Essential Techniques for Data Preprocessing (in ML)
Artificial Intelligence (AI) is a powerful force that can help us handle complex activities and business processes. But while AI, IoT, and other technologies are helping to simplify and improve business innovation, their output is fundamentally dependent on the quality of data that is fed to this system.
If the ML model has low quality or limited scope of data, the data analysis and pattern or trend analysis may not be up to the standards expected.
As per a report by NewVantage Partners, 59.% of organizations use ML-based data analytics for their business decisions, but only 20.6% of companies say they have developed a data culture, with many recognizing that data inadequacy is leading to obstacles in their business goals.
To help understand the pivotal role of data, here is a look at data preprocessing techniques that can help model performance optimization and improve business output.
Techniques of Data Preprocessing in ML
In an ideal world, ML models would be trained with the exact data that is required to help them explore multiple avenues and patterns, helping the ML model understand what is standard behavior and what needs to be flagged. However, even the best data often has inconsistencies, which is why data preprocessing techniques are crucial for training ML models.
Data preprocessing in ML is a process that allows data scientists and engineers to make updates to input data for multiple inconsistencies - such as typos, missing fields, different values, or others. These adjustments help to make the data more understandable to the ML model and ensure that only cleansed data is used for processing and training the ML model.
Data preprocessing in ML focuses on 6 key techniques:
1. Data Cleaning
The most fundamental aspect of data preprocessing techniques is data cleaning, which involves identifying and rectifying errors, outliers, and missing values within the dataset. This includes identifying null values, errors, incomplete fields, inaccurate datasets, data duplications, or irrelevant fields in the data.
2. Data Transformation
After identifying these issues, data scientists can make a decision to either delete the data or modify it to match the specific format of the ML model.
This is where data transformation comes into the picture. In this process, raw data is transformed into a suitable format for the ML model, including removal of data inconsistencies, noisy data, and missing data.
3. Feature Engineering
After data is cleaned and transformed, it can be used for feature engineering.
Feature engineering elevates the predictive power of machine learning models by crafting new features or modifying existing ones. This involves selecting, combining, and transforming data sets, enhancing the model's ability to discern patterns and make accurate predictions.
4. Handling Imbalanced Data
Often, even cleansed data can have imbalances. Effective handling of imbalanced data involves techniques such as oversampling, undersampling, or employing specialized algorithms that address the skewed distribution, preventing biases in model training.
5. Time Series Data Preprocessing for ML
Another inefficiency that can develop in data includes time series data. This often presents unique challenges, including temporal dependencies and seasonality.
Preprocessing techniques for time series data involve handling missing values, smoothing erratic patterns, and creating lag features to capture temporal relationships, ensuring models make informed predictions.
6. Data Integration for ML
The pivotal step for data preprocessing in ML is data integration. This involves merging the cleansed data into the larger data store, like a data warehouse. Since the cleansed data can have different formats than the data lake or warehouse, it needs to have a proper integration flow where information from diverse sources gets merged into a unified dataset.
This harmonization is crucial for leveraging a comprehensive pool of insights and improving the robustness of ML models.
Best Practices in Data Preprocessing
Ensuring the efficacy and sustainability of your data preprocessing endeavors involves adhering to best practices that transcend individual techniques.
1. Maintainability and Documentation
Crafting maintainable code and comprehensive documentation streamlines the data preprocessing pipeline. This practice not only facilitates collaboration but also ensures the reproducibility of results and easy troubleshooting.
2. Iterative Exploration
Data preprocessing is not a one-size-fits-all endeavor. Adopting an iterative approach allows for continuous exploration and refinement, enabling data scientists to uncover hidden patterns and make informed decisions throughout the model development lifecycle.
3. Collaboration and Communication
Effective collaboration and communication among team members are paramount. Transparent sharing of preprocessing choices, insights, and challenges fosters a collective understanding of the data, leading to more robust models.
Automating repetitive preprocessing tasks enhances efficiency and reduces the risk of human error. Embracing tools and frameworks that support automation streamlines the preprocessing workflow, freeing up valuable time for more strategic tasks.
Real-world Applications of Data Preprocessing
The impact of data preprocessing can be seen in multiple domains and business areas, but one area where it can make a big difference is in financial fraud detection.
In financial fraud detection models, ML analyses data patterns and flags activities matching fraudulent patterns. Time series preprocessing aids in recognizing temporal patterns, while feature engineering refines the discriminative power of the model.
This can help enhance the model’s overall ability to discern fraudulent activities from normal ones and even bring down the number of false positives.
As per a report by Juniper Research, there were 3.2 million fraud reports in a single year, and they estimate cybersecurity breaches to be around $5 trillion by 2024. The major issue for most financial frauds was attributed to the use of deep fakes, requiring a high level of user identity and anti-fraud checks.
Most of these frauds follow a similar pattern as per the Credit Industry Fraud Avoidance System (CIFAS), which is where machine learning can be implemented. When ML models were implemented for fraud detection, it showed a reduction in false positive results, as models learned which results were actually fraudulent over time. As the model improves, the accuracy and detection of fraudulent activities only get better, making it the ideal system for monitoring financial risks.
As we conclude this blog, we reflect on the indispensable role of data preprocessing in ML. From purifying raw data to sculpting features, each technique explored here contributes to the seamless evolution of models.
Maintaining data governance and quality is crucial for training ML models as this improves overall accuracy and detailing in the AI solution. As the model only gets complex, any inaccuracies or errors can create major issues later if they go undetected.
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