The first stage in Predictive AI is gathering relevant data, forming the entire process’s foundation. This data can come from various sources, such as transaction records, customer interactions, sensors, and online activities.
Once data is collected, it needs to be prepared for analysis. This stage involves cleaning and transforming the data into a usable format. Data cleaning includes handling missing values, removing duplicates, and correcting errors.
The prepared dataset is split into two parts: training & test sets. The training set is helpful to build and train the predictive model, while the test set is used to evaluate its performance.
The choice depends on the nature of the prediction task (classification, regression, etc.) and the data characteristics. Commonly used models include linear regression for continuous outcomes, logistic regression for binary outcomes, decision trees, random forests, and neural networks.
During this stage, the chosen model learns from the training data set. The model attempts to find patterns & relationships within the data that it can use to make predictions. This process involves adjusting the model’s parameters to minimize prediction error.
After training, the model is being tested using the test dataset. This stage assesses the model’s performance and accuracy. Metrics such as Mean Squared Error (MSE) for regression tasks or Accuracy, Precision, and Recall for classification tasks are used. If the model’s performance is unsatisfactory, adjustments are made.
Once the model is adequately trained and tested, it’s deployed into a production environment where it can start making real-world predictions. This step might involve integrating the model into existing business systems or processes.
The final stage involves continuously monitoring and updating the model’s performance as necessary. Since the real-world data can change over time, the model might need retraining with new data to maintain its accuracy and relevance.
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