We could directly load this model into the app, but to reduce dependencies of the final application, we will serve the model via API. Till now our model is ready and we can directly create an API for this model. Then we pickle the model so it can be loaded into other environments and served in different scenarios. For reference, here are the features used for building the model after combining the JSON and CSV data choosing appropriate features:Īfter trying out different approaches, Decision Tree Classifier gave satisfactory results:Īlthough the cross-validation score of logistic regression comes out to be more than the decision trees, we will still prefer Decision trees since it doesn’t require normalization/transformation of data adding up to the advantage of passing the values as received from the user with no explicit pre-processing. This article is focused more on the deployment part, so the actual processing part is skipped here. The dataset is available on Kaggle: Classify Song Genres from Audio Data. Primarily, the dataset comprises a JSON file comprising technical aspects of a song such as a tempo, valence, energy, and a CSV file with other metadata. The dataset for which we will build the model and deploying it is a music dataset. Where the website deployment requires a lot of extra effort to set up the front-end, android apps seems a reasonable solution, and that too when the app is built in Python! In this article, I will walk you through building apps using Python, which will be a cross-platform application, meaning it can be converted into android apps and IOS too. In most cases, the model is deployed via the web interfaces, android apps, or IoT. After working on the model building, the next step in the machine learning life cycle is usually the deployment in the real-world scenario to perform actionable tasks.
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