Purpose In this part of the competition, we will forecast the time (indicated as Shipping Time in the dataset) required to successfully process each shipment. The target variable to be predicted is the Shipping Time in units of days with a maximum of 5 decimals (ex. "5.3" days or "5.45673" days are acceptable in the solutions file). The train_2_pr.csv file contains historical shipments with known Shipping Times and can be used to train the model. Shipping Times should be forecasted for each Shipment ID in test_2.csv. Input Dataset • train_2_pr.csv - contains historical shipments over the 16-month period between 2019-02-14 and 2020-06-13, shipping time, and other shipment details. More than one record in the file might have the same 'shipment_id', but 'shipping_company' for such rows are different. 'shipping_company' reflects potential shipping companies which might be used, and the 'selected' column shows which company (in some cases more than one) has been selected. # shipment_id - Shipment ID # send_timestamp - Date when the order was sent to destination country (in 'source_country' timezone, e.g. UK - GMT) # pick_up_point - Pick Up Point abbreviation # drop_off_point - Drop Off Point abbreviation # source_country - Country from where the goods need to be shipped # destination_country - Country to where the goods need to be shipped # freight_cost - Cost of transportation/kg # gross_weight - Gross weight in kg which needs to be shipped # shipment_charges - Fixed cost per shipment # shipment_mode - Method of shipment (e.g. air, ocean) # shipping_company - Candidate shipping company # selected - Whether the company in 'shipping_company' was selected or not # shipping_time - The amount of time that it takes for goods to reach their destination • shipping_companies_details_1.csv - File that contains all necessary information about shipping companies. # source_country - Country from where the goods need to be shipped # destination_country - Country to where the goods need to be shipped # shipment_mode - Method of shipment (e.g. air, ocean) # cut_off_time - The time window in which goods can be picked up (e.g. "10 - 2 and 3 - 6 BST" means that pick up is available between the times of 10 a.m. and 2 p.m., and again between the ties of 3 p.m. and 6 p.m). All times are in Bangladesh Standard Time. # tat - Turnaround Time is the time it takes from a package order to shipment. T refers to the time a package was sent, and CO means cut-off time, or the time after which a package can no longer be shipped. "Before CO – T+0" and "After CO – T+1" means that if the goods are sent to the shipping company before the cut off time, then the package will be sent on the same day. If the package arrives to the shipping company after the cut off time, then it will be shipped out on the following day. # processing_days - Days on which Pick up Point works. # pick_up_point - Pick Up Point abbreviation # drop_off_point - Drop Off Point abbreviation # min_sc - Minimum cost of shipment # max_sc - Maximum cost of shipment # shipping_company - Name of shipping company # shipment_charges - Fixed cost per shipment • test_2.csv - File that contains historical shipment data. Shipping Time is unknown and should be predicted. # shipment_id - Shipment ID # send_timestamp - Date when the order was sent to destination country (in 'source_country' timezone, e.g. UK - GMT) # pick_up_point - Pick Up Point abbreviation # drop_off_point - Drop Off Point abbreviation # source_country - Country from which the goods need to be shipped # destination_country - Country to where the goods need to be shipped # freight_cost - Cost of transportation/kg # gross_weight - Gross weight in kg which needs to be shipped # shipment_charges - Fixed cost per shipment # shipment_mode - Method of shipment (e.g. air, ocean) # shipping_company - Candidate shipping company # selected - Whether the company in 'shipping_company' was selected or not Submission File • submission_2.csv - File which will be submitted by the participants. The file SHOULD NOT CONTAIN ANY ID OR FIELD TITLES; the order of the target variables should be according to the submission file format but the first row and first column should be removed prior to submission. The platform will only accept csv files that consist only of numeric values. # shipment_id - Shipment ID (should be removed before the submission) # shipping_time - Estimated time of delivery (in hours) or how long it will take to deliver the goods (target variable). This column is empty and should be filled out by the participants. Evaluation Metric Submissions are evaluated on e^(-RMSE/MAX(observed_values)) where Root Mean Squared Error is calculated between the predicted values and observed values. *While calculating the final ranking, the scores will be normalized (separately for each competition) to eliminate any inherent bias in the evaluation metric for the problem statements. We will normalize the scores before applying the formula (20% of Challenge 1) + (40% of Challenge 2) + (40% of Challenge 3). The score of each competition will have a valuable impact on the final score since they will be in the same range.