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Prediction System

Prediction systems are one of the major applications of artificial intelligence. Prediction systems deal with the uncertainty of the event. Prediction systems eliminate the guesswork in the decision making and forecasting the future trends. Predictive analytics solutions systems are based on past data or past trends. Forecasting is based on time-series methods and it required time-series data. Various techniques used for predictions include Linear regression, logistic regression, vector autoregression. These models are included in Machine learning which can use for commercial usage.
Predictive analytics solutions needs a proper understanding of mathematical and statistical principles that underlie modern data techniques. Webtunix has data behavior analysis team who are mathematicians, statisticians and computer scientists for the consumer.

Why Prediction systems are used?

Various business intelligence software like fuzzy logic emerges as an advantageous in predicting future events. Objective type of fuzzy modeling is used to build a prediction system and improve its efficiency while subjective fuzzy modeling is used to develop necessary input for the prediction system. In stock market prediction subjective fuzzy modeling is applied which helps to take a better-personalized decision, enabling customers and business for their better management.

Our technology allows to build Fully automated predictive behavior modeling system which acquires data, make decisions based, and execute transactions based on those decisions. Data Science helps us to build customer behavior predictive models of markets and behavior of financial instruments traded in markets. Prediction models fall into three wide categories are technical analysis, fundamental analysis, technological methods. Webtunix AI is a business intelligence software company that deals with building prediction systems for various industries.

Webtunix AI has provided solutions for various industries. Our prediction system applications includes:

  • Stock Market prediction systems in Finance
  • Betting prediction systems / horse racing prediction system
  • Sports gaming prediction system
  • Medical Prediction Systems
  • Customer Behaviour Prediction Systems
  • Power generation and energy consumption prediction
  • Thermal performance prediction systematic

Following steps are taken to build a prediction system.

1. Data Mining :
This is the first step in making a predictive analytics solution system. As we all know that Machine Learning Algorithms require a huge amount of data for training. This big data makes the base for a good prediction system. The aim is to gather a huge amount of data from different reliable sources. One thing to be kept in mind is that data should be of high quality. This is because low data quality can impact the result of machine learning considerably. Data mining is done with the help of web scraping tools build using AI Machine Learning. With web scrapping tools, you can collect huge data from different sources in one place.

2. Building Database :
The data you get from the previous step is in the unstructured format. In this step, we convert this data from unstructured to a structured form. Structured data is understandable by Machine Learning Algorithms. Various tools are available in the market that helps you in building the database schema for data insertion. Now, your data set is ready and you can proceed to the next step.

3. Understanding the Data :
After the data has been collected, the next step is to understand the data. The scale or level of detail in this set of data has to be taken into consideration. Machine Learning Applications has made it easy to get a better insight into the data.

4. Preparing Data :
Preparing the data includes creating features of the subset. For example, categorizing the features into match oriented features and external factors features. Other features are also to be considered but they are based on the Machine Learning algorithms that which features are to be considered or which features should not be considered. You can check your data by comparing your data selection features of Machine Learning and business intelligence software solutions with that of the expert's data selection features.

5.Modeling :
The next step is to model the data on the basis of selected items. This includes predicting the past behavior, analyzing which model results were correct for teams in the past. Thus by previous results and testing and training, most appropriate AI Machine Learning Model is selected.
Some of the commonly used Models are:

  • K nearest neighbors
  • Logistic Regression
  • Random Forest
  • Support Vector Machine

6. Evaluate Model Performance :
The next step is to evaluate the performance of the model. If there is a large deviation from, the original results, then there is a need to change the model that is used and again re-evaluating the model performance.

7. Testing and Training :
The next step is to train and test the machine learning applications. Cross-validation involves shuffling of data and therefore is not appropriate for sports results prediction. The more appropriate approach would be analyzing results in every round and following each and every season in order. Average of results can thus be obtained that help in evaluating model accuracy.

8.Deploy the Model :
AI Machine Learning helps in deploying the model through automation. New data can be obtained from the web by web scraping tools, this data can be inserted into the database using artificial intelligence systems. Training and testing of data can be adjusted according to the past results using predictive analytics solutions. The machine learning models can improve over the time as more and more data is fed into the system. Prediction results using Machine Learning Applications are found to be 70 percent accurate.

9. Big data bridging gap between machine and human prediction
One of the applications of business intelligence software is to bridge the gap between machine and human prediction. Machine Learning has helped in predicting the results to a great extent. Further research in Artificial Intelligence can lead in predicting the results with more accuracy. Machine Learning applications have helped in predicting results.

Machine learning and predictive analytics solutions have proved to be a powerful tool as a prediction of outcomes on basis of historical data is done in an efficient manner. Since our Artificial Intelligence consulting Company develops models with the intent of delivering maximal predictive accuracy. We have built models for the prediction of stocks, horse race, betting and sentiment analysis.

By collecting big data, properly analyzing the data, and using the appropriate data model, we can come close to predict the results. Webtunix AI is a business intelligence software company that offers the solutions for predicting the results in various industries. This gives time for the organizations to make policies be prepared well before time.