{
  "fetched_at": "2026-10-19T21:44:34Z",
  "source_url": "https://www.kaggle.com/competitions/datathon23-machine-learning-competition/data",
  "pages": [
    {
      "name": "data-description",
      "content": "You are provided with dataset about energy (electricity) usage of manufacturing industry. The information gathered is from the DAEWOO Steel Co. Ltd in Gwangyang, South Korea. It produces several types of coils, steel plates, and iron plates. The information on electricity consumption is held in a cloud-based system.\nThe task is to predict the variable Load_Type of energy in a industry.\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\n## Columns\n\n*   `date` - seconds\n*   `Usage_kWh` - Electricity usage in Kilo Watt Hour (KWH)\n*   `Lagging_Current_Reactive.Power_kVarh` - Lagging power in 1000 reactive volt-amps per hour (KVARH)\n*   `Leading_Current_Reactive_Power_kVarh` - Leading power in 1000 reactive volt-amps per hour (KVARH)\n*   `CO2(tCO2)` - Carbon dioxide level in Parts per million (PPM)\n*   `Lagging_Current_Power_Factor` - Lagging_Current_Power_Factor\tLagging power factor in %\n*   `Leading_Current_Power_Factor` - Leading_Current_Power_Factor\tLeading power factor in %\n*   `NSM` - Number of seconds from midnight\n*   `WeekStatus` - Weekday or weekend  \n*   `Day_of_week` - Sunday, Monday, Tuesday, Wednesday, Thursday, Friday, Saturday\n*   `Load_Type` - Light_Load, Medium_Load, Maximum_Load (**Target**)\n",
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    {
      "name": "Description",
      "content": "This challenge serves as an ML competition for the \"Datathon'23 Machine Learning Competition\" held by the IBA Data Science Society.\n\nParticipants are expected to build models that can accurately predict energy consumption for the industry based on the provided data.\n\nCan you predict the load type for the industry?\nBy solving this competition, you will be able to apply and enhance your data science skills.",
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    {
      "name": "Evaluation",
      "content": "The evaluation metric for this competition is [Accuracy]\n(https://en.wikipedia.org/wiki/Accuracy_and_precision#In_binary_classification). The accuracy is the proportion of correct predictions (both true positives and true negatives) among the total number of predictions.",
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    {
      "name": "rules",
      "content": "**Grading Scheme:** \nA judge will visit your stall any time after 3:15 pm and will mark you according to the following:\n•\tPosition on Kaggle Leaderboard (80%)\n•\tML Process and Methodology (EDA, feature selection, algorithms, performance metrics) (20%)\n\n**Plagiarism Policy:** Plagiarism will not be tolerated in any case. Exploitable use of ChatGPT is generally detectable. All instances of plagiarism will lead to instant disqualification.\n\n**Implementation Language/Tools:** Python on Jupyter Notebook is preferred. However, the use of other tools like RapidMiner and Knime is also allowed. \n\n",
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