15,000pts
Timeline
Begin
2020/08/25
2020/08/25
Finish
2021/01/25
2021/01/25
Complete
2021/02/08
2021/02/08
Competition start: 2020/08/25 00:00:00
Competition closes on: 2021/01/25 00:00:00
Final Submission Limit: 2021/02/08 00:00:00
This competition has a total duration of 5 months, within which you will be able to make your submissions and obtain results automatically. Once the first part of the competition is over, you will have one week to choose your best model and submit it to be graded and considered for cash or points prizes.
Once the whole process is completed, you will still be able to submit models as "Late Submission" as a learning experience, since, since the competition is officially over, those models will not be eligible to win prizes.
Once the whole process is completed, you will still be able to submit models as "Late Submission" as a learning experience, since, since the competition is officially over, those models will not be eligible to win prizes.
Description
The market for mobile applications is increasingly competitive and understanding which variables can be favored when designing such applications can be of great help in making business decisions.
The objective of this competition will be to analyze and classify the rating of mobile applications in the Google Play Store Android market.
The objective of this competition will be to analyze and classify the rating of mobile applications in the Google Play Store Android market.
Evaluation
The evaluation of the model will be measured using the F1 score, this is because the amount of data in both classes is not symmetric. Since we are working with an unbalanced dataset, our goal will be to optimize the model so that it properly classifies both classes and maximizes the accuracy of the classification, especially of the class with a minority of data. ## Important
F1 = 2 * (precision * recall) / (precision + recall)
The final F1 score will be the average F1 score of each class as follows:
F1_macro = (F1_class0 + F1_class1) / 2
Note:
In the sklearn library the line of code to calculate the F1 score described above would be
In the sklearn library the line of code to calculate the F1 score described above would be
f1_score(y_true, y_pred, average='macro')
In these links you can find more information about the F1 score:
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html
Rules
Rules:
- The code should not be shared in private. Any code that is shared, must be available to all participants of the competition through the platform
- The solution should use only public available open source libraries
- If two solutions get identical scores in the ranking table, the tie-breaker will be the date and time of the submission (the first solution submitted will win).
- We reserve the right to request any user's code at any time during a challenge. You will have 24 hours to submit your code following the code review rules.
- We reserve the right to update these rules at any time.
- Your solution must not infringe the rights of any third party and you must be legally authorized to assign ownership of all intellectual property in and to the winning solution code to DataSource.ai.
- Competitors may register and submit solutions as individuals (not as teams, at least for now).
- As this is a learning competition, apart from the rules in the DataSource.ai Terms of Use, no other particular rules apply.
- Maximum 10 solutions submitted per day.
At the end of the competition and if you are in the top 20, you must submit the complete model in .ipynb (Jupyter Notebook) format - no other formats will be accepted. You will have 48 hours after the end of the competition to send it to [email protected] - This model will serve us to get the real final evaluations, so the Leaderboard could change when the final private evaluation is shown.
