Predict if an AD will be clicked

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🛠 Contribute: Found a typo? Or any other change in the description that you would like to see? Please consider sending us a pull request in the public repo of the challenge here.

## 🕵️ Introduction

They popped up when you were browsing, they popped up when you were streaming videos, they popped up when you were playing an intense video game. Did you give in? Did you find those advertisements catchy enough to click! Well we made a challenge out of it! Given information about an advertisement, can you predict whether it will be clicked upon?

Understand with code! Here is `getting started code` for you.`😄`

## 💾 Dataset

The dataset provided contains advertisements that were shown along side search results and whether or not they were clicked on. For each advertisement 11 properties are given:

• `Click` - 1 if the add was clicked, 0 if it was not clicked
• `impression` - number of sessions in which the Ad was shown to the userID who issued the query
• `url_hash` - a hash of the url on which the ad was displayed
• `AdID` - an id uniquely identifying an ad
• `advertiserID` - an id uniquely identifying an advertiser
• `Depth` - number of ads displayed to a user in a session
• `Position` - order of an ad in a display
• `query_id` - an id uniquely identifying a query
• `keyword_id` - an id uniquely identifying a search keyword
• `title_id` - an id uniquely identifying the title of the ad
• `description_id` - an id uniquely identifying a description of the ad
• `user_id` - an id uniquely identifying a user

## 📁 Files

Following files are available in the `resources` section:

•  `train.csv` - (`40000` samples) This csv file contains the attributes describing an advertisement along with the binary value denoting whether or not the advertisement was clicked on .
• `test.csv` - (`10000` samples) File that will be used for actual evaluation for the leaderboard score but does not have the binary value denoting whether or not the advertisement was clicked on.

## 🚀 Submission

• Prepare a CSV containing header as `click` and predicted value as digit `0` or `1` respectively denoting whether or not the corresponding ad will be clicked upon.
• Name of the above file should be `submission.csv`.
• Sample submission format available at `sample_submission.csv` in the resorces section.

Make your first submission here 🚀 !!

## 🖊 Evaluation Criteria

During evaluation F1 score will be used to test the efficiency of the model where,

$F1 = 2 * \frac{precision*recall}{precision+recall}$