{
  "fetched_at": "2026-10-20T07:58:57Z",
  "source_url": "https://www.kaggle.com/competitions/ex-machina-apk-24/data",
  "pages": [
    {
      "name": "data-description",
      "content": "This page appears alongside the data files. It describes what files have been provided and the format of each. There is no single format for this page that is appropriate for all competitions, but you should strive to describe as much as you can here. A little time spent describing the data here can save a lot of time answering questions later.\n\nParticipants should be able to answer these types of questions after reading the data description:\n\nWhat files do I need?  \nWhat should I expect the data format to be?  \nWhat am I predicting?  \nWhat acronyms will I encounter?\n\n## Files\n\n*   **train.csv** - the training set\n*   **test.csv** - the test set\n*   **sample_submission.csv** - a sample submission file in the correct format\n*   **metaData.csv** - supplemental information about the data\n\n## Columns\n\n*`College` -\tstudent's College \n*`sex` - student's sex (binary: 'F' - female or 'M' - male)\n*`age` - student's age (numeric: from 15 to 22)\n*`address` - student's home address type (binary: 'U' - urban or 'R' - rural)\n*`Pstatus` - parent's cohabitation status (binary: 'T' - living together or 'A' - apart)\n*`Medu` - mother's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)\n*`Fedu` - father's education (numeric: 0 - none, 1 - primary education (4th grade), 2 – 5th to 9th grade, 3 – secondary education or 4 – higher education)\n*`Mjob` - mother's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')\n*`Fjob` - father's job (nominal: 'teacher', 'health' care related, civil 'services' (e.g. administrative or police), 'at_home' or 'other')\n*`reason` - reason to choose this school (nominal: close to 'home', school 'reputation', 'course' preference or 'other')\n*`guardian` - student's guardian (nominal: 'mother', 'father' or 'other')\n*`traveltime` - home to school travel time (numeric: 1 - <15 min., 2 - 15 to 30 min., 3 - 30 min. to 1 hour, or 4 - >1 hour)\n*`studytime` - weekly study time (numeric: 1 - <2 hours, 2 - 2 to 5 hours, 3 - 5 to 10 hours, or 4 - >10 hours)\n*`failures` - number of past class failures (numeric: n if 1<=n<3, else 4)\n*`schoolsup` - extra educational support (binary: yes or no)\n*`famsup` - family educational support (binary: yes or no)\n*`paid` - extra paid classes within the course subject (Math or Portuguese) (binary: yes or no)\n*`activities` - extra-curricular activities (binary: yes or no)\n*`nursery` - attended nursery school (binary: yes or no)\n*`higher` - wants to take higher education (binary: yes or no)\n*`internet` - Internet access at home (binary: yes or no)\n*`romantic` - with a romantic relationship (binary: yes or no)\n*`famrel` - quality of family relationships (numeric: from 1 - very bad to 5 - excellent)\n*`freetime` - free time after school (numeric: from 1 - very low to 5 - very high)\n*`goout` - going out with friends (numeric: from 1 - very low to 5 - very high)\n*`Dalc` - workday alcohol consumption (numeric: from 1 - very low to 5 - very high)\n*`Walc` - weekend alcohol consumption (numeric: from 1 - very low to 5 - very high)\n*`health` - current health status (numeric: from 1 - very bad to 5 - very good)\n*`absences` - number of school absences (numeric: from 0 to 93)\n*`C1` - Student's marks in C1 evaluation",
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      "name": "Description",
      "content": "Please refer to the Data section to get some in-depth information about the dataset and download the dataset.\n\nAfter the conclusion of phase 1 final standings will be calculated by incorporating the following points :\n1. Kaggle Leaderboard standings\n2. Data Visualization and EDA.\n3. Data Preprocessing and cleaning.\n\nNote: You have to submit your python notebooks which will be thoroughly analyzed for plagiarism. You can take inspiration from other publicly available models and add the reference.\n\nPlease read and follow the submission guidelines from the next section.",
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    {
      "name": "Evaluation",
      "content": "\nSubmissions will be evaluated on [R2-Score](https://scikit-learn.org/stable/modules/generated/sklearn.metrics.r2_score.html) between the predicted CGPA and the actual hidden CGPA of the test dataset. The higher the R2-Score, the better the rank on the Kaggle leaderboard. \n\n\n## Submission File\nFor each ID in the test set, you must predict the CGPA for the test dataset maintaining the exact ID for each prediction. The file should contain a header and have the following format you have to upload your predictions in CSV format: \n\nID,CGPA\n1,4.007083005743666\n2,3.773247733444112\n3,4.105744192140591\n4,7.948215714427328\n5,4.075159640563898\n6,7.847659243838525\n    etc.",
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    {
      "name": "rules",
      "content": "Welcome to Ex-Machina-24 conducted by GDSC in Aparoksha 2024. This event will be conducted in two phases. Phase-1 will be compulsory for everyone and Phase 2 will be optional for IIIT-Allahabad freshers.  \n\n - This is a team event please register your teams on kaggle.\n - Please visit each section and read all the instructions carefully before starting. \n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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    {
      "name": "abstract",
      "content": "Welcome to Ex-Machina-24 conducted by GDSC in Aparoksha 2024. This event will be conducted in two phases. Phase-1 will be compulsory for everyone and Phase 2 will be optional for IIIT-Allahabad freshers. \n\n**Problem Statement:** In IIIT Allahabad grading system is very unique. Three evaluations are performed within a semester named as C1, C2, C3. The final CG is calculated after taking the sum of each individual's C1, C2, and C3 marks. IIIT-A administration has received the C1 marks sheet and other personal data of other students of your college and wants to approximate the final CGPA of every student with the help of the past year's student data. You are given two datasets train.csv and test.csv. train.csv contains past year CGPA data while test.csv contains present year's student data. You have to create a model based on train.csv and submit the predictions for test.csv.\n\n\n**Goal:** You have to make a machine learning model that predicts the CGPA of students of IIITA with the given data.",
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