Updated March 8, 2023
Definition of Predictive Analysis
Predictive Analysis is, analyzing data using Machine Learning, Statistical Algorithms, and other Data Analysis techniques to predict future events. By taking past data or raw data as input and then applying several Predictive Analysis algorithms to provide clean data so that, we can predict the future results to the same data by analyzing the obtained clean data.
By using the results obtained in the historical and transactional data, we can predict future profits and losses. Before diving into algorithms on Predictive analysis. Let’s have a look at the structure of Predictive analysis and how to build an efficient predictive model.
Predictive Analysis Structure
Predictive analysis structure is given below:
- Defining a Project: Identify the given data set and the algorithms that we need to perform on it and define its objective.
- Collection of data: If the data is given along with the problem statement then you don’t need to surf for the data sets, but if you are not given any dataset with the statement you need to look out through the internet for the required data which matches with your problem statement. This process of collecting data from various sources is known as Data Mining.
- Analyzing the Data: Now, the data you have is raw data or unstructured data which needs to be cleaned and transformed to form structured data for analysis to discover useful information and to arrive at conclusions.
- Statistical Analysis: This analysis is needed to make hypotheses, assumptions, and tests on our data and we will treat outliers using statistical models.
- Outlier Treatment: An Outlier is an observation that lies along with other observations in the dataset which shows an abnormal distance from the other values in the dataset. It is an object that shows more deviations when compared to other observations.
For example, Let’s take a small sample dataset of 10 numbers
- 0, 14, 17, 3, 27, 30, 15, 29, 8, 19, 10000
In this case, the outlier is 10000 which lies far away from the other values when drawing a graph for this dataset, 10,000 shows more deviations than the other observations.
- Predictive Modeling: Predictive Modeling helps us to create accurate predictive models for future analysis.
- Deploying the Model: After building our model we need to deploy it for results and output to automatically make decisions on our Predictive model.
- Managing the Model: After deploying our model, we need to manage and monitor it to review the model performance and to ensure that it is giving expected results.
Algorithms in Predictive Analysis
The widely used algorithms in Predictive Analysis are:
- Linear Regression
- Logistic Regression
- Neural Network
- Decision Trees
- Naive Bayes
1. Linear Regression
Linear Regression falls under the category of Supervised learning in which the variable which needs to be predicted is known as the dependent variable and the variable through which we are predicting the dependent variable is known as the independent variable.
The data which we have collected through the data mining process will contain in a CSV file which is, then uploaded into the Jupyter Notebook in which we will perform Predictive Analysis, then by using ML algorithms we will perform actions on our data. The first step includes reading the data and performing some basic Exploratory Data Analysis and then we will train the dataset for future predictions.
2. Logistic Regression
Logistic Regression is used to predict a dependent variable by analyzing the relationship between one or more existing independent variables. This model can take into consideration many input criteria. Based on earlier results of the dependent variable, we will predict the future results of the independent variable by using the probability of falling into the particular outcome category. The main difference between Logistic and Linear Regressions is Logistic Regression is used when the response variable is categorical such as yes/no, true/false while Linear Regression is used when the response variable is continuous such as hours, height and weight.
3. Neural Network
Neural Network Algorithm is developed by considering the human brain that takes a set of units as input and transfers results to a predefined output. It tries to predict the dependent variable in a way a human brain would. A Neural Network for prediction is made by taking a web of input nodes, an output node, and a hidden node present between the two nodes. The hidden layer between the two nodes is what makes this prediction technique unique and efficient than other predictive tools. Every time data passes through the web the algorithm incorporates the data that passes through it by giving weights to the nodes in the hidden layer.
4. Decision Trees
The decision tree is an important algorithm in Predictive modeling techniques in which we can visually represent decisions. Based on certain conditions we will conclude all possible outcomes by using branching methodology.
Decision Trees are classified into two types
- Classification trees
- Regression trees
The classification tree is used to separate a dataset into different classes when we expect response variable categorical in nature.
The Regression trees are used when the response variable is numerical or continuous. A decision algorithm builds a decision tree which is used to represent classification rules. The leaves of the tree in the Decision Tree are the Predicted decisions.
5. Naive Bayes
This algorithm works on Baye’s probability theorem or alternatively known as Baye’s rule or Baye’s law. It is a simple algorithm that is known for its effectiveness to quickly build Predictive models and make predictions by using these models and algorithms.
Applications of Predictive Analysis
Predictive Analysis is used in various fields to predict future outcomes for particular conditions. Some of the applications include:
- Fraud Detection
- Health Care
- Customer Targeting
- Sales Forecasting
- Assessing Risk
Conclusion
- Predictive Analysis is analyzing data using Machine Learning, Statistical Algorithms, and other Data Analysis techniques to predict future events
- Predictive Analysis structure includes defining a project, Collecting data, Analyzing the data, Statistical Analysis, Predictive Modeling, Deploying the Model, and Managing the Model.
- The widely used Predictive modeling algorithms are Linear Regression, Logistic Regression, Neural Network, Decision trees, and Naive Baye’s models.
- Some of the Applications of modeling include Fraud Detection, Health Care, Customer Targeting, Sales Forecasting, and Risk Assessing
Recommended Articles
This is a guide to Predictive Analysis Algorithms. Here we also discuss the definition and predictive analysis structure along with algorithms. You may also have a look at the following articles to learn more –
