Predictive Analytics

What Is Predictive Analytics

Predictive analytics is a form of advanced analytics that combines historical data with statistical modeling, data mining techniques, and machine learning to predict future events. Companies use predictive analytics to detect risks and opportunities by finding trends in data. 

Big data and data science are frequently related to predictive analytics. Data from transactional databases, equipment log files, pictures, video, sensors, and other sources abound in today’s businesses. In order to gain insights from this data, data scientists use deep learning and machine learning algorithms to detect patterns and predict future events. Predictive analytics learning can then be applied to prescriptive analytics to guide actions based on predicted outcomes.

Examples of predictive analytics include:

1. Netflix’s content suggestions are driven by a complicated web of predictive analytics algorithms.

2. To detect fraud and prevent money laundering, financial services companies use big data and real-time statistical models to make relevant market comparisons.

3. To detect fraud and prevent money laundering, financial services companies use big data and real-time statistical models.

4. In order to manage the load and keep their grids adequately supplied, utility companies forecast how much energy their analytics will require.

A versatile platform for developing predictive analytics

Scalability

Data science and data engineering jobs should be automated. Train, test, and deploy models across various corporate applications in real time. Extend shared data science capabilities to hybrid and multicloud systems.

Speed

Pre-built applications and models are available from Speed Harness. You can help data scientists and business teams communicate and build models faster using cutting-edge IBM and open source tools.

Simplicity

Use a centralised platform to handle the complete data science lifecycle. Processes for development and deployment should be standardized. 

Tools for Predictive Analytics

1. IBM Watson Studio, a data science platform, assists in the successful implementation of AI by providing tools for preparing data and building models using open source code or visual modeling from anywhere.
2. IBM SPSS Statistics is a statistical analysis software that uses ad hoc analysis, hypothesis testing, geographic analysis, and predictive analytics to solve commercial and research challenges.
3. Visualisation software

With comprehensive algorithms and models that are ready for use, the IBM SPSS Modeler solution can help you access your data assets and current applications.

4. Solutions for decision optimization

Combining predictive insights from machine learning models with prescriptive analytics capabilities, IBM Decision Optimization improves outcomes.

Let’s get to the three main processes of creating a good time series model: 

ensuring that the data collected is stable, selecting the appropriate model, and assessing model accuracy The examples in this post are based on historical page views data from a well-known car marketing firm.

Obtaining Stationary Data for Forecasting

Time series forecasting makes use of data that is indexed by similarly spaced time intervals (minutes, hours, days, etc.). Many time series data sets have a seasonal and/or trend aspect due to the discrete nature of time series data.

Step 2: Create a Time Series Model

After the data has reached a state of equilibrium, the next step in time series forecasting is to create a base level prediction. It’s also worth noting that the majority of the Entry-level predictions. This requires the first step of making your data stationary. This is only necessary for more complex models like ARIMA modelling.

Step 3: Choose an Accuracy Statistic for the Model

Calculating the Mean Absolute Percent Error (MAPE) is a quick and easy way to assess the overall forecast accuracy of a model. 

Predictive analytics is an excellent technique to improve the understanding and clarity of your company’s decisions. Even if it takes a long time to collect valuable data and establish a strategy for sorting through it, it will be worthwhile when you see the consequences of what it can achieve.