Historical data is a resource in training AI models, especially in machine learning and deep learning. It is the backbone for AI training, mostly because it basically provides real-world examples which AI algorithms are taught from. In getting AI-Ready, when AI systems train on historical data, they analyze patterns, trends and relationships within the data to understand how different variables interact with one another and, hence, become AI-Ready for real-world deployment.
AI-ready data is quality data that has undergone processing and preparation for AI applications. It should be clean, consistent and well-structured so as to easily make sense and be learned by algorithms. AI-ready data has been processed through the preprocessing steps, handling of missing values, removal of duplicates and standardization of formats among others, which ensure dependability or usability of the data.
This ready-to-use information helps the AI learn from it and make predictions or decisions more accurately. In other words, AI-ready data fast-tracks the process of teaching AI and helps an organization tap into the highly intelligent capabilities of AI.
However, loading already existing data directly into the AI model without preprocessing is very challenging and full of possibilities for pitfalls. Therefore, cleaning of data has to form part of the preparation of data for use by AI applications. Inadequate or low-quality, variable data will most strongly impact the performance and reliability of AI models.
Table of Contents
Here are the steps typically involved in cleaning data for AI-Ready:
1. Data Inspection
Begin by examining the raw data to understand its structure, format and quality. Prevalaing problems that could be envisioned are missing values, outliers, duplicates, inconsistencies and formatting errors.
Goal : Analyze the raw data to understand its structure, format and quality. Tools : Excel, Python libraries (Pandas, NumPy), data visualization tools (Matplotlib, Seaborn).
2. Handling Missing Values
Handle missing values in the dataset. Depending on the nature of the data and the amount of missing-ness, strategies for handling missing values may include imputation-replacing missing values with estimates, deletion-removal of records or features with missing values, or flagging missing values for special treatment during analysis.
Goal : Address missing data through imputation, deletion, or flagging.
Tools : Python libraries (Pandas, Scikit-learn), statistical methods for imputation (mean, median, mode).
3. Removing Duplicates
Identify and eliminate duplicate records or entries from the dataset. Duplicate data causes skewness in the analysis results and overfitting to occur in AI models.
Goal : Identify and eliminate duplicate records or entries.
Tools : Python libraries (Pandas), SQL queries, Excel (Remove Duplicates function).
4. Standardising Formats
Converting the data across different features or variables to standardized formats or conventions. It would involve consistent units of measurements, date representation and coding schemes so that analyses and interpretations are easily done.
Goal : Ensure consistency in units, date formats and coding schemes.
Tools : Python libraries (Pandas), regular expressions for text processing, date parsing libraries (datetime module in Python).
5. Data Transformation
This involve transformations important for feature scaling, encoding of categorical variables, or other mathematical transformation that may be necessary to achieve good distributional properties of the data.
Goal : Prepare data for analysis by scaling, encoding, or transforming features.
Tools : Python libraries (Pandas, Scikit-learn), feature engineering techniques, scaling methods (MinMaxScaler, StandardScaler).
6. Handling Imbalanced Data
Balance a class that is highly outnumbered compared to any other class in a classification problem. Techniques for handling imbalanced data include resampling (e.g., oversampling or under sampling) and using algorithms that are robust to class imbalance.
Goal : Address class imbalance in classification tasks.
Tools : Python libraries (imbalanced-learn), resampling techniques (oversampling, under sampling), algorithm selection (XGBoost, Random Forest).
7. Data Validation and Quality Checks
Check data integrity and quality of data cleaned, cross-referencing against external sources, consistency checks, verification of data against predefined business rules or constraints.
Goal : Verify data integrity, consistency and quality.
Tools : Python libraries (Pandas), data profiling tools (D-Tale,pandas_profiling), custom scripts for consistency checks.
8. Documentation and Versioning
Document the cleaning procedure, steps performed, decisions taken and transformations applied in data cleaning. Version control provides a history of changes done over time in a dataset, hence this gives a reproducibility of data cleaning procedures.
Goal : Document the data cleaning process and maintain version control.
Tools : Version control systems (Git), documentation tools (Jupyter Notebooks, Markdown), project management platforms (Jira, Trello).
It thus follows that, through such a process, data prepared by effective cleaning will be AI-ready, improving quality and consistency in the usability of data within AI applications for both performance and reliability improvement of AI models.
How does Himcos help ?
At Himcos, we specialise in helping businesses harness the power of Gen- AI in their applications. From clearing data backlogs to refining app functionality to cleaning data, we ensure your business is primed for success in the AI era.
