Numeric Data Predictor
Numeric data prediction refers to the process of using machine learning algorithms to make predictions or estimates based on numerical features or variables. Our goal is to build a model that can learn patterns from the available numeric data and use those patterns to predict outcomes or make estimations for new, unseen data points. The developed model can be applied to a wide range of fields, including finance, sports, and healthcare. The Numeric data prediction can be evaluated using different metrics such as explained variance score, mean absolute error, mean squared error, and many more. The evolution helps determine how well the model is likely to perform on new, unseen data.
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Quick Search and Input Dataset
Generic prediction allows users to input any numerical dataset in CSV format. Once the dataset is uploaded user can be viewed and deleted it.
Sample Data
Users can view sample data that represents the features in the dataset.
Select Pre-Processing Operation
Users can perform different pre-processing operations using the given Transformers. Users can preprocess all of the data or only specific features.
Perform Prediction
Users can do prediction in the fourth step after choosing their preferred estimator, key attribute, and class label.