AI and ML for Predictive Analysis
Introduction:
What is Predictive Analysis?
It is a field in data analytics that uses historical or periodical data for understanding the trends and patterns or structure in data. By predictive it means to detect the future trend in the data. Predictive Analysis is used in fields like Business Forecasting, Customer Prediction, Fraud Detection and Risk assessment.
How AI is Used in Predictive Analysis?
Artificial Intelligence is a domain that helps in making and increasing the efficiency of outcome without using human intelligence. It utilizes various techniques and machine learning algorithm to predict or give intelligent decision on the task.
Workflow of Predictive Analysis:
• Data Collection and Preprocessing:
Data is necessary for training and learning the Machine Learning, for this data is collected in large chunks. Cleaning the data is an important task as the data needs to be structured in order to be able to use the data efficiently.
• Feature Engineering:
This step is indeed important to categorize only important and fields that really contribute to making drastic changes or prove itself in the data.
• Predictive Modelling:
AI and ML models are used for predicting the trends in data. For this Classification models and time series forecasting methods are used.
• Analysis and Evaluation:
After the model has processed the outcome this outcome is evaluated on basis of actual output and the expected output.
Applications of AI and ML Predictive Analysis:
Predictive analysis is useful to get the overall insights on the data without actually interacting with the data. Analysis is performed in the data, but the data is not changed. Predictive analysis has wide scope in many domain specifically in healthcare, business operations, inventory management etc. It is also used in Financial transactions like fraud detection, account prediction or anomaly detection.
Challenges in Predictive Analysis:
Analysis deals with data and hence the quality of data must be high to get the expected result. Hence quality of data can pose challenge to the analysis. Model complexity is an important challenge as AI and ML models are difficult to interpret. Bias and fairness is an important factor as many inaccurate or less trained model tend to be bias towards unproductive data and give wrong decisions.
Conclusion:
Prediction on data is important to understand the pattern in data on basis of AI or ML algorithm. Analysis is used to get valuable descisions on data. The quality of data is a major contributing factor of Predictive Analysis. It is used in many application like business analysis or pattern identification
