{
  "fetched_at": "2026-10-23T04:26:28Z",
  "source_url": "https://www.kaggle.com/competitions/bdaio-2024-earths-pulse/data",
  "pages": [
    {
      "name": "data-description",
      "content": "The dataset, **temperatures.npy** contains daily temperature records(in degrees Celsius) for one year (365 days)",
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      "name": "Description",
      "content": "You are required to perform the following operations for both the temperature and pollution fields of the data:\n\n**1. Calculate the Mean Temperature:** The mean is the average value of a set of numbers.\n**2. Compute the Standard Deviation of the Temperatures:** The standard deviation measures how spread out the numbers in a data set are. A low standard deviation means the numbers are close to the mean, while a high standard deviation means the numbers are spread out over a wider range.\n**3. Find the Variance of the Temperatures:** The variance is the average of the squared differences from the mean. It gives an idea of how much the numbers differ from the mean. The variance is the square of the standard deviation.\n**4. Calculate the Kurtosis of the Data:** Kurtosis measures the \"tailedness\" of the data distribution. A high kurtosis means more of the variance is due to infrequent extreme deviations, as opposed to frequent modestly sized deviations.\n**5. Calculate the Skewness of the Data:** Skewness measures the asymmetry of the data distribution. A positive skew means the tail on the right side is longer or fatter, while a negative skew means the tail on the left side is longer or fatter.\n**6. Calculate the Cumulative Moving Average:** The Cumulative Moving Average (CMA) helps you see the overall trend in your data by showing the average of all the measurements up to each day. Instead of just looking at one day at a time, the CMA smooths out the short-term ups and downs to help you understand the long-term trend.\n\nExample\nSuppose you have the following daily measurements: [5, 7, 8, 6, 10].\n\nDay 1: CMA = 5\nDay 2: CMA = (5 + 7) / 2 = 6\nDay 3: CMA = (5 + 7 + 8) / 3 = 6.67\nDay 4: CMA = (5 + 7 + 8 + 6) / 4 = 6.5\nDay 5: CMA = (5 + 7 + 8 + 6 + 10) / 5 = 7.2",
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    {
      "name": "Evaluation",
      "content": "Your submission will be evaluated based on both the correctness of the calculated values. Ensure your code is optimized and runs efficiently.\n\n\n## Submission File\nThe file should contain a header and have the following format. In case you could not find the answers of any of the subquestions, then put -1 as the default value in the column\n```python\nDay,Temperature,Cumulative_Moving_Average,Mean_Temperature,Standard_Deviation,Variance,Kurtosis,Skewness\n1,20,19,18.53,1.79,3.20,0,0.40\n2,18.1,16.7,18.53,1.79,0,-0.1,0.35\n3,16.5,16.63,29,1.79,0,-0.1,0.35\n\n\n```",
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    {
      "name": "rules",
      "content": "This is a page where you can include rules that participants must accept before joining. You may wish to include rules like:\n\n - Use of any kind of unfair means will result in disqualification\n\n\n\n",
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    {
      "name": "abstract",
      "content": "Hello young participants, in this task you'll be working with a dataset of daily temperature, collected over the course of a year. Your task is to analyse the data to uncover insights and patterns that can help in understanding the environmental health of the city.",
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    {
      "name": "Documentation",
      "content": "To check documentation from command line, refer to this notebook.\n`https://www.kaggle.com/code/promachowdhury/check-library-documentation`",
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