Data is the foundation of machine learning (ML), which models learn and predict from. Selecting the proper approaches, preparation procedures, and algorithms requires understanding machine learning data types. We’ll discuss machine learning’s main data types and their role in algorithm training in this article.
Machine Learning Data Types
1.Structured Data
Structured data, like spreadsheets and relational databases, is arranged in tables with rows and columns. Each row represents a data input (e.g., a customer, transaction, or observation), and each column represents a data property or feature.
Features of Structured Data:
A tabular format: Data is usually structured into schema-defined tables.
Good relationships: Each data point has a definite set of qualities or properties.
Highly organized: SQL and other relational database systems can query and manage it due to its clear format.
Examples of Structured Data:
- Customer databases (phone numbers, addresses, names)
- Financial data (income, expenses, transactions)
Use in Machine Learning:
Structured data is commonly utilized in machine learning methods including decision trees, random forests, logistic regression, and support vector machines. Since they accept tabular input, these models can easily process structured data. Cleaning, normalizing, encoding categorical variables, and handling missing values are structured data preprocessing tasks.
2.Unstructured Data
Data without a format or organization is unstructured Data . It is harder to process because it does not fit neatly into tables or databases. Unstructured text, photos, music, and video are examples.
Features of Unstructured Data:
Undefined structure: Free text and multimedia are examples of data.
Lots of variety: Diverse data may require advanced extraction, modification, and interpretation methods.
Direct analysis is harder: Processing unstructured data requires specialized models and algorithms.
Examples of Unstructured Data:
- Text: Books, articles, social media, emails
- Images:Photos, diagrams, satellite imagery
- Audio: Speech, music, podcasts
- Video:Movies, security camera footage, tutorials
Use in Machine Learning:
Deep learning models like CNNs for pictures and RNNs or transformers for text process unstructured data. NLP and computer vision are used to extract relevant features from unstructured data. Tokenizing text, extracting picture characteristics, and spectrographing audio are examples of preprocessing raw unstructured input for machine learning models.
3.Semi-structured data
Between structured and unstructured data is semi-structured. Organizational features make it easier to examine than unstructured data, yet it lacks structure. Semi-structured data is commonly saved in JSON or XML, where tags or key-value pairs arrange the data, but the schema may vary.
Features Semi-structured data :
Partial organization: Semi-structured data is organized but not as rigorously as structured data.
Flexible schema:The schema can be changed to add attributes without interrupting the system.
Hierarchical format: Nested data is harder to query than structured data.
Semi-structured data examples:
- JSON files (web API or log data)
- XML files (product catalogs, metadata)
- NoSQL databases (MongoDB holds semi-structured documents)
Use in Machine Learning:
Semi-structured data is increasingly used in machine learning applications, such as web scraping, API answers, and social media analysis. After parsing and structuring this data, decision trees, random forests, and gradient boosting machines can be used. Parsing, managing missing values, and flattening nested structures into tabular data are common preprocessing steps.
4.Time-series Data
Serial observations, frequently at regular intervals, make up time-series data. Finance, healthcare, and environmental science use this data for temporal correlations.
Features of Time-Series Data:
Sequential: Data points are sorted chronologically and dependent on previous observations.
Timestamped: Each data point is timestamped.
Trends and seasonality:Time-series data may show trends (long-term rises or reductions) or seasonality.
Examples of Time-Series Data:
- Stock values over time
- Weather data (hourly temperatures)
- Wearable heart rate data is sensor data.
- Daily, weekly, and monthly website traffic.
Use in Machine Learning:
In machine learning, time-series data often necessitates specialized models such as ARIMA, LSTM, and RNNs. Moving averages, temporal delays, and seasonal components may be used in time-series feature engineering. Temporal dependencies must be managed properly for reliable forecasts.
5.Category Data
Categories or groupings are represented by categorical data. Non-numeric data with fixed-value variables is typical. Each category in categorical data is different, yet there is no intrinsic ordering or ranking.
Features of Categorical Data:
Separate values: Variables indicate classes or categories.
Nominal vs. Ordinal: There are two types of categorical data: ordinal (ordered) and nominal (no order).
Few categories: Categorical variables have fixed values.
Categorical Data Examples:
- Nominal: Color (red, blue, green), Country (USA, Canada, France)
- Ordinal: High school, bachelor’s, master’s, Rating (bad, fair, good)
Use in Machine Learning:
Preprocessing is needed because machine learning algorithms cannot process category input. Many approaches use one-hot or label encoding, which assigns binary vectors or numeric values to categories. Gradient boosting machines, decision trees, and random forests handle categorical data well.
6.Numeric Data
Numerical measurements are continuous or discrete. It is useful in many machine learning models, notably regression tasks that predict a continuous output.
Features of Numerical Data:
Continuous or discrete: Height and weight are continuous numerical data, while the number of children is discrete.
Displays quantities: The numerical relationship between values is that larger values are greater or less than smaller values.
Examples of Numerical Data:
- Continuous age
- Company staff count (discrete)
- Product price (continuous)
Use in Machine Learning:
Machine learning uses numerical data, especially in regression models to predict numerical outcomes. Normalized numbers have a mean of 0 and a standard deviation of 1. Support vector machines, neural networks, and linear regression function well with numerical data.
7.Imbalanced data
Classification datasets with unequal representation of classes or categories are called imbalanced data. In machine learning, algorithms may favor the dominant class and underperform the minority class.
Features of Imbalanced data :
Uneven class distribution: One class has many more samples.
Risk of bias:Unbalanced data may bias models toward the majority class.
Imbalanced data examples:
- Fraud detection (fraudulent transactions are rare)
- Medical diagnostics (rarer diseases)
Use in Machine Learning:
Oversampling the minority class (using SMOTE), undersampling the majority class, or modifying model class weights are common ways to handle imbalanced data. These methods can modify decision trees, random forests, and support vector machines to imbalanced data.
Conclusion
Selecting relevant models, algorithms, and preprocessing stages requires understanding machine learning data types. Structured, unstructured, semi-structured, time-series, category, numerical, and imbalanced data present unique challenges, yet machine learning may be used across domains with the correct tools. By matching the technique to the data type, accurate and efficient models can be created.