedly training the model and monitoring performance metrics. While more accurate, they are computationally intensive for large datasets.
• Embedded approaches: Certain models (such as lasso regularization or decision trees) inherently emphasize significant attributes. This may be less time-consuming than exhaustive search methods.
• Dimensionality reduction: Principal component analysis (PCA) or t-distributed stochastic neighbor embedding (t-SNE) reduce the dimensionality but preserve most of the data variance.

Improving quality with advanced methods

In addition to these basic steps, additional data cleaning techniques improve data readiness for machine learning:

• Record linkage: Finds and connects records for the same object in different databases, resolving any conflicts in identification keys or naming conventions.
• Data validation rules: Autonomously checks to ensure data adheres to certain criteria. This can eliminate incorrect entries before they go into the training process.
• Fuzzy matching: Finds close matches like “Robert Williams” vs. “Bob Williams,” which is necessary for standardizing names, addresses, or product descriptions.
• NLP-based cleaning: Natural language processing techniques eliminate textual noise by stripping stopwords, synonym normalization, or lemmatization to standardize tense.
• Data governance: Instills a cultural and managerial environment where data integrity is not an ad hoc effort, but a standard business practice. This includes establishing clear data ownership, role-based access controls, quality standards, and uniform processes to ensure traceability, accountability, and sound usage of the data throughout the entire organization.

Applications: how clean data optimizes AI performance

Healthcare diagnostics

Medical facilities employ a high amount of patient information for predictive analytics. Incomplete or inaccurate patient information may result in false risk evaluations. Accurate filled records, outlier detection, and normalization of laboratory tests enable physicians to identify early warning signs of diseases like heart disease or diabetes, increasing accuracy and saving lives.

In 2020, Mount Sinai constructed an AI model based on CT scans and patient information—such as symptoms, age, and lab tests—to diagnose COVID-19. The model achieved 84% sensitivity, having been trained on more than 900 cases. This outperformed radiologists at detecting early-stage cases, including those with normal-appearance CT scans.

If the clinical data would have been inconsistent—e.g., missing symptom fields or mismatched lab formats—the AI might have overlooked subtle cases. This would have resulted in delayed isolation and increased risk of transmission. 

Fraud detection

Danske Bank struggled with a low 40% fraud detection rate and as many as 1,200 false positives daily. By adopting a modern enterprise analytic solution that utilizes AI, the bank was able to raise its fraud detection rate by 60% and lower false positives by 50%.

If Danske Bank’s data would have been unreliable or contained inaccuracies, like incorrect transaction history or outdated customer data—the AI system could have incorrectly labeled legitimate transactions as fraudulent or could have missed genuine fraud instances. 

Banks using anomaly detection depend heavily  on outlier management for credit card transactions. If erroneous or repeated data goes unnoticed, models either produce false positives or overlook suspicious patterns entirely. By using strong data cleaning techniques, financial institutions minimize these oversights, casting a tighter net over fraudulent behavior.

Supply chain analytics

Companies monitor inventory, shipping duration, and supplier performance in multiple facilities worldwide. Inconsistent measurement units or missing shipping information can hinder predictive modeling.

For instance, Amazon revolutionized its supply chain by using AI for demand prediction, inventory optimization, and logistics optimization.

If Amazon hadn’t employed data cleaning techniques, inconsistencies could have plagued its data. For example, with faulty inventory levels or incorrect shipping details—the AI models might have produced faulty forecasts and inefficient logistics plans. This would have led to stockouts, delayed shipments, and higher operational costs.

Standardized data entry practices, well-organized distribution channels for unavailable updates, and outlier identification guarantee accurate procurement and logistics forecasts. This therefore helps build confidence in the organization’s analytics capabilities.

Conclusion

The five fundamental steps—handling missing information, outlier handling, feature scaling consistently, standardization, and careful feature selection—are the basic data cleaning techniques that support machine learning activities. 

Yet more robust data pipelines also include sophisticated techniques such as record linkage, fuzzy matching, and domain-specific verification, constituting a multi-layered process that is robust even for the most challenging AI applications.

Prioritizing data quality is therefore a hallmark of mature analytics practices. Teams that do so not only gain access to more accurate short-term insights but also build a foundation that can adapt with changing business needs and new technologies. 

Through regular cleaning of data, AI practitioners make sure that each downstream step—training of models, hyperparameter optimization, and real-world deployment moves on a solid, transparent, and reliable base.

Are you on the lookout for a data strategy that is accurate and scalable? With our comprehensive data and analytics services, Netscribes assists companies in creating AI-ready data pipelines through complex cleaning, enrichment, and validation methods. 

Contact us to find out how we can optimize your data for high-impact AI and analytics.