Problem Statement
A leading pet adoption agency is planning to create a virtual tour experience for their customers showcasing all animals that are available in their shelter. To enable this tour experience, you are required to build a Machine Learning model that determines type and breed of the animal based on its physical attributes and other factors.
Data
The data consists the following columns
| Sl No. | Column Name | Description |
| 1 | pet_id | Unique Pet Id |
| 2 | issue_date | Date on which the pet was issued to the shelter |
| 3 | listing_date | Date when the pet arrived at the shelter |
| 4 | condition | Condition of the pet |
| 5 | color_type | Color of the pet |
| 6 | length(m) | Length of the pet (in meter) |
| 7 | height(cm) | Height of the pet (in centimeter) |
| 8 | X1,X2 | Anonymous columns |
| 9 | breed_category | Breed category of the pet (target variable) |
| 10 | pet_category | Category of the pet (target variable) |
Data Description:
The data folder consists of 2 CSV files
- train.csv - 18834 x 11
- test.csv - 8072 x 9
sample_submission:
pet_id,breed_category,pet_category
ANSL_69903,0,1
ANSL_66892,0,2
ANSL_69750,2,4
ANSL_71623,0,2
ANSL_57969,0,1Evaluation Metric:
s1=f1_score(actual_values[′pet_category′],predicted_values[′pet_category′],average=′weighted′)s2=f1_score(actual_values[′breed_category′],predicted_values[′breed_category′],average=′weighted′)score=100×s1+s22
Note: To avoid any discrepancies in the scoring, ensure all the index column values in submitted file matches the index column values in 'test.csv' provided.
