The name “predictive” means to predict. Predictive data analytics is used to analyse growth and changes in the future by using advanced technologies such as machine learning, artificial intelligence, big data, statistical models, etc.
It uses machine learning algorithms to predict future outcomes based on available information. Data scientists use the models and their data to find the pattern that signifies the future outcome. The model is trained on available data sets and by learning the relationships and existing patterns inside the data model.
Predictive data analytics is used in various fields, such as marketing, healthcare, finance, insurance, retail, etc. It helps businesses actively respond to changing market conditions.
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What is Predictive Analytics in Machine Learning?
Predictive Data Analytics is a part of data analytics that uses advanced technologies like machine learning, artificial intelligence, statistical modelling, and big data to assess outcomes and performance in the future based on the available datasets. It is used for predicting weather forecasts, translation, decision-making, video games, etc.
How Predictive Data Analytics Work?
Predictive data analytics uses advanced technologies like machine learning concepts to predict the outcome based on a trained model. It uses historical data and identifies the common pattern inside the model to make predictions based on future outcomes. Let us keep an eye on the important steps to implement predictive Data analytics in machine learning.
- We first need to define our problem statement and our objective.
- Data is very important for predictive analytics. We can collect data from various sources, such as spreadsheets, databases, social media platforms, text, documents, etc. Collecting a good amount of data is important for the predictions to be accurate.
- We then clean and process the collected data.
- It is followed by feature selection, which involves selecting the right variables. It ensures the efficiency of the model.
- Now, we select the proper model by using decision trees, linear regression, neural networks, etc.
- After selecting the appropriate algorithm, we train the model and generate predictions based on the input. We will analyze the report by making a graphical representation.
- If our model shows the predicted outcome, then our model is good, and it is ready to make predictions based on the input.
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Predictive Analytics Techniques
There are many predictive Data analytics techniques used to calculate the outcome based on the predefined data. Let us know about a few of the important techniques below.
1. Decision Trees
Decision trees are a supervised learning predictive Data analytics technique that represents the model in a tree-like structure used for machine learning and classification. The branch tree represents the decision, and the nodes represent an attribute. Decision trees are easy to understand, and the users have choices with more than one choice at each node. Handing missing values is easy to find using a decision tree.
2. Logistic Regression
A Logistic Regression model is used to predict the relationship between independent variables. It is used to find a common pattern in large data sets. In regression, we predict the outcome of one independent variable with the help of another variable. The main objective is to find a common formula for finding the relationship between all the inputs in the data set.
3. Neural Networks
Neural networks are used to study the workings of human brains. It uses predictive analysis, pattern recognition and artificial intelligence to discover the identity of the complex connections inside the human brain.
It is used in image recognition, predictions, etc. The Neural Network uses deep learning Predictive analytics techniques to aid in Natural Image Processing (NIP). They consist of interconnected nodes arranged in layers to represent a pattern from the extensive data set.
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Predictive Data Analytics Tools
Predictive data analytics uses available data to predict the outcome. With the help of these datasets, it generates a probability of the occurrence of a certain events. There are several tools used in predictive analytics. Here, we will learn about some important predictive data analytics tools.
- IBM SPSS: This is a data modellling tool that is used for statistical analysis. It studies both organized and unorganized data. This software is used to collect unstructured data for training purposes.
- SAS Advanced Analytics: SAS offers many tools for data analytics work. You can choose from a variety of advanced tools.
- Rapid Miner Studio: It is a data science platform used to provide a user-friendly interface to build predictive models. There are many tools for data preparation and uses many machine learning algorithms.
- BigML: It is a cloud-based machine learning platform that is used to provide a simple interface to deploy predictive models. There are many automation features available on this platform, which supports many machine learning algorithms.
- Python or R: They are open-source programming languages that are used for statistical computing and graphics. Many features, like modelling, graphical techniques, libraries, etc., are present to provide a user-interactive environment for development.
- KNIME: KNIME stands for Konstanz Information Miner, used to design workflows, data analytics, preprocess data, and build models using a wide range of advanced algorithms.
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Predictive Data Analytics Examples
1. Software Testing
With the help of the predictive data analytics process, software testing becomes easy. Predictive analytics in software testing can be implemented to know how the delays in a project will affect the project outcome. Making corrections becomes easy after knowing the root cause.
2. Retail
Retail Market uses predictive data analytics techniques to predict the customer who buys a specific product and what similar things they can buy. This helps boost their sales value with a single customer. It also helps to analyze which product will perform better in the market under certain conditions. It also helps to provide customers with better offers that will attract them to buy the product. However, predictive data analytics is making retail better and increasing its efficiency.
3. Banking and Financial Sectors
The banking and finance sectors have been using predictive analytics in some form for a long time to minimize risk, failure, and fraud attempts and maximize opportunities and value for customers.
4. Healthcare
Predictive analytics in healthcare is used to detect the risk of any severe disease and help us study the effects of the disease, the risks involved, the effects, etc. It helps to avoid ineffective treatment and invest in the best treatment possible. Predictive data analytics helps to study symptoms or diseases and predict their effects in the near future, which helps healthcare generate possible treatments with proper decision making of possible care.
5. Customer Service
Predictive data analytics is often used in customer service, like predicting market trends, and requirements of the customer, and making the products properly aligned with the user’s needs. Predictive analytics helps to decide the right customer needs and calculate the funds, capital and employees required to complete the demand.
6. Insurance Company
The insurance company uses predictive analytics to calculate the optimal policy to give the best results in the future. Along with that, it helps to calculate the risk involved, manage claims, prevent fraud, improve the customer experience, and streamline operations.
7. Weather Forecasting
Predictive data analytics helps predict the weather by reading the available information and analyzing the data insights. It uses machine learning tools to generate the output and produce values. It is used in more forecasting work, such as maintenance forecasting, etc.
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Predictive Data Analytics FAQs
Q1. What is predictive learning analytics?
Ans: Predictive Data Analytics is a part of data analytics that uses advanced technologies like machine learning, artificial intelligence, statistical modelling, and big data to assess outcomes and performance in the future based on the available datasets.
Q2. What are some examples of predictive data analytics?
Ans: Some of the important examples of predictive analytics are Software testing, banking, finance, healthcare, insurance companies, etc. Check the article for more details.
Q3. What are the four levels of Predictive data analytics?
Ans: The four steps in predictive data analytics are describe, diagnose, predict, and prescribe.
