APS is an Algorithm Predictive Studio. The app allows users to upload a dataset, select target variables and features, train various regression algorithms (Linear Regression, Decision Tree, Random Forest, Support Vector Machine, and k-Nearest Neighbors), and visualize evaluation metrics and predictions on new data.
- Dataset Upload and Visualization
- Target Variable and Feature Selection
- Model Training
- Model Evaluation
- Provides code snippet for each selected algorithm
- Accurate Predictions
- Algorithm Selection: APS is limited to Regression Algorithms only.
- Dataset Type: APS is limited to CSV and Excel files only for now. More updates will be released with diversity across all types of datasets.
- Limited Dataset Size: APS is designed to handle relatively small to medium-sized datasets (200MB). For large datasets with thousands of samples and numerous features, the app's performance might be compromised, leading to slower execution times and potential memory limitations.
- Streamlit
- sklearn.
- Pandas.
Dependencies are all listed in the
requirements.txtfile.
Gideon Ogunbanjo.