Which Type of Data Is Used for Predictive Analytics?


Larry Thompson

Which Type of Data Is Used for Predictive Analytics?

Predictive analytics is the practice of analyzing historical data to make predictions about future events. It involves using various statistical techniques, machine learning algorithms, and data mining methods to uncover patterns and trends in the data that can be used to forecast outcomes.

Types of Data

When it comes to predictive analytics, there are three main types of data that are commonly used:

  • Numerical Data: Numerical data, also known as quantitative data, consists of numbers that represent quantities or measurements. This type of data includes variables such as age, income, temperature, and sales figures. Numerical data is often used in predictive models to identify correlations and relationships between different variables.
  • Categorical Data: Categorical data, also known as qualitative or nominal data, consists of categories or labels that represent different groups or classes. Examples of categorical variables include gender, ethnicity, product categories, and customer segments.

    Categorical data is often used in predictive analytics to classify or assign objects into different groups based on their characteristics.

  • Time-Series Data: Time-series data consists of observations collected over a period of time at regular intervals. This type of data is used to analyze trends and patterns that occur over time. Examples of time-series data include stock prices, weather conditions recorded hourly or daily, and website traffic measured every minute. Time-series analysis is commonly employed in predictive analytics to make forecasts based on historical patterns.

Data Preparation

Before performing any predictive analytics tasks, it’s important to clean and preprocess the data. This involves handling missing values, removing outliers, normalizing numerical variables, and encoding categorical variables. Data preprocessing ensures that the data is in a suitable format for analysis and reduces the risk of errors or biased results.

Once the data is prepared, it can be divided into a training set and a test set. The training set is used to build the predictive model, while the test set is used to evaluate its performance. Splitting the data allows for an unbiased assessment of how well the model generalizes to new, unseen data.

Predictive Modeling Techniques

There are several predictive modeling techniques that can be applied to different types of data:

Regression Analysis

Regression analysis is used when the Target variable is continuous. It involves fitting a mathematical equation to the data that best represents the relationship between the input variables and the Target variable. Regression models are commonly used for forecasting sales, predicting housing prices, and estimating customer lifetime value.


Classification techniques are used when the Target variable is categorical. These models assign input variables to predefined classes or categories based on their characteristics. Classification algorithms are widely used in spam detection, sentiment analysis, and credit scoring.

Time-Series Forecasting

Time-series forecasting models are specifically designed to analyze time-dependent data and make predictions about future values. These models take into account trends, seasonality, and other patterns observed in historical time-series data. Time-series forecasting is commonly used in financial forecasting, demand planning, and stock market prediction.


Predictive analytics relies on different types of data such as numerical, categorical, and time-series data. By understanding these types of data and applying appropriate techniques, organizations can gain valuable insights and make informed decisions about future events. Remember to preprocess your data before building predictive models to ensure accurate and reliable results.

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