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The 2020 Data Days Competition held at Sharif university of technology.

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# Data-Days-2020
February and March of 2020.
Sharif University of Technology, Tehran, Iran.
## Ranked the 1st among 121 teams.
1153 Contestants|
121 Teams\
86 Universities

# Introduction
The 2020 Data Days Competition held at Sharif university of technology.
[The website link](https://datadays.sharif.edu)
[The link to the documentation](https://docs.google.com/document/d/1ov2xxVM1ZBHk2K3RJqDM2A-9xnyHlfh9dpCY5p_F_L8/edit#)
[The link to the dataset](https://drive.google.com/file/d/14szOHDbSmd17DXn6Iypb_NU5Ck3rktIX/view)
You are given a dataset of 9 csv files and you are expected to sort adIds that are most likely to be clicked on by a certain user.
The problem and the datasets are similar to this [kaggle](https://www.kaggle.com/c/outbrain-click-prediction/data) competiton.

You can chceck the jupyter notebook and see the algorithms and preprocessing we had done.

# Our team (March 2020):
Team **Pandas**\
Milad Soltany --- [Linkedin](https://www.linkedin.com/in/milad-soltany/), [Github](https://github.com/miladsoltany) , [Email](mailto:soltany.m.99@gmail.com)\
Omid Omidi --- [Linkedin](https://www.linkedin.com/in/omidomidi77/), [Github](https://github.com/omidomidi77), [Email](mailto:abbasomidi77@gmail.com)\
Amirhossein Heydarian ---  [Linkedin](https://www.linkedin.com/in/amirhosseinh77/), [Github](https://github.com/amirhosseinh77), [Email](mailto:amirhossein4633@gmail.com )

feel free to reach out to us.

# What we did.
We tried out many different models and algorithms.\
What worked the best was random forest with 400 estimators and max_depth of 15, you can check the preproccesings and features we extracted.

## What you find in this repo
The final notebook which consists of all the codes.

# Photos of work results
An Example of Statistical Analysis of the Number of Clicks Based on Various Parameters :

![1](https://user-images.githubusercontent.com/61683254/121142740-8d180580-c851-11eb-9cb1-dd3f93b6cc53.PNG)

View of One of the Datasets Used to Teach the Model:

![2](https://user-images.githubusercontent.com/61683254/121142923-b89af000-c851-11eb-9e99-f5cdf22083ef.PNG)

A Picture of the Importance of Our Own Features Compared to Pre-Existing Features in the Dataset:

![3](https://user-images.githubusercontent.com/61683254/121143164-fc8df500-c851-11eb-91bf-779c806a3af3.PNG)

Best.\
Team Pandas.

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