Amazon Products Analysis & RF Model.ipynb

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        "id": "GU6YA0aWOsOf"
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        "# **Analyzing Products Data from Amazon Product Search**\n",
        "\n",
        "The core objective of this project is to identify potential products suitable for sale on Amazon. This is achieved through an analysis of product data acquired from Amazon Product Search via the RapidAPI service. By utilizing data analysis techniques, the project aims to uncover insights that assist in recognizing optimal products for selling. Metrics including ratings, prices, total reviews, best seller status, and more will be scrutinized to reveal patterns that inform strategic choices and refine product positioning.\n",
        "\n",
        "The project also involves comparing revenue, demand, and ratings against other products. Moreover, it employs a Random Forest model to predict the potential success of a product in the market.\n",
        "\n",
        " Through this holistic analysis, the project seeks to offer actionable insights that steer effective business strategies and aid in the selection of the most advantageous products for the market.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-yHvJlChSfJ6"
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      "source": [
        "<a id='top'></a>\n",
        "### Table of Contents\n",
        "1. [Query Analysis Tools](##query-analysis-tools)\n",
        " - [API Query and Data Retrieval](###api-query-and-data-retrieval)\n",
        " - [Key Data Extraction](###key-data-extraction)\n",
        " - [Query Data Visualization](###query-data-visualization)\n",
        "2. [Amazon Profitability Predictor](##amazon-profitability-predictor)\n",
        " - [Data Retrieval and DataFrame Construction](###data-retrieval-and-dataframe-construction)\n",
        " - [Data Visualization and Trimming](###data-trimming-and-visualization)\n",
        " - [Query Analyzer](###query-analyzer)\n",
        " - [Random Forest Model](###random-forest-model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uTNReTA1DyQw"
      },
      "source": [
        "## <a id=\"query-analysis-tools\">Query Analysis Tools</a>\n",
        "\n",
        "This Section contains helpful resources tailored for Amazon sellers, designed to deliver valuable insights into the products you're eager to learn more about.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ki-eQP7HUxn-"
      },
      "source": [
        "### <a id=\"api-query-and-data-retrieval\">API Query and Data Retrieval</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yAPRsygA8xxn"
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      "source": [
        "# import libraries\n",
        "import requests\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from decimal import DivisionByZero"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "we1c47Ie_zIx"
      },
      "source": [
        "I'm utilizing the https://rapidapi.com/restyler/api/amazon23/ API provided by RapidAPI to fetch search data from Amazon. This API allows performing searches for products and retrieving relevant information. You can easily change the query in the querystring variable by changing the \"query\" parameter. The 'X-RapidAPI-Key' parameter should be changed to personal Api key."
      ]
    },
    {
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      "source": [
        "# URL of the API endpoint\n",
        "url = \"https://amazon23.p.rapidapi.com/product-search\"\n",
        "\n",
        "# Query parameters for the API request\n",
        "querystring = {\"query\": \"soap\", \"country\": \"US\"}\n",
        "\n",
        "# Headers containing API key and host information\n",
        "headers = {\n",
        "    \"X-RapidAPI-Key\": \"X-RapidAPI-Key\",\n",
        "    \"X-RapidAPI-Host\": \"amazon23.p.rapidapi.com\"\n",
        "}\n",
        "\n",
        "# Make a GET request to the API endpoint with the specified headers and parameters\n",
        "response = requests.get(url, headers=headers, params=querystring)\n",
        "\n",
        "# Parse the JSON response from the API\n",
        "data = response.json()\n",
        "print(data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MwMVcQQJA3KO"
      },
      "source": [
        "### <a id=\"key-data-extraction\">Key Data Extraction</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dC21lmL_Y3k4"
      },
      "source": [
        "Code below processes data from the API response to extract various statistics about products, such as ratings, reviews, and prices. It calculates the mean values of ratings, reviews, and prices for the selected products and identifies best sellers and top-ranking items. It will be used to create tables with information about query."
      ]
    },
    {
      "cell_type": "code",
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      "source": [
        "# Initialize variables to store various statistics\n",
        "ratings_sum = 0\n",
        "highest_rating = 0\n",
        "highest_rating_url = ''\n",
        "total_products = 0\n",
        "reviews_sum = 0\n",
        "price_sum = 0\n",
        "products_with_price = 0\n",
        "lowest_price = 999999999999\n",
        "highest_price = -1\n",
        "highest_price_prod_url = ''\n",
        "lowest_price_prod_url = ''\n",
        "best_seller_count = 0\n",
        "best_sellers_urls = []\n",
        "top_positions = []\n",
        "\n",
        "# Iterate through each product in the API response data\n",
        "for product in data['result']:\n",
        "    # Check if the product's page position is within the first 5 pages\n",
        "    if product['position']['page'] <= 5:\n",
        "        # Calculate cumulative statistics\n",
        "        ratings_sum += product['reviews']['rating']\n",
        "        if product['reviews']['rating'] > highest_rating:\n",
        "             highest_rating = product['reviews']['rating']\n",
        "             highest_rating_url = product['url']\n",
        "        total_products += 1\n",
        "        reviews_sum += product['reviews']['total_reviews']\n",
        "\n",
        "        # Check if the product has a non-zero price\n",
        "        if product['price']['current_price'] != 0:\n",
        "            price_sum += product['price']['current_price']\n",
        "            products_with_price += 1\n",
        "\n",
        "        # Update highest and lowest price products\n",
        "        if product['price']['current_price'] > highest_price:\n",
        "            highest_price = product['price']['current_price']\n",
        "            highest_price_prod_url = product['url']\n",
        "        if product['price']['current_price'] < lowest_price:\n",
        "            lowest_price = product['price']['current_price']\n",
        "            lowest_price_prod_url = product['url']\n",
        "\n",
        "        # Check if the product is a best seller\n",
        "        if product['bestSeller'] != False:\n",
        "            best_seller_count += 1\n",
        "            best_sellers_urls.append(product['url'])\n",
        "\n",
        "        # Check if the product's global position is within the top 3\n",
        "        if product['position']['global_position'] <= 3:\n",
        "            top_positions.append(product['url'])\n",
        "\n",
        "# Calculate mean values for ratings, reviews, and price\n",
        "if total_products > 0:\n",
        "    mean_rating = ratings_sum / total_products\n",
        "else:\n",
        "    mean_rating = None\n",
        "\n",
        "if total_products > 0:\n",
        "    mean_reviews = reviews_sum / total_products\n",
        "else:\n",
        "    mean_reviews = None\n",
        "\n",
        "if products_with_price > 0:\n",
        "    mean_price = price_sum / products_with_price\n",
        "else:\n",
        "    mean_price = None"
      ]
    },
    {
      "cell_type": "markdown",
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        "id": "6_YZXbubBfkV"
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      "source": [
        "In this section, we'll create a summary table that provides key insights about the products retrieved from the Amazon Product Search API. This table includes information such as the total number of products, category, mean price, mean rating, mean total reviews, and the count of best sellers."
      ]
    },
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              "      <td>9348</td>\n",
              "      <td>aps</td>\n",
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              "  Total products Category  Mean price  Mean rating  Mean total reviews  \\\n",
              "0           9348      aps   23.009649     4.542373               13095   \n",
              "\n",
              "   Best seller count  \n",
              "0                  1  "
            ]
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        "info_table = {\n",
        "    'Total products': [data['totalProducts']],\n",
        "    'Category': [data['category']],\n",
        "    'Mean price': [mean_price],\n",
        "    'Mean rating': [mean_rating],\n",
        "    'Mean total reviews': [int(mean_reviews)],\n",
        "    'Best seller count': [best_seller_count]\n",
        "}\n",
        "\n",
        "info_df = pd.DataFrame(info_table)\n",
        "display(info_df)"
      ]
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      "source": [
        "In this section, we will construct a DataFrame that compiles information about the product with the lowest price, the product with the highest price, product with the highest rating, best-selling products, and top-ranking products. This DataFrame serves as a valuable resource to discern essential attributes of products that exhibit strong positioning and successful sales performance."
      ]
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Lowest_price_prduct</th>\n",
              "      <th>highest_price_product</th>\n",
              "      <th>highest_rating_url</th>\n",
              "      <th>best seller 0</th>\n",
              "      <th>1 position product</th>\n",
              "      <th>2 position product</th>\n",
              "      <th>3 position product</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>https://www.amazon.com/dp/B08GGR5HLS</td>\n",
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              "      <td>https://www.amazon.com/dp/B0BDMZS8SX</td>\n",
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            "text/plain": [
              "                    Lowest_price_prduct                 highest_price_product  \\\n",
              "0  https://www.amazon.com/dp/B08GGR5HLS  https://www.amazon.com/dp/B0BDMZS8SX   \n",
              "\n",
              "                     highest_rating_url                         best seller 0  \\\n",
              "0  https://www.amazon.com/dp/B07FY17L3N  https://www.amazon.com/dp/B09MFMCTRK   \n",
              "\n",
              "                     1 position product                    2 position product  \\\n",
              "0  https://www.amazon.com/dp/B08GGR5HLS  https://www.amazon.com/dp/B0BDMZS8SX   \n",
              "\n",
              "                     3 position product  \n",
              "0  https://www.amazon.com/dp/B01LWIAJ3S  "
            ]
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      "source": [
        "unique_table = {\n",
        "    'Lowest_price_prduct': [lowest_price_prod_url],\n",
        "    'highest_price_product': [highest_price_prod_url],\n",
        "    'highest_rating_url': [highest_rating_url]\n",
        "}\n",
        "\n",
        "unique_df = pd.DataFrame(unique_table)\n",
        "\n",
        "j = 0\n",
        "for prod_url in best_sellers_urls:\n",
        " unique_df[f'best seller {j}'] = prod_url\n",
        " j +=1\n",
        "\n",
        "k = 1\n",
        "for prod_url in top_positions:\n",
        "  unique_df[f'{k} position product'] = prod_url\n",
        "  k += 1\n",
        "\n",
        "display(unique_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1XT8t3mhJT4h"
      },
      "source": [
        "### <a id=\"query-data-visualization\">Query Data Visualization</a>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_kHs2EOBYtPB"
      },
      "source": [
        "This code processes data from the API response to extract and categorize key attributes of products in order to visualize the data later on."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "grfIcE7lH-Cp"
      },
      "outputs": [],
      "source": [
        "# Lists to store various attributes\n",
        "total_reviews = []\n",
        "ratings = []\n",
        "prices = []\n",
        "best_sellers = []\n",
        "amzn_primes = []\n",
        "amzn_choices = []\n",
        "glob_positions = []\n",
        "sponsored = []\n",
        "\n",
        "# Loop through each product in the API response\n",
        "for product in data['result']:\n",
        "    # Check if the product's page position is within the first 5 pages\n",
        "    if product['position']['page'] <= 5:\n",
        "        # Append attributes to respective lists\n",
        "        ratings.append(product['reviews']['rating'])  # Ratings\n",
        "        total_reviews.append(product['reviews']['total_reviews'])  # Total reviews\n",
        "\n",
        "        # Check if the product's current price is not 0\n",
        "        if product['price']['current_price'] != 0:\n",
        "            prices.append(product['price']['current_price'])  # Prices\n",
        "\n",
        "        # Handle the case where the current price is 0\n",
        "        if product['price']['current_price'] == 0:\n",
        "            try:\n",
        "                prices.append(sum(prices)/len(prices))  # Calculate average price\n",
        "            except ZeroDivisionError:\n",
        "                prices.append(1)  # Handle division by zero\n",
        "\n",
        "        # Check if the product is marked as Amazon Prime\n",
        "        if product['amazonPrime'] == True:\n",
        "            amzn_primes.append(1)  # Amazon Prime\n",
        "        if product['amazonPrime'] == False:\n",
        "            amzn_primes.append(0)  # Not Amazon Prime\n",
        "\n",
        "        # Check if the product is marked as Amazon Choice\n",
        "        if product['amazonChoice'] == True:\n",
        "            amzn_choices.append(1)  # Amazon Choice\n",
        "        if product['amazonChoice'] == False:\n",
        "            amzn_choices.append(0)  # Not Amazon Choice\n",
        "\n",
        "        # Check if the product is a best seller\n",
        "        if product['bestSeller'] == True:\n",
        "            best_sellers.append(1)  # Best seller\n",
        "        if product['bestSeller'] == False:\n",
        "            best_sellers.append(0)  # Not a best seller\n",
        "\n",
        "        # Store global position of the product\n",
        "        glob_positions.append(product['position']['global_position'])  # Global position\n",
        "\n",
        "        # Check if the product is sponsored\n",
        "        if product['sponsored'] == True:\n",
        "            sponsored.append(1)  # Sponsored\n",
        "        if product['sponsored'] == False:\n",
        "            sponsored.append(0)  # Not sponsored\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CcHLxKt9KiW8"
      },
      "source": [
        "This code generates a histogram to analyze the distribution of total reviews across products. The histogram visualization of total reviews provides a clear view of how reviews are distributed across products. Peaks in the histogram indicate popular products with a specific range of review counts, while the shape and tail behavior help identify the concentration of reviews and the presence of exceptional cases."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "O_P12Mg5NK6f",
        "outputId": "43da6c84-714c-4f48-e1ad-7c5a0d109e02"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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IMIULFzalS5c2I0aMMJcuXXJ6/uXLl80rr7xiwsLCjLe3t3nggQfMrl27TIkSJZwuh85qLMbk/LWU1Tayeu307t3b+Pr6Znl8gfxgM4aZWgBgJXFxcSpevLjGjRunkSNHurocwKWYIwMAbuzixYsZ2q7Orbn2lgnA7Yg5MgDgxmbNmqXp06erTZs2Klq0qFatWqVvvvlGLVu2dNyiAbidEWQAwI3dfffd8vT01Jtvvqn4+HjHBOBx48a5ujTALTBHBgAAWBZzZAAAgGURZAAAgGW5dI7MhAkTNHfuXO3evVve3t667777NHHiRFWqVMnRp1mzZlq+fLnT85566ilNmzYtR/tIT0/XX3/9pWLFit2S+5oAAIC8Z4xRQkKCwsPDVahQ1uddXDpHplWrVurWrZvuueceXb58WS+99JJ27NihnTt3Or7au1mzZqpYsaJeffVVx/N8fHzk5+eXo30cO3Yswx1bAQCANRw9elSlSpXKcr1Lz8jExMQ4LU+fPl0hISHatGmTmjRp4mj38fFRWFhYrvZx9avCjx49muPwAwAAXCs+Pl6lS5d2uvFuZtzq8uvz589LunIX23/66quv9OWXXyosLEzt2rXTK6+8Ih8fn0y3kZyc7HSjuISEBEmSn58fQQYAAIu53rQQtwky6enpGjx4sBo2bKhq1ao52h955BGVLVtW4eHh2r59u1588UXt2bNHc+fOzXQ7EyZM0NixY/OrbAAA4EJu8z0yzzzzjH7++WetWrUq28/ClixZoubNm2vfvn1OdxC+6tozMldPTZ0/f54zMgAAWER8fLz8/f2v+/7tFmdkBgwYoB9//FErVqzINsRIUv369SUpyyBjt9tlt9tvSZ0AAMC9uDTIGGP03HPPad68eVq2bJkiIiKu+5ytW7dKkkqWLHmLqwMAAO7OpUGmf//++vrrr/Xdd9+pWLFiOnnypCTJ399f3t7e2r9/v77++mu1adNGJUqU0Pbt2/X888+rSZMmuvvuu11ZOgAAcAMunSOT1Uzk6Oho9enTR0ePHlXPnj21Y8cOJSUlqXTp0nrwwQf18ssv53i+S04/YwMAAO7DEnNkrpehSpcuneFbfQEAAK7iXksAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCy3OJeS1YVGxur+Pj4bPv4+fkpODg4nyoCAOD2QpDJpdjYWPXs+7jOJlzItl9gMR99Gf0JYQYAgFuAIJNL8fHxOptwQcENHpJvYGimfZLOnlLs2m8VHx9PkAEA4BYgyNwk38BQ+YWUynJ9bD7WAgDA7YbJvgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLJcGmQmTJige+65R8WKFVNISIg6duyoPXv2OPW5dOmS+vfvrxIlSqho0aJ66KGHdOrUKRdVDAAA3IlLg8zy5cvVv39/rVu3TgsXLlRqaqpatmyppKQkR5/nn39eP/zwg+bMmaPly5frr7/+UqdOnVxYNQAAcBeertx5TEyM0/L06dMVEhKiTZs2qUmTJjp//rw+/fRTff3113rggQckSdHR0apSpYrWrVune++91xVlAwAAN+FWc2TOnz8vSQoMDJQkbdq0SampqWrRooWjT+XKlVWmTBmtXbs2020kJycrPj7e6QEAAAomtwky6enpGjx4sBo2bKhq1apJkk6ePCkvLy8FBAQ49Q0NDdXJkycz3c6ECRPk7+/veJQuXfpWlw4AAFzEbYJM//79tWPHDs2cOfOmtjNixAidP3/e8Th69GgeVQgAANyNS+fIXDVgwAD9+OOPWrFihUqVKuVoDwsLU0pKiuLi4pzOypw6dUphYWGZbstut8tut9/qkgEAgBtw6RkZY4wGDBigefPmacmSJYqIiHBaX6dOHRUuXFiLFy92tO3Zs0dHjhxRgwYN8rtcAADgZlx6RqZ///76+uuv9d1336lYsWKOeS/+/v7y9vaWv7+/+vXrpyFDhigwMFB+fn567rnn1KBBA65YAgAArg0yU6dOlSQ1a9bMqT06Olp9+vSRJE2ZMkWFChXSQw89pOTkZEVFRemDDz7I50oBAIA7cmmQMcZct0+RIkX0/vvv6/3338+HigAAgJW4zVVLAAAAN4ogAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALIsgAwAALMulQWbFihVq166dwsPDZbPZNH/+fKf1ffr0kc1mc3q0atXKNcUCAAC349Igk5SUpBo1auj999/Psk+rVq104sQJx+Obb77JxwoBAIA783Tlzlu3bq3WrVtn28dutyssLCyfKgIAAFbi9nNkli1bppCQEFWqVEnPPPOMzpw54+qSAACAm3DpGZnradWqlTp16qSIiAjt379fL730klq3bq21a9fKw8Mj0+ckJycrOTnZsRwfH59f5QIAgHzm1kGmW7dujn9Xr15dd999t8qXL69ly5apefPmmT5nwoQJGjt2bH6VCAAAXMjtP1r6pzvvvFNBQUHat29fln1GjBih8+fPOx5Hjx7NxwoBAEB+ytUZmQMHDujOO+/M61qu69ixYzpz5oxKliyZZR+73S673Z6PVQEAAFfJ1RmZChUq6P7779eXX36pS5cu5XrniYmJ2rp1q7Zu3SpJOnjwoLZu3aojR44oMTFRw4cP17p163To0CEtXrxYHTp0UIUKFRQVFZXrfQIAgIIjV0Fm8+bNuvvuuzVkyBCFhYXpqaee0oYNG254Oxs3blStWrVUq1YtSdKQIUNUq1YtjRo1Sh4eHtq+fbvat2+vihUrql+/fqpTp45WrlzJGRcAACAplx8t1axZU++8844mTZqk77//XtOnT1ejRo1UsWJFPfbYY+rVq5eCg4Ovu51mzZrJGJPl+l9++SU35QEAgNvETU329fT0VKdOnTRnzhxNnDhR+/bt07Bhw1S6dGk9+uijOnHiRF7VCQAAkMFNBZmNGzfq2WefVcmSJTV58mQNGzZM+/fv18KFC/XXX3+pQ4cOeVUnAABABrn6aGny5MmKjo7Wnj171KZNG33++edq06aNChW6kosiIiI0ffp0lStXLi9rBQAAcJKrIDN16lQ99thj6tOnT5aXQoeEhOjTTz+9qeIAAACyk6sgs3fv3uv28fLyUu/evXOzeQAAgBzJ1RyZ6OhozZkzJ0P7nDlzNGPGjJsuCgAAICdyFWQmTJigoKCgDO0hISF6/fXXb7ooAACAnMhVkDly5IgiIiIytJctW1ZHjhy56aIAAAByIldBJiQkRNu3b8/Qvm3bNpUoUeKmiwIAAMiJXAWZ7t27a+DAgVq6dKnS0tKUlpamJUuWaNCgQerWrVte1wgAAJCpXF219Nprr+nQoUNq3ry5PD2vbCI9PV2PPvooc2QAAEC+yVWQ8fLy0qxZs/Taa69p27Zt8vb2VvXq1VW2bNm8rg8AACBLuQoyV1WsWFEVK1bMq1oAAABuSK6CTFpamqZPn67Fixfr9OnTSk9Pd1q/ZMmSPCkOAAAgO7kKMoMGDdL06dPVtm1bVatWTTabLa/rAgAAuK5cBZmZM2dq9uzZatOmTV7XAwAAkGO5uvzay8tLFSpUyOtaAAAAbkiugszQoUP1zjvvyBiT1/UAAADkWK4+Wlq1apWWLl2qn3/+WVWrVlXhwoWd1s+dOzdPigMAAMhOroJMQECAHnzwwbyuBQAA4IbkKshER0fndR0AAAA3LFdzZCTp8uXLWrRokT788EMlJCRIkv766y8lJibmWXEAAADZydUZmcOHD6tVq1Y6cuSIkpOT9a9//UvFihXTxIkTlZycrGnTpuV1nQAAABnk6ozMoEGDVLduXZ07d07e3t6O9gcffFCLFy/Os+IAAACyk6szMitXrtSaNWvk5eXl1F6uXDkdP348TwoDAAC4nlydkUlPT1daWlqG9mPHjqlYsWI3XRQAAEBO5CrItGzZUm+//bZj2WazKTExUaNHj+a2BQAAIN/k6qOlSZMmKSoqSnfddZcuXbqkRx55RHv37lVQUJC++eabvK4RAAAgU7kKMqVKldK2bds0c+ZMbd++XYmJierXr5969OjhNPkXAADgVspVkJEkT09P9ezZMy9rAQAAuCG5CjKff/55tusfffTRXBUDAABwI3IVZAYNGuS0nJqaqgsXLsjLy0s+Pj4EGQAAkC9yddXSuXPnnB6JiYnas2ePGjVqxGRfAACQb3J9r6VrRUZG6o033shwtgYAAOBWybMgI12ZAPzXX3/l5SYBAACylKs5Mt9//73TsjFGJ06c0H//+181bNgwTwoDAAC4nlwFmY4dOzot22w2BQcH64EHHtCkSZPyoi4AAIDrylWQSU9Pz+s6AAAAbliezpEBAADIT7k6IzNkyJAc9508eXJudgEAAHBduQoyW7Zs0ZYtW5SamqpKlSpJkv788095eHiodu3ajn42my1vqgQAAMhEroJMu3btVKxYMc2YMUPFixeXdOVL8vr27avGjRtr6NCheVokAABAZnI1R2bSpEmaMGGCI8RIUvHixTVu3DiuWgIAAPkmV0EmPj5esbGxGdpjY2OVkJBw00UBAADkRK6CzIMPPqi+fftq7ty5OnbsmI4dO6Zvv/1W/fr1U6dOnfK6RgAAgEzlao7MtGnTNGzYMD3yyCNKTU29siFPT/Xr109vvfVWnhYIAACQlVwFGR8fH33wwQd66623tH//fklS+fLl5evrm6fFAQAAZOemvhDvxIkTOnHihCIjI+Xr6ytjTF7VBQAAcF25CjJnzpxR8+bNVbFiRbVp00YnTpyQJPXr149LrwEAQL7JVZB5/vnnVbhwYR05ckQ+Pj6O9q5duyomJibPigMAAMhOrubI/Prrr/rll19UqlQpp/bIyEgdPnw4TwoDAAC4nlydkUlKSnI6E3PV2bNnZbfbb7ooAACAnMhVkGncuLE+//xzx7LNZlN6errefPNN3X///XlWHAAAQHZy9dHSm2++qebNm2vjxo1KSUnRCy+8oD/++ENnz57V6tWr87pGAACATOXqjEy1atX0559/qlGjRurQoYOSkpLUqVMnbdmyReXLl8/rGgEAADJ1w2dkUlNT1apVK02bNk0jR468FTUBAADkyA2fkSlcuLC2b99+K2oBAAC4Ibn6aKlnz5769NNP87oWAACAG5Kryb6XL1/WZ599pkWLFqlOnToZ7rE0efLkPCkOAAAgOzcUZA4cOKBy5cppx44dql27tiTpzz//dOpjs9nyrjoAAIBs3FCQiYyM1IkTJ7R06VJJV25J8O677yo0NPSWFAcAAJCdG5ojc+3drX/++WclJSXlaUEAAAA5lavJvlddG2wAAADy0w0FGZvNlmEOzM3MiVmxYoXatWun8PBw2Ww2zZ8/32m9MUajRo1SyZIl5e3trRYtWmjv3r253h8AAChYbmiOjDFGffr0cdwY8tKlS3r66aczXLU0d+7cHG0vKSlJNWrU0GOPPaZOnTplWP/mm2/q3Xff1YwZMxQREaFXXnlFUVFR2rlzp4oUKXIjpQMAgALohoJM7969nZZ79ux5Uztv3bq1Wrdunek6Y4zefvttvfzyy+rQoYMk6fPPP1doaKjmz5+vbt263dS+AQCA9d1QkImOjr5VdWRw8OBBnTx5Ui1atHC0+fv7q379+lq7dm2WQSY5OVnJycmO5fj4+FteKwAAcI2bmux7K508eVKSMlzaHRoa6liXmQkTJsjf39/xKF269C2tEwAAuI7bBpncGjFihM6fP+94HD161NUlAQCAW8Rtg0xYWJgk6dSpU07tp06dcqzLjN1ul5+fn9MDAAAUTG4bZCIiIhQWFqbFixc72uLj47V+/Xo1aNDAhZUBAAB3kaubRuaVxMRE7du3z7F88OBBbd26VYGBgSpTpowGDx6scePGKTIy0nH5dXh4uDp27Oi6ogEAgNtwaZDZuHGj7r//fsfykCFDJF25zHv69Ol64YUXlJSUpCeffFJxcXFq1KiRYmJi+A4ZAAAgycVBplmzZtne5sBms+nVV1/Vq6++mo9VAQAAq3DbOTIAAADXQ5ABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACW5dLvkcEVsbGxio+Pv24/Pz8/BQcH50NFAABYA0HGxWJjY9Wz7+M6m3Dhun0Di/noy+hPCDMAAPw/BBkXi4+P19mECwpu8JB8A0Oz7Jd09pRi136r+Ph4ggwAAP8PQcZN+AaGyi+kVLZ9YvOpFgAArILJvgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLIIMgAAwLI8XV1AQZeakqLDhw9nuf7w4cO6nHo5HysCAKDgIMjcQsmJ53Xo4AENfmmM7HZ7pn0uXbygY8dPqExqaj5XBwCA9RFkbqHU5ItKt3kq6N5OKhFeNtM+p/fv0OGjnyntMkEGAIAbRZDJBz7Fg+UXUirTdYlnTuZzNQAAFBxM9gUAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJbFvZYsJDUlRYcPH862j5+fn4KDg/OpIgAAXIsgYxHJied16OABDX5pjOx2e5b9Aov56MvoTwgzAIDbAkHGIlKTLyrd5qmgezupRHjZTPsknT2l2LXfKj4+niADALgtEGQsxqd4sPxCSmW5PjYfawEAwNWY7AsAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACzLrYPMmDFjZLPZnB6VK1d2dVkAAMBNuP0X4lWtWlWLFi1yLHt6un3JAAAgn7h9KvD09FRYWJirywAAAG7IrT9akqS9e/cqPDxcd955p3r06KEjR45k2z85OVnx8fFODwAAUDC5dZCpX7++pk+frpiYGE2dOlUHDx5U48aNlZCQkOVzJkyYIH9/f8ejdOnS+VgxAADIT24dZFq3bq3OnTvr7rvvVlRUlBYsWKC4uDjNnj07y+eMGDFC58+fdzyOHj2ajxUDAID85PZzZP4pICBAFStW1L59+7LsY7fbZbfb87EqAADgKm59RuZaiYmJ2r9/v0qWLOnqUgAAgBtw6yAzbNgwLV++XIcOHdKaNWv04IMPysPDQ927d3d1aQAAwA249UdLx44dU/fu3XXmzBkFBwerUaNGWrdunYKDg11dGgAAcANuHWRmzpzp6hIAAIAbc+uPlgAAALJDkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJZFkAEAAJbl6eoCkP9iY2MVHx+fbR8/Pz8FBwfnU0UAAOQOQeY2Exsbq559H9fZhAvZ9gss5qMvoz8hzAAA3BpB5jYTHx+vswkXFNzgIfkGhmbaJ+nsKcWu/Vbx8fEEGQCAWyPI3KZ8A0PlF1Iqy/Wx+VgLAAC5xWRfAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWdyioIBJTUnR4cOHs1x/+PBhXU69nCf74i7ayAqvDaBgsMLvMkGmAElOPK9DBw9o8EtjZLfbM+1z6eIFHTt+QmVSU29qX9xFG1nhtQEUDFb5XSbIFCCpyReVbvNU0L2dVCK8bKZ9Tu/focNHP1Pa5ZsLMtxFG1nhtQEUDFb5XSbIFEA+xYOzvLN14pmTebov7qKNrPDaAAoGd/9dZrIvAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLO61BJfLz9vEW+GW9ACAnCPIwKXy8zbxVrklPQAg5wgycKn8vE28VW5JDwDIOYIM3EJ+3ibe3W9JDwDIOSb7AgAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAy7JEkHn//fdVrlw5FSlSRPXr19eGDRtcXRIAAHADbh9kZs2apSFDhmj06NHavHmzatSooaioKJ0+fdrVpQEAABdz+yAzefJkPfHEE+rbt6/uuusuTZs2TT4+Pvrss89cXRoAAHAxtw4yKSkp2rRpk1q0aOFoK1SokFq0aKG1a9e6sDIAAOAO3PpeS3///bfS0tIUGup8g7/Q0FDt3r070+ckJycrOTnZsXz+/HlJV24YmJcSEhKUdvmy4k4cUuqlzO+mHH/6mEx6uuJPHpWnLfPt5KRPXm4rJ32Szp1W8sWL2rlzpxISEjLtc/ToUaVcupTt+PNzOzmRn/u6nXGcgYIhp7/LaZcvKyEhIc/fZ69uzxiTfUfjxo4fP24kmTVr1ji1Dx8+3NSrVy/T54wePdpI4sGDBw8ePHgUgMfRo0ezzQpufUYmKChIHh4eOnXqlFP7qVOnFBYWlulzRowYoSFDhjiW09PTdfbsWZUoUUI2WzanPG5QfHy8SpcuraNHj8rPzy/PtmsVt/P4b+exS7f3+G/nsUu39/hv57FLrhm/MUYJCQkKDw/Ptp9bBxkvLy/VqVNHixcvVseOHSVdCSaLFy/WgAEDMn2O3W6X3W53agsICLhlNfr5+d2WL+qrbufx385jl27v8d/OY5du7/HfzmOX8n/8/v7+1+3j1kFGkoYMGaLevXurbt26qlevnt5++20lJSWpb9++ri4NAAC4mNsHma5duyo2NlajRo3SyZMnVbNmTcXExGSYAAwAAG4/bh9kJGnAgAFZfpTkKna7XaNHj87wMdbt4nYe/+08dun2Hv/tPHbp9h7/7Tx2yb3HbzPmetc1AQAAuCe3/kI8AACA7BBkAACAZRFkAACAZRFkAACAZRFkcun9999XuXLlVKRIEdWvX18bNmxwdUnZmjBhgu655x4VK1ZMISEh6tixo/bs2ePU59KlS+rfv79KlCihokWL6qGHHsrwrcpHjhxR27Zt5ePjo5CQEA0fPlyXL1926rNs2TLVrl1bdrtdFSpU0PTp0zPU48rj98Ybb8hms2nw4MGOtoI+9uPHj6tnz54qUaKEvL29Vb16dW3cuNGx3hijUaNGqWTJkvL29laLFi20d+9ep22cPXtWPXr0kJ+fnwICAtSvXz8lJiY69dm+fbsaN26sIkWKqHTp0nrzzTcz1DJnzhxVrlxZRYoUUfXq1bVgwYJbM2hJaWlpeuWVVxQRESFvb2+VL19er732mtO9WwrS2FesWKF27dopPDxcNptN8+fPd1rvTmPNSS15Of7U1FS9+OKLql69unx9fRUeHq5HH31Uf/31V4EY//V+9v/09NNPy2az6e233y4QY3frey25q5kzZxovLy/z2WefmT/++MM88cQTJiAgwJw6dcrVpWUpKirKREdHmx07dpitW7eaNm3amDJlypjExERHn6efftqULl3aLF682GzcuNHce++95r777nOsv3z5sqlWrZpp0aKF2bJli1mwYIEJCgoyI0aMcPQ5cOCA8fHxMUOGDDE7d+407733nvHw8DAxMTGOPq48fhs2bDDlypUzd999txk0aNBtMfazZ8+asmXLmj59+pj169ebAwcOmF9++cXs27fP0eeNN94w/v7+Zv78+Wbbtm2mffv2JiIiwly8eNHRp1WrVqZGjRpm3bp1ZuXKlaZChQqme/fujvXnz583oaGhpkePHmbHjh3mm2++Md7e3ubDDz909Fm9erXx8PAwb775ptm5c6d5+eWXTeHChc3vv/9+S8Y+fvx4U6JECfPjjz+agwcPmjlz5piiRYuad955p0COfcGCBWbkyJFm7ty5RpKZN2+e03p3GmtOasnL8cfFxZkWLVqYWbNmmd27d5u1a9eaevXqmTp16jhtw6rjv97P/qq5c+eaGjVqmPDwcDNlypQCMXaCTC7Uq1fP9O/f37GclpZmwsPDzYQJE1xY1Y05ffq0kWSWL19ujLnyS164cGEzZ84cR59du3YZSWbt2rXGmCu/KIUKFTInT5509Jk6darx8/MzycnJxhhjXnjhBVO1alWnfXXt2tVERUU5ll11/BISEkxkZKRZuHChadq0qSPIFPSxv/jii6ZRo0ZZrk9PTzdhYWHmrbfecrTFxcUZu91uvvnmG2OMMTt37jSSzG+//ebo8/PPPxubzWaOHz9ujDHmgw8+MMWLF3ccj6v7rlSpkmO5S5cupm3btk77r1+/vnnqqadubpBZaNu2rXnsscec2jp16mR69OhhjCnYY7/2zcydxpqTWm5Wdm/mV23YsMFIMocPHzbGFJzxZzX2Y8eOmTvuuMPs2LHDlC1b1inIWHnsfLR0g1JSUrRp0ya1aNHC0VaoUCG1aNFCa9eudWFlN+b8+fOSpMDAQEnSpk2blJqa6jSuypUrq0yZMo5xrV27VtWrV3f6VuWoqCjFx8frjz/+cPT55zau9rm6DVcev/79+6tt27YZ6ivoY//+++9Vt25dde7cWSEhIapVq5Y+/vhjx/qDBw/q5MmTTnX5+/urfv36TuMPCAhQ3bp1HX1atGihQoUKaf369Y4+TZo0kZeXl6NPVFSU9uzZo3Pnzjn6ZHeM8tp9992nxYsX688//5Qkbdu2TatWrVLr1q0lFeyxX8udxpqTWvLD+fPnZbPZHPfjK8jjT09PV69evTR8+HBVrVo1w3orj50gc4P+/vtvpaWlZbhFQmhoqE6ePOmiqm5Menq6Bg8erIYNG6patWqSpJMnT8rLyyvDDTb/Oa6TJ09mOu6r67LrEx8fr4sXL7rs+M2cOVObN2/WhAkTMqwr6GM/cOCApk6dqsjISP3yyy965plnNHDgQM2YMcOp/uzqOnnypEJCQpzWe3p6KjAwME+O0a0a///5P/9H3bp1U+XKlVW4cGHVqlVLgwcPVo8ePZzqKohjv5Y7jTUntdxqly5d0osvvqju3bs7boJYkMc/ceJEeXp6auDAgZmut/LYLXGLAuSt/v37a8eOHVq1apWrS8kXR48e1aBBg7Rw4UIVKVLE1eXku/T0dNWtW1evv/66JKlWrVrasWOHpk2bpt69e7u4ultr9uzZ+uqrr/T111+ratWq2rp1qwYPHqzw8PACP3ZkLTU1VV26dJExRlOnTnV1Obfcpk2b9M4772jz5s2y2WyuLifPcUbmBgUFBcnDwyPDFS2nTp1SWFiYi6rKuQEDBujHH3/U0qVLVapUKUd7WFiYUlJSFBcX59T/n+MKCwvLdNxX12XXx8/PT97e3i45fps2bdLp06dVu3ZteXp6ytPTU8uXL9e7774rT09PhYaGFtixS1LJkiV11113ObVVqVJFR44ccao/u7rCwsJ0+vRpp/WXL1/W2bNn8+QY3arxDx8+3HFWpnr16urVq5eef/55x5m5gjz2a7nTWHNSy61yNcQcPnxYCxcudJyNuVpXQRz/ypUrdfr0aZUpU8bxN/Dw4cMaOnSoypUr56jJqmMnyNwgLy8v1alTR4sXL3a0paena/HixWrQoIELK8ueMUYDBgzQvHnztGTJEkVERDitr1OnjgoXLuw0rj179ujIkSOOcTVo0EC///6704v96h+Cq2+UDRo0cNrG1T5Xt+GK49e8eXP9/vvv2rp1q+NRt25d9ejRw/Hvgjp2SWrYsGGGS+3//PNPlS1bVpIUERGhsLAwp7ri4+O1fv16p/HHxcVp06ZNjj5LlixRenq66tev7+izYsUKpaamOvosXLhQlSpVUvHixR19sjtGee3ChQsqVMj5z5yHh4fS09MlFeyxX8udxpqTWm6FqyFm7969WrRokUqUKOG0vqCOv1evXtq+fbvT38Dw8HANHz5cv/zyi/XHnqspwre5mTNnGrvdbqZPn2527txpnnzySRMQEOB0RYu7eeaZZ4y/v79ZtmyZOXHihONx4cIFR5+nn37alClTxixZssRs3LjRNGjQwDRo0MCx/uolyC1btjRbt241MTExJjg4ONNLkIcPH2527dpl3n///UwvQXb18fvnVUvGFOyxb9iwwXh6eprx48ebvXv3mq+++sr4+PiYL7/80tHnjTfeMAEBAea7774z27dvNx06dMj0stxatWqZ9evXm1WrVpnIyEinSzPj4uJMaGio6dWrl9mxY4eZOXOm8fHxyXBppqenp/nPf/5jdu3aZUaPHn1LL7/u3bu3ueOOOxyXX8+dO9cEBQWZF154oUCOPSEhwWzZssVs2bLFSDKTJ082W7ZscVyV405jzUkteTn+lJQU0759e1OqVCmzdetWp7+D/7wKx6rjv97P/lrXXrVk5bETZHLpvffeM2XKlDFeXl6mXr16Zt26da4uKVuSMn1ER0c7+ly8eNE8++yzpnjx4sbHx8c8+OCD5sSJE07bOXTokGndurXx9vY2QUFBZujQoSY1NdWpz9KlS03NmjWNl5eXufPOO532cZWrj9+1Qaagj/2HH34w1apVM3a73VSuXNl89NFHTuvT09PNK6+8YkJDQ43dbjfNmzc3e/bscepz5swZ0717d1O0aFHj5+dn+vbtaxISEpz6bNu2zTRq1MjY7XZzxx13mDfeeCNDLbNnzzYVK1Y0Xl5epmrVquann37K+wH/P/Hx8WbQoEGmTJkypkiRIubOO+80I0eOdHrjKkhjX7p0aaa/571793a7seaklrwc/8GDB7P8O7h06VLLj/96P/trZRZkrDp2mzH/+IpLAAAAC2GODAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDAAAsCyCDACXs9lsmj9/fr7vd8yYMapZs2a+7xdA3iHIAHCw2WzZPsaMGZPlcw8dOiSbzaatW7fmW703a9iwYRnuCwPAWjxdXQAA93HixAnHv2fNmqVRo0Y53XCyaNGirigrg5SUFHl5ed30dooWLeo2YwKQO5yRAeAQFhbmePj7+8tmszmWQ0JCNHnyZJUqVUp2u101a9ZUTEyM47lX76heq1Yt2Ww2NWvWTJL022+/6V//+peCgoLk7++vpk2bavPmzTdUV7NmzTRgwAANHjxYQUFBioqKkiTt2LFDrVu3VtGiRRUaGqpevXrp77//liR99NFHCg8Pd9zp+qoOHTrosccek5T5R0uffPKJqlSpoiJFiqhy5cr64IMPHOsefvhhDRgwwLE8ePBg2Ww27d69W9KVgOXr66tFixZJkv73v/+pevXq8vb2VokSJdSiRQslJSXd0NgBZI8gAyBH3nnnHU2aNEn/+c9/tH37dkVFRal9+/bau3evJGnDhg2SpEWLFunEiROaO3euJCkhIUG9e/fWqlWrtG7dOkVGRqpNmzZKSEi4of3PmDFDXl5eWr16taZNm6a4uDg98MADqlWrljZu3KiYmBidOnVKXbp0kSR17txZZ86c0dKlSx3bOHv2rGJiYtSjR49M9/HVV19p1KhRGj9+vHbt2qXXX39dr7zyimbMmCFJatq0qZYtW+bov3z5cgUFBTnafvvtN6Wmpuq+++7TiRMn1L17dz322GPatWuXli1bpk6dOonb2wF5LNe3mwRQoEVHRxt/f3/Hcnh4uBk/frxTn3vuucc8++yzxhjjuLvwli1bst1uWlqaKVasmPnhhx8cbZLMvHnzsnxO06ZNTa1atZzaXnvtNdOyZUuntqNHjxpJjjvpdujQwTz22GOO9R9++KEJDw83aWlpxhhjRo8ebWrUqOFYX758efP1119n2E+DBg2MMcZs377d2Gw2c/r0aXP27Fnj5eVlXnvtNdO1a1djjDHjxo0z9913nzHGmE2bNhlJ5tChQ9keDwA3hzMyAK4rPj5ef/31lxo2bOjU3rBhQ+3atSvb5546dUpPPPGEIiMj5e/vLz8/PyUmJurIkSM3VEOdOnWclrdt26alS5c65rkULVpUlStXliTt379fktSjRw99++23Sk5OlnTljEu3bt1UqFDGP31JSUnav3+/+vXr57TNcePGObZXrVo1BQYGavny5Vq5cqVq1aqlf//731q+fLmkK2dorn6kVqNGDTVv3lzVq1dX586d9fHHH+vcuXM3NGYA18dkXwC3VO/evXXmzBm98847Klu2rOx2uxo0aKCUlJQb2o6vr6/TcmJiotq1a6eJEydm6FuyZElJUrt27WSM0U8//aR77rlHK1eu1JQpUzLdfmJioiTp448/Vv369Z3WeXh4SLpyVVeTJk20bNky2e12NWvWTHfffbeSk5O1Y8cOrVmzRsOGDXM8Z+HChVqzZo1+/fVXvffeexo5cqTWr1/vmE8E4OYRZABcl5+fn8LDw7V69Wo1bdrU0b569WrVq1dPkhxXEaWlpTk9d/Xq1frggw/Upk0bSdLRo0cdE3JvRu3atfXtt9+qXLly8vTM/E9ZkSJF1KlTJ3311Vfat2+fKlWqpNq1a2faNzQ0VOHh4Tpw4ECWc2ikK/NkPv74Y9ntdo0fP16FChVSkyZN9NZbbyk5OdnprJXNZlPDhg3VsGFDjRo1SmXLltW8efM0ZMiQmxs8AAeCDIAcGT58uEaPHq3y5curZs2aio6O1tatW/XVV19JkkJCQuTt7a2YmBiVKlVKRYoUkb+/vyIjI/XFF1+obt26io+P1/Dhw+Xt7X3T9fTv318ff/yxunfvrhdeeEGBgYHat2+fZs6cqU8++cRxFqVHjx7697//rT/++EM9e/bMdptjx47VwIED5e/vr1atWik5OVkbN27UuXPnHOGjWbNmev755+Xl5aVGjRo52oYNG6Z77rnHceZo/fr1Wrx4sVq2bKmQkBCtX79esbGxqlKlyk2PHcD/xxwZADkycOBADRkyREOHDlX16tUVExOj77//XpGRkZIkT09Pvfvuu/rwww8VHh6uDh06SJI+/fRTnTt3TrVr11avXr00cOBAhYSE3HQ9V88QpaWlqWXLlqpevboGDx6sgIAApzkwDzzwgAIDA7Vnzx498sgj2W7z8ccf1yeffKLo6GhVr15dTZs21fTp050+CqpevboCAgJUs2ZNx3fQNGvWTGlpaY75MdKVs1grVqxQmzZtVLFiRb388suaNGmSWrdufdNjB/D/2YzhWkAAAGBNnJEBAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACWRZABAACW9X8B5+gt09+Js1gAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# create total_reviews histogram\n",
        "plt.hist(total_reviews, bins=50, edgecolor='black', alpha=0.7)\n",
        "plt.xlabel('Total reviews')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Total reviews histogram')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tw5VxlLzK-NV"
      },
      "source": [
        "The histogram below visualizes the distribution of ratings across products. Peaks and patterns in the histogram offer insights into the most common rating ranges, revealing product popularity levels and potential customer preferences. The visualization can also highlight any extreme or outlier ratings that could impact purchasing decisions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "HAl_JEZzNLBh",
        "outputId": "2768199c-3f49-4628-fd3c-50d44c1d0307"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# create ratings histogram\n",
        "plt.hist(ratings, bins=50, edgecolor='black', alpha=0.7)\n",
        "plt.xlabel('Ratings')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Ratings histogram')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VgxlZe5yLQJu"
      },
      "source": [
        "This histogram illustrates the distribution of prices among products. Peaks and clusters in the histogram unveil common price ranges, aiding in identifying price points that attract customers. Additionally, the visualization can highlight products at the extremes, indicating premium or budget offerings that may influence consumer choices."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Cyvd86eINLH2",
        "outputId": "f1ee4f39-bd86-402a-977d-d13034c7a75b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# create prices histogram\n",
        "plt.hist(prices, bins=50, edgecolor='black', alpha=0.7)\n",
        "plt.xlabel('Price USD')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Price histogram')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lhKrGjVcLa2G"
      },
      "source": [
        "The heatmap showcases the Pearson correlation coefficients between various attributes of products. Colors and values in the heatmap indicate the strength and direction of relationships. By observing patterns, analysts can discern connections such as how ratings, prices, total reviews, best seller status, and other attributes interrelate, aiding in identifying factors that influence product performance."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 807
        },
        "id": "ny_ggO6JT8UH",
        "outputId": "98187a94-a721-4ef5-f92a-9fe64b59ae29"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# create pearson correlation heatmap\n",
        "Vals = {'ratings': ratings,\n",
        "        'prices': prices,\n",
        "        'total_reviews': total_reviews,\n",
        "        'best_sellers': best_sellers,\n",
        "        'amzn_primes': amzn_primes,\n",
        "        'amzn_choices': amzn_choices,\n",
        "        'glob_positions': glob_positions,\n",
        "        'sponsored': sponsored\n",
        "}\n",
        "\n",
        "df = pd.DataFrame(Vals)\n",
        "\n",
        "corr_matrix = df.corr()\n",
        "plt.figure(figsize=(12, 8))\n",
        "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0)\n",
        "plt.title('Pearson Correlation Heatmap')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o-LlgmVW9-Fa"
      },
      "source": [
        "## <a id=\"amazon-profitability-predictor\">Amazon Profitability Predictor</a>\n",
        "\n",
        "In this section I developed two Random Forest models to predict whether the product will be succeseful to sell. Additionally, I implemented a Query Analyzer function to assess products against medians for indicators like revenue and demand ratio. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WydAAJQeBTQF"
      },
      "source": [
        "### <a id=\"data-retrieval-and-dataframe-construction\">Data Retrieval and DataFrame Construction</a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Sa-exoiJBIct"
      },
      "outputs": [],
      "source": [
        "# import libraries\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.ensemble import RandomForestClassifier"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i09u-MwaHhSx"
      },
      "source": [
        "Below I created list with popular amazon queries. It will be used to create DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-lhmUP5Zqdtj",
        "outputId": "256edfb9-c73f-4e6f-b013-25b56e904e0c"
      },
      "outputs": [],
      "source": [
        "products = [\n",
        "    \"Wireless%Headpones\",\n",
        "    \"Smart%Home%Device\",\n",
        "    \"Camera\",\n",
        "    \"Health%and%Fitness%Tracker\",\n",
        "    \"Portable%Bluetooth%Speaker\",\n",
        "    \"Personal%Care%Appliance\",\n",
        "    \"Kitchen%Essential\",\n",
        "    \"Wireless%Charging%Solution\",\n",
        "    \"LED%Desk%Light\",\n",
        "    \"Outdoor%Adventure%Gear\",\n",
        "    \"Fitness%Training%Kit\",\n",
        "    \"Home%and%Kitchen%Tool\",\n",
        "    \"Automated%Cleaning%Device\",\n",
        "    \"On-the-Go%Drinkware\",\n",
        "    \"Sleep%and%Relaxation%Device\",\n",
        "    \"Kitchen%Appliance\",\n",
        "    \"Outdoor%Illumination\",\n",
        "    \"Exercise%Mat\",\n",
        "    \"Noise-Isolating%Headphones\",\n",
        "    \"Home%Security%Solution\",\n",
        "    \"Car%Phone%Mount\",\n",
        "    \"ElectricTransport\",\n",
        "    \"Portable%Powerbank\",\n",
        "    \"Plant%Container\",\n",
        "    \"Foldable%Fitness%Equipment\",\n",
        "    \"Portable%Media%Display\",\n",
        "    \"Smart%Thermostat\",\n",
        "    \"Kitchen%Appliance\",\n",
        "    \"Temperature%Monitor\",\n",
        "    \"Smart%Door%Access\",\n",
        "    \"Outdoor%Relaxation\",\n",
        "    \"Home%Light\",\n",
        "    \"Virtual%Reality\",\n",
        "    \"Culinary%Tool%Set\",\n",
        "    \"Ergonomic%Office%Seat\",\n",
        "    \"Hair%Styling\",\n",
        "    \"Gaming%Controller\",\n",
        "    \"Kitchen%Prep%Essential\",\n",
        "    \"Space-Saving%Containers\",\n",
        "    \"Fidget%Tool\",\n",
        "    \"Digital%Creative%Tool\",\n",
        "    \"UV-C%Sanitizing%Device\",\n",
        "    \"Eco-Friendly%Storage\",\n",
        "    \"Wireless%Car%Audio%Adapter\",\n",
        "    \"Travel%Comfort%Accessory\",\n",
        "    \"Portable%Screen%Solution\",\n",
        "    \"Gel%Nail%Kit\",\n",
        "    \"Fitness%Core%Strengthening%Tool\",\n",
        "    \"Wireless%Microphone\",\n",
        "    \"Foldable%Transportation\",\n",
        "    \"Yoga%Support\",\n",
        "    \"Coffee%Maker\",\n",
        "    \"Adjustable%Work%Stand\",\n",
        "    \"Sustainable%Kitchen\",\n",
        "    \"Fitness%Resistance%Gear\",\n",
        "    \"Air%Purification%Device\",\n",
        "    \"Smart%Garden\",\n",
        "    \"Gaming%Accessories%Stand\",\n",
        "    \"Indoor%Cycling\",\n",
        "    \"Automated%Pet%Feeder\",\n",
        "    \"Travel%Organizer%Set\",\n",
        "    \"Mini%Kitchen%Appliance\",\n",
        "    \"Video%Communication%Tool\",\n",
        "    \"Smart%Hydration%Solution\",\n",
        "    \"Fitness%Core%Training%Tool\",\n",
        "    \"Adjustable%Weight%Set\",\n",
        "    \"Compact%Cookware%Set\",\n",
        "    \"Decorative%Illumination\",\n",
        "    \"Digital%Voice%Recording%Device\",\n",
        "    \"Smart%Tracking%Device\",\n",
        "    \"Outdoor%Cooking%Gear\",\n",
        "    \"Portable%Recreational%Device\",\n",
        "    \"Bluetooth%Car%Upgrade\",\n",
        "    \"Ergonomic%Gaming%Seating\",\n",
        "    \"Smart%Security%Accessory\",\n",
        "    \"Reusable%Coffee%Tool\",\n",
        "    \"Adjustable%Fitness%Gear\",\n",
        "    \"Stainless%Steel%Food%Container\",\n",
        "    \"Electric%Beverage%Opener\",\n",
        "    \"Wireless%Audio%Hat\",\n",
        "    \"Portable%Personal%Cooling\",\n",
        "    \"Space-Saving%Yoga%Mat\",\n",
        "    \"Automated%Lawn%Care\",\n",
        "    \"Digital%Baggage%Weight%Monitor\",\n",
        "    \"Portable%Audio%Playback\",\n",
        "    \"Indoor%Gardening%Kit\",\n",
        "    \"Portable%Hand%Heater\",\n",
        "    \"Home%Network%Extender\",\n",
        "    \"Travel%Container\",\n",
        "    \"Smart%Bike%Safety%Gear\",\n",
        "    \"Fitness%Tracking%Device\",\n",
        "    \"Adjustable%Work%Surface\",\n",
        "    \"Phone%Sanitization\",\n",
        "    \"Portable%Monitor%Stand\",\n",
        "    \"Wireless%Charging\",\n",
        "    \"Resistance%Training\",\n",
        "    \"Portable%Heating\",\n",
        "    \"Digital%Notebook\",\n",
        "    \"Wine%Access%Tool\",\n",
        "    \"Food%Cover\",\n",
        "    \"Outdoor%Illumination\",\n",
        "    \"Indoor%Cycling%Support\",\n",
        "    \"Cleaning%Tool\",\n",
        "    \"Light%Control\",\n",
        "    \"Outdoor%Seating\",\n",
        "    \"Portable%Hygiene\",\n",
        "    \"Dumbbell%Set\",\n",
        "    \"Outdoor%Recreation%Accessory\",\n",
        "    \"Bluetooth%Gaming%Accessory\",\n",
        "    \"Stainless%Steel%Cooking%Set\",\n",
        "    \"Collapsible%Outdoor%Furniture\",\n",
        "    \"Indoor%Monitoring\",\n",
        "    \"Fitness%Play%Tool\",\n",
        "    \"Portable%Water%Transport\",\n",
        "    \"Gaming%Surface\",\n",
        "    \"Foldable%Outdoor%Seat\"\n",
        "]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "07cWLB6DH_FY"
      },
      "source": [
        "Data frame initialization with blank values."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "fuksiGQAxgQZ"
      },
      "outputs": [],
      "source": [
        "query_df = pd.DataFrame({\n",
        "    'total products': [],\n",
        "    'mean price': [],\n",
        "    'mean reviews': [],\n",
        "    'mean rating': [],\n",
        "    'best sellers count': [],\n",
        "    'amazon prime count':[],\n",
        "    'amazon choice count': [],\n",
        "    'revenue indicator': [],\n",
        "    'demand ratio': []\n",
        "})"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Od42MvzcIliE"
      },
      "source": [
        "This code gathers information from Amazon product listings via the Hexxondiv Amazon Data Scraper API. It collects data such as prices, ratings, and reviews for specified products, computes averages and counts for these attributes, and stores outcomes in a DataFrame. Two extra columns have been added for use in constructing a prediction model and product analysis against other products. The API was switched due to exhausting free calls from the prior API, resulting in a DataFrame of 81 rows, providing satisfactory insights despite incomplete list coverage. The 'X-RapidAPI-Key' parameter should be changed to personal API key."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "zq5OtiLfzdW1",
        "outputId": "c0d78a0f-0548-419c-ff7b-52ed2968c7c1"
      },
      "outputs": [],
      "source": [
        "# Loop through each product in the 'products' list\n",
        "for prod in products:\n",
        "\n",
        "  # Construct the URL for the API request using the product name\n",
        "  url = \"https://hexxondiv-amazon-data-scraper.p.rapidapi.com/search/\" + prod\n",
        "\n",
        "  # Set the query parameters for the API request\n",
        "  querystring = {\"api_key\":\"260bb6aace1bbf4df4a7cd832a4661a1\"}\n",
        "\n",
        "  # Set the headers for the API request\n",
        "  headers = {\n",
        "    \"X-RapidAPI-Key\": \"X-RapidAPI-Key\",\n",
        "    \"X-RapidAPI-Host\": \"hexxondiv-amazon-data-scraper.p.rapidapi.com\"\n",
        "  }\n",
        "\n",
        "  # Make a GET request to the API\n",
        "  response = requests.get(url, headers=headers, params=querystring)\n",
        "\n",
        "  # Parse the response JSON data\n",
        "  data = response.json()\n",
        "\n",
        "  # Initialize variables to store various statistics\n",
        "  total_products = len(data['results'])\n",
        "  prices = []\n",
        "  ratings = []\n",
        "  reviews = []\n",
        "  best_sellers = 0\n",
        "  amzn_primes = 0\n",
        "  amzn_choices = 0\n",
        "\n",
        "  # Iterate over the first 10 results from the API response\n",
        "  for x in range(10):\n",
        "    try:\n",
        "      # Check if the product has a price and is denominated in dollars\n",
        "      if data['results'][x]['price_symbol'] == '$' and data['results'][x]['price'] is not None:\n",
        "        prices.append(int(data['results'][x]['price']))\n",
        "\n",
        "      # Check if the product has a star rating\n",
        "      if data['results'][x]['stars'] is not None:\n",
        "        ratings.append(data['results'][x]['stars'])\n",
        "\n",
        "      # Check if the product has a total review count\n",
        "      if data['results'][x]['total_reviews'] is not None:\n",
        "        reviews.append(data['results'][x]['total_reviews'])\n",
        "\n",
        "      # Check if the product is marked as a best seller\n",
        "      if data['results'][x]['is_best_seller'] is not None:\n",
        "        if data['results'][x]['is_best_seller'] == True:\n",
        "          best_sellers += 1\n",
        "\n",
        "      # Check if the product is marked as an Amazon Choice\n",
        "      if data['results'][x]['is_amazon_choice'] is not None:\n",
        "        if data['results'][x]['is_amazon_choice'] == True:\n",
        "          amzn_choices  += 1\n",
        "\n",
        "      # Check if the product is marked as having Amazon Prime availability\n",
        "      if data['results'][x]['has_prime'] is not None:\n",
        "        if data['results'][x]['has_prime'] == True:\n",
        "          amzn_primes += 1\n",
        "    except IndexError:\n",
        "      break\n",
        "\n",
        "  try:\n",
        "    # Calculate the mean price, mean rating, and mean reviews\n",
        "    mean_price = sum(prices)/len(prices)\n",
        "    mean_rating = sum(ratings)/len(ratings)\n",
        "    mean_reviews = sum(reviews)/len(reviews)\n",
        "  except ZeroDivisionError:\n",
        "    continue\n",
        "\n",
        "  # Create a list with various statistics for the current product\n",
        "  query_info = [total_products, mean_price, mean_reviews, mean_rating, best_sellers, amzn_primes, amzn_choices, mean_price*mean_reviews, mean_reviews/total_products]\n",
        "\n",
        "  # Add the product's statistics to the query_df DataFrame\n",
        "  query_df.loc[prod] = query_info\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "muJolBDpPydj"
      },
      "source": [
        "The fisrt column i created is called 'revenue indicator'. It is a product of mean price and mean number of reviews. It informs how much money the products from the query generate assuming that mean number of reviews is strongly corelated to product sales.\n",
        "\n",
        "Secound new column is 'demand ratio'. It is a ratio between mean number of reviews and total products. It shows how high is the demand for the product also assuming that mean number of reviews ~ product sales."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "wMXX3K_fvTOx",
        "outputId": "f95354af-e6e0-49d0-b694-5dda7c83944c"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-cedba652-a8cb-4d4f-b832-06b75a496868\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total products</th>\n",
              "      <th>mean price</th>\n",
              "      <th>mean reviews</th>\n",
              "      <th>mean rating</th>\n",
              "      <th>best sellers count</th>\n",
              "      <th>amazon prime count</th>\n",
              "      <th>amazon choice count</th>\n",
              "      <th>revenue indicator</th>\n",
              "      <th>demand ratio</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wireless%Headpones</th>\n",
              "      <td>18.0</td>\n",
              "      <td>44.200000</td>\n",
              "      <td>8006.000000</td>\n",
              "      <td>4.610000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.538652e+05</td>\n",
              "      <td>444.777778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smart%Home%Device</th>\n",
              "      <td>48.0</td>\n",
              "      <td>62.300000</td>\n",
              "      <td>60.285714</td>\n",
              "      <td>4.442857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.755800e+03</td>\n",
              "      <td>1.255952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Camera</th>\n",
              "      <td>56.0</td>\n",
              "      <td>68.666667</td>\n",
              "      <td>171.900000</td>\n",
              "      <td>4.510000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.180380e+04</td>\n",
              "      <td>3.069643</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health%and%Fitness%Tracker</th>\n",
              "      <td>34.0</td>\n",
              "      <td>83.100000</td>\n",
              "      <td>5976.900000</td>\n",
              "      <td>4.240000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.966804e+05</td>\n",
              "      <td>175.791176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portable%Bluetooth%Speaker</th>\n",
              "      <td>18.0</td>\n",
              "      <td>60.777778</td>\n",
              "      <td>43633.700000</td>\n",
              "      <td>4.660000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.651959e+06</td>\n",
              "      <td>2424.094444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Phone%Sanitization</th>\n",
              "      <td>16.0</td>\n",
              "      <td>36.200000</td>\n",
              "      <td>2334.300000</td>\n",
              "      <td>4.360000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.450166e+04</td>\n",
              "      <td>145.893750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portable%Monitor%Stand</th>\n",
              "      <td>26.0</td>\n",
              "      <td>21.900000</td>\n",
              "      <td>96.500000</td>\n",
              "      <td>4.530000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.113350e+03</td>\n",
              "      <td>3.711538</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wireless%Charging</th>\n",
              "      <td>18.0</td>\n",
              "      <td>17.900000</td>\n",
              "      <td>45030.800000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.060513e+05</td>\n",
              "      <td>2501.711111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resistance%Training</th>\n",
              "      <td>48.0</td>\n",
              "      <td>29.100000</td>\n",
              "      <td>438.700000</td>\n",
              "      <td>4.530000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.276617e+04</td>\n",
              "      <td>9.139583</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Digital%Notebook</th>\n",
              "      <td>49.0</td>\n",
              "      <td>80.800000</td>\n",
              "      <td>159.600000</td>\n",
              "      <td>4.260000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.289568e+04</td>\n",
              "      <td>3.257143</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81 rows × 9 columns</p>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-cedba652-a8cb-4d4f-b832-06b75a496868 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cedba652-a8cb-4d4f-b832-06b75a496868');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-18876a30-bdd9-4374-9d70-a3e3e6076e2d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-18876a30-bdd9-4374-9d70-a3e3e6076e2d')\"\n",
              "            title=\"Suggest charts.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "    filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const charts = await google.colab.kernel.invokeFunction(\n",
              "          'suggestCharts', [key], {});\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-18876a30-bdd9-4374-9d70-a3e3e6076e2d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                            total products  mean price  mean reviews  \\\n",
              "Wireless%Headpones                    18.0   44.200000   8006.000000   \n",
              "Smart%Home%Device                     48.0   62.300000     60.285714   \n",
              "Camera                                56.0   68.666667    171.900000   \n",
              "Health%and%Fitness%Tracker            34.0   83.100000   5976.900000   \n",
              "Portable%Bluetooth%Speaker            18.0   60.777778  43633.700000   \n",
              "...                                    ...         ...           ...   \n",
              "Phone%Sanitization                    16.0   36.200000   2334.300000   \n",
              "Portable%Monitor%Stand                26.0   21.900000     96.500000   \n",
              "Wireless%Charging                     18.0   17.900000  45030.800000   \n",
              "Resistance%Training                   48.0   29.100000    438.700000   \n",
              "Digital%Notebook                      49.0   80.800000    159.600000   \n",
              "\n",
              "                            mean rating  best sellers count  \\\n",
              "Wireless%Headpones             4.610000                 1.0   \n",
              "Smart%Home%Device              4.442857                 0.0   \n",
              "Camera                         4.510000                 1.0   \n",
              "Health%and%Fitness%Tracker     4.240000                 1.0   \n",
              "Portable%Bluetooth%Speaker     4.660000                 1.0   \n",
              "...                                 ...                 ...   \n",
              "Phone%Sanitization             4.360000                 0.0   \n",
              "Portable%Monitor%Stand         4.530000                 0.0   \n",
              "Wireless%Charging              4.350000                 1.0   \n",
              "Resistance%Training            4.530000                 0.0   \n",
              "Digital%Notebook               4.260000                 0.0   \n",
              "\n",
              "                            amazon prime count  amazon choice count  \\\n",
              "Wireless%Headpones                         8.0                  1.0   \n",
              "Smart%Home%Device                          6.0                  1.0   \n",
              "Camera                                    10.0                  1.0   \n",
              "Health%and%Fitness%Tracker                 9.0                  2.0   \n",
              "Portable%Bluetooth%Speaker                 9.0                  0.0   \n",
              "...                                        ...                  ...   \n",
              "Phone%Sanitization                         8.0                  0.0   \n",
              "Portable%Monitor%Stand                    10.0                  3.0   \n",
              "Wireless%Charging                          9.0                  0.0   \n",
              "Resistance%Training                       10.0                  1.0   \n",
              "Digital%Notebook                           9.0                  1.0   \n",
              "\n",
              "                            revenue indicator  demand ratio  \n",
              "Wireless%Headpones               3.538652e+05    444.777778  \n",
              "Smart%Home%Device                3.755800e+03      1.255952  \n",
              "Camera                           1.180380e+04      3.069643  \n",
              "Health%and%Fitness%Tracker       4.966804e+05    175.791176  \n",
              "Portable%Bluetooth%Speaker       2.651959e+06   2424.094444  \n",
              "...                                       ...           ...  \n",
              "Phone%Sanitization               8.450166e+04    145.893750  \n",
              "Portable%Monitor%Stand           2.113350e+03      3.711538  \n",
              "Wireless%Charging                8.060513e+05   2501.711111  \n",
              "Resistance%Training              1.276617e+04      9.139583  \n",
              "Digital%Notebook                 1.289568e+04      3.257143  \n",
              "\n",
              "[81 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "display(query_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TyohA8bnaUbF"
      },
      "outputs": [],
      "source": [
        "query_df.to_csv('amazon_queries.csv', index=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8LRgAUi6Cwbf"
      },
      "source": [
        "### <a id=\"data-visualization-and-trimming\">Data Visualization and Trimming</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MEdXs5gBTkne"
      },
      "source": [
        "Below there are scatter plots for revenue indicator, demand ratio and ratings."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Ye0og2V-8HNc",
        "outputId": "3d69aaca-6fc2-4417-f143-6c9e06650c57"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "x = [x for x in range(81)]\n",
        "y = query_df['revenue indicator'].values.tolist()\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(x, y, color='blue')\n",
        "plt.xlabel('Query')\n",
        "plt.ylabel('Revenue Indicator Value')\n",
        "plt.title('Revenue Indicator Scatter Plot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "GKd9u6Bv-ObV",
        "outputId": "9d88bc37-3c51-4fee-be72-b7fe02a25a17"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "x = [x for x in range(81)]\n",
        "y = query_df['demand ratio'].values.tolist()\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(x, y, color='blue')\n",
        "plt.xlabel('Demand Ratio')\n",
        "plt.ylabel('Query')\n",
        "plt.title('Demand Ratio Scatter Plot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "fFwebgjy_idm",
        "outputId": "9fe8675d-e2a1-406d-91cd-7c3ab743ac6a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "x = [x for x in range(81)]\n",
        "y = query_df['mean rating'].values.tolist()\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(x, y, color='blue')\n",
        "plt.xlabel('Mean Rating')\n",
        "plt.ylabel('Query')\n",
        "plt.title('Mean rating Scatter Plot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P4flTNuQPM4N"
      },
      "source": [
        "\n",
        "Due to widely scattered values in the demand ratio and revenue indicator, I have created a new DataFrame in which I deleted rows with extreme values for both columns, so the DataFrame will be a bit less scattered."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hsapu0ZuDDdW"
      },
      "outputs": [],
      "source": [
        "amazon_df = query_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "5T6uDeAPBZ0Y",
        "outputId": "58b74a84-0252-4fb9-e46b-0f0d8e553319"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-d0854d7d-d485-4cd9-af3c-9ee6e871821c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total products</th>\n",
              "      <th>mean price</th>\n",
              "      <th>mean reviews</th>\n",
              "      <th>mean rating</th>\n",
              "      <th>best sellers count</th>\n",
              "      <th>amazon prime count</th>\n",
              "      <th>amazon choice count</th>\n",
              "      <th>revenue indicator</th>\n",
              "      <th>demand ratio</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Smart%Home%Device</th>\n",
              "      <td>48.0</td>\n",
              "      <td>62.300000</td>\n",
              "      <td>60.285714</td>\n",
              "      <td>4.442857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3755.800000</td>\n",
              "      <td>1.255952</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Camera</th>\n",
              "      <td>56.0</td>\n",
              "      <td>68.666667</td>\n",
              "      <td>171.900000</td>\n",
              "      <td>4.510000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11803.800000</td>\n",
              "      <td>3.069643</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health%and%Fitness%Tracker</th>\n",
              "      <td>34.0</td>\n",
              "      <td>83.100000</td>\n",
              "      <td>5976.900000</td>\n",
              "      <td>4.240000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>496680.390000</td>\n",
              "      <td>175.791176</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Personal%Care%Appliance</th>\n",
              "      <td>60.0</td>\n",
              "      <td>38.900000</td>\n",
              "      <td>179.714286</td>\n",
              "      <td>4.128571</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6990.885714</td>\n",
              "      <td>2.995238</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kitchen%Essential</th>\n",
              "      <td>48.0</td>\n",
              "      <td>21.700000</td>\n",
              "      <td>17004.200000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>368991.140000</td>\n",
              "      <td>354.254167</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adjustable%Work%Surface</th>\n",
              "      <td>50.0</td>\n",
              "      <td>122.700000</td>\n",
              "      <td>190.100000</td>\n",
              "      <td>4.390000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>23325.270000</td>\n",
              "      <td>3.802000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Phone%Sanitization</th>\n",
              "      <td>16.0</td>\n",
              "      <td>36.200000</td>\n",
              "      <td>2334.300000</td>\n",
              "      <td>4.360000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>84501.660000</td>\n",
              "      <td>145.893750</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portable%Monitor%Stand</th>\n",
              "      <td>26.0</td>\n",
              "      <td>21.900000</td>\n",
              "      <td>96.500000</td>\n",
              "      <td>4.530000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2113.350000</td>\n",
              "      <td>3.711538</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resistance%Training</th>\n",
              "      <td>48.0</td>\n",
              "      <td>29.100000</td>\n",
              "      <td>438.700000</td>\n",
              "      <td>4.530000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12766.170000</td>\n",
              "      <td>9.139583</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Digital%Notebook</th>\n",
              "      <td>49.0</td>\n",
              "      <td>80.800000</td>\n",
              "      <td>159.600000</td>\n",
              "      <td>4.260000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12895.680000</td>\n",
              "      <td>3.257143</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>65 rows × 10 columns</p>\n",
              "</div>\n",
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              "        const element = document.querySelector('#df-d0854d7d-d485-4cd9-af3c-9ee6e871821c');\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "  <script>\n",
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              "      const charts = await google.colab.kernel.invokeFunction(\n",
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              "        document.querySelector('#df-079dd8a5-1709-4601-9529-6f8f6a064f26 button');\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                            total products  mean price  mean reviews  \\\n",
              "Smart%Home%Device                     48.0   62.300000     60.285714   \n",
              "Camera                                56.0   68.666667    171.900000   \n",
              "Health%and%Fitness%Tracker            34.0   83.100000   5976.900000   \n",
              "Personal%Care%Appliance               60.0   38.900000    179.714286   \n",
              "Kitchen%Essential                     48.0   21.700000  17004.200000   \n",
              "...                                    ...         ...           ...   \n",
              "Adjustable%Work%Surface               50.0  122.700000    190.100000   \n",
              "Phone%Sanitization                    16.0   36.200000   2334.300000   \n",
              "Portable%Monitor%Stand                26.0   21.900000     96.500000   \n",
              "Resistance%Training                   48.0   29.100000    438.700000   \n",
              "Digital%Notebook                      49.0   80.800000    159.600000   \n",
              "\n",
              "                            mean rating  best sellers count  \\\n",
              "Smart%Home%Device              4.442857                 0.0   \n",
              "Camera                         4.510000                 1.0   \n",
              "Health%and%Fitness%Tracker     4.240000                 1.0   \n",
              "Personal%Care%Appliance        4.128571                 0.0   \n",
              "Kitchen%Essential              4.700000                 2.0   \n",
              "...                                 ...                 ...   \n",
              "Adjustable%Work%Surface        4.390000                 0.0   \n",
              "Phone%Sanitization             4.360000                 0.0   \n",
              "Portable%Monitor%Stand         4.530000                 0.0   \n",
              "Resistance%Training            4.530000                 0.0   \n",
              "Digital%Notebook               4.260000                 0.0   \n",
              "\n",
              "                            amazon prime count  amazon choice count  \\\n",
              "Smart%Home%Device                          6.0                  1.0   \n",
              "Camera                                    10.0                  1.0   \n",
              "Health%and%Fitness%Tracker                 9.0                  2.0   \n",
              "Personal%Care%Appliance                    9.0                  0.0   \n",
              "Kitchen%Essential                         10.0                  0.0   \n",
              "...                                        ...                  ...   \n",
              "Adjustable%Work%Surface                    9.0                  1.0   \n",
              "Phone%Sanitization                         8.0                  0.0   \n",
              "Portable%Monitor%Stand                    10.0                  3.0   \n",
              "Resistance%Training                       10.0                  1.0   \n",
              "Digital%Notebook                           9.0                  1.0   \n",
              "\n",
              "                            revenue indicator  demand ratio  label  \n",
              "Smart%Home%Device                 3755.800000      1.255952      0  \n",
              "Camera                           11803.800000      3.069643      0  \n",
              "Health%and%Fitness%Tracker      496680.390000    175.791176      0  \n",
              "Personal%Care%Appliance           6990.885714      2.995238      0  \n",
              "Kitchen%Essential               368991.140000    354.254167      1  \n",
              "...                                       ...           ...    ...  \n",
              "Adjustable%Work%Surface          23325.270000      3.802000      0  \n",
              "Phone%Sanitization               84501.660000    145.893750      0  \n",
              "Portable%Monitor%Stand            2113.350000      3.711538      0  \n",
              "Resistance%Training              12766.170000      9.139583      0  \n",
              "Digital%Notebook                 12895.680000      3.257143      0  \n",
              "\n",
              "[65 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "revenue_indicator_median = amazon_df['revenue indicator'].median()\n",
        "demand_ratio_median = amazon_df[\"demand ratio\"].median()\n",
        "\n",
        "amazon_df = amazon_df[amazon_df['revenue indicator'] <= 25 * revenue_indicator_median]\n",
        "amazon_df = amazon_df[amazon_df['revenue indicator'] >= revenue_indicator_median / 25]\n",
        "\n",
        "amazon_df = amazon_df[amazon_df['demand ratio'] >= demand_ratio_median / 25]\n",
        "amazon_df = amazon_df[amazon_df['demand ratio'] <= demand_ratio_median * 25]\n",
        "\n",
        "display(amazon_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ykTKEguBTFwP"
      },
      "source": [
        "Now we have 2 DataFrames which will be used to make 2 separate predicting models. Below there is visualization of the trimmed data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "J9PKfau3JaYZ",
        "outputId": "2921e3d7-f4b0-41a5-cd3f-464606f2ae8a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "y = amazon_df['revenue indicator'].values.tolist()\n",
        "x = [x for x in range(len(y))]\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(x, y, color='blue')\n",
        "plt.xlabel('Query')\n",
        "plt.ylabel('Revenue Indicator Value')\n",
        "plt.title('Revenue Indicator Scatter Plot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "TV4dSOWUKvjc",
        "outputId": "7133e7ca-a0a8-4379-88d3-ec029d0f0d73"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "y = amazon_df['demand ratio'].values.tolist()\n",
        "x = [x for x in range(len(y))]\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(x, y, color='blue')\n",
        "plt.xlabel('Demand Ratio')\n",
        "plt.ylabel('Query')\n",
        "plt.title('Demand Ratio Scatter Plot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "UWxlMI8ELLay",
        "outputId": "07c4c681-6575-4489-817d-8675681d79ad"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "y = amazon_df['mean rating'].values.tolist()\n",
        "x = [x for x in range(len(y))]\n",
        "\n",
        "plt.figure(figsize=(8, 6))\n",
        "plt.scatter(x, y, color='blue')\n",
        "plt.xlabel('Mean Rating')\n",
        "plt.ylabel('Query')\n",
        "plt.title('Mean rating Scatter Plot')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CT-IMI_REAx0"
      },
      "source": [
        "### <a id=\"query-analyzer\">Query Analyzer</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X30XtCfmVIAl"
      },
      "source": [
        "This function can be used to obtain insights on how the revenue indicator, demand ratio, and mean rating of a query compare to their respective medians."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "scGnp-QBcod4"
      },
      "outputs": [],
      "source": [
        "def QueryAnalyzer(query, revenue_indicator_median, demand_ratio_median, rating_median):\n",
        "  # Construct the URL for the API request using the provided query\n",
        "  url = \"https://hexxondiv-amazon-data-scraper.p.rapidapi.com/search/\" + query\n",
        "\n",
        "  # Set the query parameters for the API request\n",
        "  querystring = {\"api_key\":\"260bb6aace1bbf4df4a7cd832a4661a1\"}\n",
        "\n",
        "  # Set the headers for the API request\n",
        "  headers = {\n",
        "    \"X-RapidAPI-Key\": \"77e19541e2msh0b74d6e3120b38ep1eb0ddjsn8ca2b7c09c00\",\n",
        "    \"X-RapidAPI-Host\": \"hexxondiv-amazon-data-scraper.p.rapidapi.com\"\n",
        "  }\n",
        "\n",
        "  # Make a GET request to the API\n",
        "  response = requests.get(url, headers=headers, params=querystring)\n",
        "  data = response.json()\n",
        "\n",
        "  # Initialize variables to store various statistics\n",
        "  total_products = len(data['results'])\n",
        "  prices = []\n",
        "  ratings = []\n",
        "  reviews = []\n",
        "  best_sellers = 0\n",
        "  amzn_primes = 0\n",
        "  amzn_choices = 0\n",
        "\n",
        "  # Iterate over the first 10 results from the API response\n",
        "  for x in range(10):\n",
        "    try:\n",
        "      # Check if the product has a price denoted in dollars and is not None, then add it to 'prices'\n",
        "      if data['results'][x]['price_symbol'] == '$' and data['results'][x]['price'] is not None:\n",
        "        prices.append(int(data['results'][x]['price']))\n",
        "\n",
        "      # Check if the product has a star rating, then add it to 'ratings'\n",
        "      if data['results'][x]['stars'] is not None:\n",
        "        ratings.append(data['results'][x]['stars'])\n",
        "\n",
        "      # Check if the product has a total review count, then add it to 'reviews'\n",
        "      if data['results'][x]['total_reviews'] is not None:\n",
        "        reviews.append(data['results'][x]['total_reviews'])\n",
        "\n",
        "      # Count the number of best sellers\n",
        "      if data['results'][x]['is_best_seller'] is not None:\n",
        "        if data['results'][x]['is_best_seller'] == True:\n",
        "          best_sellers += 1\n",
        "\n",
        "      # Count the number of Amazon Choice products\n",
        "      if data['results'][x]['is_amazon_choice'] is not None:\n",
        "        if data['results'][x]['is_amazon_choice'] == True:\n",
        "          amzn_choices  += 1\n",
        "\n",
        "      # Count the number of products with Amazon Prime availability\n",
        "      if data['results'][x]['has_prime'] is not None:\n",
        "        if data['results'][x]['has_prime'] == True:\n",
        "          amzn_primes += 1\n",
        "    except IndexError:\n",
        "      break\n",
        "\n",
        "  try:\n",
        "    # Calculate mean values for price, rating, and reviews\n",
        "    mean_price = sum(prices)/len(prices)\n",
        "    mean_rating = sum(ratings)/len(ratings)\n",
        "    mean_reviews = sum(reviews)/len(reviews)\n",
        "\n",
        "    # Generate result string based on calculated values and provided medians\n",
        "    result = f'Query {query} has better mean ratings than {mean_rating/rating_median}% queries, has higher mean revenue indicator than {mean_price*mean_reviews/revenue_indicator_median}% queries and has higher mean demand ratio than {(mean_reviews/total_products)/demand_ratio_median}% queries'\n",
        "  except ZeroDivisionError:\n",
        "    # Handle ZeroDivisionError when there's not enough data\n",
        "    result = f'Not enough data for query {query}'\n",
        "\n",
        "  return result\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fkvNeO5tEglF"
      },
      "source": [
        "### <a id=\"random-forest-model\">Random Forest Model</a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0vZF2O-QWCuW"
      },
      "source": [
        "I added column with label 0 or 1 for every query in DataFrames (The initial 1 and the 1 after trimming). 1 means that the products in query are predicted to sell well and 0 means that they will not be good products to sell."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "Mxax7Jo6XMDv",
        "outputId": "4a5086c0-9b96-43eb-a81c-c31a6ec56668"
      },
      "outputs": [
        {
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              "<style scoped>\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total products</th>\n",
              "      <th>mean price</th>\n",
              "      <th>mean reviews</th>\n",
              "      <th>mean rating</th>\n",
              "      <th>best sellers count</th>\n",
              "      <th>amazon prime count</th>\n",
              "      <th>amazon choice count</th>\n",
              "      <th>revenue indicator</th>\n",
              "      <th>demand ratio</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Smart%Home%Device</th>\n",
              "      <td>48.0</td>\n",
              "      <td>62.300000</td>\n",
              "      <td>60.285714</td>\n",
              "      <td>4.442857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3755.800000</td>\n",
              "      <td>1.255952</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Camera</th>\n",
              "      <td>56.0</td>\n",
              "      <td>68.666667</td>\n",
              "      <td>171.900000</td>\n",
              "      <td>4.510000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>11803.800000</td>\n",
              "      <td>3.069643</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health%and%Fitness%Tracker</th>\n",
              "      <td>34.0</td>\n",
              "      <td>83.100000</td>\n",
              "      <td>5976.900000</td>\n",
              "      <td>4.240000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>496680.390000</td>\n",
              "      <td>175.791176</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Personal%Care%Appliance</th>\n",
              "      <td>60.0</td>\n",
              "      <td>38.900000</td>\n",
              "      <td>179.714286</td>\n",
              "      <td>4.128571</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6990.885714</td>\n",
              "      <td>2.995238</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Kitchen%Essential</th>\n",
              "      <td>48.0</td>\n",
              "      <td>21.700000</td>\n",
              "      <td>17004.200000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>2.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>368991.140000</td>\n",
              "      <td>354.254167</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Adjustable%Work%Surface</th>\n",
              "      <td>50.0</td>\n",
              "      <td>122.700000</td>\n",
              "      <td>190.100000</td>\n",
              "      <td>4.390000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>23325.270000</td>\n",
              "      <td>3.802000</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Phone%Sanitization</th>\n",
              "      <td>16.0</td>\n",
              "      <td>36.200000</td>\n",
              "      <td>2334.300000</td>\n",
              "      <td>4.360000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>84501.660000</td>\n",
              "      <td>145.893750</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portable%Monitor%Stand</th>\n",
              "      <td>26.0</td>\n",
              "      <td>21.900000</td>\n",
              "      <td>96.500000</td>\n",
              "      <td>4.530000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2113.350000</td>\n",
              "      <td>3.711538</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resistance%Training</th>\n",
              "      <td>48.0</td>\n",
              "      <td>29.100000</td>\n",
              "      <td>438.700000</td>\n",
              "      <td>4.530000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12766.170000</td>\n",
              "      <td>9.139583</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Digital%Notebook</th>\n",
              "      <td>49.0</td>\n",
              "      <td>80.800000</td>\n",
              "      <td>159.600000</td>\n",
              "      <td>4.260000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>12895.680000</td>\n",
              "      <td>3.257143</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>65 rows × 10 columns</p>\n",
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              "                                                    [key], {});\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ],
            "text/plain": [
              "                            total products  mean price  mean reviews  \\\n",
              "Smart%Home%Device                     48.0   62.300000     60.285714   \n",
              "Camera                                56.0   68.666667    171.900000   \n",
              "Health%and%Fitness%Tracker            34.0   83.100000   5976.900000   \n",
              "Personal%Care%Appliance               60.0   38.900000    179.714286   \n",
              "Kitchen%Essential                     48.0   21.700000  17004.200000   \n",
              "...                                    ...         ...           ...   \n",
              "Adjustable%Work%Surface               50.0  122.700000    190.100000   \n",
              "Phone%Sanitization                    16.0   36.200000   2334.300000   \n",
              "Portable%Monitor%Stand                26.0   21.900000     96.500000   \n",
              "Resistance%Training                   48.0   29.100000    438.700000   \n",
              "Digital%Notebook                      49.0   80.800000    159.600000   \n",
              "\n",
              "                            mean rating  best sellers count  \\\n",
              "Smart%Home%Device              4.442857                 0.0   \n",
              "Camera                         4.510000                 1.0   \n",
              "Health%and%Fitness%Tracker     4.240000                 1.0   \n",
              "Personal%Care%Appliance        4.128571                 0.0   \n",
              "Kitchen%Essential              4.700000                 2.0   \n",
              "...                                 ...                 ...   \n",
              "Adjustable%Work%Surface        4.390000                 0.0   \n",
              "Phone%Sanitization             4.360000                 0.0   \n",
              "Portable%Monitor%Stand         4.530000                 0.0   \n",
              "Resistance%Training            4.530000                 0.0   \n",
              "Digital%Notebook               4.260000                 0.0   \n",
              "\n",
              "                            amazon prime count  amazon choice count  \\\n",
              "Smart%Home%Device                          6.0                  1.0   \n",
              "Camera                                    10.0                  1.0   \n",
              "Health%and%Fitness%Tracker                 9.0                  2.0   \n",
              "Personal%Care%Appliance                    9.0                  0.0   \n",
              "Kitchen%Essential                         10.0                  0.0   \n",
              "...                                        ...                  ...   \n",
              "Adjustable%Work%Surface                    9.0                  1.0   \n",
              "Phone%Sanitization                         8.0                  0.0   \n",
              "Portable%Monitor%Stand                    10.0                  3.0   \n",
              "Resistance%Training                       10.0                  1.0   \n",
              "Digital%Notebook                           9.0                  1.0   \n",
              "\n",
              "                            revenue indicator  demand ratio  label  \n",
              "Smart%Home%Device                 3755.800000      1.255952      0  \n",
              "Camera                           11803.800000      3.069643      0  \n",
              "Health%and%Fitness%Tracker      496680.390000    175.791176      0  \n",
              "Personal%Care%Appliance           6990.885714      2.995238      0  \n",
              "Kitchen%Essential               368991.140000    354.254167      1  \n",
              "...                                       ...           ...    ...  \n",
              "Adjustable%Work%Surface          23325.270000      3.802000      0  \n",
              "Phone%Sanitization               84501.660000    145.893750      0  \n",
              "Portable%Monitor%Stand            2113.350000      3.711538      0  \n",
              "Resistance%Training              12766.170000      9.139583      0  \n",
              "Digital%Notebook                 12895.680000      3.257143      0  \n",
              "\n",
              "[65 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "revenue_indicator_median = amazon_df['revenue indicator'].median()\n",
        "demand_ratio_median = amazon_df[\"demand ratio\"].median()\n",
        "rating_median = amazon_df['mean rating'].median()\n",
        "\n",
        "amazon_df['label'] = 0\n",
        "\n",
        "for idx, query in amazon_df.iterrows():\n",
        "  if query['demand ratio'] > 1.2 * demand_ratio_median and query['revenue indicator'] > 1.2 * revenue_indicator_median and query['mean rating'] > 1 * rating_median:\n",
        "    amazon_df.loc[idx, 'label'] = 1\n",
        "\n",
        "display(amazon_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 441
        },
        "id": "5Bd78h84Uotn",
        "outputId": "bbb7ce04-6c8d-4ca1-9984-af95a400786b"
      },
      "outputs": [
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          "data": {
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              "\n",
              "  <div id=\"df-884511e4-9424-41df-9688-3522cc05f061\" class=\"colab-df-container\">\n",
              "    <div>\n",
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              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>total products</th>\n",
              "      <th>mean price</th>\n",
              "      <th>mean reviews</th>\n",
              "      <th>mean rating</th>\n",
              "      <th>best sellers count</th>\n",
              "      <th>amazon prime count</th>\n",
              "      <th>amazon choice count</th>\n",
              "      <th>revenue indicator</th>\n",
              "      <th>demand ratio</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Wireless%Headpones</th>\n",
              "      <td>18.0</td>\n",
              "      <td>44.200000</td>\n",
              "      <td>8006.000000</td>\n",
              "      <td>4.610000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.538652e+05</td>\n",
              "      <td>444.777778</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Smart%Home%Device</th>\n",
              "      <td>48.0</td>\n",
              "      <td>62.300000</td>\n",
              "      <td>60.285714</td>\n",
              "      <td>4.442857</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.755800e+03</td>\n",
              "      <td>1.255952</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Camera</th>\n",
              "      <td>56.0</td>\n",
              "      <td>68.666667</td>\n",
              "      <td>171.900000</td>\n",
              "      <td>4.510000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.180380e+04</td>\n",
              "      <td>3.069643</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Health%and%Fitness%Tracker</th>\n",
              "      <td>34.0</td>\n",
              "      <td>83.100000</td>\n",
              "      <td>5976.900000</td>\n",
              "      <td>4.240000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.966804e+05</td>\n",
              "      <td>175.791176</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portable%Bluetooth%Speaker</th>\n",
              "      <td>18.0</td>\n",
              "      <td>60.777778</td>\n",
              "      <td>43633.700000</td>\n",
              "      <td>4.660000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.651959e+06</td>\n",
              "      <td>2424.094444</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Phone%Sanitization</th>\n",
              "      <td>16.0</td>\n",
              "      <td>36.200000</td>\n",
              "      <td>2334.300000</td>\n",
              "      <td>4.360000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.450166e+04</td>\n",
              "      <td>145.893750</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Portable%Monitor%Stand</th>\n",
              "      <td>26.0</td>\n",
              "      <td>21.900000</td>\n",
              "      <td>96.500000</td>\n",
              "      <td>4.530000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2.113350e+03</td>\n",
              "      <td>3.711538</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wireless%Charging</th>\n",
              "      <td>18.0</td>\n",
              "      <td>17.900000</td>\n",
              "      <td>45030.800000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>8.060513e+05</td>\n",
              "      <td>2501.711111</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Resistance%Training</th>\n",
              "      <td>48.0</td>\n",
              "      <td>29.100000</td>\n",
              "      <td>438.700000</td>\n",
              "      <td>4.530000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.276617e+04</td>\n",
              "      <td>9.139583</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Digital%Notebook</th>\n",
              "      <td>49.0</td>\n",
              "      <td>80.800000</td>\n",
              "      <td>159.600000</td>\n",
              "      <td>4.260000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.289568e+04</td>\n",
              "      <td>3.257143</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>81 rows × 10 columns</p>\n",
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            ],
            "text/plain": [
              "                            total products  mean price  mean reviews  \\\n",
              "Wireless%Headpones                    18.0   44.200000   8006.000000   \n",
              "Smart%Home%Device                     48.0   62.300000     60.285714   \n",
              "Camera                                56.0   68.666667    171.900000   \n",
              "Health%and%Fitness%Tracker            34.0   83.100000   5976.900000   \n",
              "Portable%Bluetooth%Speaker            18.0   60.777778  43633.700000   \n",
              "...                                    ...         ...           ...   \n",
              "Phone%Sanitization                    16.0   36.200000   2334.300000   \n",
              "Portable%Monitor%Stand                26.0   21.900000     96.500000   \n",
              "Wireless%Charging                     18.0   17.900000  45030.800000   \n",
              "Resistance%Training                   48.0   29.100000    438.700000   \n",
              "Digital%Notebook                      49.0   80.800000    159.600000   \n",
              "\n",
              "                            mean rating  best sellers count  \\\n",
              "Wireless%Headpones             4.610000                 1.0   \n",
              "Smart%Home%Device              4.442857                 0.0   \n",
              "Camera                         4.510000                 1.0   \n",
              "Health%and%Fitness%Tracker     4.240000                 1.0   \n",
              "Portable%Bluetooth%Speaker     4.660000                 1.0   \n",
              "...                                 ...                 ...   \n",
              "Phone%Sanitization             4.360000                 0.0   \n",
              "Portable%Monitor%Stand         4.530000                 0.0   \n",
              "Wireless%Charging              4.350000                 1.0   \n",
              "Resistance%Training            4.530000                 0.0   \n",
              "Digital%Notebook               4.260000                 0.0   \n",
              "\n",
              "                            amazon prime count  amazon choice count  \\\n",
              "Wireless%Headpones                         8.0                  1.0   \n",
              "Smart%Home%Device                          6.0                  1.0   \n",
              "Camera                                    10.0                  1.0   \n",
              "Health%and%Fitness%Tracker                 9.0                  2.0   \n",
              "Portable%Bluetooth%Speaker                 9.0                  0.0   \n",
              "...                                        ...                  ...   \n",
              "Phone%Sanitization                         8.0                  0.0   \n",
              "Portable%Monitor%Stand                    10.0                  3.0   \n",
              "Wireless%Charging                          9.0                  0.0   \n",
              "Resistance%Training                       10.0                  1.0   \n",
              "Digital%Notebook                           9.0                  1.0   \n",
              "\n",
              "                            revenue indicator  demand ratio  label  \n",
              "Wireless%Headpones               3.538652e+05    444.777778      1  \n",
              "Smart%Home%Device                3.755800e+03      1.255952      0  \n",
              "Camera                           1.180380e+04      3.069643      0  \n",
              "Health%and%Fitness%Tracker       4.966804e+05    175.791176      0  \n",
              "Portable%Bluetooth%Speaker       2.651959e+06   2424.094444      1  \n",
              "...                                       ...           ...    ...  \n",
              "Phone%Sanitization               8.450166e+04    145.893750      0  \n",
              "Portable%Monitor%Stand           2.113350e+03      3.711538      0  \n",
              "Wireless%Charging                8.060513e+05   2501.711111      0  \n",
              "Resistance%Training              1.276617e+04      9.139583      0  \n",
              "Digital%Notebook                 1.289568e+04      3.257143      0  \n",
              "\n",
              "[81 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "amazon_df_v2 = query_df\n",
        "revenue_indicator_median = amazon_df_v2['revenue indicator'].median()\n",
        "demand_ratio_median = amazon_df_v2[\"demand ratio\"].median()\n",
        "rating_median = amazon_df_v2['mean rating'].median()\n",
        "\n",
        "\n",
        "amazon_df_v2['label'] = 0\n",
        "\n",
        "for idx, query in amazon_df_v2.iterrows():\n",
        "  if query['demand ratio'] > 1.2 * demand_ratio_median and query['revenue indicator'] > 1.2 * revenue_indicator_median and query['mean rating'] > 1 * rating_median:\n",
        "    amazon_df_v2.loc[idx, 'label'] = 1\n",
        "\n",
        "display(amazon_df_v2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qAD7de51W38L"
      },
      "source": [
        "Below 2 Random Forest models are created. What is interesting model trained on the initial DataDrame(amazon_df_v2) have better accuracy than the one trained on preprocessed DataFrame(amazon_df). It may be caused beacuse of the less amount of data in the preprocessed DataFrame."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zuyGxAH2g2PR",
        "outputId": "38fddd0d-9c5f-4601-f60e-624082b4ebe2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.0\n",
            "0.9230769230769231\n"
          ]
        }
      ],
      "source": [
        "y = amazon_df['label']\n",
        "X = amazon_df.drop(['label', 'revenue indicator', 'demand ratio'], axis = 1)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=26)\n",
        "\n",
        "rf_model = RandomForestClassifier()\n",
        "rf_model.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "print(f'Training accuracy -- {rf_model.score(X_train, y_train)}')\n",
        "print(f'Test accuracy -- {rf_model.score(X_test, y_test)}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GRd_mRKRtxuP",
        "outputId": "85b4c641-812e-4d09-827d-5ad35e38ec79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1.0\n",
            "0.9411764705882353\n"
          ]
        }
      ],
      "source": [
        "y = amazon_df_v2['label']\n",
        "X = amazon_df_v2.drop(['label', 'revenue indicator', 'demand ratio'], axis = 1)\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=26)\n",
        "\n",
        "rf_model_v2 = RandomForestClassifier()\n",
        "rf_model_v2.fit(X_train, y_train)\n",
        "\n",
        "\n",
        "print(f'Training accuracy -- {rf_model_v2.score(X_train, y_train)}')\n",
        "print(f'Test accuracy -- {rf_model_v2.score(X_test, y_test)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2RnRiUEvYNPY"
      },
      "source": [
        "Lastly the function to get information about query from API in the format that will be ready to be used by either of the Random Forest Models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6AS8ULZSutT8"
      },
      "outputs": [],
      "source": [
        "def QueryDataExtraction(query):\n",
        "  url = \"https://hexxondiv-amazon-data-scraper.p.rapidapi.com/search/\" + query\n",
        "\n",
        "  querystring = {\"api_key\":\"260bb6aace1bbf4df4a7cd832a4661a1\"}\n",
        "\n",
        "  headers = {\n",
        "    \"X-RapidAPI-Key\": \"77e19541e2msh0b74d6e3120b38ep1eb0ddjsn8ca2b7c09c00\",\n",
        "    \"X-RapidAPI-Host\": \"hexxondiv-amazon-data-scraper.p.rapidapi.com\"\n",
        "  }\n",
        "\n",
        "  response = requests.get(url, headers=headers, params=querystring)\n",
        "  data = response.json()\n",
        "\n",
        "  total_products = len(data['results'])\n",
        "  prices = []\n",
        "  ratings = []\n",
        "  reviews = []\n",
        "  best_sellers = 0\n",
        "  amzn_primes = 0\n",
        "  amzn_choices = 0\n",
        "\n",
        "  for x in range(10):\n",
        "    try:\n",
        "      if data['results'][x]['price_symbol'] == '$' and data['results'][x]['price'] is not None:\n",
        "        prices.append(int(data['results'][x]['price']))\n",
        "      if data['results'][x]['stars'] is not None:\n",
        "        ratings.append(data['results'][x]['stars'])\n",
        "      if data['results'][x]['total_reviews'] is not None:\n",
        "        reviews.append(data['results'][x]['total_reviews'])\n",
        "      if data['results'][x]['is_best_seller'] is not None:\n",
        "        if data['results'][x]['is_best_seller'] == True:\n",
        "          best_sellers += 1\n",
        "      if data['results'][x]['is_amazon_choice'] is not None:\n",
        "        if data['results'][x]['is_amazon_choice'] == True:\n",
        "          amzn_choices  += 1\n",
        "      if data['results'][x]['has_prime'] is not None:\n",
        "        if data['results'][x]['has_prime'] == True:\n",
        "          amzn_primes += 1\n",
        "    except IndexError:\n",
        "      break\n",
        "  try:\n",
        "   mean_price = sum(prices)/len(prices)\n",
        "   mean_rating = sum(ratings)/len(ratings)\n",
        "   mean_reviews = sum(reviews)/len(reviews)\n",
        "   result = np.array([total_products,\tmean_price,\tmean_reviews,\tmean_rating,\tbest_sellers,\tamzn_primes,\tamzn_choices]).reshape(1, -1)\n",
        "  except ZeroDivisionError:\n",
        "    result = f'Not enough data for query {query}'\n",
        "\n",
        "  return result"
      ]
    }
  ],
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    "colab": {
      "collapsed_sections": [
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        "MwMVcQQJA3KO",
        "1XT8t3mhJT4h"
      ],
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
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}