{
  "fetched_at": "2026-10-24T04:11:10Z",
  "source_url": "https://www.kaggle.com/competitions/SalesAnalysis/data",
  "pages": [
    {
      "name": "data-description",
      "content": "\n**hh_demographic :**\nThis table contains demographic information for a portion of households. Due to nature of the \ndata, the demographic information is not available for all households.\n\n**train_transaction**\nThis table contains all products purchased by households within this study. Each line found in \nthis table is essentially the same line that would be found on a store receipt. Data is available for 639 days.\n\n**campaign_table**\nThis table lists the campaigns received by each household in the study. Each household \nreceived a different set of campaigns\n\n**coupon_redempt**\nThis table identifies the coupons that each household redeemed.\n",
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      "name": "Description",
      "content": "The given data contains transaction and household level data for around 21 months. There is a group of 2,500 households who are frequent shoppers at a retail store. For certain households, demographic information as well as direct marketing/ campaign history is included.\nPlease use EDA to answer below questions. Analysis on any other point/category apart from questions mentioned below is always welcome.\n\n1. How many customers are spending more over the time? Lesser over the time? Describe these customers.\n\n2. Of those customers who are spending more over the time, which categories are growing at a faster rate?\n\n3. Of those customers who are spending lesser over the time, with which categories are they becoming less engaged?\n\n4. Which demographic factors (e.g. household size, presence of children, income) appear to affect customer spend? Engagement with certain categories?\n\n5.  Is there evidence to suggest that direct marketing improves overall engagement?\n\n6. Retail store is planning to send out a campaign for Nutritional Products and wants a list of potential customers who are more likely to buy in next three months. Use the available data to build a model which predicts if a customer is likely to buy a NUTRITIONAL product in next 3 months. A list of all 2500 household keys followed by prediction in 1/0 needs to be provided in an excel file. Use DEPARTMENT = “NUTRITION” to filter out the products.\n\nPlease provide proper analysis based on data and it should be backed up with relevant tables/graphs.",
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    {
      "name": "rules",
      "content": "Rules of the Completion.\n\n - Don't cheat!\n - Apply yourself!\n - Have fun!",
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}