1) Define goals. Before proceeding with model development, it’s essential to have a well-defined business question or problem that needs to be addressed. This means that you should identify what you want to predict precisely. Having a clear understanding of the desired project outcome will help you determine the necessary data and enable your predictive model to produce an actionable result.
2) Build team. Although new tools have made predictive modeling more accessible, it is still important to have a team with five critical members:
- An executive sponsor who can secure funding and prioritize the project.
- A line-of-business manager with a deep understanding of the business problem you want to solve.
- A data wrangler or someone with expertise in data management to clean, prepare, and integrate the data. Although modern analytics and BI tools often have data integration capabilities.
- An IT manager responsible for implementing the appropriate AI analytics infrastructure.
- A data scientist to build, refine, and deploy models. However, with the rise of AutoML tools, data analysts can now perform these tasks if the prediction model is not too complex.
3) Collect and prepare data. Now you’re ready to gather relevant data from various sources. This includes structured data like sales history and demographic information, as well as unstructured data like social media content, customer service notes, and web logs. Once you have all the data, your team will preprocess it to clean, transform, and normalize the data to remove any noise or inconsistencies. To properly prep your data, follow these steps:
- Correctly label and format your dataset.
- Ensure data integrity by cleaning up incomplete, missing, or inconsistent data.
- Avoid data leakage and training-serving skew.
- Review your dataset after importing to ensure accuracy.
Since you'll likely be working with big data, including real-time streaming data, you'll need the appropriate tools. Cloud data warehouses can now provide the necessary storage, power, and speed at an affordable cost.
4) Select predictors. This step, called feature engineering, is when you choose and create relevant features (predictors) that can help improve the accuracy of your predictive model. You want to transform raw data into meaningful features that capture the underlying patterns and relationships in the data. Some techniques you can use include data exploration, scaling, normalization, dimensionality reduction, encoding categorical variables, creating new variables through mathematical operations, and feature selection based on statistical tests or domain knowledge. Your goal is to extract the most informative features that can help the model learn the underlying patterns in the data and make accurate predictions.
5) Choose model. To select the predictive modeling technique for your problem, you need to consider the type of data you have and the specific problem you’re trying to solve. Some models work better for certain types of data than others. For example, if you have a lot of numerical data, you might consider linear regression or a decision tree model. If you have image data, you might consider a convolutional neural network.
It's also important to consider the complexity of the model and the interpretability of its output. If you need explainable AI (being able to understand the relationship between the input features and the output prediction), you might want to choose a simpler model like linear regression. If you need a highly accurate prediction and explainability is less important, you might consider a more complex one like a deep neural network.
Ultimately, the best way to select an appropriate prediction model is through experimentation and evaluation. Try out different models and compare their performance on a validation set or through cross-validation. Choose the one that gives you the best accuracy and meets your specific needs for interpretability, complexity, and performance.
6) Train model. Once you’ve selected the appropriate model, the next step is to optimize its parameters and fine-tune it for accuracy. This involves finding the best set of parameter values that will result in the highest accuracy on your training data.
To optimize the parameters, you can use techniques like grid search or randomized search, which involve systematically testing different combinations of parameter values and evaluating their performance. Once you’ve found the optimal set of parameters, you can fine-tune the model by adjusting the learning rate or regularization to improve its accuracy further.
It's important to validate the performance of the optimized model on a validation set or through cross-validation to ensure that it is not overfitting to the training data. Overfitting can occur when the model is too complex and fits too closely to the training data, resulting in poor performance on new data.
7) Evaluate model. To evaluate the performance of your model, you can use a validation set or cross-validation. This involves testing the model on a separate dataset that was not used for training, to ensure that it can generalize well to new data. With a validation set, you can split your data into a training set and a validation set. You can train your model on the training set and then evaluate its performance on the validation set. You can use metrics like accuracy, precision, recall, and F1 score to assess the model's performance and refine it if necessary.
With cross-validation, you can partition your data into multiple folds, train the model on each fold, and then evaluate its performance on the remaining folds. This allows you to test the model's performance on different subsets of the data and reduce the risk of overfitting.
Based on the results of the evaluation, you can refine your model by adjusting the hyperparameters, selecting different features, or choosing a different model altogether. By iteratively evaluating and refining your model, you can improve its performance and make it more effective for making accurate predictions on new data.
8) Adjust hyperparameters. Hyperparameters are parameters that are set before training the model, such as the learning rate, regularization strength, or the number of hidden layers in a neural network. To prevent overfitting and improve the performance of your predictive model, you can adjust these hyperparameters. Techniques like grid search or randomized search can help you find the optimal hyperparameter values. Validating the performance of the optimized model on a separate test set is crucial to ensure its generalization ability.
9) Validate model. You’re almost there! Your last step before deployment is to measure the final performance of your model and verify that it meets the desired accuracy and other requirements. Here you use a test set, which is a separate dataset that was not used for training or validation and is used to evaluate the model's performance on unseen data. It's important to ensure that the test set is representative of the data your model will encounter in the real world. This means that the distribution of the test set should be similar to the distribution of the data the model will encounter in production.
10) Deploy your model. Now you’re finally ready to integrate your model into the relevant application or system and deploy it in production to start making predictions. Integrating into an application or system may involve creating an API or a library that can be called from the application to make predictions based on new data. The model can also be integrated into a database or a data processing pipeline to automatically make predictions on incoming data.
Before deploying your model in production, it's important to ensure that it meets the performance and reliability requirements of the application or system. This may involve setting up monitoring and alerting systems to detect and address any issues that may arise during deployment. Plus, you may need to regularly maintain and update your model to ensure it remains effective and accurate over time.