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<!DOCTYPE html>
<html lang="en">
  <head>
    <meta charset="UTF-8" />
    <meta name="viewport" content="width=device-width, initial-scale=1.0" />
    <title>Analytica Data Science Competition Guide</title>
    <style>
      body {
        font-family: Arial, sans-serif;
        line-height: 1.6;
        margin: 20px;
        background-color: #f4f4f4;
        color: #333;
      }
      .container {
        max-width: 900px;
        margin: auto;
        background: #fff;
        padding: 20px 40px;
        border-radius: 8px;
        box-shadow: 0 0 10px rgba(0, 0, 0, 0.1);
      }
      h1,
      h2,
      h3 {
        color: #2c3e50;
      }
      ul {
        list-style-type: none;
        padding: 0;
      }
      li {
        background: #ecf0f1;
        padding: 10px;
        margin-bottom: 8px;
        border-radius: 5px;
      }
      strong {
        color: #2980b9;
      }
      code {
        background-color: #e8e8e8;
        padding: 2px 5px;
        border-radius: 3px;
      }
      .note {
        background-color: #fff3cd;
        border-left: 5px solid #ffc107;
        padding: 10px;
        margin: 20px 0;
        border-radius: 4px;
      }
    </style>
  </head>
  <body>
    <div class="container">
      <h1>Analytica: A Guide to the Data Analysis Event</h1>
      <p>
        This document is a breakdown and roadmap for the Unstop "Analytica" data
        science competition, based on our discussion.
      </p>

      <h2>🎯 The Core Problem</h2>
      <p>
        Your task is to analyze sales data from
        <strong>Urban Grocers Pvt. Ltd.</strong> to evaluate the success of
        their new market strategy. You'll use data to answer key business
        questions and provide evidence-based recommendations to the management
        team.
      </p>

      <h2>🗺️ Your Step-by-Step Roadmap</h2>
      <ol>
        <li>
          <h3>1. Data Loading and Preparation</h3>
          <ul>
            <li>
              <strong>Load the Data:</strong> Use a tool like pandas to read the
              <code>Urban_Grocers.csv.xlsx</code> file.
            </li>
            <li>
              <strong>Inspect and Clean:</strong> Convert the
              <code>Date</code> column to a proper datetime format. Check for
              and handle any missing values.
            </li>
          </ul>
        </li>
        <li>
          <h3>2. Exploratory Data Analysis (EDA)</h3>
          <ul>
            <li>
              <strong>Identify Peak Sales:</strong> Aggregate
              <code>Units_Sold</code> by <code>Date</code> to find overall sales
              trends and peak periods.
            </li>
            <li>
              <strong>Analyze Holiday/Weekend Impact:</strong> Compare average
              sales on holiday/weekend days (<code>Holiday_Weekend = 1</code>)
              vs. regular days (<code>Holiday_Weekend = 0</code>). Do this for
              overall sales and for each <code>Food_Category</code>.
            </li>
          </ul>
        </li>
        <li>
          <h3>3. Answering Key Questions</h3>
          <ul>
            <li>
              <strong>Unpredictable Demand:</strong> Group data by
              <code>Store_ID</code> and calculate the standard deviation of
              <code>Units_Sold</code>. The store with the highest standard
              deviation is the most unpredictable.
            </li>
            <li>
              <strong>Promotion Impact:</strong> Compare the average
              <code>Units_Sold</code> when <code>Promotion = 1</code> versus
              when <code>Promotion = 0</code>. This quantifies the effect of
              promotions.
            </li>
          </ul>
        </li>
        <li>
          <h3>4. Demand Patterns and Forecasting</h3>
          <ul>
            <li>
              <strong>Analyze Patterns:</strong> Visualize sales trends for each
              <code>Food_Category</code> and <code>Store_ID</code> to identify
              specific demand patterns.
            </li>
            <li>
              <strong>Forecast Demand:</strong> Choose a single food category.
              Aggregate its sales by date and use a time-series model (like
              ARIMA) to predict sales for the next quarter.
            </li>
          </ul>
        </li>
        <li>
          <h3>5. Investment and Profitability Analysis</h3>
          <ul>
            <li>
              <strong>The 14% Profit Margin:</strong> This is a fixed value
              provided for the competition. It's the gross profit margin. You
              use it to calculate the company's total profit.
            </li>
            <li>
              <strong>Calculation:</strong>
              <ol>
                <li>
                  Calculate <strong>Total Revenue</strong>:
                  <code>Sum(Units_Sold * Price_per_Unit)</code> for all
                  transactions.
                </li>
                <li>
                  Calculate <strong>Total Profit</strong>:
                  <code>Total Revenue * 0.14</code>.
                </li>
              </ol>
            </li>
            <li>
              <strong>Investment Evaluation:</strong> Compare the
              <strong>Total Profit</strong> to the initial investment of
              <strong>₹20 crores</strong> to see if the company has recovered
              its investment.
            </li>
          </ul>
        </li>
        <li>
          <h3>6. Formulating Recommendations</h3>
          <ul>
            <li>
              Based on all your analysis (peak sales, promotion impact,
              unpredictable demand, and profit analysis), create a list of
              actionable, data-driven strategies for Urban Grocers to improve
              their business.
            </li>
          </ul>
        </li>
      </ol>
    </div>
  </body>
</html>