kasati
Santiago Serna
Sidereus
1.0974240703460731
0.8484965514846635
Adam Michaels
1.0551794064434958
1.3188986464605459
Machine Learning Problem
Let's say you work as senior Data Scientist in a marketing agency that offers Search Engine Optimization SEO services for corporate clients. Within the SEO services the agency recommends to all its clients to publish a greater number of articles in their blog posts, and as a result have a bigger impact, because Google rewards the creation of unique and frequent content.
So far, the agency has historical data on its clients' posts with a column that has recorded the number of unique and total visitors for each blog post. The agency believes that with this information it can predict whether a client's new post will be successful, as measured by the number of visits.
This will allow the client to focus on creating relevant posts that are expected to have a high number of visitors (and impact), and avoid creating posts that don't have that same impact. This prediction will save valuable resources (like time and money) for the client and the agency.
This will allow the client to focus on creating relevant posts that are expected to have a high number of visitors (and impact), and avoid creating posts that don't have that same impact. This prediction will save valuable resources (like time and money) for the client and the agency.
Evaluation
The evaluation of the model will be done using the RMSLE (Root Mean Squared Logarithmic Error). What we do is calculate the Square Root to the MLSE metric that implements Scikit-learn. If you want to know more about the MLSE metric that Scikit Learn calculates, you can find it here:
Where:
N = Number of rows in your submission file
= true values
= predicted valuesRules
- The code should not be shared privately. Any code that is shared, must be available to all participants of the competition through the platform
- The solution should use only publicly 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 tournament. You will have 48 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 copyrights 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).
- Apart from the rules in the DataSource.ai Terms of Use, no other particular rules apply.
- Maximum 50 solutions submitted per day.
If you reach the Final Stage, at the end of this stage you must submit the complete model in .ipynb (Jupyter Notebook) format through our form (as an attachment) that we’ll display for you inside the platform- no other file formats or submission channels will be accepted. Normally, you'll have 3 days (Final Shot Stage) to send it through our "Submit Modal" button - This final machine learning model will help us to get the final evaluations, so the winners will be determined on the basis of the final score and this Notebook.