Aerofit_10009.ipynb

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      "source": [
        "# **Business case : Aerofit**\n",
        "\n",
        "---\n",
        "\n",
        "\n"
      ],
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        "\n",
        " **About Aerofit :**  \n",
        "\n",
        "Aerofit is a leading brand in the field of fitness equipment. Aerofit provides a product range\n",
        "including machines such as treadmills, exercise bikes, gym equipment, and fitness\n",
        "accessories to cater to the needs of all categories of people.\n",
        "\n",
        " **Business Problem :**\n",
        "\n",
        "The market research team at AeroFit wants to identify the characteristics of the target\n",
        "audience for each type of treadmill offered by the company, to provide a better\n",
        "recommendation of the treadmills to the new customers. The team decides to investigate\n",
        "whether there are differences across the product with respect to customer characteristics.\n",
        "1. Perform descriptive analytics to create a customer profile for each AeroFit treadmill\n",
        "product by developing appropriate tables and charts.\n",
        "2. For each AeroFit treadmill product, construct two-way contingency tables and compute\n",
        "all conditional and marginal probabilities along with their insights/impact on the\n",
        "business.*italicized text*"
      ],
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      "cell_type": "markdown",
      "source": [
        "# **Dataset**\n"
      ],
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        "id": "3XxB_4J_-3M-"
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      "source": [
        "The company collected the data on individuals who purchased a treadmill from the AeroFit\n",
        "stores during the prior three months. The dataset has the following features:\n",
        "Dataset link: Aerofit_treadmill.csv\n",
        "(https://d2beiqkhq929f0.cloudfront.net/public_assets/assets/000/001/125/original/aerofit_treadm\n",
        "1639992749)\n",
        "\n",
        "**Product Purchased:** KP281, KP481, or KP781\n",
        "\n",
        "**Age:** In years\n",
        "\n",
        "**Gender:** Male/Female\n",
        "\n",
        "**Education:** In years\n",
        "\n",
        "**MaritalStatus:** Single or partnered\n",
        "\n",
        "**Usage:** The average number of times the customer plans to use the treadmill each week.\n",
        "\n",
        "**Income:** Annual income (in $)\n",
        "\n",
        "**Fitness:** Self-rated fitness on a 1-to-5 scale, where 1 is the poor shape and 5 is the excellent\n",
        "\n",
        "**Miles:** The average number of miles the customer expects to walk/run each week"
      ],
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        "id": "MCnxJq32_t4M"
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    {
      "cell_type": "markdown",
      "source": [
        "# **Data** **Analysis**"
      ],
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      "cell_type": "code",
      "source": [
        "#import the required libraries for data analysis.\n",
        "#basic data analysis checking the structure, characteristics.\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from scipy.stats import binom"
      ],
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      "source": [
        "#Download the given dataset.\n",
        "!gdown https://d2beiqkhq929f0.cloudfront.net/public_assets/assets/000/001/125/original/aerofit_treadmill.csv?1639992749"
      ],
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        "outputId": "16b412b7-d178-4bba-911a-6183e0772d3d"
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      "outputs": [
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          "text": [
            "Downloading...\n",
            "From: https://d2beiqkhq929f0.cloudfront.net/public_assets/assets/000/001/125/original/aerofit_treadmill.csv?1639992749\n",
            "To: /content/aerofit_treadmill.csv?1639992749\n",
            "\r  0% 0.00/7.28k [00:00<?, ?B/s]\r100% 7.28k/7.28k [00:00<00:00, 23.7MB/s]\n"
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      "source": [
        "#Read the data\n",
        "data=pd.read_csv('aerofit_treadmill.csv?1639992749')\n",
        "data"
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              "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "0     KP281   18    Male         14        Single      3        4   29562   \n",
              "1     KP281   19    Male         15        Single      2        3   31836   \n",
              "2     KP281   19  Female         14     Partnered      4        3   30699   \n",
              "3     KP281   19    Male         12        Single      3        3   32973   \n",
              "4     KP281   20    Male         13     Partnered      4        2   35247   \n",
              "..      ...  ...     ...        ...           ...    ...      ...     ...   \n",
              "175   KP781   40    Male         21        Single      6        5   83416   \n",
              "176   KP781   42    Male         18        Single      5        4   89641   \n",
              "177   KP781   45    Male         16        Single      5        5   90886   \n",
              "178   KP781   47    Male         18     Partnered      4        5  104581   \n",
              "179   KP781   48    Male         18     Partnered      4        5   95508   \n",
              "\n",
              "     Miles  \n",
              "0      112  \n",
              "1       75  \n",
              "2       66  \n",
              "3       85  \n",
              "4       47  \n",
              "..     ...  \n",
              "175    200  \n",
              "176    200  \n",
              "177    160  \n",
              "178    120  \n",
              "179    180  \n",
              "\n",
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              "  <div id=\"id_4f6e9fcd-2c51-4a14-a9bd-4c941d786964\">\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 180,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"KP281\",\n          \"KP481\",\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 18,\n        \"max\": 50,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          45,\n          33,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 12,\n        \"max\": 21,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Partnered\",\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16506,\n        \"min\": 29562,\n        \"max\": 104581,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          88396,\n          103336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51,\n        \"min\": 21,\n        \"max\": 360,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          95,\n          169\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#No. of rows and columns in the data\n",
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cj0CMz3VBSSe",
        "outputId": "a48e2a88-5113-40ac-9aac-c7c78f2459f2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(180, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WJVWdBSjBk8Q",
        "outputId": "63fd958c-971d-4f01-d47b-b0476eaa22e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  Miles\n",
              "0   KP281   18    Male         14        Single      3        4   29562    112\n",
              "1   KP281   19    Male         15        Single      2        3   31836     75\n",
              "2   KP281   19  Female         14     Partnered      4        3   30699     66\n",
              "3   KP281   19    Male         12        Single      3        3   32973     85\n",
              "4   KP281   20    Male         13     Partnered      4        2   35247     47"
            ],
            "text/html": [
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              "      <td>32973</td>\n",
              "      <td>85</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>KP281</td>\n",
              "      <td>20</td>\n",
              "      <td>Male</td>\n",
              "      <td>13</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>2</td>\n",
              "      <td>35247</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 180,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"KP281\",\n          \"KP481\",\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 18,\n        \"max\": 50,\n        \"num_unique_values\": 32,\n        \"samples\": [\n          45,\n          33,\n          43\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 12,\n        \"max\": 21,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          15,\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Partnered\",\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2,\n        \"max\": 7,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          3,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 5,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16506,\n        \"min\": 29562,\n        \"max\": 104581,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          88396,\n          103336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51,\n        \"min\": 21,\n        \"max\": 360,\n        \"num_unique_values\": 37,\n        \"samples\": [\n          95,\n          169\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "0HpedCkZCIjQ",
        "outputId": "edfec94e-7e1d-4190-a4b0-76b086ca0be2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product  Age Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "175   KP781   40   Male         21        Single      6        5   83416   \n",
              "176   KP781   42   Male         18        Single      5        4   89641   \n",
              "177   KP781   45   Male         16        Single      5        5   90886   \n",
              "178   KP781   47   Male         18     Partnered      4        5  104581   \n",
              "179   KP781   48   Male         18     Partnered      4        5   95508   \n",
              "\n",
              "     Miles  \n",
              "175    200  \n",
              "176    200  \n",
              "177    160  \n",
              "178    120  \n",
              "179    180  "
            ],
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              "      <th>175</th>\n",
              "      <td>KP781</td>\n",
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              "      <td>Male</td>\n",
              "      <td>21</td>\n",
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              "      <td>KP781</td>\n",
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              "      <th>178</th>\n",
              "      <td>KP781</td>\n",
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              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>104581</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>KP781</td>\n",
              "      <td>48</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
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              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-4ec6b270-faf9-4fff-9a80-f22209bb3f58 button.colab-df-convert');\n",
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              "\n",
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              "        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",
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              "</svg>\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
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              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 40,\n        \"max\": 48,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          42\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 16,\n        \"max\": 21,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Partnered\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 6,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7871,\n        \"min\": 83416,\n        \"max\": 104581,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          89641\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 33,\n        \"min\": 120,\n        \"max\": 200,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          160\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "jhuHSqN5CKmQ",
        "outputId": "e8ab64d3-acd2-4360-8935-5bbf170a80b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pandas.core.frame.DataFrame"
            ],
            "text/html": [
              "<div style=\"max-width:800px; border: 1px solid var(--colab-border-color);\"><style>\n",
              "      pre.function-repr-contents {\n",
              "        overflow-x: auto;\n",
              "        padding: 8px 12px;\n",
              "        max-height: 500px;\n",
              "      }\n",
              "\n",
              "      pre.function-repr-contents.function-repr-contents-collapsed {\n",
              "        cursor: pointer;\n",
              "        max-height: 100px;\n",
              "      }\n",
              "    </style>\n",
              "    <pre style=\"white-space: initial; background:\n",
              "         var(--colab-secondary-surface-color); padding: 8px 12px;\n",
              "         border-bottom: 1px solid var(--colab-border-color);\"><b>pandas.core.frame.DataFrame</b><br/>def __init__(data=None, index: Axes | None=None, columns: Axes | None=None, dtype: Dtype | None=None, copy: bool | None=None) -&gt; None</pre><pre class=\"function-repr-contents function-repr-contents-collapsed\" style=\"\"><a class=\"filepath\" style=\"display:none\" href=\"#\">/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py</a>Two-dimensional, size-mutable, potentially heterogeneous tabular data.\n",
              "\n",
              "Data structure also contains labeled axes (rows and columns).\n",
              "Arithmetic operations align on both row and column labels. Can be\n",
              "thought of as a dict-like container for Series objects. The primary\n",
              "pandas data structure.\n",
              "\n",
              "Parameters\n",
              "----------\n",
              "data : ndarray (structured or homogeneous), Iterable, dict, or DataFrame\n",
              "    Dict can contain Series, arrays, constants, dataclass or list-like objects. If\n",
              "    data is a dict, column order follows insertion-order. If a dict contains Series\n",
              "    which have an index defined, it is aligned by its index.\n",
              "\n",
              "    .. versionchanged:: 0.25.0\n",
              "       If data is a list of dicts, column order follows insertion-order.\n",
              "\n",
              "index : Index or array-like\n",
              "    Index to use for resulting frame. Will default to RangeIndex if\n",
              "    no indexing information part of input data and no index provided.\n",
              "columns : Index or array-like\n",
              "    Column labels to use for resulting frame when data does not have them,\n",
              "    defaulting to RangeIndex(0, 1, 2, ..., n). If data contains column labels,\n",
              "    will perform column selection instead.\n",
              "dtype : dtype, default None\n",
              "    Data type to force. Only a single dtype is allowed. If None, infer.\n",
              "copy : bool or None, default None\n",
              "    Copy data from inputs.\n",
              "    For dict data, the default of None behaves like ``copy=True``.  For DataFrame\n",
              "    or 2d ndarray input, the default of None behaves like ``copy=False``.\n",
              "    If data is a dict containing one or more Series (possibly of different dtypes),\n",
              "    ``copy=False`` will ensure that these inputs are not copied.\n",
              "\n",
              "    .. versionchanged:: 1.3.0\n",
              "\n",
              "See Also\n",
              "--------\n",
              "DataFrame.from_records : Constructor from tuples, also record arrays.\n",
              "DataFrame.from_dict : From dicts of Series, arrays, or dicts.\n",
              "read_csv : Read a comma-separated values (csv) file into DataFrame.\n",
              "read_table : Read general delimited file into DataFrame.\n",
              "read_clipboard : Read text from clipboard into DataFrame.\n",
              "\n",
              "Notes\n",
              "-----\n",
              "Please reference the :ref:`User Guide &lt;basics.dataframe&gt;` for more information.\n",
              "\n",
              "Examples\n",
              "--------\n",
              "Constructing DataFrame from a dictionary.\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [1, 2], &#x27;col2&#x27;: [3, 4]}\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d)\n",
              "&gt;&gt;&gt; df\n",
              "   col1  col2\n",
              "0     1     3\n",
              "1     2     4\n",
              "\n",
              "Notice that the inferred dtype is int64.\n",
              "\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int64\n",
              "col2    int64\n",
              "dtype: object\n",
              "\n",
              "To enforce a single dtype:\n",
              "\n",
              "&gt;&gt;&gt; df = pd.DataFrame(data=d, dtype=np.int8)\n",
              "&gt;&gt;&gt; df.dtypes\n",
              "col1    int8\n",
              "col2    int8\n",
              "dtype: object\n",
              "\n",
              "Constructing DataFrame from a dictionary including Series:\n",
              "\n",
              "&gt;&gt;&gt; d = {&#x27;col1&#x27;: [0, 1, 2, 3], &#x27;col2&#x27;: pd.Series([2, 3], index=[2, 3])}\n",
              "&gt;&gt;&gt; pd.DataFrame(data=d, index=[0, 1, 2, 3])\n",
              "   col1  col2\n",
              "0     0   NaN\n",
              "1     1   NaN\n",
              "2     2   2.0\n",
              "3     3   3.0\n",
              "\n",
              "Constructing DataFrame from numpy ndarray:\n",
              "\n",
              "&gt;&gt;&gt; df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),\n",
              "...                    columns=[&#x27;a&#x27;, &#x27;b&#x27;, &#x27;c&#x27;])\n",
              "&gt;&gt;&gt; df2\n",
              "   a  b  c\n",
              "0  1  2  3\n",
              "1  4  5  6\n",
              "2  7  8  9\n",
              "\n",
              "Constructing DataFrame from a numpy ndarray that has labeled columns:\n",
              "\n",
              "&gt;&gt;&gt; data = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)],\n",
              "...                 dtype=[(&quot;a&quot;, &quot;i4&quot;), (&quot;b&quot;, &quot;i4&quot;), (&quot;c&quot;, &quot;i4&quot;)])\n",
              "&gt;&gt;&gt; df3 = pd.DataFrame(data, columns=[&#x27;c&#x27;, &#x27;a&#x27;])\n",
              "...\n",
              "&gt;&gt;&gt; df3\n",
              "   c  a\n",
              "0  3  1\n",
              "1  6  4\n",
              "2  9  7\n",
              "\n",
              "Constructing DataFrame from dataclass:\n",
              "\n",
              "&gt;&gt;&gt; from dataclasses import make_dataclass\n",
              "&gt;&gt;&gt; Point = make_dataclass(&quot;Point&quot;, [(&quot;x&quot;, int), (&quot;y&quot;, int)])\n",
              "&gt;&gt;&gt; pd.DataFrame([Point(0, 0), Point(0, 3), Point(2, 3)])\n",
              "   x  y\n",
              "0  0  0\n",
              "1  0  3\n",
              "2  2  3</pre>\n",
              "      <script>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Info extraction of all columns\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TC0zv7RpCOmA",
        "outputId": "40e91b57-5b0a-45ba-d511-b82aceaaa586"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 180 entries, 0 to 179\n",
            "Data columns (total 9 columns):\n",
            " #   Column         Non-Null Count  Dtype \n",
            "---  ------         --------------  ----- \n",
            " 0   Product        180 non-null    object\n",
            " 1   Age            180 non-null    int64 \n",
            " 2   Gender         180 non-null    object\n",
            " 3   Education      180 non-null    int64 \n",
            " 4   MaritalStatus  180 non-null    object\n",
            " 5   Usage          180 non-null    int64 \n",
            " 6   Fitness        180 non-null    int64 \n",
            " 7   Income         180 non-null    int64 \n",
            " 8   Miles          180 non-null    int64 \n",
            "dtypes: int64(6), object(3)\n",
            "memory usage: 12.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#statistical information of data\n",
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "Vi4Mg-h9Cz0a",
        "outputId": "962f11c2-7426-4fe2-8d8f-22aa5fd5708e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              Age   Education       Usage     Fitness         Income  \\\n",
              "count  180.000000  180.000000  180.000000  180.000000     180.000000   \n",
              "mean    28.788889   15.572222    3.455556    3.311111   53719.577778   \n",
              "std      6.943498    1.617055    1.084797    0.958869   16506.684226   \n",
              "min     18.000000   12.000000    2.000000    1.000000   29562.000000   \n",
              "25%     24.000000   14.000000    3.000000    3.000000   44058.750000   \n",
              "50%     26.000000   16.000000    3.000000    3.000000   50596.500000   \n",
              "75%     33.000000   16.000000    4.000000    4.000000   58668.000000   \n",
              "max     50.000000   21.000000    7.000000    5.000000  104581.000000   \n",
              "\n",
              "            Miles  \n",
              "count  180.000000  \n",
              "mean   103.194444  \n",
              "std     51.863605  \n",
              "min     21.000000  \n",
              "25%     66.000000  \n",
              "50%     94.000000  \n",
              "75%    114.750000  \n",
              "max    360.000000  "
            ],
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              "      <th>Age</th>\n",
              "      <th>Education</th>\n",
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              "      <th>min</th>\n",
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              "      <th>25%</th>\n",
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              "      <td>3.000000</td>\n",
              "      <td>44058.750000</td>\n",
              "      <td>66.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>16.000000</td>\n",
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              "      <td>3.000000</td>\n",
              "      <td>50596.500000</td>\n",
              "      <td>94.000000</td>\n",
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              "      <th>75%</th>\n",
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              "      <td>4.000000</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 55.58832332198464,\n        \"min\": 6.943498135399795,\n        \"max\": 180.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          28.788888888888888,\n          26.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 59.04362112875324,\n        \"min\": 1.6170548978065569,\n        \"max\": 180.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          180.0,\n          15.572222222222223,\n          16.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.474604277313155,\n        \"min\": 1.0847970343962436,\n        \"max\": 180.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          180.0,\n          3.4555555555555557,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 62.63086276036247,\n        \"min\": 0.958868565619312,\n        \"max\": 180.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          180.0,\n          3.311111111111111,\n          4.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31403.855763201762,\n        \"min\": 180.0,\n        \"max\": 104581.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          53719.57777777778,\n          50596.5,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 106.52090041797727,\n        \"min\": 21.0,\n        \"max\": 360.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          103.19444444444444,\n          94.0,\n          180.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(data.dtypes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MM9sFbVjOqpB",
        "outputId": "4d914e41-c9ca-4881-db3b-1e48ea2b9394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Product          object\n",
            "Age               int64\n",
            "Gender           object\n",
            "Education         int64\n",
            "MaritalStatus    object\n",
            "Usage             int64\n",
            "Fitness           int64\n",
            "Income            int64\n",
            "Miles             int64\n",
            "dtype: object\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Observations:**\n",
        "\n",
        "From above code we can have a basic analysis of our data.\n",
        "\n",
        "\n",
        "*   I have imported the required libraries for analysis\n",
        "*   data.shape() gives no. of rows and columns are 180 and 9.\n",
        "*   The data.info()function gives you a quick idea about data like summary. what kind of data each column holds (numbers, text, etc.), and if any values are missing from those columns.\n",
        "*  There are no null values.\n",
        "*  The data types of the data are integer and object(String).\n",
        "\n",
        "\n",
        "*   data.describe() gives stats for numbers in the table.\n",
        "*   The data is about people aged 18-50, with schooling from 12 to 21 years, and earnings between $29, 562  and $104,581.\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "-w02_cfaDhs3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#**Non-Graphical Analysis**"
      ],
      "metadata": {
        "id": "21slZDe4IRkR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Frequency analysis\n",
        "data[\"Age\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dNBET44fIcg_",
        "outputId": "85ee2b60-26fb-4645-8ee5-55b7a3e92237"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "25    25\n",
              "23    18\n",
              "24    12\n",
              "26    12\n",
              "28     9\n",
              "35     8\n",
              "33     8\n",
              "30     7\n",
              "38     7\n",
              "21     7\n",
              "22     7\n",
              "27     7\n",
              "31     6\n",
              "34     6\n",
              "29     6\n",
              "20     5\n",
              "40     5\n",
              "32     4\n",
              "19     4\n",
              "48     2\n",
              "37     2\n",
              "45     2\n",
              "47     2\n",
              "46     1\n",
              "50     1\n",
              "18     1\n",
              "44     1\n",
              "43     1\n",
              "41     1\n",
              "39     1\n",
              "36     1\n",
              "42     1\n",
              "Name: Age, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "ZRyGjBNHIk-v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Gender\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IP1nPtlpLGTv",
        "outputId": "ba117668-cceb-48a9-b0e4-f93fc2dda120"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      104\n",
              "Female     76\n",
              "Name: Gender, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['MaritalStatus'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Cc3PiyDLPuD",
        "outputId": "21d3b81a-36c3-404f-9792-8e71d60a71ab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Partnered    107\n",
              "Single        73\n",
              "Name: MaritalStatus, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data[\"Fitness\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UbojDOaOLeox",
        "outputId": "e1cd2bb6-6935-44b7-cdc8-1817c40d8a35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    97\n",
              "5    31\n",
              "2    26\n",
              "4    24\n",
              "1     2\n",
              "Name: Fitness, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#differect attribute count\n",
        "data.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "daAzS6yWM0xS",
        "outputId": "3a9bebbc-553d-4dcf-a4dc-adc4d645a335"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product           3\n",
              "Age              32\n",
              "Gender            2\n",
              "Education         8\n",
              "MaritalStatus     2\n",
              "Usage             6\n",
              "Fitness           5\n",
              "Income           62\n",
              "Miles            37\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Types of products\n",
        "data['Product'].unique().tolist()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JnHdIYELNrJL",
        "outputId": "ad81cdcf-dfc6-4df1-fe7f-0211d176c303"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['KP281', 'KP481', 'KP781']"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Number of customers used the three types of products\n",
        "customerCount = data['Product'].value_counts()\n",
        "customerCount.sort_index ()\n",
        "customerCount"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUBQ2xNtN91z",
        "outputId": "cb07094d-ce0c-46f0-c911-9cc151cc4d30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KP281    80\n",
              "KP481    60\n",
              "KP781    40\n",
              "Name: Product, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data['Age'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E_R2L1wuSbYO",
        "outputId": "8d35f110-a094-451b-9c1c-3aa95495d16b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
              "       35, 36, 37, 38, 39, 40, 41, 43, 44, 46, 47, 50, 45, 48, 42])"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data['Education'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EMqwXosHSfYY",
        "outputId": "5fc9f9a7-ac93-40ae-ed52-7eecd9e133ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([14, 15, 12, 13, 16, 18, 20, 21])"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data['Income'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64J7-kCPSi0p",
        "outputId": "cc2c0b47-3d80-4b73-d32d-feeeaf1abba2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 29562,  31836,  30699,  32973,  35247,  37521,  36384,  38658,\n",
              "        40932,  34110,  39795,  42069,  44343,  45480,  46617,  48891,\n",
              "        53439,  43206,  52302,  51165,  50028,  54576,  68220,  55713,\n",
              "        60261,  67083,  56850,  59124,  61398,  57987,  64809,  47754,\n",
              "        65220,  62535,  48658,  54781,  48556,  58516,  53536,  61006,\n",
              "        57271,  52291,  49801,  62251,  64741,  70966,  75946,  74701,\n",
              "        69721,  83416,  88396,  90886,  92131,  77191,  52290,  85906,\n",
              "       103336,  99601,  89641,  95866, 104581,  95508])"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "---\n",
        "\n",
        "\n",
        "**Observations :**\n",
        "From the above frequency analysis,\n",
        "\n",
        "*   Age : Median age is 25 years, maximum age of 50 years and minimum age of 18 years.\n",
        "*   There are more number of male users compared to females.\n",
        "*   Mostly the customers who purchased treadmills fall under fitness scale of 3.\n",
        "*  Couples purchased more treadmills than singles.\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ],
      "metadata": {
        "id": "bMBMOIwxTjiw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Missing values\n",
        "data.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AKu51CzpTx3Z",
        "outputId": "580280dc-cbd9-4102-c97d-7b6fa8939638"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product          0\n",
              "Age              0\n",
              "Gender           0\n",
              "Education        0\n",
              "MaritalStatus    0\n",
              "Usage            0\n",
              "Fitness          0\n",
              "Income           0\n",
              "Miles            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "data.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RZ_9FvQhUDe7",
        "outputId": "1311244f-564a-4f3f-fd5b-c2e11b08b747"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no missing values and no duplicates."
      ],
      "metadata": {
        "id": "o6-jvVaMUL2l"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Visual Analysis**"
      ],
      "metadata": {
        "id": "Oxq9mSF6Ulg8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Correlation Analysis**"
      ],
      "metadata": {
        "id": "DRbxOZCN34Z1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "9Yyk_LfC4RU5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#heatmap\n",
        "sns.heatmap(data.corr(), annot=True, cmap='YlGnBu')\n",
        "plt.title('Correlation heatmap')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 620
        },
        "id": "MJJbkHQ24aMD",
        "outputId": "f4f35202-838f-4cab-8884-b4563b60e509"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-68-a0765374b867>:2: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  sns.heatmap(data.corr(), annot=True, cmap='YlGnBu')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "x0jW1TMA480V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#pairplot\n",
        "sns.pairplot(data)\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "jr9wmdJ75rhG",
        "outputId": "0cfe346e-6767-4f68-abf9-4c7c8b91a60a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 42 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "from both visualizations,\n",
        "1. Pearson co-efficient is used to evalute the correlation between numerical data points.\n",
        "2. Pearson evalutes the linear relationship between data points.\n",
        "\n",
        "Observations which are higher than 0.5.\n",
        "\n",
        "- Correlation between Income & Miles is 0.54.\n",
        "- Correlation between Age & Income is 0.51\n",
        "- Correlation between Usage & Fitness is 0.67.\n",
        "- Correlation between Usage & Miles is 0.76.\n",
        "- Correlation between Education & Income is 0.63.\n",
        "- Correlation between Fitness & Miles is 0.79.\n",
        "- Correlation between Usage & Income is 0.52.\n",
        "- Correlation between Fitness & Income is 0.54."
      ],
      "metadata": {
        "id": "wIh8aMR26w8T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Bivariate**\n"
      ],
      "metadata": {
        "id": "hDSbmi1djTDZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Bivariate using histplot\n",
        "#Analysis among product and Gender\n",
        "sns.histplot(data=data, x=\"Product\", hue=\"Gender\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "ZN_SWP9sjY5-",
        "outputId": "d9132f92-3008-4bea-dcbc-d86263976377"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Product', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation:**\n",
        "* Gender distribution :\n",
        "  -  KP281, there are more female customers compared to males.\n",
        "  -  KP481 and KP781, the distribution balanced.\n",
        "\n",
        "* Product preferences:\n",
        "\n",
        "KP281 appears to be slightly more popular among males, while KP481 and KP781 have similar popularity among both genders."
      ],
      "metadata": {
        "id": "JFwfd7fqkYnZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Countplot\n",
        "#Comparisiom between maritalStatus and product.\n",
        "sns.countplot(data=data, x=\"MaritalStatus\", hue=\"Product\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "v7QHwIpEjubL",
        "outputId": "8f5ec592-f690-410e-cc53-1e0e151f0528"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "* Product Preference :\n",
        "    1. Singles: KP281 is the most popular.\n",
        "    2. Partnered: KP781 is slightly more popular than KP481.\n",
        "\n",
        "* Usage:\n",
        "   1. Singles use KP281 more frequently,  partnered individuals have a more balanced usage."
      ],
      "metadata": {
        "id": "SKDFaoK0lIKz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Boxplot\n",
        "sns.boxplot(data=data, x=\"Age\", hue=\"Product\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "uvjv0EGgkC3S",
        "outputId": "274e9f97-266e-44ba-ad4f-4164b188e7cf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "\n",
        "* **Product Preference :**\n",
        "    1. Younger customers (18-29)  higher preference is KP281.\n",
        "    2. In the middle age range (30-49) balanced distribution among all three products.\n",
        "* **Usage Patterns:**\n",
        "    1. Younger customers tend to use treadmills more frequently, regardless of the product type.\n",
        "    2. Usage patterns for different products are relatively similar across all age groups.\n"
      ],
      "metadata": {
        "id": "X8UCQqk0l3pM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Univariate**"
      ],
      "metadata": {
        "id": "olAzeJ7EfDVq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#univariate\n",
        "sns.countplot(data=data, x='Product', palette='YlGnBu')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "bsarQ1d0Xsyt",
        "outputId": "856dbb4e-62d0-4451-9a43-ddf61a53b394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-38-aa922ac1b6b8>:2: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.countplot(data=data, x='Product', palette='YlGnBu')\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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e09KlSzVv3rzWngcAAOCiBfVzZL788ktNmDChtWcBAABokaBCZsKECfrjH//Y2rMAAAC0SFBvLfXu3Vvz58/Xvn37NGDAAHXo0CFg+89//vNWGQ4AAOBCggqZZ599Vh07dlRxcbGKi4sDtjkcDkIGAABcFkGFTFlZWWvPAQAA0GJB3SMDAAAQCoK6IjN16tQLbl+3bl1QwwAAALREUCHz5ZdfBjyur6/XBx98oKqqqmZ/mSQAAEBbCCpktmzZ0mRdY2OjHnjgAfXq1euShwIAALgYrXaPTFhYmLKzs7Vy5crWOiQAAMAFterNvn/961917ty51jwkAADAeQX11lJ2dnbAY8uyVFFRof/6r/9SZmZmqwwGAADwbYIKmb/85S8Bj8PCwtStWzc9/vjj3/qJJgAAgNYSVMjs3r27tefQsmXLlJOTo4cfflh5eXmSpJqaGs2ePVubNm1SbW2t0tPTtWrVKrnd7lZ/fQAAYJ5Lukfm1KlT2rt3r/bu3atTp04FfZyDBw/qmWee0cCBAwPWz5o1S9u2bdPmzZtVXFysEydOaPz48ZcyMgAAaEeCCpnq6mpNnTpV8fHxuummm3TTTTcpISFB06ZN09mzZ1t0rDNnzmjSpElau3atrrzySv96r9ergoICPfHEE0pNTdWQIUNUWFioP//5z9q3b18wYwMAgHYmqJDJzs5WcXGxtm3bpqqqKlVVVenVV19VcXGxZs+e3aJjZWVl6Y477lBaWlrA+tLSUtXX1wes79u3r5KSklRSUnLe49XW1srn8wUsAACgfQrqHpmXX35Zv/vd73TLLbf4191+++2Kjo7Wj3/8Y61evfqijrNp0ya98847OnjwYJNtHo9HkZGR6ty5c8B6t9stj8dz3mPm5uZq8eLFF/X6AIDWc+PkNXaPgBDy9vr7L8vrBHVF5uzZs83ecNu9e/eLfmupvLxcDz/8sDZs2KCoqKhgxmhWTk6OvF6vfykvL2+1YwMAgNASVMikpKRo4cKFqqmp8a/7+9//rsWLFyslJeWijlFaWqqTJ0/q+9//viIiIhQREaHi4mI9+eSTioiIkNvtVl1dnaqqqgKeV1lZqbi4uPMe1+l0KjY2NmABAADtU1BvLeXl5em2225Tjx49NGjQIEnSu+++K6fTqT/+8Y8XdYwf/vCHev/99wPWTZkyRX379tW8efOUmJioDh06qKioSBkZGZKkI0eO6Pjx4xcdSwAAoH0LKmQGDBigo0ePasOGDfroo48kSRMnTtSkSZMUHR19Ucfo1KmTrr/++oB1MTEx6tq1q3/9tGnTlJ2drS5duig2NlYzZsxQSkqKhg8fHszYAACgnQkqZHJzc+V2u3XfffcFrF+3bp1OnTqlefPmtcpwK1euVFhYmDIyMgJ+IB4AAIAU5D0yzzzzjPr27dtk/XXXXac1a4K/a33Pnj3+n+orSVFRUcrPz9cXX3yh6upqvfLKKxe8PwYAAHy3BBUyHo9H8fHxTdZ369ZNFRUVlzwUAADAxQgqZBITE/XWW281Wf/WW28pISHhkocCAAC4GEHdI3Pfffdp5syZqq+vV2pqqiSpqKhIc+fObfFP9gUAAAhWUCHzi1/8Qp9//rkefPBB1dXVSfrH/Szz5s1TTk5Oqw4IAABwPkGFjMPh0PLlyzV//nwdPnxY0dHR6tOnj5xOZ2vPBwAAcF5BhcxXOnbsqKFDh7bWLAAAAC0S1M2+AAAAoYCQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsWwNmdWrV2vgwIGKjY1VbGysUlJS9Nprr/m319TUKCsrS127dlXHjh2VkZGhyspKGycGAAChxNaQ6dGjh5YtW6bS0lK9/fbbSk1N1dixY/Xf//3fkqRZs2Zp27Zt2rx5s4qLi3XixAmNHz/ezpEBAEAIibDzxceMGRPweOnSpVq9erX27dunHj16qKCgQBs3blRqaqokqbCwUP369dO+ffs0fPhwO0YGAAAhJGTukWloaNCmTZtUXV2tlJQUlZaWqr6+Xmlpaf59+vbtq6SkJJWUlJz3OLW1tfL5fAELAABon2wPmffff18dO3aU0+nU/fffry1btqh///7yeDyKjIxU586dA/Z3u93yeDznPV5ubq5cLpd/SUxMbOOvAAAA2MX2kLn22mt16NAh7d+/Xw888IAyMzP14YcfBn28nJwceb1e/1JeXt6K0wIAgFBi6z0ykhQZGanevXtLkoYMGaKDBw/qP//zP3X33Xerrq5OVVVVAVdlKisrFRcXd97jOZ1OOZ3Oth4bAACEANuvyHxTY2OjamtrNWTIEHXo0EFFRUX+bUeOHNHx48eVkpJi44QAACBU2HpFJicnR6NHj1ZSUpJOnz6tjRs3as+ePdq5c6dcLpemTZum7OxsdenSRbGxsZoxY4ZSUlL4xBIAAJBkc8icPHlS9957ryoqKuRyuTRw4EDt3LlTP/rRjyRJK1euVFhYmDIyMlRbW6v09HStWrXKzpEBAEAIsTVkCgoKLrg9KipK+fn5ys/Pv0wTAQAAk4TcPTIAAAAXi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGMvWkMnNzdXQoUPVqVMnde/eXePGjdORI0cC9qmpqVFWVpa6du2qjh07KiMjQ5WVlTZNDAAAQomtIVNcXKysrCzt27dPr7/+uurr6/Uv//Ivqq6u9u8za9Ysbdu2TZs3b1ZxcbFOnDih8ePH2zg1AAAIFRF2vviOHTsCHq9fv17du3dXaWmpbrrpJnm9XhUUFGjjxo1KTU2VJBUWFqpfv37at2+fhg8fbsfYAAAgRITUPTJer1eS1KVLF0lSaWmp6uvrlZaW5t+nb9++SkpKUklJSbPHqK2tlc/nC1gAAED7FDIh09jYqJkzZ2rkyJG6/vrrJUkej0eRkZHq3LlzwL5ut1sej6fZ4+Tm5srlcvmXxMTEth4dAADYJGRCJisrSx988IE2bdp0ScfJycmR1+v1L+Xl5a00IQAACDW23iPzlYceekjbt2/Xm2++qR49evjXx8XFqa6uTlVVVQFXZSorKxUXF9fssZxOp5xOZ1uPDAAAQoCtV2Qsy9JDDz2kLVu26I033lBycnLA9iFDhqhDhw4qKiryrzty5IiOHz+ulJSUyz0uAAAIMbZekcnKytLGjRv16quvqlOnTv77Xlwul6Kjo+VyuTRt2jRlZ2erS5cuio2N1YwZM5SSksInlgAAgL0hs3r1aknSLbfcErC+sLBQkydPliStXLlSYWFhysjIUG1trdLT07Vq1arLPCkAAAhFtoaMZVnfuk9UVJTy8/OVn59/GSYCAAAmCZlPLQEAALQUIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwFiEDAACMRcgAAABjETIAAMBYhAwAADAWIQMAAIxFyAAAAGMRMgAAwFiEDAAAMBYhAwAAjEXIAAAAYxEyAADAWIQMAAAwlq0h8+abb2rMmDFKSEiQw+HQ1q1bA7ZblqUFCxYoPj5e0dHRSktL09GjR+0ZFgAAhBxbQ6a6ulqDBg1Sfn5+s9tXrFihJ598UmvWrNH+/fsVExOj9PR01dTUXOZJAQBAKIqw88VHjx6t0aNHN7vNsizl5eXpl7/8pcaOHStJeuGFF+R2u7V161bdc889l3NUAAAQgkL2HpmysjJ5PB6lpaX517lcLg0bNkwlJSXnfV5tba18Pl/AAgAA2qeQDRmPxyNJcrvdAevdbrd/W3Nyc3Plcrn8S2JiYpvOCQAA7BOyIROsnJwceb1e/1JeXm73SAAAoI2EbMjExcVJkiorKwPWV1ZW+rc1x+l0KjY2NmABAADtU8iGTHJysuLi4lRUVORf5/P5tH//fqWkpNg4GQAACBW2fmrpzJkzOnbsmP9xWVmZDh06pC5duigpKUkzZ87UY489pj59+ig5OVnz589XQkKCxo0bZ9/QAAAgZNgaMm+//bZuvfVW/+Ps7GxJUmZmptavX6+5c+equrpa06dPV1VVlUaNGqUdO3YoKirKrpEBAEAIsTVkbrnlFlmWdd7tDodDS5Ys0ZIlSy7jVAAAwBQhe48MAADAtyFkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYyImTy8/N11VVXKSoqSsOGDdOBAwfsHgkAAISAkA+Z3/72t8rOztbChQv1zjvvaNCgQUpPT9fJkyftHg0AANgs5EPmiSee0H333acpU6aof//+WrNmja644gqtW7fO7tEAAIDNIuwe4ELq6upUWlqqnJwc/7qwsDClpaWppKSk2efU1taqtrbW/9jr9UqSfD5f0HNUn64O+rlony7lfGot9WfP2j0CQkgonJMNdX+3ewSEkEs9J796vmVZF9wvpEPms88+U0NDg9xud8B6t9utjz76qNnn5ObmavHixU3WJyYmtsmMABAKXFPsngAI5Pp/2a1ynNOnT8vlcp13e0iHTDBycnKUnf1/f3mNjY364osv1LVrVzkcDhsnM5/P51NiYqLKy8sVGxtr9zgA5yRCDudk67EsS6dPn1ZCQsIF9wvpkPne976n8PBwVVZWBqyvrKxUXFxcs89xOp1yOp0B6zp37txWI34nxcbG8h8oQgrnJEIN52TruNCVmK+E9M2+kZGRGjJkiIqKivzrGhsbVVRUpJSUFBsnAwAAoSCkr8hIUnZ2tjIzM3XjjTfqn//5n5WXl6fq6mpNmcIbwgAAfNeFfMjcfffdOnXqlBYsWCCPx6PBgwdrx44dTW4ARttzOp1auHBhk7fuALtwTiLUcE5efg7r2z7XBAAAEKJC+h4ZAACACyFkAACAsQgZAABgLEIGAAAYi5BpxyZPnqxx48YFrPvd736nqKgoPf7445o8ebIcDoccDociIyPVu3dvLVmyROfOnZMk7dmzR2PHjlV8fLxiYmI0ePBgbdiwocnr5OXl6dprr1V0dLQSExM1a9Ys1dTU+Le/+eabGjNmjBISEuRwOLR169a2/LIRwi71nPy6Y8eOqVOnTs3+wEvOSbTEpZ6XixYt8m//+hITExNwTM7LtkHIfIc899xzmjRpklavXq3Zs2dLkm677TZVVFTo6NGjmj17thYtWqR///d/lyT9+c9/1sCBA/Xyyy/rvffe05QpU3Tvvfdq+/bt/mNu3LhRjzzyiBYuXKjDhw+roKBAv/3tb/Xoo4/696murtagQYOUn59/eb9ghLyWnpNfqa+v18SJE/WDH/ygyTE5J3GpWnpezpkzRxUVFQFL//79NWHCBP8xOS/bkIV2KzMz0xo7dqxlWZa1fPlyKyoqynrllVea3f6VH/3oR9bw4cPPe8zbb7/dmjJliv9xVlaWlZqaGrBPdna2NXLkyGafL8nasmVLy74QtButdU7OnTvX+slPfmIVFhZaLpcrYBvnJFqqtb9XHjp0yJJkvfnmm/51nJdthysy3wHz5s3Tr371K23fvl133XXXBfeNjo5WXV3debd7vV516dLF/3jEiBEqLS3VgQMHJEl/+9vf9Ic//EG333576wyPdulSzsk33nhDmzdvPu//tXJOIlit9b3yueee0zXXXBNwxZDzsu2E/E/2xaV57bXX9Oqrr6qoqEipqann3c+yLBUVFWnnzp2aMWNGs/u89NJLOnjwoJ555hn/un/7t3/TZ599plGjRsmyLJ07d073339/wOVS4Osu5Zz8/PPPNXnyZL344ovn/YV8nJMIRmt9r6ypqdGGDRv0yCOPBKznvGxDdl4OQtvKzMy0brzxRuuqq66yRo0aZZ0+fbrJ9vDwcCsmJsaKjIy0IiIirHvvvdc6c+ZMk2O98cYb1hVXXGE9//zzAet3795tud1ua+3atdZ7771nvfLKK1ZiYqK1ZMmSZmcSl0u/0y71nLzrrrusefPm+fdv7q0lzkm0VGt+r9y4caMVERFheTyegPWcl22HkGnHvnpf93/+53+s5ORka+TIkZbP5wvYnpaWZh09etT65JNPrPr6+maPs2fPHismJsZ65plnmmwbNWqUNWfOnIB1v/nNb6zo6GiroaGhyf78x/nddqnnpMvlssLDw/1LWFiYJckKDw+3CgoKLMvinETLtdb3SsuyrNTUVGvcuHFN1nNeth3ukfkO6Nmzp4qLi+XxeHTbbbfp9OnT/m0xMTHq3bu3kpKSFBHR9J3GPXv26I477tDy5cs1ffr0JtvPnj2rsLDA0yg8PFzSPy7BAs0J9pwsKSnRoUOH/MuSJUvUqVMnHTp0yH9PA+ckgnUp3yslqaysTLt379a0adOabOO8bDvcI/MdkZiYqD179ujWW29Venq6duzY8a3P2b17t+688049/PDDysjIkMfjkSRFRkb6b/gdM2aMnnjiCd1www0aNmyYjh07pvnz52vMmDH+/0jPnDmjY8eO+Y9bVlamQ4cOqUuXLkpKSmqDrxYmCOac7NevX8Djt99+W2FhYbr++uv96zgncSmCOS+/sm7dOsXHx2v06NFNtnFetiGbrwihDTX3kcFPP/3U6tOnjzV8+HDrrrvuarL9m8+X1GS5+eab/fvU19dbixYtsnr16mVFRUVZiYmJ1oMPPmh9+eWX/n12797d7HEyMzNb9etF6LvUc/KbmrtHhnMSLdUa52VDQ4PVo0cP69FHH212O+dl23FYFte0AACAmbhHBgAAGIuQAQAAxiJkAACAsQgZAABgLEIGAAAYi5ABAADGImQAAICxCBkAAGAsQgaA8SZPnqxx48bZPQYAGxAyANrM5MmT5XA45HA4FBkZqd69e2vJkiU6d+6c3aNd0Pr169W5c2e7xwBwEfilkQDa1G233abCwkLV1tbqD3/4g7KystShQwfl5OQE7FdXV6fIyEibpgRgKq7IAGhTTqdTcXFx6tmzpx544AGlpaXp97//vf/toKVLlyohIUHXXnutJOn9999XamqqoqOj1bVrV02fPl1nzpzxH6+hoUHZ2dnq3Lmzunbtqrlz5+qbvzLuqquuUl5eXsC6wYMHa9GiRf7HVVVV+tnPfia3262oqChdf/312r59u/bs2aMpU6bI6/X6ryZ9/XkAQgshA+Cyio6OVl1dnSSpqKhIR44c0euvv67t27erurpa6enpuvLKK3Xw4EFt3rxZu3bt0kMPPeR//uOPP67169dr3bp12rt3r7744gtt2bKlRTM0NjZq9OjReuutt/Tiiy/qww8/1LJlyxQeHq4RI0YoLy9PsbGxqqioUEVFhebMmdOqfwcAWg9vLQG4LCzLUlFRkXbu3KkZM2bo1KlTiomJ0XPPPed/S2nt2rWqqanRCy+8oJiYGEnS008/rTFjxmj58uVyu93Ky8tTTk6Oxo8fL0las2aNdu7c2aJZdu3apQMHDujw4cO65pprJElXX321f7vL5ZLD4VBcXFxrfOkA2hBXZAC0qe3bt6tjx46KiorS6NGjdffdd/vfqhkwYEDAfTGHDx/WoEGD/BEjSSNHjlRjY6OOHDkir9eriooKDRs2zL89IiJCN954Y4tmOnTokHr06OGPGADm4ooMgDZ16623avXq1YqMjFRCQoIiIv7v287Xg6U1hYWFNblvpr6+3v/n6OjoNnldAJcfV2QAtKmYmBj17t1bSUlJARHTnH79+undd99VdXW1f91bb72lsLAwXXvttXK5XIqPj9f+/fv928+dO6fS0tKA43Tr1k0VFRX+xz6fT2VlZf7HAwcO1KeffqqPP/642TkiIyPV0NDQoq8TgD0IGQAhY9KkSYqKilJmZqY++OAD7d69WzNmzNBPf/pTud1uSdLDDz+sZcuWaevWrfroo4/04IMPqqqqKuA4qamp+s1vfqM//elPev/995WZmanw8HD/9ptvvlk33XSTMjIy9Prrr6usrEyvvfaaduzYIekfn3o6c+aMioqK9Nlnn+ns2bOX7e8AQMsQMgBCxhVXXKGdO3fqiy++0NChQ/Wv//qv+uEPf6inn37av8/s2bP105/+VJmZmUpJSVGnTp101113BRwnJydHN998s+68807dcccdGjdunHr16hWwz8svv6yhQ4dq4sSJ6t+/v+bOneu/CjNixAjdf//9uvvuu9WtWzetWLGi7b94AEFxWN98IxkAAMAQXJEBAADGImQAAICxCBkAAGAsQgYAABiLkAEAAMYiZAAAgLEIGQAAYCxCBgAAGIuQAQAAxiJkAACAsQgZAABgrP8PPh9Sj67lN68AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "*  KP281 is the most purchased product count of 80.\n",
        "*  KP481 is the second most purchased product, count of 60\n",
        "*  KP781 is the purchased product having the count of 40.\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "mfQJJJIAYwFN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Count plot\n",
        "sns.countplot(data=data, x='MaritalStatus')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "texepq2WZIHN",
        "outputId": "6d9b3b9d-4da0-46e0-9b0d-a9e7630b8bf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='MaritalStatus', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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KAACgrjh1gfK5c+c0ZswYhYWF6d5779W9996r8PBwJSUl6fz583U9IwAAgNOcip3U1FTl5+frvffeU2lpqUpLS/Xuu+8qPz9fjz/+eF3PCAAA4DSn3sZ6++239dZbb+n++++3L/v9738vX19fDRs2TEuXLq2r+QAAAGrFqTM758+fV0hIyEXLmzdvzttYAADArTgVOzExMZo1a5YuXLhgX/avf/1Lc+bMUUxMTJ0NBwAAUFtOvY21cOFCxcfHq0WLFoqKipIk7du3T97e3vroo4/qdEAAAIDacCp2OnfurMOHD2vNmjX65ptvJEkPPfSQRowYIV9f3zodEAAAoDacip2MjAyFhIRo3LhxDstXrVql06dPa8aMGXUyHAAAQG05dc3OK6+8osjIyIuWd+zYUcuWLav1UAAAAHXFqdgpLi5WWFjYRcuDg4N18uTJWg8FAABQV5yKHavVqm3btl20fNu2bQoPD6/1UAAAAHXFqWt2xo0bp5SUFFVVValv376SpLy8PE2fPp2/oAwAANyKU7HzxBNP6MyZM5o4caIqKyslST4+PpoxY4bS0tLqdEAAAIDacCp2LBaLMjMzlZ6eroMHD8rX11dt2rSRt7d3Xc8HAABQK07Fzi/8/f3Vo0ePupoFAACgzjl1gTIAAEB9QewAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADBavYqdv/71r7JYLEpJSbEvu3DhgpKTkxUUFCR/f38lJiaqpKTEdUMCAAC3Um9iZ/fu3XrllVfUpUsXh+VTp07Ve++9pzfffFP5+fk6ceKEhgwZ4qIpAQCAu6kXsXP27FmNGDFCK1asUNOmTe3Ly8rK9OqrryorK0t9+/ZV9+7dlZ2drX/84x/asWPHZY9XUVGh8vJyhxsAADBTvYid5ORkDRgwQLGxsQ7LCwoKVFVV5bA8MjJSLVu21Pbt2y97vIyMDAUGBtpvVqv1hs0OAABcy+1jZ926dfr888+VkZFx0bri4mJ5eXmpSZMmDstDQkJUXFx82WOmpaWprKzMfjt+/Hhdjw0AANyEp6sHuJLjx4/rscce0+bNm+Xj41Nnx/X29pa3t3edHQ8AALgvtz6zU1BQoFOnTunOO++Up6enPD09lZ+fr0WLFsnT01MhISGqrKxUaWmpw34lJSUKDQ11zdAAAMCtuPWZnX79+umrr75yWDZ69GhFRkZqxowZslqtatiwofLy8pSYmChJKiws1LFjxxQTE+OKkQEAgJtx69hp3LixOnXq5LDMz89PQUFB9uVJSUlKTU1Vs2bNFBAQoMmTJysmJkZ33323K0YGAABuxq1j51osWLBAHh4eSkxMVEVFheLi4vTyyy+7eiwAAOAm6l3sbN261eG+j4+PlixZoiVLlrhmIAAA4Nbc+gJlAACA2iJ2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDR3Dp2MjIy1KNHDzVu3FjNmzfX4MGDVVhY6LDNhQsXlJycrKCgIPn7+ysxMVElJSUumhgAALgbt46d/Px8JScna8eOHdq8ebOqqqr0H//xHzp37px9m6lTp+q9997Tm2++qfz8fJ04cUJDhgxx4dQAAMCdeLp6gCvZtGmTw/2cnBw1b95cBQUFuvfee1VWVqZXX31Va9euVd++fSVJ2dnZat++vXbs2KG7777bFWMDAAA34tZndv5dWVmZJKlZs2aSpIKCAlVVVSk2Nta+TWRkpFq2bKnt27df9jgVFRUqLy93uAEAADPVm9ipqalRSkqKevfurU6dOkmSiouL5eXlpSZNmjhsGxISouLi4sseKyMjQ4GBgfab1Wq9kaMDAAAXqjexk5ycrK+//lrr1q2r9bHS0tJUVlZmvx0/frwOJgQAAO7Ira/Z+cWkSZP0/vvv65NPPlGLFi3sy0NDQ1VZWanS0lKHszslJSUKDQ297PG8vb3l7e19I0cGAABuwq3P7NhsNk2aNEnvvPOOtmzZooiICIf13bt3V8OGDZWXl2dfVlhYqGPHjikmJuZmjwsAANyQW5/ZSU5O1tq1a/Xuu++qcePG9utwAgMD5evrq8DAQCUlJSk1NVXNmjVTQECAJk+erJiYGH4TCwAASHLz2Fm6dKkk6f7773dYnp2drVGjRkmSFixYIA8PDyUmJqqiokJxcXF6+eWXb/KkAADAXbl17Nhstqtu4+PjoyVLlmjJkiU3YSIAAFDfuPU1OwAAALVF7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMbEzpIlS9SqVSv5+PioZ8+e2rVrl6tHAgAAbsCI2Hn99deVmpqqWbNm6fPPP1dUVJTi4uJ06tQpV48GAABczIjYycrK0rhx4zR69Gh16NBBy5YtU6NGjbRq1SpXjwYAAFzM09UD1FZlZaUKCgqUlpZmX+bh4aHY2Fht3779kvtUVFSooqLCfr+srEySVF5efsPmrK741w07NlCf3cjX3c3C6xu4tBv9+v7l+Dab7Yrb1fvY+b//+z9VV1crJCTEYXlISIi++eabS+6TkZGhOXPmXLTcarXekBkBXF7g4kddPQKAG+Rmvb5//PFHBQYGXnZ9vY8dZ6SlpSk1NdV+v6amRt9//72CgoJksVhcOBluhvLyclmtVh0/flwBAQGuHgdAHeL1/dtis9n0448/Kjw8/Irb1fvYueWWW9SgQQOVlJQ4LC8pKVFoaOgl9/H29pa3t7fDsiZNmtyoEeGmAgIC+J8hYChe378dVzqj84t6f4Gyl5eXunfvrry8PPuympoa5eXlKSYmxoWTAQAAd1Dvz+xIUmpqqkaOHKno6GjdddddWrhwoc6dO6fRo0e7ejQAAOBiRsTO8OHDdfr0ac2cOVPFxcXq2rWrNm3adNFFy4D089uYs2bNuuitTAD1H69vXIrFdrXf1wIAAKjH6v01OwAAAFdC7AAAAKMROwAAwGjEDoxgsVi0YcOGOj3m7Nmz1bVr1zo9JgBztWrVSgsXLnT1GLgEYgf1wunTpzVhwgS1bNlS3t7eCg0NVVxcnLZt2yZJOnnypPr37+/iKQGMGjVKFotFFotFXl5eat26tZ5++mn99NNPtTrm4MGD625I/OYY8avnMF9iYqIqKyuVm5ur22+/XSUlJcrLy9OZM2ck6bJ/LRvAzRcfH6/s7GxVVFTob3/7m5KTk9WwYUOHD2y+FtXV1Tf9I3wqKyvl5eV1Ux8TNx5nduD2SktL9emnnyozM1N9+vTRbbfdprvuuktpaWn6z//8T0mOb2MdPXpUFotF69evV58+fdSoUSNFRUVp+/btDsddsWKFrFarGjVqpD/84Q/Kysq66seGrFy5Uu3bt5ePj48iIyP18ssv34inDNRrv5x9ve222zRhwgTFxsZq48aNysrKUufOneXn5yer1aqJEyfq7Nmz9v1ycnLUpEkTbdy4UR06dJC3t7fGjBmj3Nxcvfvuu/YzRlu3br3m1/lnn32m3/3ud/L19ZXVatWUKVN07tw5+/pWrVrpmWee0Z///GcFBARo/Pjx17TfqVOnlJCQIF9fX0VERGjNmjU3+LuK2iB24Pb8/f3l7++vDRs2qKKi4pr3+6//+i9NmzZNe/fuVdu2bfXQQw/ZT6Vv27ZNjz76qB577DHt3btXDzzwgObNm3fF461Zs0YzZ87UvHnzdPDgQT377LNKT09Xbm5urZ4fYDpfX19VVlbKw8NDixYt0v79+5Wbm6stW7Zo+vTpDtueP39emZmZWrlypfbv369FixZp2LBhio+P18mTJ3Xy5En16tXLvv2VXudHjhxRfHy8EhMT9eWXX+r111/XZ599pkmTJjk85vPPP6+oqCh98cUXSk9Pv6b9Ro0apePHj+vjjz/WW2+9pZdfflmnTp26gd9F1IoNqAfeeustW9OmTW0+Pj62Xr162dLS0mz79u2zr5dke+edd2w2m81WVFRkk2RbuXKlff3+/fttkmwHDx602Ww22/Dhw20DBgxweIwRI0bYAgMD7fdnzZpli4qKst+/4447bGvXrnXY55lnnrHFxMTU0bME6r+RI0faBg0aZLPZbLaamhrb5s2bbd7e3rZp06ZdtO2bb75pCwoKst/Pzs62SbLt3bv3ssf8xbW8zpOSkmzjx4932O/TTz+1eXh42P71r3/ZbDab7bbbbrMNHjzYYZur7VdYWGiTZNu1a5d9/cGDB22SbAsWLLjCdweuwpkd1AuJiYk6ceKENm7cqPj4eG3dulV33nmncnJyLrtPly5d7F+HhYVJkv1fXoWFhbrrrrsctv/3+7927tw5HTlyRElJSfYzTf7+/po7d66OHDlSi2cGmOf999+Xv7+/fHx81L9/fw0fPlyzZ8/W3//+d/Xr10+33nqrGjdurIcfflhnzpzR+fPn7ft6eXk5vHav5kqv83379iknJ8fhNRsXF6eamhoVFRXZ94uOjnY45tX2O3jwoDw9PdW9e3f7PpGRkVd9GxyuwwXKqDd8fHz0wAMP6IEHHlB6errGjh2rWbNmadSoUZfcvmHDhvavf7nIsaamxqnH/uW6ghUrVqhnz54O6xo0aODUMQFT9enTR0uXLpWXl5fCw8Pl6empo0ePauDAgZowYYLmzZunZs2a6bPPPlNSUpIqKyvVqFEjST+/5XU9FyVf6XV+9uxZPfLII5oyZcpF+7Vs2dL+tZ+fn8O6q+136NCha54P7oHYQb3VoUMHp/+2Trt27bR7926HZf9+/9dCQkIUHh6ub7/9ViNGjHDqMYHfCj8/P7Vu3dphWUFBgWpqavTCCy/Iw+PnNxXeeOONazqel5eXqqurr3uOO++8UwcOHLholtruFxkZqZ9++kkFBQXq0aOHpJ/PFpeWll73jLg5iB24vTNnzmjo0KEaM2aMunTposaNG2vPnj2aP3++Bg0a5NQxJ0+erHvvvVdZWVlKSEjQli1b9MEHH1zxX5Rz5szRlClTFBgYqPj4eFVUVGjPnj364YcflJqa6uzTA34TWrduraqqKi1evFgJCQnatm2bli1bdk37tmrVSh9++KEKCwsVFBSkwMDAa9pvxowZuvvuuzVp0iSNHTtWfn5+OnDggDZv3qyXXnrJ6f3atWun+Ph4PfLII1q6dKk8PT2VkpIiX1/fa5oLNx/X7MDt+fv7q2fPnlqwYIHuvfdederUSenp6Ro3btwV/4d1Jb1799ayZcuUlZWlqKgobdq0SVOnTpWPj89l9xk7dqxWrlyp7Oxsde7cWffdd59ycnIUERHh7FMDfjOioqKUlZWlzMxMderUSWvWrFFGRsY17Ttu3Di1a9dO0dHRCg4Otv8x0avp0qWL8vPzdejQIf3ud79Tt27dNHPmTIWHh9d6v+zsbIWHh+u+++7TkCFDNH78eDVv3vya5sLNZ7HZbDZXDwG4g3Hjxumbb77Rp59+6upRAAB1iLex8Jv1/PPP64EHHpCfn58++OAD5ebm8kcCAcBAnNnBb9awYcO0detW/fjjj7r99ts1efJkPfroo64eCwBQx4gdAABgNC5QBgAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAuJ2cnJw6+QTpujoOgPqN2AFwXUaNGiWLxXLJv0mUnJwsi8Vy2U+iv1bDhw93+GTp2bNnq2vXrrU6piRVV1frr3/9qyIjI+Xr66tmzZqpZ8+eWrlypX2b+++/XykpKdd97FGjRmnw4MG1nhFA3eMvKAO4blarVevWrdOCBQvsH3544cIFrV27Vi1btqzVsauqquTr63tDPlRxzpw5euWVV/TSSy8pOjpa5eXl9g9zBWAuzuwAuG533nmnrFar1q9fb1+2fv16tWzZUt26dbMv27Rpk+655x41adJEQUFBGjhwoI4cOWJff/ToUVksFr3++uu677775OPjozVr1ji8/ZSTk6M5c+Zo3759slgsslgsysnJkSRlZWWpc+fO8vPzk9Vq1cSJE3X27NnLzr1x40ZNnDhRQ4cOVUREhKKiopSUlKRp06ZJ+vnsTH5+vl588UX7Yx09elTV1dVKSkpSRESEfH191a5dO7344ov2486ePVu5ubl699137ftt3bpVW7dulcViUWlpqX3bvXv32o8rSd99950SEhLUtGlT+fn5qWPHjvrb3/7m7H8aAJdA7ABwypgxY5SdnW2/v2rVKo0ePdphm3Pnzik1NVV79uxRXl6ePDw89Ic//EE1NTUO2z355JN67LHHdPDgQcXFxTmsGz58uB5//HF17NhRJ0+e1MmTJzV8+HBJkoeHhxYtWqT9+/crNzdXW7Zs0fTp0y87c2hoqLZs2aLTp09fcv2LL76omJgYjRs3zv5YVqtVNTU1atGihd58800dOHBAM2fO1F/+8he98cYbkqRp06Zp2LBhio+Pt+/Xq1eva/o+Jicnq6KiQp988om++uorZWZmyt/f/5r2BXBteBsLgFP+9Kc/KS0tTd99950kadu2bVq3bp22bt1q3yYxMdFhn1WrVik4OFgHDhxQp06d7MtTUlI0ZMiQSz6Or6+v/P395enpqdDQUId1v762plWrVpo7d64effTRy36ga1ZWlv74xz8qNDRUHTt2VK9evTRo0CD1799fkhQYGCgvLy81atTI4bEaNGigOXPm2O9HRERo+/bteuONNzRs2DD5+/vL19dXFRUVF814NceOHVNiYqI6d+4sSbr99tuva38AV8eZHQBOCQ4O1oABA5STk6Ps7GwNGDBAt9xyi8M2hw8f1kMPPaTbb79dAQEBatWqlaSff8D/WnR0tFMz/P3vf1e/fv106623qnHjxnr44Yd15swZnT9//pLbd+jQQV9//bV27NihMWPG6NSpU0pISNDYsWOv+lhLlixR9+7dFRwcLH9/fy1fvvyi5+GMKVOmaO7cuerdu7dmzZqlL7/8stbHBOCI2AHgtDFjxignJ0e5ubkaM2bMResTEhL0/fffa8WKFdq5c6d27twpSaqsrHTYzs/P77of++jRoxo4cKC6dOmit99+WwUFBVqyZMklj/9rHh4e6tGjh1JSUrR+/Xrl5OTo1VdfVVFR0WX3WbdunaZNm6akpCR99NFH2rt3r0aPHn3Fx/nlsSTp15+3XFVV5bDN2LFj9e233+rhhx/WV199pejoaC1evPiqzx/AtSN2ADgtPj5elZWVqqqquuhamzNnzqiwsFBPPfWU+vXrp/bt2zv9W09eXl6qrq52WFZQUKCamhq98MILuvvuu9W2bVudOHHiuo/doUMHST9fX3S5x9q2bZt69eqliRMnqlu3bmrdurXDhdaX2y84OFiSdPLkSfuyvXv3XjSD1WrVo48+qvXr1+vxxx/XihUrrvt5ALg8rtkB4LQGDRro4MGD9q9/rWnTpgoKCtLy5csVFhamY8eO6cknn3TqcVq1aqWioiLt3btXLVq0UOPGjdW6dWtVVVVp8eLFSkhI0LZt27Rs2bIrHuePf/yjevfurV69eik0NFRFRUVKS0tT27ZtFRkZaX+snTt36ujRo/L391ezZs3Upk0brV69Wh9++KEiIiL03//939q9e7ciIiIcZvzwww9VWFiooKAgBQYGqnXr1rJarZo9e7bmzZunQ4cO6YUXXnCYKSUlRf3791fbtm31ww8/6OOPP1b79u2d+j4BuDTO7AColYCAAAUEBFy03MPDQ+vWrVNBQYE6deqkqVOn6rnnnnPqMRITExUfH68+ffooODhYr732mqKiopSVlaXMzEx16tRJa9asUUZGxhWPExcXp/fee08JCQlq27atRo4cqcjISH300Ufy9Pz5337Tpk1TgwYN1KFDBwUHB+vYsWN65JFHNGTIEA0fPlw9e/bUmTNnNHHiRIdjjxs3Tu3atVN0dLSCg4O1bds2NWzYUK+99pq++eYbdenSRZmZmZo7d67DftXV1UpOTlb79u0VHx+vtm3bXvYCawDOsdh+/WYyAACAYTizAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGj/D62o8vNsndbmAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Displot\n",
        "sns.displot(data=data, x='Gender')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "JrHMdYMKZm4L",
        "outputId": "a24eeccc-f35d-4c96-ee03-fa92d9b42652"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7ceb666cbd60>"
            ]
          },
          "metadata": {},
          "execution_count": 44
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#histogram\n",
        "plt.hist(data[\"Age\"])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "aiIKiQubazRM",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "0e7fb06b-a46d-45b5-b38d-ddd48c3e4376"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Countplot\n",
        "sns.countplot(data=data,x='Education')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "giNV-9o3d776",
        "outputId": "f3542680-92f9-47e5-bbba-692940f42d65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Education', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "\n",
        "From above visualizations using countplot, displot, histogram we can\n",
        "\n",
        "\n",
        "observe,\n",
        "\n",
        "\n",
        "*   **Product**: KP281 is the most purchased followed by KP481, KP781\n",
        "*  **MaritalStatus:** couples are the most frequent buyers than singles.\n",
        "*   **Gender :** Males are frequent buyer with count more than 100.\n",
        "*   **Age :** Distribution of age is in bell-shaped, most of the customers are in middle age range.\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "2uGDkhy4fQNe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#boxplot\n",
        "sns.boxplot(data=data, x='Usage')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "TnCiZZ1M07-u",
        "outputId": "2cc90d6d-f636-4b87-a326-1802df738054"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "1. Most of the customers use treadmill 3 - 4 times per week.\n",
        "2. Very few people walk/run on treadmill 6 - 7 times per week (Outlier)."
      ],
      "metadata": {
        "id": "dUeZ-nxS1ALQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#boxplot\n",
        "\n",
        "sns.boxplot(data=data, x='Income')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "wG8d7UWc1lXF",
        "outputId": "9fbf4fe2-fc1d-470b-851b-26162cb8fd33"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "1. Most of the customers  income  is between 45K-60k\n",
        "2. few people have income more than  85K-100k\n"
      ],
      "metadata": {
        "id": "D3ePlLau1rVz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#miles\n",
        "sns.boxplot(data=data, x='Miles')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "b5aUyECk2ENU",
        "outputId": "a29f5ee9-cb6e-4311-a20e-56afb4b1ab40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "1. Distance travelled by most of the customer on treadmill is between 75-120 Miles.\n",
        "2. Very few people have travelled more than  200 Miles.{outliers}"
      ],
      "metadata": {
        "id": "Lh7KqhYR2T8_"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "-l0dYAWk2pe0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "wq9Zx-5f0TAi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Outliers**"
      ],
      "metadata": {
        "id": "YY5_uS_577dO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Outliers in age\n",
        "Age_data = data['Age']\n",
        "Q1,Q2,Q3 = np.percentile(Age_data,25),np.percentile(Age_data,50),np.percentile(Age_data,75)\n",
        "IQR = Q3 - Q1\n",
        "W1,W2 = Q1-1.5*IQR,Q3+1.5*IQR\n",
        "data[(data['Age']<W1) | (data['Age'] > W2)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "LVZjGBI58N_J",
        "outputId": "a3b68dc1-b7f9-425b-dc44-47f804d1add8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "78    KP281   47    Male         16     Partnered      4        3   56850   \n",
              "79    KP281   50  Female         16     Partnered      3        3   64809   \n",
              "139   KP481   48    Male         16     Partnered      2        3   57987   \n",
              "178   KP781   47    Male         18     Partnered      4        5  104581   \n",
              "179   KP781   48    Male         18     Partnered      4        5   95508   \n",
              "\n",
              "     Miles  \n",
              "78      94  \n",
              "79      66  \n",
              "139     64  \n",
              "178    120  \n",
              "179    180  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f074a501-e3c4-4a6d-b6c9-5bb758d89069\" 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>Product</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Usage</th>\n",
              "      <th>Fitness</th>\n",
              "      <th>Income</th>\n",
              "      <th>Miles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>78</th>\n",
              "      <td>KP281</td>\n",
              "      <td>47</td>\n",
              "      <td>Male</td>\n",
              "      <td>16</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>56850</td>\n",
              "      <td>94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>79</th>\n",
              "      <td>KP281</td>\n",
              "      <td>50</td>\n",
              "      <td>Female</td>\n",
              "      <td>16</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>64809</td>\n",
              "      <td>66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>139</th>\n",
              "      <td>KP481</td>\n",
              "      <td>48</td>\n",
              "      <td>Male</td>\n",
              "      <td>16</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>57987</td>\n",
              "      <td>64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>178</th>\n",
              "      <td>KP781</td>\n",
              "      <td>47</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>104581</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>KP781</td>\n",
              "      <td>48</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>95508</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f074a501-e3c4-4a6d-b6c9-5bb758d89069')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-f074a501-e3c4-4a6d-b6c9-5bb758d89069 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-f074a501-e3c4-4a6d-b6c9-5bb758d89069');\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-518a8070-1f26-4870-8203-7729f3beb9c4\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-518a8070-1f26-4870-8203-7729f3beb9c4')\"\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",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-518a8070-1f26-4870-8203-7729f3beb9c4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[(data['Age']<W1) | (data['Age'] > W2)]\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"KP281\",\n          \"KP481\",\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 47,\n        \"max\": 50,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          47,\n          50,\n          48\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 16,\n        \"max\": 18,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          18,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"Partnered\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 4,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22437,\n        \"min\": 56850,\n        \"max\": 104581,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          64809\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 47,\n        \"min\": 64,\n        \"max\": 180,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          66\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(x = data['Age'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "LlBfzzdr-DhI",
        "outputId": "88e7f8e9-f876-43d5-d8ab-cc6dd10962e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age'>"
            ]
          },
          "metadata": {},
          "execution_count": 73
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Outliers in income\n",
        "income_data = data['Income']\n",
        "Q1,Q2,Q3 = np.percentile(income_data,25),np.percentile(income_data,50),np.percentile(income_data,75)\n",
        "IQR = Q3 - Q1\n",
        "W1,W2 = Q1-1.5*IQR,Q3+1.5*IQR\n",
        "data[(data['Income']<W1) | (data['Income'] > W2)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 645
        },
        "id": "ZiNiIR-G-I41",
        "outputId": "2da714df-e14c-44d9-f5a7-bc1d30c43029"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "159   KP781   27    Male         16     Partnered      4        5   83416   \n",
              "160   KP781   27    Male         18        Single      4        3   88396   \n",
              "161   KP781   27    Male         21     Partnered      4        4   90886   \n",
              "162   KP781   28  Female         18     Partnered      6        5   92131   \n",
              "164   KP781   28    Male         18        Single      6        5   88396   \n",
              "166   KP781   29    Male         14     Partnered      7        5   85906   \n",
              "167   KP781   30  Female         16     Partnered      6        5   90886   \n",
              "168   KP781   30    Male         18     Partnered      5        4  103336   \n",
              "169   KP781   30    Male         18     Partnered      5        5   99601   \n",
              "170   KP781   31    Male         16     Partnered      6        5   89641   \n",
              "171   KP781   33  Female         18     Partnered      4        5   95866   \n",
              "172   KP781   34    Male         16        Single      5        5   92131   \n",
              "173   KP781   35    Male         16     Partnered      4        5   92131   \n",
              "174   KP781   38    Male         18     Partnered      5        5  104581   \n",
              "175   KP781   40    Male         21        Single      6        5   83416   \n",
              "176   KP781   42    Male         18        Single      5        4   89641   \n",
              "177   KP781   45    Male         16        Single      5        5   90886   \n",
              "178   KP781   47    Male         18     Partnered      4        5  104581   \n",
              "179   KP781   48    Male         18     Partnered      4        5   95508   \n",
              "\n",
              "     Miles  \n",
              "159    160  \n",
              "160    100  \n",
              "161    100  \n",
              "162    180  \n",
              "164    150  \n",
              "166    300  \n",
              "167    280  \n",
              "168    160  \n",
              "169    150  \n",
              "170    260  \n",
              "171    200  \n",
              "172    150  \n",
              "173    360  \n",
              "174    150  \n",
              "175    200  \n",
              "176    200  \n",
              "177    160  \n",
              "178    120  \n",
              "179    180  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>Product</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Usage</th>\n",
              "      <th>Fitness</th>\n",
              "      <th>Income</th>\n",
              "      <th>Miles</th>\n",
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              "  <tbody>\n",
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              "      <th>159</th>\n",
              "      <td>KP781</td>\n",
              "      <td>27</td>\n",
              "      <td>Male</td>\n",
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              "      <td>5</td>\n",
              "      <td>83416</td>\n",
              "      <td>160</td>\n",
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              "    <tr>\n",
              "      <th>160</th>\n",
              "      <td>KP781</td>\n",
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              "      <th>161</th>\n",
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              "      <th>164</th>\n",
              "      <td>KP781</td>\n",
              "      <td>28</td>\n",
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              "      <th>166</th>\n",
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              "      <td>5</td>\n",
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              "      <th>167</th>\n",
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              "      <td>30</td>\n",
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              "      <td>5</td>\n",
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              "      <th>168</th>\n",
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              "      <td>30</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>103336</td>\n",
              "      <td>160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>169</th>\n",
              "      <td>KP781</td>\n",
              "      <td>30</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
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              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>99601</td>\n",
              "      <td>150</td>\n",
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              "    <tr>\n",
              "      <th>170</th>\n",
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              "      <td>31</td>\n",
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              "      <td>6</td>\n",
              "      <td>5</td>\n",
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              "      <th>171</th>\n",
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              "      <td>18</td>\n",
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              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>95866</td>\n",
              "      <td>200</td>\n",
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              "    <tr>\n",
              "      <th>172</th>\n",
              "      <td>KP781</td>\n",
              "      <td>34</td>\n",
              "      <td>Male</td>\n",
              "      <td>16</td>\n",
              "      <td>Single</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>92131</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173</th>\n",
              "      <td>KP781</td>\n",
              "      <td>35</td>\n",
              "      <td>Male</td>\n",
              "      <td>16</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>92131</td>\n",
              "      <td>360</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>174</th>\n",
              "      <td>KP781</td>\n",
              "      <td>38</td>\n",
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              "      <th>175</th>\n",
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              "      <td>40</td>\n",
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              "      <td>21</td>\n",
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              "      <th>176</th>\n",
              "      <td>KP781</td>\n",
              "      <td>42</td>\n",
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              "      <td>5</td>\n",
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              "      <td>89641</td>\n",
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              "      <td>KP781</td>\n",
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              "      <td>47</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>104581</td>\n",
              "      <td>120</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>179</th>\n",
              "      <td>KP781</td>\n",
              "      <td>48</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>95508</td>\n",
              "      <td>180</td>\n",
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              "  </tbody>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[(data['Income']<W1) | (data['Income'] > W2)]\",\n  \"rows\": 19,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 27,\n        \"max\": 48,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 14,\n        \"max\": 21,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          18\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6438,\n        \"min\": 83416,\n        \"max\": 104581,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          103336\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 68,\n        \"min\": 100,\n        \"max\": 360,\n        \"num_unique_values\": 10,\n        \"samples\": [\n          360\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#box-plot\n",
        "sns.boxplot(x = data['Income'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "GMXwgxui-VtO",
        "outputId": "89cfc481-af2b-4dc2-e739-9666ca15209b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Income'>"
            ]
          },
          "metadata": {},
          "execution_count": 75
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Outliers for education\n",
        "Education_data = data['Education']\n",
        "Q1,Q2,Q3 = np.percentile(Education_data,25),np.percentile(Education_data,50),np.percentile(Education_data,75)\n",
        "IQR = Q3 - Q1\n",
        "W1,W2 = Q1-1.5*IQR,Q3+1.5*IQR\n",
        "data[(data['Education']<W1) | (data['Education'] > W2)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "qWPxWFZ_-Yn7",
        "outputId": "9ef7cc1d-7b46-4dc9-f4e9-07c7336e0763"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "156   KP781   25    Male         20     Partnered      4        5   74701   \n",
              "157   KP781   26  Female         21        Single      4        3   69721   \n",
              "161   KP781   27    Male         21     Partnered      4        4   90886   \n",
              "175   KP781   40    Male         21        Single      6        5   83416   \n",
              "\n",
              "     Miles  \n",
              "156    170  \n",
              "157    100  \n",
              "161    100  \n",
              "175    200  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a2eaab2f-44a7-4481-b168-67044524d1c1\" 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>Product</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Usage</th>\n",
              "      <th>Fitness</th>\n",
              "      <th>Income</th>\n",
              "      <th>Miles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>KP781</td>\n",
              "      <td>25</td>\n",
              "      <td>Male</td>\n",
              "      <td>20</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>74701</td>\n",
              "      <td>170</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>157</th>\n",
              "      <td>KP781</td>\n",
              "      <td>26</td>\n",
              "      <td>Female</td>\n",
              "      <td>21</td>\n",
              "      <td>Single</td>\n",
              "      <td>4</td>\n",
              "      <td>3</td>\n",
              "      <td>69721</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>161</th>\n",
              "      <td>KP781</td>\n",
              "      <td>27</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>4</td>\n",
              "      <td>90886</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>KP781</td>\n",
              "      <td>40</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>Single</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>83416</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
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              "        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",
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              "\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
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              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    60% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        document.querySelector('#df-61666139-c774-45e0-a38d-12a70512f6d2 button');\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[(data['Education']<W1) | (data['Education'] > W2)]\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 25,\n        \"max\": 40,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 20,\n        \"max\": 21,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 4,\n        \"max\": 6,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9372,\n        \"min\": 69721,\n        \"max\": 90886,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          69721\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 50,\n        \"min\": 100,\n        \"max\": 200,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          170\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#box-plot for education\n",
        "sns.boxplot(x = data['Education'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "XKWN388p-hz3",
        "outputId": "f99aa366-9b54-4533-f798-9e51419579c2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Education'>"
            ]
          },
          "metadata": {},
          "execution_count": 77
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Outliers for fitness\n",
        "Fitness_data = data['Fitness']\n",
        "Q1,Q2,Q3 = np.percentile(Fitness_data,25),np.percentile(Fitness_data,50),np.percentile(Fitness_data,75)\n",
        "IQR = Q3 - Q1\n",
        "W1,W2 = Q1-1.5*IQR,Q3+1.5*IQR\n",
        "data[(data['Fitness']<W1) | (data['Fitness'] > W2)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "9gYc_wxS-oEo",
        "outputId": "95a454a0-de6f-4d4e-d615-cdf522d206ac"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "14    KP281   23    Male         16     Partnered      3        1   38658   \n",
              "117   KP481   31  Female         18        Single      2        1   65220   \n",
              "\n",
              "     Miles  \n",
              "14      47  \n",
              "117     21  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[(data['Fitness']<W1) | (data['Fitness'] > W2)]\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"KP481\",\n          \"KP281\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 23,\n        \"max\": 31,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          31,\n          23\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 16,\n        \"max\": 18,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          18,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Single\",\n          \"Partnered\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 2,\n        \"max\": 3,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          2,\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 1,\n        \"max\": 1,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18782,\n        \"min\": 38658,\n        \"max\": 65220,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          65220\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 21,\n        \"max\": 47,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#box-plot for fitness\n",
        "sns.boxplot(x = data['Fitness'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "n6spxEnw-xU4",
        "outputId": "0bd4939c-0e6c-40b5-b6ad-905b81eb8f98"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Fitness'>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Outliers for miles\n",
        "Miles_data = data['Miles']\n",
        "Q1,Q2,Q3 = np.percentile(Miles_data,25),np.percentile(Miles_data,50),np.percentile(Miles_data,75)\n",
        "IQR = Q3 - Q1\n",
        "W1,W2 = Q1-1.5*IQR,Q3+1.5*IQR\n",
        "data[(data['Miles']<W1) | (data['Miles'] > W2)]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "4VjrxiPT-2Do",
        "outputId": "4865b438-93ae-4764-debf-9fd8dc7de144"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "23    KP281   24  Female         16     Partnered      5        5   44343   \n",
              "84    KP481   21  Female         14     Partnered      5        4   34110   \n",
              "142   KP781   22    Male         18        Single      4        5   48556   \n",
              "148   KP781   24  Female         16        Single      5        5   52291   \n",
              "152   KP781   25  Female         18     Partnered      5        5   61006   \n",
              "155   KP781   25    Male         18     Partnered      6        5   75946   \n",
              "166   KP781   29    Male         14     Partnered      7        5   85906   \n",
              "167   KP781   30  Female         16     Partnered      6        5   90886   \n",
              "170   KP781   31    Male         16     Partnered      6        5   89641   \n",
              "171   KP781   33  Female         18     Partnered      4        5   95866   \n",
              "173   KP781   35    Male         16     Partnered      4        5   92131   \n",
              "175   KP781   40    Male         21        Single      6        5   83416   \n",
              "176   KP781   42    Male         18        Single      5        4   89641   \n",
              "\n",
              "     Miles  \n",
              "23     188  \n",
              "84     212  \n",
              "142    200  \n",
              "148    200  \n",
              "152    200  \n",
              "155    240  \n",
              "166    300  \n",
              "167    280  \n",
              "170    260  \n",
              "171    200  \n",
              "173    360  \n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[(data['Miles']<W1) | (data['Miles'] > W2)]\",\n  \"rows\": 13,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"KP281\",\n          \"KP481\",\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6,\n        \"min\": 21,\n        \"max\": 42,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          30,\n          24,\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 14,\n        \"max\": 21,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          14,\n          21\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Single\",\n          \"Partnered\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 7,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          4,\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          4,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21502,\n        \"min\": 34110,\n        \"max\": 95866,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          92131,\n          95866\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52,\n        \"min\": 188,\n        \"max\": 360,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          212,\n          280\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#box-plot\n",
        "sns.boxplot(x = data['Miles'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "A0Mdf-rK_CyU",
        "outputId": "61071087-53da-46bb-ac76-188791c944ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Miles'>"
            ]
          },
          "metadata": {},
          "execution_count": 82
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 1 Axes>"
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Outliers for usage\n",
        "Usage_data = data['Usage']\n",
        "Q1,Q2,Q3 = np.percentile(Usage_data,25),np.percentile(Usage_data,50),np.percentile(Usage_data,75)\n",
        "IQR = Q3 - Q1\n",
        "W1,W2 = Q1-1.5*IQR,Q3+1.5*IQR\n",
        "data[(data['Usage']<W1) | (data['Usage'] > W2)] # These are the outliers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 331
        },
        "id": "xV6l_aAK_RO4",
        "outputId": "ad9d3333-d6a5-4d73-8aa1-e4b1c58266b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "154   KP781   25    Male         18     Partnered      6        4   70966   \n",
              "155   KP781   25    Male         18     Partnered      6        5   75946   \n",
              "162   KP781   28  Female         18     Partnered      6        5   92131   \n",
              "163   KP781   28    Male         18     Partnered      7        5   77191   \n",
              "164   KP781   28    Male         18        Single      6        5   88396   \n",
              "166   KP781   29    Male         14     Partnered      7        5   85906   \n",
              "167   KP781   30  Female         16     Partnered      6        5   90886   \n",
              "170   KP781   31    Male         16     Partnered      6        5   89641   \n",
              "175   KP781   40    Male         21        Single      6        5   83416   \n",
              "\n",
              "     Miles  \n",
              "154    180  \n",
              "155    240  \n",
              "162    180  \n",
              "163    180  \n",
              "164    150  \n",
              "166    300  \n",
              "167    280  \n",
              "170    260  \n",
              "175    200  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Product</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education</th>\n",
              "      <th>MaritalStatus</th>\n",
              "      <th>Usage</th>\n",
              "      <th>Fitness</th>\n",
              "      <th>Income</th>\n",
              "      <th>Miles</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>154</th>\n",
              "      <td>KP781</td>\n",
              "      <td>25</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>6</td>\n",
              "      <td>4</td>\n",
              "      <td>70966</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>155</th>\n",
              "      <td>KP781</td>\n",
              "      <td>25</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>75946</td>\n",
              "      <td>240</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>162</th>\n",
              "      <td>KP781</td>\n",
              "      <td>28</td>\n",
              "      <td>Female</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>92131</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163</th>\n",
              "      <td>KP781</td>\n",
              "      <td>28</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>77191</td>\n",
              "      <td>180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>164</th>\n",
              "      <td>KP781</td>\n",
              "      <td>28</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
              "      <td>Single</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>88396</td>\n",
              "      <td>150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>166</th>\n",
              "      <td>KP781</td>\n",
              "      <td>29</td>\n",
              "      <td>Male</td>\n",
              "      <td>14</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>7</td>\n",
              "      <td>5</td>\n",
              "      <td>85906</td>\n",
              "      <td>300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>KP781</td>\n",
              "      <td>30</td>\n",
              "      <td>Female</td>\n",
              "      <td>16</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>90886</td>\n",
              "      <td>280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>170</th>\n",
              "      <td>KP781</td>\n",
              "      <td>31</td>\n",
              "      <td>Male</td>\n",
              "      <td>16</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>89641</td>\n",
              "      <td>260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>KP781</td>\n",
              "      <td>40</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>Single</td>\n",
              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>83416</td>\n",
              "      <td>200</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f7d7cbd0-5612-4e24-b77d-19be7cc059fd')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\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-f7d7cbd0-5612-4e24-b77d-19be7cc059fd 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-f7d7cbd0-5612-4e24-b77d-19be7cc059fd');\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-cbf43a77-3eee-4dd7-8348-b2add18391ee\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-cbf43a77-3eee-4dd7-8348-b2add18391ee')\"\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",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-cbf43a77-3eee-4dd7-8348-b2add18391ee button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data[(data['Usage']<W1) | (data['Usage'] > W2)] # These are the outliers\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"Product\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"KP781\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 25,\n        \"max\": 40,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          25\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Education\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 14,\n        \"max\": 21,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          14\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaritalStatus\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Single\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Usage\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 6,\n        \"max\": 7,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fitness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 4,\n        \"max\": 5,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Income\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7495,\n        \"min\": 70966,\n        \"max\": 92131,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          89641\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Miles\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52,\n        \"min\": 150,\n        \"max\": 300,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          180\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#box-plot for usage\n",
        "sns.boxplot(x =data['Usage'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 526
        },
        "id": "QPf6UwJJ_Ycr",
        "outputId": "a5cf4c31-f090-4595-9c71-5931927e6976"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Usage'>"
            ]
          },
          "metadata": {},
          "execution_count": 85
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**"
      ],
      "metadata": {
        "id": "mHEBcJpv_pND"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Probability**"
      ],
      "metadata": {
        "id": "azVFwLnC_-8D"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Marginal Probability**"
      ],
      "metadata": {
        "id": "17PhZqVlEy6o"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Marginal Probability P(Product)\n",
        "data['Product'].value_counts(normalize=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mHRfnJtMFXVO",
        "outputId": "aec3d618-4ec3-48c9-adbd-59cf8185e4f5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KP281    0.444444\n",
              "KP481    0.333333\n",
              "KP781    0.222222\n",
              "Name: Product, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "\n",
        "probability of buying the product\n",
        "1.  P(Product=KP281) = 0.44.\n",
        "2. P(Product=KP481) = 0.33.\n",
        "3. P(Product=KP781) = 0.22."
      ],
      "metadata": {
        "id": "kM5Q91z4FdNL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Marginal Probability P(Gender)\n",
        "data ['Gender'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xlaMaSmMFuvG",
        "outputId": "be97e662-2b8f-45fa-da2b-f9e6e9b276b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Male      0.577778\n",
              "Female    0.422222\n",
              "Name: Gender, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation:**\n",
        "\n",
        "Probability of customer is female or male\n",
        "1.  P(Gender=Male) = 0.58.\n",
        "2.  P(Gender=Female) = 0.42.\n"
      ],
      "metadata": {
        "id": "PgkB_n7QF1dY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Marginal Probability P(MaritalStatus)\n",
        "data['MaritalStatus'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIftEBLjGV7I",
        "outputId": "3cb24445-d6a4-49fa-f1c9-aec270d2c13b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Partnered    0.594444\n",
              "Single       0.405556\n",
              "Name: MaritalStatus, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "1.  P(MaritalStatus=Partnered) = 0.60.\n",
        "2.  P(MaritalStatus=Single) = 0.40."
      ],
      "metadata": {
        "id": "D72IZ1umGcw5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Marginal Probability P(Fitness)\n",
        "data['Fitness'].value_counts(normalize=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fj6jHMZXGlcb",
        "outputId": "c7179970-2400-406a-9325-adec3f4a1fc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    0.538889\n",
              "5    0.172222\n",
              "2    0.144444\n",
              "4    0.133333\n",
              "1    0.011111\n",
              "Name: Fitness, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations**\n",
        "\n",
        "Probability of customer having fitness rating,\n",
        "1. P(Fitness=3) = 0.53\n",
        "2. P(Fitness=5) = 0.17\n",
        "3. P(Fitness=2) = 0.14\n",
        "4. P(Fitness=4) = 0.13\n",
        "5. P(Fitness=1) = 0.01"
      ],
      "metadata": {
        "id": "38pvkbzmG8pf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Marginal Probability P(Usage)\n",
        "data['Usage'].value_counts(normalize=True)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_wI1fIJpHUNc",
        "outputId": "f592a096-9382-4656-8eab-223bc232e154"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3    0.383333\n",
              "4    0.288889\n",
              "2    0.183333\n",
              "5    0.094444\n",
              "6    0.038889\n",
              "7    0.011111\n",
              "Name: Usage, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "Probability of customer having usage,\n",
        "\n",
        "P(Usage=3) = 0.38\n",
        "\n",
        "P(Usage=4) = 0.29\n",
        "\n",
        "P(Usage=2) = 0.18\n",
        "\n",
        "P(Usage=5) = 0.09\n",
        "\n",
        "P(Usage=6) = 0.03\n",
        "\n",
        "P(Usage=7) = 0.01\n"
      ],
      "metadata": {
        "id": "WPzY-cRTHfOp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#cross tab\n",
        "pd.crosstab(data[\"Gender\"],data[\"Product\"])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "JtPcRay7JP_P",
        "outputId": "b8da0fc9-df08-4c86-c94f-b0929b8fb80c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product  KP281  KP481  KP781\n",
              "Gender                      \n",
              "Female      40     29      7\n",
              "Male        40     31     33"
            ],
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              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 2,\n  \"fields\": [\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KP281\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 40,\n        \"max\": 40,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          40\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KP481\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 29,\n        \"max\": 31,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"KP781\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18,\n        \"min\": 7,\n        \"max\": 33,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          33\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#probability of customers buying KP281 treadmill?\n",
        "80/180\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NyUZOj5eJgwP",
        "outputId": "c0a32415-2df6-4926-9718-230cd71ae451"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4444444444444444"
            ]
          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#probability of customers buying KP781 treadmill?\n",
        "40/180"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "59V_OUWoJiK4",
        "outputId": "50a47a1d-15d2-4a99-8e19-930b9030a978"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2222222222222222"
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#probability of customers buying KP781 treadmill?\n",
        "40/180"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0T9WKSpoJkpa",
        "outputId": "7366b672-3546-48e8-db92-079160a21032"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.2222222222222222"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Conditional probability**"
      ],
      "metadata": {
        "id": "w6GjYphVCULC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Conditional Probability, P(Product | Gender)\n",
        "\n",
        "sns.heatmap(pd.crosstab(data['Gender'], data['Product'], normalize='index'), annot=True, cmap='Spectral_r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "luXnf7arCZt1",
        "outputId": "54427861-c87f-422d-e5a7-28ef5c3cacb4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "1.  P(Product=KP281 | Customer=Male) = 0.38.\n",
        "2.  P(Product=KP481 | Customer=Male) = 0.3.\n",
        "3. P(Product=KP781 | Customer=Male) = 0.32.\n",
        "4. P(Product=KP281 | Customer=Female) = 0.53.\n",
        "5. P(Product=KP481 | Customer=Female) = 0.38.\n",
        "6.  P(Product=KP781 | Customer=Female) = 0.092."
      ],
      "metadata": {
        "id": "9aUIIGEmCncG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Conditional Probability, P(Product | MaritalStatus)\n",
        "sns.heatmap(pd.crosstab(data['MaritalStatus'], data['Product'], normalize='index'), annot=True, cmap='Spectral_r')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "nEpbhti5CT1S",
        "outputId": "9ae9683f-0eba-4105-aac7-0f8584d19881"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "1. (Product=KP281 | MaritalStatus=Single) = 0.44.\n",
        "2.  P(Product=KP481 | MaritalStatus=Single) = 0.33.\n",
        "3.  P(Product=KP781 | MaritalStatus=Single) = 0.23.\n",
        "4.  P(Product=KP281 | MaritalStatus=Partnered) = 0.45.\n",
        "5. P(Product=KP481 | MaritalStatus=partnered) = 0.34.\n",
        "6. P(Product=KP781 | MaritalStatus=Partnered) = 0.21."
      ],
      "metadata": {
        "id": "jyU2xpWKDGXI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Conditional Probability, P(Gender | Product)\n",
        "\n",
        "sns.heatmap(pd.crosstab(data['Gender'], data['Product'], normalize='columns'), annot=True, cmap='Spectral_r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "Ph__L2o9Dt9d",
        "outputId": "ce2681de-555b-4073-f6f0-b39cde049902"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 2 Axes>"
            ],
            "image/png": 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9tlmRXweAVmynXTbMoV/ZIeN/cn9efG529tp/y5wyes+cdtxNeefthUvsX13TPrNmzMuD907LYV/ZfqnnPOeUW1O1WIKw9wZr57RzP5sH73t1hX0PYCXWTu9kca1i7MYnP/nJ3HPPPUmSffbZJyeffHLOP//8fOUrX8knP/nJClcHK4err/ljvnjILjn44MHp12+9nHPOYenQYbX85jf3fegxpVIp667buWnr1m3pTWoAVm3XXjM5Bx+ycw48aFA26dczo0YfktU7VOe3E+7/0GPq6xty+qk/z3HHfy7r91lnic8fffiV7H/Ajtlxp37p3btrDvni4Gy2+Xp5/PFpK/KrANDKfe4LW+XuPzyfP9/5Yv7x97dzzU/+kkV19fn0npssdf+XX3gjN46fmvvveSXvvd+w1H3emVuXt99a2LQN3HH9zJw+N888MXNFfhWAVUKraD5fcsklGTRoUJLknHPOyZ577pkbb7wxffv2zVVXXVXh6qDtW7To/Tz55LQMHrxl01pVVVUG77xlHn7kpQ897t1367L7Hmdkt8+MzLHf+HGef/4fLVEuAG3Ie4vez1NP/T2f/ORmTWtVVVX55M6b5tFHPjwxdvmPb0/XrmvmoIOXHjQYsF3fTL7ricyc+VYaGxvzwP3P59VXZmXwLpsX/h0AaBvata9K30265snHZjStNTYmTz46Pf02X7ewawzebaP8adKLhZwPWAW1K7Xc1ga0irEbG2/8r1+17NixYy6//PJlPrauri51dXVlazXVi1JTU11YfdDWvfnmvNTXNywxXmOdbmvmpZdnLPWYjTbqmQvOH5bNN++dd95ZkHHj7siXDr0ot9w8Oj17dmmJsgFoA958a/4Hz5hu5eM11llnzbz80utLPWbqQy9lwoT78+sJp3zoec/49sE5Z/SNGbL7OWnfviqlUilnnzs0O+yw9GQbACu/NdesSbt2VXn7rQVl62+/vTC91u9cyDW2H9Qna3Sszj2azwCFaBXJ58XNmzcvc+fOLdv+nTFjxqRz585l25gxv2yhamHltd12G+eAAz6ZLbfsk5122iw/+tEx6dp1zdxw458rXRoAbdj8+Qtzxum/yNnnDE2XLku+3Omffnndn/PYo6/mR5cdlRt+dXJOOfULOf+832TKfc+2YLUArGo+PaRfHpv6j7z15oKP3hlgKUpVpRbb2oJWkXx++eWXc/zxx2fy5MlZuPBfLwhobGxMqVRKfX39hx47cuTI1NbWlq3VVE9ZYbVCW9SlS6e0a1eVN94o/8ecN2a/s8xznFdbrV223LJPpr269BQbAKumLmt3/OAZM/udsvU33ngn6yzlGfO3aW/ktdfm5ITjrmxaa2hoTJIM3Pbk/P6WkVm3+1r5f2Nvyf/70Yh8ercPXkK9+ebr5dlnXsv4ayZn58FGbwCsit55py719Q3pvPbqZeudO3fI2wU0i9dZt2O27t8zP/zu3R/7XAB8oFU0nw8//PA0NjZm3Lhx6dGjR0qlZe/c19TUpKampnyx0cgNWFx1dftsvfUGmTLlmQwZMjBJ0tDQkCl/eSaHf/kzy3SO+vqGPPfca9nt09usuEIBaHNWq26frbZaP/f/5bnsOWTbJB88Y/7yl+dz6GGfWmL/jTbungn/c2rZ2o/+3615d35dTjvjwPTsuXbqFr2f99+vT6lU/kt6VVVVaWhY+suiAFj51b/fkFdenJOt+vfM1Pv/liQplZKt+vfMH2/9+L8Zs+uem2Tu2wvz6F9f+9jnAuADraL5/Oijj+ahhx7K5ptLscCKMuLIITnt9GuyzTYbpn//vhk//s4sWLAoBx00OEly6mlXp0f3tXPyyQcmSS697JYMHLBRNtxw3cyduyBXXfWH/OMfc3LIIbtU8msA0AoNO/Iz+fbIX2brbfpk2203zM+vvTsLFizKAQd+8ELpM07/Rbp375z/qv18ampWy6ab9io7fs21Pkiw/XN9ter22WHHTXLJ929Khw6rpdd6XfLXB1/M72/6a7512hda9ssB0KpM/J+ncvSJu+TlF97IS8/Pzl77bZmaDu3z5/+d0fy1EwfnzTcW5FfXPZzkgxcI9u7zwTzo9u2r0qXrGtlgoy5ZuOD9vD7jX7+1Uyolu+6xSe6566Wm38gBWC5t5EWALaVVNJ933HHH/O1vf9N8hhVon312yJw57+SHP/p9Zs2amy23XD9XXnFC09iN6f+Yk6rFfutg7tz5OWvUdZk1a246d14jW2+9QW64/lvp12+9Sn0FAFqpz+29XebMmZfLfjQxs2fPzRZb9M7lP/16uv3vSwinT3+z2TPpvvf9YRn7g1ty+qnX5e23302v9brkhBP3yReHDl4RXwGANuKBe1/NWp075KBDB6Rzl9Uz7eU38/1z7szctz8Y4dl13Y5ZvHfcpevqOe8Hn2/6eZ8Dt84+B26dp5+YkQvPvKNpfesBvdKte6f8adILLfZdAFYFpcbGxor/k96LL76YY445Jocffni22WabrLbaamWf9+/fv3knbLyrwOoA4N9b1OCFNAC0jKMPfqPSJQCwihj/uyMqXUKbtOjSL7bYtaqP/+8Wu9byahXJ51mzZuXFF1/MiBEjmtZKpdIyvXAQAAAAAIDWp1U0n7/yla9ku+22y/XXX9/sFw4CAAAAALQGJTOfy7SK5vOrr76am266Kf369at0KQAAAAAAFKCq0gUkyR577JFHH3200mUAAAAAACy/qlLLbW1Aq0g+77fffjnppJPy+OOPZ9ttt13ihYP7779/hSoDAAAAAGB5tIrm8zHHHJMkOffcc5f4zAsHAQAAAIA2wcznMq2i+dzQ0FDpEgAAAAAAKFCraD4vbuHChenQoUOlywAAAAAAaJZSG5nF3FJaxQsH6+vrc95556V3797p1KlTXnrppSTJWWedlauuuqrC1QEAAAAA0Fytovl8/vnn55prrslFF12U6urqpvVtttkmV155ZQUrAwAAAABYRu2qWm5rA1pFlddee21+9rOf5ctf/nLatWvXtD5gwIA888wzFawMAAAAAIDl0SpmPr/22mvp16/fEusNDQ157733KlARAAAAAEAztTPzeXGtIvm81VZb5c9//vMS67/+9a+z3XbbVaAiAAAAAAA+jlaRfB41alSGDx+e1157LQ0NDZkwYUKeffbZXHvttbn55psrXR4AAAAAwEcqVUk+L66iyeeXXnopjY2N+cIXvpDf//73+eMf/5iOHTtm1KhRefrpp/P73/8+n/3sZytZIgAAAAAAy6GiyedNN90006dPT/fu3bPrrruma9euefzxx9OjR49KlgUAAAAAwMdU0eZzY2Nj2c+33XZb5s+fX6FqAAAAAAA+Bi8cLNMqXjj4T/+3GQ0AAAAAQNtU0eRzqVRKqVRaYg0AAAAAoM3xwsEyFR+7ceSRR6ampiZJsnDhwhxzzDHp2LFj2X4TJkyoRHkAAAAAACynijafhw8fXvbz4YcfXqFKAAAAAAA+npKZz2Uq2ny++uqrK3l5AAAAAABWkIo2nwEAAAAAVhpVVZWuoFVxNwAAAAAAKJzkMwAAAABAEarMfF6c5DMAAAAAAIWTfAYAAAAAKIKZz2XcDQAAAAAACif5DAAAAABQBMnnMu4GAAAAAACFk3wGAAAAAChCVanSFbQqks8AAAAAABRO8hkAAAAAoAhmPpdxNwAAAAAAKJzkMwAAAABAESSfy7gbAAAAAAAUTvMZAAAAAIDCGbsBAAAAAFCEqlKlK2hVJJ8BAAAAACic5DMAAAAAQBG8cLCMuwEAAAAAQOEknwEAAAAAiiD5XMbdAAAAAABYyV122WXp27dvOnTokEGDBuWBBx74t/u/9dZbOe6449KrV6/U1NRks802y6233tqsa0o+AwAAAAAUoapU6QqW6sYbb0xtbW0uv/zyDBo0KGPHjs1ee+2VZ599Nt27d19i/0WLFuWzn/1sunfvnl//+tfp3bt3Xn311ay99trNuq7mMwAAAADASuySSy7J0UcfnREjRiRJLr/88txyyy0ZN25cTj/99CX2HzduXObMmZP77rsvq622WpKkb9++zb6usRsAAAAAAEWoqmqxra6uLnPnzi3b6urqlihp0aJFeeihhzJkyJDFyqzKkCFDMmXKlKV+jZtuuik777xzjjvuuPTo0SPbbLNNLrjggtTX1zfvdjTv7gEAAAAAUGljxoxJ586dy7YxY8Yssd/s2bNTX1+fHj16lK336NEjM2bMWOq5X3rppfz6179OfX19br311px11lm5+OKL853vfKdZNRq7AQAAAABQhKqWy/qOHDkytbW1ZWs1NTWFnLuhoSHdu3fPz372s7Rr1y7bb799XnvttXzve9/L6NGjl/k8ms8AAAAAAG1MTU3NMjWbu3Xrlnbt2mXmzJll6zNnzkzPnj2XekyvXr2y2mqrpV27dk1rW265ZWbMmJFFixalurp6mWo0dgMAAAAAoAClUqnFtmVVXV2d7bffPpMmTWpaa2hoyKRJk7Lzzjsv9ZhddtklL7zwQhoaGprWnnvuufTq1WuZG8+J5jMAAAAAwEqttrY2V1xxRcaPH5+nn346xx57bObPn58RI0YkSYYNG5aRI0c27X/sscdmzpw5OfHEE/Pcc8/llltuyQUXXJDjjjuuWdc1dgMAAAAAoAgtOPO5OYYOHZpZs2Zl1KhRmTFjRgYOHJiJEyc2vYRw2rRpqVqs9j59+uT222/PSSedlP79+6d379458cQTc9pppzXruqXGxsbGQr9Ja9B4V6UrAGAVsqhhQaVLAGAVcfTBb1S6BABWEeN/d0SlS2iTGh88o8WuVdrxgha71vKSfAYAAAAAKEIrTT5XirsBAAAAAEDhNJ8BAAAAACicsRsAAAAAAEWoKlW6glZF8hkAAAAAgMJJPgMAAAAAFMELB8u4GwAAAAAAFE7yGQAAAACgCGY+l5F8BgAAAACgcJLPAAAAAABFMPO5jLsBAAAAAEDhJJ8BAAAAAIog+VzG3QAAAAAAoHCSzwAAAAAARagqVbqCVkXyGQAAAACAwkk+AwAAAAAUwcznMu4GAAAAAACFk3wGAAAAACiC5HMZdwMAAAAAgMJJPgMAAAAAFKGqVOkKWhXJZwAAAAAACif5DAAAAABQBDOfy7gbAAAAAAAUTvMZAAAAAIDCGbsBAAAAAFCEkqzv4twNAAAAAAAKJ/kMAAAAAFAEyecy7gYAAAAAAIWTfAYAAAAAKEKVrO/i3A0AAAAAAAon+QwAAAAAUAQzn8u4GwAAAAAAFE7yGQAAAACgCJLPZdwNAAAAAAAKJ/kMAAAAAFAEyecy7gYAAAAAAIWTfAYAAAAAKEKVrO/i3A0AAAAAAAon+QwAAAAAUAQzn8u4GwAAAAAAFE7yGQAAAACgCJLPZdwNAAAAAAAKp/kMAAAAAEDhjN0AAAAAACiCsRtl3A0AAAAAAAon+QwAAAAAUATJ5zLuBgAAAAAAhZN8BgAAAAAoQpWs7+LcDQAAAAAACif5DAAAAABQgFKpXaVLaFUknwEAAAAAKJzkMwAAAABAEUqyvotr9t1obGzMtGnTsnDhwhVRDwAAAAAAK4Hlaj7369cvf/vb31ZEPQAAAAAAbVOpquW2NqDZVVZVVWXTTTfNG2+8sSLqAQAAAABgJbBcLfILL7ww3/rWt/LEE08UXQ8AAAAAQNsk+VxmuV44OGzYsLz77rsZMGBAqqurs/rqq5d9PmfOnEKKAwAAAACgbVqu5vPYsWMLLgMAAAAAoI2rahuJ5JayXM3n4cOHF10HAAAAAAArkeVuxb/44os588wzc+ihh+b1119Pktx222158sknCysOAAAAAKDNMPO5zHJVeffdd2fbbbfN/fffnwkTJmTevHlJkkcffTSjR48utEAAAAAAANqe5Wo+n3766fnOd76TO+64I9XV1U3re+yxR/7yl78UVhwAAAAAQJsh+Vxmuap8/PHHc+CBBy6x3r1798yePftjFwUAAAAAQNu2XM3ntddeO9OnT19i/eGHH07v3r0/dlEAAAAAALRty9V8/tKXvpTTTjstM2bMSKlUSkNDQ+69996ccsopGTZsWNE1AgAAAAC0fsZulFmuKi+44IJsscUW6dOnT+bNm5etttoqn/70pzN48OCceeaZRdcIAAAAAEAb0355Dqqurs4VV1yRs846K0888UTmzZuX7bbbLptuumnR9QEAAAAAtA1VbSOR3FKWq/n8TxtssEE22GCDomoBAAAAAGAlsczN59ra2mU+6SWXXLJcxQAAAAAAtFltZBZzS1nm5vPDDz9c9vPUqVPz/vvvZ/PNN0+SPPfcc2nXrl223377YisEAAAAAKDNWebm81133dX035dccknWXHPNjB8/Pl26dEmSvPnmmxkxYkR23XXX4qsEAAAAAGjtJJ/LLNfduPjiizNmzJimxnOSdOnSJd/5zndy8cUXF1YcAAAAAABt03I1n+fOnZtZs2YtsT5r1qy88847H7soAAAAAIA2p1TVclszXXbZZenbt286dOiQQYMG5YEHHvjQfa+55pqUSqWyrUOHDs2+5nI1nw888MCMGDEiEyZMyN///vf8/e9/z29+85scddRROeigg5bnlAAAAAAArAA33nhjamtrM3r06EydOjUDBgzIXnvtlddff/1Dj1lrrbUyffr0pu3VV19t9nWXeebz4i6//PKccsopOeyww/Lee+99cKL27XPUUUfle9/73vKcEgAAAACgbWulM58vueSSHH300RkxYkSSD/q7t9xyS8aNG5fTTz99qceUSqX07NnzY113ue7GGmuskR//+Md544038vDDD+fhhx/OnDlz8uMf/zgdO3b8WAUBAAAAAPDv1dXVZe7cuWVbXV3dEvstWrQoDz30UIYMGdK0VlVVlSFDhmTKlCkfev558+Zlww03TJ8+ffKFL3whTz75ZLNrXK7k8z917Ngx/fv3/zinAIA2r/q9+kqXAMAq4jM3XVPpEgBYZRxR6QLaplLLXWrMmDE555xzytZGjx6ds88+u2xt9uzZqa+vT48ePcrWe/TokWeeeWap5958880zbty49O/fP2+//Xa+//3vZ/DgwXnyySez/vrrL3ONy9V8nj9/fi688MJMmjQpr7/+ehoaGso+f+mll5bntAAAAAAALIORI0emtra2bK2mpqaQc++8887Zeeedm34ePHhwttxyy/z0pz/Neeedt8znWa7m81e/+tXcfffdOeKII9KrV6+USi3Y0gcAAAAAaIUaGxtb7Fo1NTXL1Gzu1q1b2rVrl5kzZ5atz5w5c5lnOq+22mrZbrvt8sILLzSrxuVqPt9222255ZZbsssuuyzP4QAAAAAAtIDq6upsv/32mTRpUg444IAkSUNDQyZNmpTjjz9+mc5RX1+fxx9/PPvss0+zrr1czecuXbqka9euy3MoAAAAAMBKqTENH71TQZozi6K2tjbDhw/PDjvskJ122iljx47N/PnzM2LEiCTJsGHD0rt374wZMyZJcu655+aTn/xk+vXrl7feeivf+9738uqrr+arX/1qs2pcrubzeeedl1GjRmX8+PFZY401lucUAAAAAAC0gKFDh2bWrFkZNWpUZsyYkYEDB2bixIlNLyGcNm1aqqqqmvZ/8803c/TRR2fGjBnp0qVLtt9++9x3333ZaqutmnXdUuNyDCLZbrvt8uKLL6axsTF9+/bNaqutVvb51KlTm3vKYjXeVdnrA7BqqZtX6QoAWEVcvcbYSpcAwCpiRMOkSpfQJjU0/rHFrlVVGtJi11pey5V8/udsEAAAAAAAPtCYlnvhYFuwXM3n0aNHF10HAAAAAAArkaqP3mXp3nrrrVx55ZUZOXJk5syZk+SDcRuvvfZaYcUBAAAAALQVjY0NLba1BcuVfH7ssccyZMiQdO7cOa+88kqOPvrodO3aNRMmTMi0adNy7bXXFl0nAAAAAABtyHIln2tra3PkkUfm+eefT4cOHZrW99lnn/zpT38qrDgAAAAAgLaisQX/ry1Yrubzgw8+mK9//etLrPfu3TszZsz42EUBAAAAANC2LdfYjZqamsydO3eJ9eeeey7rrrvuxy4KAAAAAKCtaSuzmFvKciWf999//5x77rl57733kiSlUinTpk3LaaedloMPPrjQAgEAAAAAaHuWq/l88cUXZ968eenevXsWLFiQ3XbbLf369UunTp1y/vnnF10jAAAAAECrZ+ZzueUau9G5c+fccccduffee/Poo49m3rx5+cQnPpEhQ4YUXR8AAAAAAG1Qs5LPCxYsyM0339z0880335wXXnghM2bMyK233ppTTz01CxcuLLxIAAAAAIDWrjENLba1Bc1KPo8fPz633HJLPv/5zydJLr300my99dZZffXVkyTPPPNMevXqlZNOOqn4SgEAAAAAaDOa1Xz+xS9+kVNPPbVs7Ze//GU23njjJMl1112Xyy67TPMZAAAAAFjlNDa2jURyS2nW2I0XXngh2267bdPPHTp0SFXVv06x00475amnniquOgAAAAAA2qRmJZ/feuut1NXVNf08a9asss8bGhrKPgcAAAAAWFU0prHSJbQqzUo+r7/++nniiSc+9PPHHnss66+//scuCgAAAACAtq1Zzed99tkno0aNysKFC5f4bMGCBTnnnHOy7777FlYcAAAAAEBb0djY0GJbW9CssRtnnHFG/vu//zubb755jj/++Gy22WZJkmeffTaXXnpp3n///ZxxxhkrpFAAAAAAANqOZjWfe/Tokfvuuy/HHntsTj/99DQ2fjDDpFQq5bOf/Wx+/OMfp0ePHiukUAAAAACA1szM53LNaj4nyUYbbZSJEydmzpw5eeGFF5Ik/fr1S9euXQsvDgAAAACAtqnZzed/6tq1a3baaaciawEAAAAAYCWx3M1nAAAAAAD+pTFt40WALaWq0gUAAAAAALDykXwGAAAAAChAY6MXDi5O8hkAAAAAgMJJPgMAAAAAFMDM53KSzwAAAAAAFE7yGQAAAACgAI0x83lxks8AAAAAABRO8hkAAAAAoACNjWY+L07yGQAAAACAwkk+AwAAAAAUwMzncpLPAAAAAAAUTvIZAAAAAKAAZj6Xk3wGAAAAAKBwks8AAAAAAAUw87mc5DMAAAAAAIWTfAYAAAAAKEBjzHxenOQzAAAAAACF03wGAAAAAKBwxm4AAAAAABSgsdELBxcn+QwAAAAAQOEknwEAAAAACuCFg+UknwEAAAAAKJzkMwAAAABAAcx8Lif5DAAAAABA4SSfAQAAAAAKYOZzOclnAAAAAAAKJ/kMAAAAAFCABiOfy0g+AwAAAABQOMlnAAAAAIAC1DeWKl1CqyL5DAAAAABA4SSfAQAAAAAKYOZzOclnAAAAAAAKJ/kMAAAAAFCABjOfy0g+AwAAAABQOMlnAAAAAIAC1Jv5XEbyGQAAAACAwkk+AwAAAAAUwMzncpLPAAAAAAAUTvMZAAAAAIDCGbsBAAAAAFCABi8cLCP5DAAAAABA4SSfAQAAAAAKUO+Fg2UknwEAAAAAKJzkMwAAAABAARoqXUArI/kMAAAAAEDhJJ8BAAAAAArQYOZzGclnAAAAAAAKJ/kMAAAAAFCAhsZKV9C6SD4DAAAAAFA4yWcAAAAAgALUm/lcRvIZAAAAAGAld9lll6Vv377p0KFDBg0alAceeGCZjrvhhhtSKpVywAEHNPuams8AAAAAAAVoaGy5rTluvPHG1NbWZvTo0Zk6dWoGDBiQvfbaK6+//vq/Pe6VV17JKaeckl133XW57ofmMwAAAADASuySSy7J0UcfnREjRmSrrbbK5ZdfnjXWWCPjxo370GPq6+vz5S9/Oeecc0423njj5bqu5jMAAAAAQAEaGkstttXV1WXu3LllW11d3RI1LVq0KA899FCGDBnStFZVVZUhQ4ZkypQpH/pdzj333HTv3j1HHXXUct8PzWcAAAAAgDZmzJgx6dy5c9k2ZsyYJfabPXt26uvr06NHj7L1Hj16ZMaMGUs99z333JOrrroqV1xxxceqsf3HOhoAAAAAgCRJfTNnMX8cI0eOTG1tbdlaTU3Nxz7vO++8kyOOOCJXXHFFunXr9rHOpfkMAAAAANDG1NTULFOzuVu3bmnXrl1mzpxZtj5z5sz07Nlzif1ffPHFvPLKK9lvv/2a1hoaGpIk7du3z7PPPptNNtlkmWo0dgMAAAAAYCVVXV2d7bffPpMmTWpaa2hoyKRJk7Lzzjsvsf8WW2yRxx9/PI888kjTtv/++2f33XfPI488kj59+izztSWfAQAAAAAK0NBYqnQJS1VbW5vhw4dnhx12yE477ZSxY8dm/vz5GTFiRJJk2LBh6d27d8aMGZMOHTpkm222KTt+7bXXTpIl1j+K5jMAAAAAwEps6NChmTVrVkaNGpUZM2Zk4MCBmThxYtNLCKdNm5aqquKHZJQaGxtbcAx2C2m8q9IVALAqqZtX6QoAWEVcvcbYSpcAwCpiRMOkj96JJUz+x/gWu9Zn1hveYtdaXmY+AwAAAABQOGM3AAAAAAAKUL/yzZj4WCSfAQAAAAAonOQzAAAAAEABGhpLlS6hVZF8BgAAAACgcJLPAAAAAAAFaDDzuYzkMwAAAAAAhZN8BgAAAAAoQL2Zz2UknwEAAAAAKJzkMwAAAABAAcx8Lif5DAAAAABA4SSfAQAAAAAK0GDmcxnJZwAAAAAACif5DAAAAABQgHozn8tIPgMAAAAAUDjJZwAAAACAApj5XE7yGQAAAACAwmk+AwAAAABQOGM3AAAAAAAK0OCFg2UknwEAAAAAKJzkMwAAAABAAeoln8tIPgMAAAAAUDjJZwAAAACAAkg+l5N8BgAAAACgcJLPAAAAAAAFaJB8LiP5DAAAAABA4SSfAQAAAAAKYOZzOclnAAAAAAAKJ/kMAAAAAFAAyedyks8AAAAAABRO8hkAAAAAoACSz+UknwEAAAAAKFyraj6/8MILuf3227NgwYIkSWOjfyoAAAAAANqGhsaW29qCVtF8fuONNzJkyJBsttlm2WeffTJ9+vQkyVFHHZWTTz65wtUBAAAAANBcraL5fNJJJ6V9+/aZNm1a1lhjjab1oUOHZuLEiRWsDAAAAABg2dQ3ttzWFrSKFw7+4Q9/yO23357111+/bH3TTTfNq6++WqGqAAAAAABYXq0i+Tx//vyyxPM/zZkzJzU1NRWoCAAAAACAj6NVNJ933XXXXHvttU0/l0qlNDQ05KKLLsruu+9ewcoAAAAAAJaNsRvlWsXYjYsuuih77rln/vrXv2bRokU59dRT8+STT2bOnDm59957K10eAAAAAADN1Cqaz9tss02ee+65XHrppVlzzTUzb968HHTQQTnuuOPSq1evSpcHK41f/GJyrrrqD5k1e2622GL9nHXm0PTvv9FS950w4b6MPOPasrXq6vZ5/LFLW6JUANqYX9xwb64aPzmzZr+TLTbrlbNOPzD9t91gqftO+J8HM3LUjWVr1dXt8/iDFyZJ3nuvPmMvvS1/uueZ/O3vb6TTmqtn8KBNc/KJ+6RH984r/LsA0Lpt8Y0vZJtTvpjVe3bNm4++mL9880eZ/eCzH7r/VicelC2O2T8dN+ieutlv55Xf/CkPjbwy9XXvJUm2Pf3QbHjgp7L2Fhvk/QV1ef2+p/LX03+Wuc/9vaW+ErASaWgjieSW0iqaz0nSuXPnfPvb3650GbDSuvXWv2bMhb/OOWcflgED+mb8+Dtz1Fd/lIm3nZ111llrqcd06tQhE287p+nnUqnUUuUC0IbcOvGRjPn+TTnnzIMzYNsNMv4Xf85Rx16Rif9zatZZZ82lHtOpU4dM/J9Tm35e/BmzcOGiPPXMazn2a0OyxebrZe7cBTn/u7/LsSdenQnX/9eK/joAtGIbffEz2eniY3LfsWMz6/5nsvV/HZT/mPjdTNjiyCyc9dYS+2986B7ZfszRufeo7+X1+57MWputn12vPjWNjcmDJ/8kSdLz0/3zzI9vyuwHn0mpfbtsf/5R2ev2i/Lbrb+S999d2MLfEGDlUrHm82OPPbbM+/bv338FVgKrhquv+WO+eMguOfjgwUmSc845LJPvfjy/+c19+drXPrfUY0qlUtZdV8IMgH/v6p/fnS8eNCgHH7BTkuScMw/O5D89nd/87sF87ag9lnpMqZSs223p//i55pqr5+qffr1s7ayRB+aQL/8w/5j+Ztbr1aXYLwBAm7H1Sf+Z5668NS9cc3uS5L5jxmb9fT6ZTb/yuTz+3RuW2L/74K3z+r1P5KXr70ySzHt1Zl664a6su9MWTfvcsc/IsmP+POKiHPb6hKyz/aaZ+efHV+C3AVZGbWUWc0upWPN54MCBKZVKaWz89/8fKZVKqa+vb6GqYOW0aNH7efLJafn6Yk3mqqqqDN55yzz8yEsfety779Zl9z3OSENDY7baqk9qTzogm266XkuUDEAbsei99/Pk06/l60ft2bRWVVWVwZ/cNA8/9uqHHvfuu4uy++e+88EzZsv1U3vC3tm0X88P3X/evIUplUpZa83VC60fgLajarX2WWf7zfLYhdf/a7GxMdP/ODXdP7nVUo95/b4ns/GXh6Tbjptn9oPPptNGvbL+3jvlxev++KHXqe7cMUlSN+edQusHWBVVrPn88ssvV+rSsMp58815qa9vWGK8xjrd1sxLL89Y6jEbbdQzF5w/LJtv3jvvvLMg48bdkS8delFuuXl0evaUOAPgA2++Of9/nzGdytbXWWfNvPTy60s9ZqO+6+aCc76YzTftlXfmLcy48ZPzpeGX5pYJp6Rnj7WX2L+u7r18f+wt2XfvgenUqcOK+BoAtAE13Tqnqn27LJj5Ztn6gtffTOct+iz1mJeuvzM13Tpnnz//v5RKpVSt1j7PXH5THhvzy6VfpFTKoB8cl5n3PJ63nnyl4G8ArAokn8tVrPm84YYbFnKeurq61NXVla3VVC9KTU11IeeHVdV2222c7bbbeLGfN8k++56dG278c/7rxP0rWBkAbd12A/pmuwF9y37e58CLcsOv/pL/Or58FNR779XnxG/9PI2NyTnfPriFKwWgreu524D0H3lYphz3w8y+/+ms2W+9DBp7XAaceXge/c51S+y/82XfzNrb9M2tu55YgWoBVj6t5oWDSfLUU09l2rRpWbRoUdn6/vt/eKNrzJgxOeecc8rWRo8alrPPPnJFlAhtUpcundKuXVXeeGNu2fobs99Jtw+Zt/l/rbZau2y5ZZ9Me3XpKTYAVk1dunT832fMvLL1N95o5jNmi96Z9rfZZevvvVef//rWz/OP6W9m/BXHSD0DrOLqZr+dhvfrs3qP8t/EXL17lyyYMWepx2x37oi8eN0def6qW5Mkbz7xctp3XD27/PSkPHr+L5LFRoF+8kcnpM++n8ytu52Ud1+bvdTzAXyU+sbSR++0CmkVzeeXXnopBx54YB5//PGyOdD/fOv5v5v5PHLkyNTW1pat1VRPWXHFQhtUXd0+W2+9QaZMeSZDhgxMkjQ0NGTKX57J4V/+zDKdo76+Ic8991p2+/Q2K65QANqc6tXaZ+ste2fK/c9nyB4fPCMaGhoy5f4XcviXdlmmc9TXN+S556dnt09t2bT2z8bzq9Nm5dorj02XtTuukPoBaDsa3ns/bzz0XHrtuV2m/c+9HyyWSum153Z5+rLfLfWY9mvUJA3lvwPf+L89hsX7D5/80QnZ4IBPZeLutZn3ytJHEwLQfK2i+XziiSdmo402yqRJk7LRRhvlgQceyBtvvJGTTz453//+9//tsTU1NampqSlfbDRyA/6vEUcOyWmnX5Ntttkw/fv3zfjxd2bBgkU56KDBSZJTT7s6PbqvnZNPPjBJcullt2TggI2y4YbrZu7cBbnqqj/kH/+Yk0MOWbZGAgCrjhFH7JbTzroh22y9fvpvs0HGX/fnD54xB+yYJDn129enR/fOOfnEfZIkl17+hwzsv2E23KBb5r6zIFddMzn/mP5mDjlopyQfNJ6/ecq1eerpv+enPzoq9Q0NmTX7g9/e6dx5jVSv1ir+CAtABTz5g1/nU9ecljf++lxmPfBMtv6vg9O+Y4c8f/XtSZJdrzkt7/5jdh4646okyd9unpKtT/rPvPHwC5l1/9NZq1/vfOLcEfnb76eksaEhSfLJy76ZjQ/dM5MOOCvvvfNuU7J60dvzU79w0dILAfgQDWY+l2kVf3KfMmVK7rzzznTr1i1VVVWpqqrKpz71qYwZMybf/OY38/DDD1e6RGjz9tlnh8yZ805++KPfZ9asudlyy/Vz5RUnNP1K9PR/zElV6V+/GjJ37vycNeq6zJo1N507r5Gtt94gN1z/rfTrt16lvgIArdQ+nxuYOW/Oyw9/fHtmzX4nW26+Xq788VfTbZ01kyTTZ7yZqqrFnjHvLMhZ5/4qs2a/k85rrZGtt+qdG8afkH6b9EySzHz97dw5+ckkyRe+eEnZta698pgM2rFfC30zAFqbl/97cjqs2znbnXNkVu/ZJXMeeTF/2Pv0LHz9g5cQdtygexoX6/w8+p3rksbGfOK8EVmjd7csnPVW/nbzXzL121c17bPlsV9Ikuwz+Qdl1/rziIvywvjbW+BbAay8So2NjRXvx3fp0iVTp07NRhttlE022SRXXnlldt9997z44ovZdttt8+677zbvhI13rZhCAWBp6uZ99D4AUICr1xhb6RIAWEWMaJhU6RLapHOn/rLFrjXqE4e12LWWV6tIPm+zzTZ59NFHs9FGG2XQoEG56KKLUl1dnZ/97GfZeOONK10eAAAAAADN1Cqaz2eeeWbmz5+fJDnnnHOy3377Zdddd80666yTG264ocLVAQAAAAB8tPqKz5hoXVpF83mvvfZq+u9NN900zzzzTObMmZMuXbqktNgMWgAAAAAA2oaKNp+/8pWvLNN+48aNW8GVAAAAAAB8PA0Nla6gdalo8/maa67JhhtumO222y6t4L2HAAAAAAAUpKLN52OPPTbXX399Xn755YwYMSKHH354unbtWsmSAAAAAACWi5nP5aoqefHLLrss06dPz6mnnprf//736dOnT774xS/m9ttvl4QGAAAAAGjDKtp8TpKampoceuihueOOO/LUU09l6623zje+8Y307ds38+bNq3R5AAAAAAAsh4qO3fi/qqqqUiqV0tjYmPr6+kqXAwAAAACwzIzdKFfx5HNdXV2uv/76fPazn81mm22Wxx9/PJdeemmmTZuWTp06Vbo8AAAAAACWQ0WTz9/4xjdyww03pE+fPvnKV76S66+/Pt26datkSQAAAAAAy6VB8rlMRZvPl19+eTbYYINsvPHGufvuu3P33Xcvdb8JEya0cGUAAAAAAHwcFW0+Dxs2LKVSqZIlAAAAAAAUwsznchVtPl9zzTWVvDwAAAAAACtIRZvPAAAAAAArC8nnclWVLgAAAAAAgJWP5DMAAAAAQAEkn8tJPgMAAAAAUDjJZwAAAACAAjRIPpeRfAYAAAAAoHCSzwAAAAAABTDzuZzkMwAAAAAAhdN8BgAAAAAoQENDqcW25rrsssvSt2/fdOjQIYMGDcoDDzzwoftOmDAhO+ywQ9Zee+107NgxAwcOzM9//vNmX1PzGQAAAABgJXbjjTemtrY2o0ePztSpUzNgwIDstddeef3115e6f9euXfPtb387U6ZMyWOPPZYRI0ZkxIgRuf3225t13VJjY+PKN4mk8a5KVwDAqqRuXqUrAGAVcfUaYytdAgCriBENkypdQpt01J03tNi1rtrjS8u876BBg7Ljjjvm0ksvTZI0NDSkT58+OeGEE3L66acv0zk+8YlPZN9998155523zNeVfAYAAAAAaGPq6uoyd+7csq2urm6J/RYtWpSHHnooQ4YMaVqrqqrKkCFDMmXKlI+8TmNjYyZNmpRnn302n/70p5tVo+YzAAAAAEAbM2bMmHTu3LlsGzNmzBL7zZ49O/X19enRo0fZeo8ePTJjxowPPf/bb7+dTp06pbq6Ovvuu29+9KMf5bOf/WyzamzfrL0BAAAAAFiq5XkR4PIaOXJkamtry9ZqamoKO/+aa66ZRx55JPPmzcukSZNSW1ubjTfeOJ/5zGeW+RyazwAAAAAAbUxNTc0yNZu7deuWdu3aZebMmWXrM2fOTM+ePT/0uKqqqvTr1y9JMnDgwDz99NMZM2ZMs5rPxm4AAAAAABSgoaHUYtuyqq6uzvbbb59Jk/71EsmGhoZMmjQpO++8czO+W8NSZ0r/O5LPAAAAAAArsdra2gwfPjw77LBDdtppp4wdOzbz58/PiBEjkiTDhg1L7969m2ZGjxkzJjvssEM22WST1NXV5dZbb83Pf/7z/OQnP2nWdTWfAQAAAAAK0JIzn5tj6NChmTVrVkaNGpUZM2Zk4MCBmThxYtNLCKdNm5aqqn8NyZg/f36+8Y1v5O9//3tWX331bLHFFrnuuusydOjQZl231NjY2FjoN2kNGu+qdAUArErq5lW6AgBWEVevMbbSJQCwihjRMOmjd2IJR0z8VYtd6+efO6TFrrW8JJ8BAAAAAArQUN86k8+V4oWDAAAAAAAUTvIZAAAAAKAArXXmc6VIPgMAAAAAUDjJZwAAAACAAkg+l5N8BgAAAACgcJLPAAAAAAAFaGiodAWti+QzAAAAAACFk3wGAAAAAChAQ72Zz4uTfAYAAAAAoHCSzwAAAAAABWhokHxenOQzAAAAAACF03wGAAAAAKBwxm4AAAAAABTA2I1yks8AAAAAABRO8hkAAAAAoAD19ZLPi5N8BgAAAACgcJLPAAAAAAAFMPO5nOQzAAAAAACFk3wGAAAAACiA5HM5yWcAAAAAAAon+QwAAAAAUADJ53KSzwAAAAAAFE7yGQAAAACgAA31ks+Lk3wGAAAAAKBwks8AAAAAAAUw87mc5DMAAAAAAIWTfAYAAAAAKIDkcznJZwAAAAAACif5DAAAAABQAMnncpLPAAAAAAAUTvIZAAAAAKAADfWSz4uTfAYAAAAAoHCazwAAAAAAFM7YDQAAAACAAnjhYDnJZwAAAAAACif5DAAAAABQAMnncpLPAAAAAAAUTvIZAAAAAKAADfWSz4uTfAYAAAAAoHCSzwAAAAAABTDzuZzkMwAAAAAAhZN8BgAAAAAoQkNjpStoVSSfAQAAAAAonOQzAAAAAEABqiSfy0g+AwAAAABQOMlnAAAAAIAClOolnxcn+QwAAAAAQOEknwEAAAAACmDmcznJZwAAAAAACif5DAAAAABQAMnncpLPAAAAAAAUTvMZAAAAAIDCGbsBAAAAAFCAkrEbZSSfAQAAAAAonOQzAAAAAEABquolnxcn+QwAAAAAQOEknwEAAAAAClBl5nMZyWcAAAAAAAon+QwAAAAAUADJ53KSzwAAAAAAFE7yGQAAAACgACXJ5zKSzwAAAAAAFE7yGQAAAACgAGY+l5N8BgAAAACgcJLPAAAAAAAFqKqXfF6c5DMAAAAAAIWTfAYAAAAAKICZz+UknwEAAAAAKJzkMwAAAABAAUqSz2UknwEAAAAAKJzkMwAAAABAAcx8Lif5DAAAAACwkrvsssvSt2/fdOjQIYMGDcoDDzzwofteccUV2XXXXdOlS5d06dIlQ4YM+bf7fxjNZwAAAACAldiNN96Y2trajB49OlOnTs2AAQOy11575fXXX1/q/pMnT86hhx6au+66K1OmTEmfPn3yH//xH3nttdeadd1SY2PjypcFb7yr0hUAsCqpm1fpCgBYRVy9xthKlwDAKmJEw6RKl9Am7f+1CS12rZt+dtAy7zto0KDsuOOOufTSS5MkDQ0N6dOnT0444YScfvrpH3l8fX19unTpkksvvTTDhg1b5utKPgMAAAAAtDF1dXWZO3du2VZXV7fEfosWLcpDDz2UIUOGNK1VVVVlyJAhmTJlyjJd69133817772Xrl27NqtGzWcAAAAAgAKUGhpbbBszZkw6d+5cto0ZM2aJmmbPnp36+vr06NGjbL1Hjx6ZMWPGMn2v0047Leutt15ZA3tZtG/W3gAAAAAAVNzIkSNTW1tbtlZTU1P4dS688MLccMMNmTx5cjp06NCsYzWfAQAAAAAKUNXQ0GLXqqmpWaZmc7du3dKuXbvMnDmzbH3mzJnp2bPnvz32+9//fi688ML88Y9/TP/+/Ztdo7EbAAAAAAArqerq6my//faZNOlfL5FsaGjIpEmTsvPOO3/ocRdddFHOO++8TJw4MTvssMNyXVvyGQAAAACgAFUNjZUuYalqa2szfPjw7LDDDtlpp50yduzYzJ8/PyNGjEiSDBs2LL17926aGf3d7343o0aNyi9/+cv07du3aTZ0p06d0qlTp2W+ruYzAAAAAMBKbOjQoZk1a1ZGjRqVGTNmZODAgZk4cWLTSwinTZuWqqp/Dcn4yU9+kkWLFuU///M/y84zevTonH322ct8Xc1nAAAAAIACVNW3zuRzkhx//PE5/vjjl/rZ5MmTy35+5ZVXCrmmmc8AAAAAABRO8hkAAAAAoAClVjrzuVIknwEAAAAAKJzkMwAAAABAAaokn8tIPgMAAAAAUDjJZwAAAACAAkg+l5N8BgAAAACgcJLPAAAAAAAFqKqXfF6c5DMAAAAAAIXTfAYAAAAAoHDGbgAAAAAAFKDkhYNlJJ8BAAAAACic5DMAAAAAQAGqJJ/LSD4DAAAAAFC4UmNjo3Y8kLq6uowZMyYjR45MTU1NpcsBYCXmmQNAS/LcAagczWcgSTJ37tx07tw5b7/9dtZaa61KlwPASswzB4CW5LkDUDnGbgAAAAAAUDjNZwAAAAAACqf5DAAAAABA4TSfgSRJTU1NRo8e7QUcAKxwnjkAtCTPHYDK8cJBAAAAAAAKJ/kMAAAAAEDhNJ8BAAAAACic5jMAAAAAAIXTfAYAAAAAoHCaz9BGHXnkkTnggAPK1n7961+nQ4cOufjii3PkkUemVCqlVCqluro6/fr1y7nnnpv3338/STJ58uR84QtfSK9evdKxY8cMHDgwv/jFL5a4ztixY7P55ptn9dVXT58+fXLSSSdl4cKFTZ//6U9/yn777Zf11lsvpVIpv/vd71bk1wagQj7uc2dxL7zwQtZcc82svfbaS3zmuQPAx33mnH322U2fL7517Nix7JyeOQArnuYzrCSuvPLKfPnLX85PfvKTnHzyyUmSz33uc5k+fXqef/75nHzyyTn77LPzve99L0ly3333pX///vnNb36Txx57LCNGjMiwYcNy8803N53zl7/8ZU4//fSMHj06Tz/9dK666qrceOONOeOMM5r2mT9/fgYMGJDLLrusZb8wABXV3OfOP7333ns59NBDs+uuuy5xTs8dAJamuc+cU045JdOnTy/bttpqqxxyyCFN5/TMAWgZ7StdAPDxXXTRRRk9enRuuOGGHHjggU3rNTU16dmzZ5Lk2GOPzW9/+9vcdNNNGTlyZNkfqpLkxBNPzB/+8IdMmDAhn//855N80KDeZZddcthhhyVJ+vbtm0MPPTT3339/03F777139t577xX9FQFoRZbnufNPZ555ZrbYYovsueeeue+++8rO67kDwP+1PM+cTp06pVOnTk37Pvroo3nqqady+eWXN6155gC0DMlnaONOO+20nHfeebn55pvL/jC2NKuvvnoWLVr0oZ+//fbb6dq1a9PPgwcPzkMPPZQHHnggSfLSSy/l1ltvzT777FNM8QC0OR/nuXPnnXfmV7/61YcmyDx3AFhcUX/XufLKK7PZZpuV/daNZw5Ay5B8hjbstttuy//8z/9k0qRJ2WOPPT50v8bGxkyaNCm33357TjjhhKXu89///d958MEH89Of/rRp7bDDDsvs2bPzqU99Ko2NjXn//fdzzDHHLJGaBmDV8HGeO2+88UaOPPLIXHfddVlrrbWWepznDgD/VNTfdRYuXJhf/OIXOf3008vWPXMAWobkM7Rh/fv3T9++fTN69OjMmzdvic9vvvnmdOrUKR06dMjee++doUOH5uyzz15iv7vuuisjRozIFVdcka233rppffLkybngggvy4x//OFOnTs2ECRNyyy235LzzzluRXwuAVurjPHeOPvroHHbYYfn0pz/9oef33AHgn4r6u85vf/vbvPPOOxk+fHjZumcOQMvQfIY2rHfv3pk8eXJee+21fO5zn8s777xT9vnuu++eRx55JM8//3wWLFiQ8ePHL/GG57vvvjv77bdffvCDH2TYsGFln5111lk54ogj8tWvfjXbbrttDjzwwFxwwQUZM2ZMGhoaVvj3A6B1+TjPnTvvvDPf//730759+7Rv3z5HHXVU3n777bRv3z7jxo1L4rkDwL8U8Xed5IORG5///OfTo0ePsnXPHICWofkMbdyGG26Yu+++OzNmzFjiD2UdO3ZMv379ssEGG6R9+yWn7EyePDn77rtvvvvd7+ZrX/vaEp+/++67qaoq/5+Jdu3aJfng19sAWPUs73NnypQpeeSRR5q2c889N2uuuWYeeeSRpjmenjsALO7j/F0nSV5++eXcddddOeqoo5b4zDMHoGWY+QwrgT59+mTy5MnZfffds9dee2XixIkfecxdd92Vz3/+8znxxBNz8MEHZ8aMGUmS6urqppcO7rfffrnkkkuy3XbbZdCgQXnhhRdy1llnZb/99mv6g9m8efPywgsvNJ335ZdfziOPPJKuXbtmgw02WAHfFoBKW57nzpZbbln281//+tdUVVVlm222aVrz3AHg/1qeZ84/jRs3Lr169cree++9xGeeOQAtQ/MZVhLrr79+2R/KevXq9W/3Hz9+fN59992MGTMmY8aMaVrfbbfdMnny5CTJmWeemVKplDPPPDOvvfZa1l133ey33345//zzm/b/61//mt13373p59ra2iTJ8OHDc8011xT3BQFoVZr73FkWnjsALM3yPHMaGhpyzTXX5Mgjj2xqJi/OMwegZZQa/T4JAAAAAAAFM/MZAAAAAIDCaT4DAAAAAFA4zWcAAAAAAAqn+QwAAAAAQOE0nwEAAAAAKJzmMwAAAAAAhdN8BgAAAACgcJrPAABUxJFHHpkDDjig0mUAAAAriOYzAABljjzyyJRKpZRKpVRXV6dfv34599xz8/7771e6tH/rmmuuydprr13pMgAAgP/VvtIFAADQ+nzuc5/L1Vdfnbq6utx666057rjjstpqq2XkyJFl+y1atCjV1dUVqhIAAGjNJJ8BAFhCTU1NevbsmQ033DDHHntshgwZkptuuqlpVMb555+f9dZbL5tvvnmS5PHHH88ee+yR1VdfPeuss06+9rWvZd68eU3nq6+vT21tbdZee+2ss846OfXUU9PY2Fh2zb59+2bs2LFlawMHDszZZ5/d9PNbb72Vr3/96+nRo0c6dOiQbbbZJjfffHMmT56cESNG5O23325KbS9+HAAA0PI0nwEA+Eirr756Fi1alCSZNGlSnn322dxxxx25+eabM3/+/Oy1117p0qVLHnzwwfzqV7/KH//4xxx//PFNx1988cW55pprMm7cuNxzzz2ZM2dOfvvb3zarhoaGhuy999659957c9111+Wpp57KhRdemHbt2mXw4MEZO3Zs1lprrUyfPj3Tp0/PKaecUug9AAAAmsfYDQAAPlRjY2MmTZqU22+/PSeccEJmzZqVjh075sorr2wat3HFFVdk4cKFufbaa9OxY8ckyaWXXpr99tsv3/3ud9OjR4+MHTs2I0eOzEEHHZQkufzyy3P77bc3q5Y//vGPeeCBB/L0009ns802S5JsvPHGTZ937tw5pVIpPXv2LOKrAwAAH5PkMwAAS7j55pvTqVOndOjQIXvvvXeGDh3aNMZi2223LZvz/PTTT2fAgAFNjeck2WWXXdLQ0JBnn302b7/9dqZPn55BgwY1fd6+ffvssMMOzarpkUceyfrrr9/UeAYAAFo3yWcAAJaw++675yc/+Umqq6uz3nrrpX37f/2xcfEmc5GqqqqWmAP93nvvNf336quvvkKuCwAArBiSzwAALKFjx47p169fNthgg7LG89JsueWWefTRRzN//vymtXvvvTdVVVXZfPPN07lz5/Tq1Sv3339/0+fvv/9+HnroobLzrLvuupk+fXrTz3Pnzs3LL7/c9HP//v3z97//Pc8999xS66iurk59fX2zvicAALDiaD4DAPCxfPnLX06HDh0yfPjwPPHEE7nrrrtywgkn5IgjjkiPHj2SJCeeeGIuvPDC/O53v8szzzyTb3zjG3nrrbfKzrPHHnvk5z//ef785z/n8ccfz/Dhw9OuXbumz3fbbbd8+tOfzsEHH5w77rgjL7/8cm677bZMnDgxSdK3b9/MmzcvkyZNyuzZs/Puu++22D0AAACWpPkMAMDHssYaa+T222/PnDlzsuOOO+Y///M/s+eee+bSSy9t2ufkk0/OEUcckeHDh2fnnXfOmmuumQMPPLDsPCNHjsxuu+2Wz3/+89l3331zwAEHZJNNNinb5ze/+U123HHHHHroodlqq61y6qmnNqWdBw8enGOOOSZDhw7Nuuuum4suumjFf3kAAOBDlRr/72A9AAAAAAD4mCSfAQAAAAAonOYzAAAAAACF03wGAAAAAKBwms8AAAAAABRO8xkAAAAAgMJpPgMAAAAAUDjNZwAAAAAACqf5DAAAAABA4TSfAQAAAAAonOYzAAAAAACF03wGAAAAAKBwms8AAAAAABTu/wNNDRB+cjDtlAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "1.  P(Customer=Male | Producty=KP281) = 0.50.\n",
        "3.  P(Customer=Male | Product=KP481) = 0.52.\n",
        "5.  P(Customer=Male | Product=781) = 0.82.\n",
        "2.  P(Customer=Female | Product=KP281) = 0.50.\n",
        "4.  P(Customer=Female | Product=KP481) = 0.48.\n",
        "6.  P(Customer=Female | Product=KP781) = 0.17."
      ],
      "metadata": {
        "id": "9Ba74Oi8D3es"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Joint probability**"
      ],
      "metadata": {
        "id": "xgCDIg1uAGGE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#using heatmap\n",
        "#Joint Probability, P(Product Intersection Gender)\n",
        "\n",
        "sns.heatmap(pd.crosstab(data['Gender'], data['Product'], normalize=True), annot=True, cmap='Spectral_r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "zLzSj6wNAFld",
        "outputId": "3a86da73-7fe9-4cb1-aede-5040092b9381"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "1.  P(KP281 Intersection Male) = 0.22.\n",
        "2.  P(KP481 Intersection Male) = 0.17.\n",
        "3.  P(KP781 Intersection Male) = 0.18.\n",
        "4.  P(KP281 5. Intersection Female) = 0.22.\n",
        "5.  P(KP481 Intersection Female) = 0.16.\n",
        "6.  P(KP781 Intersection Female) = 0.039."
      ],
      "metadata": {
        "id": "aPizlJMJAyCB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Joint Probability, P(Product Intersection MartialStatus)\n",
        "#heatmap\n",
        "sns.heatmap(pd.crosstab(data['MaritalStatus'], data['Product'], normalize=True), annot=True, cmap='Spectral_r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        },
        "id": "Tu2bwm7rBkpq",
        "outputId": "41cae4d5-ae0c-407b-f1fa-936868f519fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observations:**\n",
        "1. P(KP281 Intersection Single) = 0.18.\n",
        "2. P(KP481 Intersection Single) = 0.13.\n",
        "3. P(KP781 Intersection Single) = 0.094.\n",
        "4. P(KP281 Intersection Partnered) = 0.27.\n",
        "5. P(KP481 Intersection Partnered) = 0.2.\n",
        "6. P(KP781 Intersection Partnered) = 0.13."
      ],
      "metadata": {
        "id": "jFs4JIJdBqaz"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8MBAU7-ICIfv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Statistics for all features**"
      ],
      "metadata": {
        "id": "KLY-VrHhIh-b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sPNFge3JIsPg",
        "outputId": "c432a6a9-08e7-4e60-a23b-1f184bb9e32c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-96-abc01cf6c622>:1: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  data.mean()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age             28.788889\n",
              "Education       15.572222\n",
              "Usage            3.455556\n",
              "Fitness          3.311111\n",
              "Income       53719.577778\n",
              "Miles          103.194444\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.median()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQZWZIZ2Iv_X",
        "outputId": "b8afcf1a-6712-4f20-c5a2-20a88f883e19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-97-135339ac59ce>:1: FutureWarning: The default value of numeric_only in DataFrame.median is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
            "  data.median()\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age             26.0\n",
              "Education       16.0\n",
              "Usage            3.0\n",
              "Fitness          3.0\n",
              "Income       50596.5\n",
              "Miles           94.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('Product')['Fitness'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jewiokBIzNI",
        "outputId": "bd9f8323-160e-4787-d2a7-142bc786dcd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product\n",
              "KP281    46418.025\n",
              "KP481    48973.650\n",
              "KP781    75441.575\n",
              "Name: Income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('Product')['Usage'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nCFG0QJHI3rB",
        "outputId": "10be0fa8-0d65-4abe-942b-6434a8acac62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product\n",
              "KP281    3.087500\n",
              "KP481    3.066667\n",
              "KP781    4.775000\n",
              "Name: Usage, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('Product')['Income'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LD7S7c8HI6vb",
        "outputId": "244549c6-2b41-4ad0-9c22-fadd342baf4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product\n",
              "KP281    46418.025\n",
              "KP481    48973.650\n",
              "KP781    75441.575\n",
              "Name: Income, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Customer-Profiling**"
      ],
      "metadata": {
        "id": "kmPVzyg0J4Wr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Business insights from this case-study :**\n",
        "\n",
        "**KP281:**\n",
        "\n",
        "1. KP281 is the most purchased treadmill model,44.44% of the sales.\n",
        "2. Both genders prefer KP281 model.\n",
        "7. Fitness Scale - 2 to 4\n",
        "6. Weekly Usage - 2 to 4 times\n",
        "8. Weekly Running Mileage - 50 to 100 miles\n",
        "5. Annual Income of customer below USD 60,000\n",
        "4. Education level of customer 13 years and above\n",
        "3. Mean income of customers buying KP281 is 46.4K dollars.\n",
        "3. Age of customer mainly between 18 to 35 years with few between 35 to 50 years\n",
        "\n",
        "**KP481 :**\n",
        "\n",
        "1. KP481 is the second most product sold, 33.33% of sales.\n",
        "2. Mean income of KP481 is 49K dollars.\n",
        "7. Fitness Scale - 2 to 4\n",
        "6. Weekly Usage - 2 to 4 times\n",
        "8. Weekly Running Mileage - 50 to 200 miles\n",
        "4. Education level of customer 13 years and above\n",
        "5. Annual Income of customer between USD 40,000 to USD 80,000\n",
        "3. Age of customer mainly between 18 to 35 years with few between 35 to 50 years\n",
        "\n",
        "**KP781 :**\n",
        "\n",
        "1. KP781 is a rare bought treadmill model because it is expensive.\n",
        "7. Fitness Scale - 3 to 5\n",
        "6. Weekly Usage - 4 to 7 times.\n",
        "3. Age of customer between 18 to 35 years\n",
        "8. Weekly Running Mileage - 100 miles and above.\n",
        "2. Mean income of KP781 buyers is 75.4K dollars.\n",
        "4. Education level of customer 15 years and above.\n",
        "5. Annual Income of customer USD 80,000 and above.\n",
        "\n"
      ],
      "metadata": {
        "id": "1mbYLKkyJ-xr"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Recommendations**\n"
      ],
      "metadata": {
        "id": "fxAeT4k0PVtX"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "mztKNUXu0JdG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "9cXGL_ti0M3a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. KP281 & KP481 treadmills are preferred by the customers whose annual income lies in the range of 39K - 53K Dollars. These models should promote as budget treadmills.\n",
        "2. Not many women here seem to like using exercise equipment. So, we should start a campaign to inspire women to exercise more.\n",
        "3. Provide customer support and recommend users to upgrade from lower versions to next level versions after consistent usages.\n",
        "4. We should research how to expand our market for customers beyond 50 years of age. We can recommend basic treadmill models (KP281/KP481) for starters.\n",
        "5. Aerofit should spread the word about health and our products, especially to people who have studied for 15, 20, or 21 years. Education often links to how much money people make, and we've noticed that folks with 14 years of schooling have bought 11 times more machines (55 machines) than those with 15 years of schooling (5 machines).\n",
        "6. There's a big difference in sales for the KP784 model—only 18% of all KP781 sales are to women. To get more women interested, we should offer them special deals and let them try out the product.\n",
        "7. Provide personalized recommendations based on customer profiles to improve satisfaction.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "PZHEfjEERjWx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!sudo apt-get install git\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvnd9hYo0aEr",
        "outputId": "26ad86f8-c013-4cf3-d3e0-978b49357f7c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Reading package lists... Done\n",
            "Building dependency tree... Done\n",
            "Reading state information... Done\n",
            "git is already the newest version (1:2.34.1-1ubuntu1.11).\n",
            "0 upgraded, 0 newly installed, 0 to remove and 45 not upgraded.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global user.name \"sanjanaynvsdl\""
      ],
      "metadata": {
        "id": "QE--Kcfh0ytj"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!git config --global user.email \"sanjanayalmarthi@gmail.com\""
      ],
      "metadata": {
        "id": "aRd3kbBi07ip"
      },
      "execution_count": 4,
      "outputs": []
    }
  ]
}