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Your Go-To Tutorial for Machine Learning Data Prep is here/ai-insights/your-go-to-tutorial-for-machine-learning-data-prep-is-here

Your Go-To Tutorial for Machine Learning Data Prep is here

August 21, 2023

Your Go-To Tutorial for Machine Learning Data Prep is here

With the tech generation going on and AI taking over the functional domains, making everything easier and swifter at the point of fingertips, AI professionals are in high demand. But all this is ruled by Machine Learning Algorithm. With tons of data generated daily that pulls off the entire intelligent framework, preparing these data for proper machine learning input is essential to produce productive output. But before acquiring a career as an AI Engineer, it's vital to have a comprehensive knowledge of the critical function of data preparation for machine learning.

1. Introduction to Data Preparation

  • 1.1 What is Data Preparation?

    Turning raw data into usable form making it suitable for machine learning models, is known as Data Preparation. It involves cleaning, organizing, and transforming data to ensure quality and consistency. AI professionals must understand the importance of data preparation to create accurate and reliable machine learning models, as poorly prepared data can lead to incorrect or misleading results.

  • 1.2 Importance of Data Preparation

    Data preparation is crucial for the following reasons:

    • Eliminate errors, inconsistencies, and inaccuracies, improving machine learning model performance.
    • Reduces the time required to train ML models by ensuring the data is in the correct format.
    • Understands and explores the data, making it easier for AI professionals to identify patterns and trends.

2. Steps in Data Preparation

The following steps make up the process of preparing data for machine learning:

Data Collection:

  • 2.1 Identifying Data Sources

    Data preparation begins with data. Identifying the relevant origin of data collection that can be produced as input for the machine learning process is of primary importance for AI professionals. They can be data from the organization, i.e., the internal data, or outsourced from third-party sources.

  • 2.2 Data Acquisition

    Once the data sources are identified, the next step is to acquire the data. Data acquisition involves extracting, downloading, or scraping data from recognized authorities. AI professionals should ensure the data is collected in a structured format, such as CSV, JSON, or XML, to facilitate data preparation.

  • 2.3 Data Storage

    After acquiring the data, storing it in a suitable format is essential to ensure easy accessibility and processing. AI professionals can opt for databases, cloud storage, or local storage, depending on the size and complexity of the data.

3. Data Cleaning

  • 3.1 Handling Missing Values

    Missing values are shared in datasets, leading to inaccurate machine-learning model predictions. AI professionals can handle missing values in one of the following ways:

    • Imputation
    • Deletion
  • 3.2 Removing Duplicates

    Duplicate data can lead to biases in machine learning models. AI professionals should identify and remove identical instances from the dataset to ensure the model's accuracy.

  • 3.3 Outliers Detection and Treatment

    Outliers are data points that deviate significantly from the rest of the dataset. They can negatively impact the performance of machine learning models. AI professionals can detect outliers using statistical techniques like Z-score, IQR or visualization methods like boxplots. Once detected, outliers can be treated by removing or transforming them using suitable techniques.

4. Data Integration

Data integration is merging information from several sources into a single, unified dataset. AI professionals must ensure that the integrated data is consistent and discrepancies-free. This can be done by employing methods like:

  • Data Mapping
  • Data Transformation
  • Entity Resolution

5. Data Transformation

  • 5.1 Feature Scaling

    Feature scaling is a critical step in data preparation, ensuring all features have the same range of values. This can improve the performance of machine learning models, especially those that rely on distance-based calculations. AI professionals can use Min-Max Scaling, Standard Scaling, or Log Transformation for feature scaling.

  • 5.2 Feature Encoding

    Machine learning models require features to be in a numerical format. AI professionals must convert categorical features into numerical values using One-Hot Encoding, Label Encoding, or Binary Encoding.

  • 5.3 Feature Selection

    Feature selection involves selecting the most relevant features for the machine learning model. AI professionals can use Filter, Wrapper, or Embedded Methods to choose the best features contributing to the model's predictive power.

6. Data Reduction

Data reduction involves reducing the dataset size without compromising its quality or integrity. AI professionals can use data reduction techniques such as:

  • Dimensionality Reduction
  • Sampling

7. Data Splitting

Data splitting involves dividing the dataset into training, validation, and testing sets. AI professionals should ensure that the data is split in a way that maintains its representativeness and prevents overfitting or underfitting. Standard data-splitting techniques include:

  • Random Split
  • Stratified Split

8. Validation and Iteration

AI professionals should validate the data preparation process by training and evaluating machine learning models on the prepared data. If the models do not perform as expected, AI professionals can iterate the data preparation process, adjusting and improvements until the desired performance is achieved.

Conclusion

With no doubt in mind, in the Machine Learning Process, data preparation is one of the most critical steps.AI Professionals can be sure that the data they have provided is perfect, flawless, consistent, and ready for use in machine learning models, with proper data preparation. This can give them different results in organizational productivity and triumphant career growth, and they can make the most of their Artificial Intelligence Certification or Machine Learning Certifications.

Investing time and effort in data preparation can significantly improve the performance of ML models, paving the way for success in the AI industry. So, take the time to learn and apply the techniques outlined in this guide to enhance your skills as an ML Engineer and set yourself apart in the competitive world of AI.