MPG Prediction Model

intern-basics/Job Recommendation System/Data.md

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+# Data
+
+For the Job Recommendation System model we consider the data from the [Kaggle](https://www.kaggle.com/c/job-recommendation).
+
+The dataset consist of many files in different formats, but we only consider the following files
+* apps.tsv
+* jobs.tsv
+* test_users.tsv
+* user_history.tsv
+* users.tsv
+
+In order to understand the content of the data files, you need to understand the structure of this contest.
+
+In outline, we give you data on users, job postings, and job applications that users have made to job postings. In total, the applications span 13 weeks. 
+We have split the applications into 7 groups, each group representing a 13-day window. Each 13-day window is split into two parts:
+The first 9 days are the training period, and the last 4 days are the test period. All the other details on window split per user you can find in the
+[Kaggle](https://www.kaggle.com/c/job-recommendation/data) site.
+
+## File Formats
+Each of the files is in .tsv (tab-seperated value) format. This means that each line in a .tsv file consists of several fields, which are separated by tabs.
+To accommodate this file format, fields composed of text have been changed in the following ways to escape tabs, newlines, and carriage returns.
+
+1. Tabs have been replaced by '\t'
+2. Newlines have been replaced by '\n'
+3. Carriage returns have been replaced by '\r'
+4. Backslashes have been replaced by '\\\'
+
+## Files
+
+**users.tsv** contains information about the users. Each row of this file describes a user. The *UserID* column contains a user's unique id number,
+the *WindowID* column contains which of the 7 windows the user is assigned to, and the *Split* column tells whether the user is in the Train group or
+the Test group. The remaining columns contain *demographic and professional information* about the users.
+