## Blog > How to Select a Predictive Modeling Technique?

**What is Predictive Analysis?**

Predictive analysis is the branch of data analysis that is mainly used to predict future events or outcomes. It is solely based on data-driven approaches and techniques to reach conclusions or solutions. The analysis mainly uses analytical techniques and predictive modeling to find relevant patterns in large data sets; in turn, these patterns can be used to make various opportunities in the businesses by identifying the risk and benefits. The predictive Modeling Technique is an anticipatory technique for forward-looking solutions and insights to assess any situation.

Most of the processes in predictive analysis incorporate machine learning terminologies and algorithms for model building, especially to train the models.

**How to Choose a Correct Predictive Technique?**

It is significantly important to understand how to choose a correct predictive technique for model building.

**Predictive Analysis Techniques**

**Regression**

The primary role of regression is the construction of an efficient model to predict the dependent attributes from a bunch of attribute variables. A regression problem is when the output variable is either real or a continuous variable that can be weighed, area, or salary. Regression can also be defined as a statistical means that is used in applications like housing investing etc.

It is used to predict the relationship between a dependent variable and a bunch of independent variables and a simple linear regression technique in which the independent variable has a linear relationship with the dependent variable. It is a technique to analyze a data set with the dependent variable and one or more independent variables to predict the outcome in a binary variable, meaning it will have only two outcomes, and the dependent variable is categorical.

**Logistic Regression**

It is a special case of linear regression where only one needs to predict the outcome in a categorical variable. It predicts the probability of the event using the log function.

**Classification**

Classification is a process of a given set of data into classes, and it can be performed on both structured or unstructured data. The process starts with predicting the class of given data points, and classes are often referred to as target labels or categories. The classification predictive modeling approximates the mapping function from input variables to discrete output variables. The main goal is to identify which class or the category where the new data will fit into. For example, a heart disease detection can be identified as a classification problem, and it’s a binary classification since there can only be two classes with heart disease or do not have heart disease.

So, in this case, the classifier needs training data to understand how the given input variables are related to the class. Once the classifier is trained accurately, it can detect whether heart disease is there or not for a particular patient since the classifier is also a type of supervised learning; even the targets are also provided with the input data.

**Clustering**

Clustering means dividing data points into homogeneous classes or clusters. The points in the same group are as similar as possible, and then the points in different groups are as dissimilar as possible. So, when a collection of objects is given, the object will be grouped based on similarity.

**Time Series Model**

The Time series model comprises a sequence of data points captured using time as the input parameter. It uses the last year of data or previous data to develop a numerical metric. It predicts the data using that metric to understand a singular metric is developing over time with a level of accuracy beyond simple averages.

**Forecasting**

Forecasting is nothing but using the historical data to make predictions or numeric predictions on new data based on the learning from the previous.

**Choosing Predictive Analysis Technique**

Before choosing the best predictive analysis technique for a project, first understand some important points such as:

**Problem Statement**

Before building a model, first, we need to understand the problem statement, which helps to understand what kind of target result is required. For example, we have a problem statement to decide if a patient has heart disease or not.

So this problem statement is categorical, and it will have only two values: one has heart disease or does not have heart disease. In this particular example, we can use the classification technique to model this data, but there are few problems where it is difficult to choose a target variable.

**Target Variable**

If the target variable is continuous, we can choose regression analysis, and if the target variable is categorical, we can use classification analysis. And if the target variable is identified we can also go for clustering analysis.

**Linearly Separable Data**

There is no direct way to determine linearly separable data; we can determine it by choosing different models or comparing them.

**Size of The Data**

The size of data helps to determine the possibility of overfitting and underfitting a model. Also, some models may not work efficiently with small data, so these are some deciding factors for choosing a model before training a model with the training data.

**Machine Learning Models for Predictive Analysis**

**Linear Regression**

Linear regression is to be used when a target variable is continuous, and the dependent variable or variables are continuous or a mixture of continuous and categorical. The relationship between the independent variable and the dependent variable has to be linear.

**Logistic Regression**

It does not require a linear relationship between the target variable and the dependent variables. The target variable is the binary assuming value of either 1 or 0.

**Neural Networks**

The Neural networks help to understand or help to cluster and classify the data.

**K-Means Clustering**

K-Means involves placing unlabeled data points in separate groups based on similarities, and this algorithm is used for the clustering model.

**Decision Trees**

The decision tree is a map of possible outcomes of a series of related choices; it allows an individual or organization to weigh possible actions against one another based on their costs, probabilities, and benefits. It is useful to drive informal discussion or map out an algorithm that predicts the best choice mathematically.

**Time Series**

The time series regression analysis is a method for predicting future responses based on response history. The data for a time series should be a set of observations on the values that a variable takes at different points in time.

**Predictive Analysis Applications**

- It helps finance identify risk detection or the risk detection model to identify any fraudulent transactions or loans.
- It helps to forecast predictions that can be taken into account for different stocks. Stock prediction is a valuable application of predictive analytics in finance.

- Predictive analysis is also working to predict the weather, campaigning, and disaster management.
- We can also use predictive analysis in health care to detect diseases such as cancer or heart elements that may or may not be a part of a person’s life several years later. So, it helps to predict all these diseases and conditions at an early stage.
- It is also helpful in manufacturing to identify production failures.
- Telecom industries use predictive analysis for customer support to segregate or segment the customers into groups that they can work on based on their predictions.

*Author: SVCIT Editorial*

*Copyright Silicon Valley Cloud IT, LLC.*