ADS-overview

Access

Access

1. Automatically download data and upload files to the database (use a progress bar whenever possible).
2. APIs for joining tables, converting queried results into DataFrames, and getting POI data from OSM.

Fynesse

House Price Prediction

Assess

Address

Assess

1. Visualise POIs for different locations & tags (e.g., tourism POIs around Cambridge train station).
2. Use a heatmap for visualising the OSM feature correlation.
3. Apply PCA on the correlation matrix, and visualise relative distances between features with an interactive 3D plot (users can change elevation & azimuth).

Address

1. Select an optimal bounding box and date range automatically.
2. Transform prices: a constrained domain to an unconstrained one using log.
3. Preprocess data: handle missing and invalid data, merge with OSM features, apply one-hot encoding, and preserve only quantitative data.
4. Apply cross-validation with RMSE for evaluating model performance.
5. Linear models include: RidgeCV, BayesianRidge, and TweedieRegressor.
6. Predict prices and report model performance (warn if the model is poor).