This project aims to explore and understand the data to identify potential anamolies. After which, the anamolies are resolved using suitable imputation methods.
- branches.csv : contains the code and location of the various brances of the restaurant.
- nodes.csv : contains the location of the all the nodes - customers and branches.
- edges.csv : contains details of the source (restaurant branch) and the destination (customer) along with the distance between them.
Datasets:
Food delivery data of 500 customers of a restaurant in Melbourne, Australia.
- dirty_data.csv : carries rows with at most one anomaly in it.
- missing_data.csv : contains rows with one or more missing values.
- outlier_data.csv : contains data with outliers in them.