aerofit.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CFl4-inPf13I"
      },
      "source": [
        "# **Aerofit Case Study**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M5uPCdGdiCqm"
      },
      "source": [
        "# **1. Objective**\n",
        "## Identify the customer characteristics for each type of treadmill offered by Aerofit, so as to provide a better recommendation to the new customers."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Qhjo-d7KFXg"
      },
      "source": [
        "## Importing the Packages"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
        "id": "Me7wRBtqfr1y"
      },
      "outputs": [],
      "source": [
        "# import required librariies & packages\n",
        "\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 norm, poisson"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YlbQzQbLKVxt"
      },
      "source": [
        "## Import and read the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Gw93vN-ogY2P"
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      "source": [
        "# import dataset\n",
        "\n",
        "dataset_url = \"https://d2beiqkhq929f0.cloudfront.net/public_assets/assets/000/001/125/original/aerofit_treadmill.csv?1639992749\"\n",
        "aero_df = pd.read_csv(dataset_url)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wPnmgp5KKub-"
      },
      "source": [
        "# **2. Statistical Summary**\n",
        "## List the data properties"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vEhyycCfLDdL"
      },
      "source": [
        "#### Basic info of the dataset"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
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        "outputId": "dc24ae90-566a-4364-f8ac-62004fe2364e"
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          "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"
          ]
        }
      ],
      "source": [
        "# Basic info of the dataset\n",
        "aero_df.info()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AQurMpj-LIHu"
      },
      "source": [
        "#### Description of the dataset"
      ]
    },
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              "      <th>Age</th>\n",
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              "      <th>count</th>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
              "      <td>180.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>28.788889</td>\n",
              "      <td>15.572222</td>\n",
              "      <td>3.455556</td>\n",
              "      <td>3.311111</td>\n",
              "      <td>53719.577778</td>\n",
              "      <td>103.194444</td>\n",
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              "      <th>std</th>\n",
              "      <td>6.943498</td>\n",
              "      <td>1.617055</td>\n",
              "      <td>1.084797</td>\n",
              "      <td>0.958869</td>\n",
              "      <td>16506.684226</td>\n",
              "      <td>51.863605</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>18.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>29562.000000</td>\n",
              "      <td>21.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>24.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>44058.750000</td>\n",
              "      <td>66.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>26.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>50596.500000</td>\n",
              "      <td>94.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>58668.000000</td>\n",
              "      <td>114.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>21.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>104581.000000</td>\n",
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            "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",
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          "execution_count": 10,
          "metadata": {},
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      ],
      "source": [
        "# Description of the dataset\n",
        "aero_df.describe()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mj-oHrqLLL8p"
      },
      "source": [
        "#### Size of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qxHskHDEmIP4",
        "outputId": "80ef1742-e0df-460a-dd22-6a4c95a66d9e"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "1620"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Size of the dataset\n",
        "aero_df.size"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBgBtJPJLPVQ"
      },
      "source": [
        "#### Shape of the dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        {
          "data": {
            "text/plain": [
              "(180, 9)"
            ]
          },
          "execution_count": 17,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Shape of the dataset\n",
        "aero_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wVJRUd7NiZFo"
      },
      "source": [
        "##### Shape of the dataset shows there are 180 records with 9 attributes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GLyd_knVMQcF"
      },
      "source": [
        "#### Columns/Product Attributes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        },
        "id": "9UqCKsGRoZM6",
        "outputId": "0075394c-9aee-44b5-df85-d2ae48568e72"
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      "outputs": [
        {
          "data": {
            "text/plain": [
              "Index(['Product', 'Age', 'Gender', 'Education', 'MaritalStatus', 'Usage',\n",
              "       'Fitness', 'Income', 'Miles'],\n",
              "      dtype='object')"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Column names in the dataset\n",
        "aero_df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3y79LFjNii2w"
      },
      "source": [
        "##### the 9 attributes of the given dataset are - Product, Age, Gender, Education, Marital Status, Usage, Fitness, Income and Miles. These attributes will help to identify the characterisitcs associated with each Product."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lbCJObCqMeAt"
      },
      "source": [
        "#### Data Types of Product Attributes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "NyDhtqkNpdbH",
        "outputId": "564a0de3-6b72-48ae-c0c0-9cd9db486ca6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "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"
            ]
          },
          "execution_count": 21,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Data types of all the attributes\n",
        "aero_df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S6JODD92MjuL"
      },
      "source": [
        "#### Top 5 rows of dataset"
      ]
    },
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "        \n",
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              "  </svg>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-8864e9da-0784-4684-a441-69a9ba4ba6c2 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-8864e9da-0784-4684-a441-69a9ba4ba6c2');\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",
              "  </div>\n",
              "  "
            ],
            "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"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# top 5 rows from the dataset\n",
        "aero_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YjdYU2phMrtl"
      },
      "source": [
        "#### Bottom 5 rows of dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8oo0i72lr9r2",
        "outputId": "473e8410-a158-41df-b894-81199c970922"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>Product</th>\n",
              "      <th>Age</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Education</th>\n",
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              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>KP781</td>\n",
              "      <td>40</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
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              "      <td>6</td>\n",
              "      <td>5</td>\n",
              "      <td>83416</td>\n",
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              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>KP781</td>\n",
              "      <td>42</td>\n",
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              "      <td>18</td>\n",
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              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>89641</td>\n",
              "      <td>200</td>\n",
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              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>KP781</td>\n",
              "      <td>45</td>\n",
              "      <td>Male</td>\n",
              "      <td>16</td>\n",
              "      <td>Single</td>\n",
              "      <td>5</td>\n",
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              "      <th>178</th>\n",
              "      <td>KP781</td>\n",
              "      <td>47</td>\n",
              "      <td>Male</td>\n",
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              "      <td>Partnered</td>\n",
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              "      <td>104581</td>\n",
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              "    <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",
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              "                                                     [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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "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  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# bottom 5 rows\n",
        "aero_df.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "115dZFONM640"
      },
      "source": [
        "# **3. Data Pre-processing**\n",
        "## Data Preparation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IpXEaTMeNGkS"
      },
      "source": [
        "#### Check for Null Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mBmcPlL-sqzU",
        "outputId": "39fa1fe5-1a47-4325-d8bb-3f68e05e0f18"
      },
      "outputs": [
        {
          "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"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Check for Null values\n",
        "aero_df.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SsLCtnTqi-7a"
      },
      "source": [
        "##### The above result indicates that, there is no null value for any attribute in the given dataset, which is good. "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JydkaYzYf4NK"
      },
      "source": [
        "#### Data Types & Sanctity\n",
        "\n",
        "##### The Usage and Fitness attribute values don't indicate anything significant  "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pr3-4sVCtGAM"
      },
      "source": [
        "# **4. Exploratory Data Analysis**\n",
        "## Graphical & non-graphical analysis of Uni & Bi variate elements"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O4hoq0XBNUa3"
      },
      "source": [
        "#### Count by Products"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "4FbcUfzP87LJ",
        "outputId": "501124df-d691-4d07-b478-e0e8f18a5bcc"
      },
      "outputs": [
        {
          "data": {
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              "\n",
              "  <div id=\"df-89793708-cbc5-4af1-8a38-75f1e16e2d32\">\n",
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              "\n",
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              "      <th></th>\n",
              "      <th>Products Count</th>\n",
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              "  <tbody>\n",
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              "      <th>KP281</th>\n",
              "      <td>80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KP481</th>\n",
              "      <td>60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KP781</th>\n",
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              "</table>\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "       Products Count \n",
              "KP281               80\n",
              "KP481               60\n",
              "KP781               40"
            ]
          },
          "execution_count": 54,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Count of each products\n",
        "product_cnt = {'Products Count ' : aero_df['Product'].value_counts()}\n",
        "product_cnt = pd.DataFrame(product_cnt)\n",
        "product_cnt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Yv8XU0ojN1s"
      },
      "source": [
        "#### *As we can see, KP281 is sold more and KP781 is sold less amongst three products*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CCoDKI2wNq_f"
      },
      "source": [
        "#### Customers count by Age"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "y4Rr0hpR6Yrt",
        "outputId": "7d7a8496-29b0-4b4f-d43d-932180659dd3"
      },
      "outputs": [
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              "                                                     [key], {});\n",
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              "\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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            "text/plain": [
              "    Age Count\n",
              "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"
            ]
          },
          "execution_count": 302,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Customers count by age group\n",
        "age_group_cnt = {'Age Count': aero_df['Age'].value_counts()}\n",
        "age_group_cnt = pd.DataFrame(age_group_cnt)\n",
        "age_group_cnt"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Gm52YzzRmI5S"
      },
      "source": [
        "#### *The above result indicates that the most popular age group which purchased threadmill is between 23 & 26*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "42jsApFoN1_T"
      },
      "outputs": [],
      "source": [
        "#### Customers count by Gender"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "qTgxQz5f724-",
        "outputId": "d35caec9-dfd9-477e-bc4c-cf066dee2a81"
      },
      "outputs": [
        {
          "data": {
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              "      <td>104</td>\n",
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              "    <tr>\n",
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              "\n",
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              "          const element = document.querySelector('#df-cec62d82-9796-4f74-9da3-aa2325aa39e5');\n",
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              "            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",
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            "text/plain": [
              "        Male Female\n",
              "Male            104\n",
              "Female           76"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Customers by Gender\n",
        "gender_cnt = {'Male Female': aero_df['Gender'].value_counts()}\n",
        "gender_cnt = pd.DataFrame(gender_cnt)\n",
        "gender_cnt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "c3-48ohxizah",
        "outputId": "bdd7a7e1-6fbc-4d66-dfc9-1b6a5213e42e"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = sns.countplot(data=aero_df, x='Gender')\n",
        "for p in ax.patches:\n",
        "  ax.annotate(p.get_height(), (p.get_x()+0.40, p.get_height()+0.05))\n",
        "plt.title(\"Count by Gender\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qhjlVcwjmii1"
      },
      "source": [
        "#### *From the buyers list the Male community is large in number than Females*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xulg9GZ3ODuG"
      },
      "source": [
        "#### Customers count by Marital Status"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_XI4hury1s04",
        "outputId": "21bbef1a-88f3-441f-e704-af97cbc8bab6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Partnered    107\n",
              "Single        73\n",
              "Name: MaritalStatus, dtype: int64"
            ]
          },
          "execution_count": 69,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Customers count by their marital status\n",
        "marital_status_cnt = aero_df['MaritalStatus'].value_counts()\n",
        "marital_status_cnt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "KukbsnNB2b7s",
        "outputId": "32044fdf-14d8-44e2-8fb4-c44dd9604880"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = sns.countplot(data=aero_df, x='MaritalStatus')\n",
        "for p in ax.patches:\n",
        "  ax.annotate(p.get_height(), (p.get_x()+0.35, p.get_height()+0.05))\n",
        "plt.title('Customers by Marital Status')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DGB2g2jvnKAp"
      },
      "source": [
        "#### *Interestingly 'Partnered' seem to have purchased the Threadmill more than the Singles*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SiE27OWcAzv-",
        "outputId": "5dc7ae58-80df-488a-89ec-ec72fd702b8f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Gender  MaritalStatus\n",
              "Female  Partnered        46\n",
              "        Single           30\n",
              "Male    Partnered        61\n",
              "        Single           43\n",
              "Name: MaritalStatus, dtype: int64"
            ]
          },
          "execution_count": 110,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "mari_status_gender = aero_df.groupby('Gender')['MaritalStatus'].value_counts()\n",
        "temp_df = pd.DataFrame(mari_status_gender)\n",
        "mari_status_gender"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TF-hcFC3nmrI"
      },
      "source": [
        "#### *About 61% females who are partnered have purchased Threadmills. Similarly amongst partnered males percentage is: 59. Amongst the Singles - Male Vs Female percentages are 41% and 39%. This indicates that purchase of Threadmill is higher amonghs male singles compared to female singles; wherease it becomes reverse amongnst 'partnered' community*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "bgECBA5uhdmV",
        "outputId": "d8e269e4-09c5-44c9-8347-f4de6e83a138"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 6 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "aero_df.hist()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 281
        },
        "id": "bLJbdiy5d7yZ",
        "outputId": "7acd41d1-745e-4ddd-b414-18046b9dd10f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = mari_status_gender.hist()\n",
        "plt.title(\"Gender & MaritalStatus\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "id": "Sag2c5DSgi53",
        "outputId": "a7b83783-6d43-4a10-e327-796df3db80a9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.displot(data=aero_df, x='Gender', y='MaritalStatus')\n",
        "plt.title('Gender Vs MaritalStatus')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YMD2MxLAQWDL",
        "outputId": "79e4030c-d8c4-400f-d628-6eb2ec486088"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "3    69\n",
              "4    52\n",
              "2    33\n",
              "5    17\n",
              "6     7\n",
              "7     2\n",
              "Name: Usage, dtype: int64"
            ]
          },
          "execution_count": 99,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Usage Count\n",
        "usage_count = aero_df['Usage'].value_counts()\n",
        "usage_count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "4f0rbqLUQrhp",
        "outputId": "104ac01d-a959-49ba-83a4-289285ccfcaa"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "ax = sns.countplot(data=aero_df, x='Usage')\n",
        "for p in ax.patches:\n",
        "  ax.annotate(p.get_height(), (p.get_x()+0.30, p.get_height()+0.03))\n",
        "plt.title('Count by Usage')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8YncFehsQtrM"
      },
      "source": [
        "#### *Usage of 3 or 4 times per day is higher.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "mb0tEQ3SOZXD",
        "outputId": "c70e942c-f430-4396-b61c-593850527119"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7faa7df86710>"
            ]
          },
          "execution_count": 44,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "usage_df = aero_df['Usage']\n",
        "sns.distplot(usage_df, kde=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mOIfE_CmE51J"
      },
      "source": [
        "#### *Usage Units - Assuming the given Usage numbers are of number of times usage per day, majority of the customers use 3 times.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "reI1spdxTAgV",
        "outputId": "9e475730-58d7-4276-96eb-9452e9526549"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-88d15cf9-8ce0-4692-8fb6-d5b22fa540d2\">\n",
              "    <div 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>Usage Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-88d15cf9-8ce0-4692-8fb6-d5b22fa540d2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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",
              "    [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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-88d15cf9-8ce0-4692-8fb6-d5b22fa540d2 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-88d15cf9-8ce0-4692-8fb6-d5b22fa540d2');\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",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Usage Count\n",
              "3           37\n",
              "4           22\n",
              "2           19\n",
              "5            2"
            ]
          },
          "execution_count": 32,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# KP281 Product by Usage\n",
        "KP1_Usage = {'Usage Count': aero_df[aero_df['Product']=='KP281']['Usage'].value_counts()}\n",
        "KP1_Usage_df = pd.DataFrame(KP1_Usage)\n",
        "KP1_Usage_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "q4bAd1kaPRQM",
        "outputId": "aa0357ff-70f6-46f5-fa75-16bec9bdff16"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.distplot(KP1_Usage_df, bins=5, kde=False)\n",
        "plt.title(\"Prodcut KP281 Usage\")\n",
        "plt.xlabel(\"Usage\")\n",
        "plt.ylabel('Count')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "17g-5gUtRlja",
        "outputId": "c6b11fea-8968-4966-b1fc-004310f1eb6d"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-c764db5a-2981-4376-86af-c11f1c9ea1fe\">\n",
              "    <div 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>Usage Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c764db5a-2981-4376-86af-c11f1c9ea1fe')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-c764db5a-2981-4376-86af-c11f1c9ea1fe');\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",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Usage Count\n",
              "3           31\n",
              "2           14\n",
              "4           12\n",
              "5            3"
            ]
          },
          "execution_count": 106,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# KP481 Product by Usage\n",
        "KP2_Usage = {'Usage Count': aero_df[aero_df['Product']=='KP481']['Usage'].value_counts()}\n",
        "KP2_Usage_df = pd.DataFrame(KP2_Usage)\n",
        "KP2_Usage_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "EkMEGifZTJPU",
        "outputId": "2b554899-a85f-4eba-89a0-9d9a3f958f52"
      },
      "outputs": [
        {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Usage Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
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              "      <td>7</td>\n",
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              "      <th>7</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-90111dd6-15b3-4a2b-9a53-f41091957f74');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "   Usage Count\n",
              "4           18\n",
              "5           12\n",
              "6            7\n",
              "7            2\n",
              "3            1"
            ]
          },
          "execution_count": 108,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# KP781 Product by Usage\n",
        "KP3_Usage = {'Usage Count': aero_df[aero_df['Product']=='KP781']['Usage'].value_counts()}\n",
        "KP3_Usage_df = pd.DataFrame(KP3_Usage)\n",
        "KP3_Usage_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bbfBQhZ8HeY_"
      },
      "source": [
        "#### *Interestingly the product KP781 is used 4 or more times a day, but that's the one lowest number sold. The other two products are sold in high numbers but mostly used 3 times a day.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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        "id": "2vyP8BxWpNAo",
        "outputId": "e1a3f7cc-a4a0-40f6-d9a0-525c79a52b52"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-1ead755d-9aa3-4b23-b4fa-44a10fcac9e2\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>7</td>\n",
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              "      <th>26</th>\n",
              "      <td>7</td>\n",
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              "      <th>28</th>\n",
              "      <td>6</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>4</td>\n",
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              "      <th>21</th>\n",
              "      <td>4</td>\n",
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              "      <th>22</th>\n",
              "      <td>4</td>\n",
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              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>3</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      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",
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              "\n",
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              "          const element = document.querySelector('#df-1ead755d-9aa3-4b23-b4fa-44a10fcac9e2');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Age Count\n",
              "23          8\n",
              "25          7\n",
              "26          7\n",
              "28          6\n",
              "24          5\n",
              "38          4\n",
              "21          4\n",
              "22          4\n",
              "29          3\n",
              "19          3"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Top 10 users of the product KP281 by Age\n",
        "top10_KP1 = {'Age Count': aero_df[aero_df['Product']=='KP281']['Age'].value_counts()}\n",
        "top10_KP1_df = pd.DataFrame(top10_KP1)\n",
        "top10_KP1_df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LAam2ZV681ea"
      },
      "source": [
        "#### *The first product KP281 seems to be popular amongst the age groups 23 to 28.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "hQTTWWC7q7NX",
        "outputId": "f810d58e-b1c1-41dc-988f-90fcaaad24ad"
      },
      "outputs": [
        {
          "data": {
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              "      <th>25</th>\n",
              "      <td>11</td>\n",
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              "      <td>3</td>\n",
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              "      <th>26</th>\n",
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              "      <th>40</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec230e8c-332a-4395-a8ef-7ca98bc08254')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\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",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Age Count\n",
              "25         11\n",
              "23          7\n",
              "33          5\n",
              "35          4\n",
              "31          3\n",
              "21          3\n",
              "24          3\n",
              "26          3\n",
              "40          3\n",
              "20          3"
            ]
          },
          "execution_count": 76,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Top 10 users of the product KP481 by Age\n",
        "top10_KP2 = {'Age Count': aero_df[aero_df['Product']=='KP481']['Age'].value_counts()}\n",
        "top10_KP2_df = pd.DataFrame(top10_KP2)\n",
        "top10_KP2_df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y8_SkreK9e1x"
      },
      "source": [
        "#### *The second product KP481 seems to be popular amongst the age groups 25 and 23.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "NXFNojOtrNYX",
        "outputId": "c0393bbe-e390-49ce-dd3f-d06e0f5bff81"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>7</td>\n",
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              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>4</td>\n",
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              "      <th>22</th>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>1</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<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",
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            "text/plain": [
              "    Age Count\n",
              "25          7\n",
              "24          4\n",
              "22          3\n",
              "27          3\n",
              "28          3\n",
              "30          3\n",
              "23          3\n",
              "26          2\n",
              "29          2\n",
              "40          1"
            ]
          },
          "execution_count": 77,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Top 10 users of the product KP781 by Age\n",
        "top10_KP3 = {'Age Count': aero_df[aero_df['Product']=='KP781']['Age'].value_counts()}\n",
        "top10_KP3_df = pd.DataFrame(top10_KP3)\n",
        "top10_KP3_df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gdtBtVRH9xQw"
      },
      "source": [
        "#### *The third product KP781 seems to be popular amongst the age group 25 and 24.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zpg0Rqj6-Hm2",
        "outputId": "bb3ae2e8-bcc4-4340-8245-a1f43de4e8c4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Product  Age\n",
              "KP481    25     11\n",
              "KP281    23      8\n",
              "KP481    23      7\n",
              "KP281    25      7\n",
              "         26      7\n",
              "KP781    25      7\n",
              "KP281    28      6\n",
              "KP481    33      5\n",
              "KP281    24      5\n",
              "         38      4\n",
              "dtype: int64"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# top age group which purchased the threadmils\n",
        "top_age_group = aero_df[['Product', 'Age']].value_counts()\n",
        "top_age_group.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "tpTE-4pqH8wM",
        "outputId": "4980ef61-6f4c-4db1-895b-5695ab04f54b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.countplot(data=aero_df, x=\"Age\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "id": "JkXQ1iwrVimH",
        "outputId": "3ecdc2f8-eb71-4c82-dff5-9220240215d8"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "from pandas.core.ops.dispatch import Any\n",
        "sns.displot(data=aero_df, x='Age')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tjFkt1a_NUO"
      },
      "source": [
        "#### *This indicates that generally threadmills are mostly bought by the age groups between 23 to 28.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CkO3qZAa-X6J",
        "outputId": "ab79f4a3-d8a8-44f3-978b-e2f200219f5a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "*** KP281 Count by Gender ***\n",
            " Male      40\n",
            "Female    40\n",
            "Name: Gender, dtype: int64\n",
            "\n",
            "*** KP281 Count by gender & age ***\n",
            " Gender  Age\n",
            "Male    23     5\n",
            "        26     4\n",
            "Female  25     4\n",
            "        28     4\n",
            "Male    25     3\n",
            "Female  22     3\n",
            "        23     3\n",
            "        24     3\n",
            "        26     3\n",
            "Male    38     3\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Top 10 users of KP281 by gender & by Age\n",
        "count_by_gender = aero_df[aero_df['Product']=='KP281']['Gender'].value_counts()\n",
        "count_by_gender_by_age = aero_df[aero_df['Product']==\"KP281\"][['Gender', 'Age']].sort_values(by='Gender').value_counts()\n",
        "print(\"*** KP281 Count by Gender ***\\n\", count_by_gender)\n",
        "print()\n",
        "print(\"*** KP281 Count by gender & age ***\\n\", count_by_gender_by_age.head(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sf8yb18eS7IS"
      },
      "source": [
        "#### *the Product KP281 is equally purchased by both Male & Females. Amongst the Age group for Male it is 23 & 26; for Female it is 25 & 28.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4TNnGGbpSwtR",
        "outputId": "9f209570-23c9-4678-9110-1c176be3cee5"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "*** KP281 Count by Gender ***\n",
            " Male      31\n",
            "Female    29\n",
            "Name: Gender, dtype: int64\n",
            "\n",
            "*** KP281 Count by gender & age ***\n",
            " Gender  Age\n",
            "Male    25     6\n",
            "Female  25     5\n",
            "Male    23     4\n",
            "Female  23     3\n",
            "        33     3\n",
            "Male    20     2\n",
            "        35     2\n",
            "        32     2\n",
            "        21     2\n",
            "        33     2\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Top 10 users of KP481 by gender & by Age\n",
        "count_by_gender = aero_df[aero_df['Product']=='KP481']['Gender'].value_counts()\n",
        "count_by_gender_by_age = aero_df[aero_df['Product']==\"KP481\"][['Gender', 'Age']].sort_values(by='Gender').value_counts()\n",
        "print(\"*** KP281 Count by Gender ***\\n\", count_by_gender)\n",
        "print()\n",
        "print(\"*** KP281 Count by gender & age ***\\n\", count_by_gender_by_age.head(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9n0AacefHWr8"
      },
      "source": [
        "#### *More Males have purchased the Product KP481 than Females. Amongst the Age group for both Male & Female it is 25 and 23 who purchased in high numbers.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9l1OkY-NTZGn",
        "outputId": "35c07434-4b86-484a-a570-29215755adcb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "*** KP281 Count by Gender ***\n",
            " Male      33\n",
            "Female     7\n",
            "Name: Gender, dtype: int64\n",
            "\n",
            "*** KP281 Count by gender & age ***\n",
            " Gender  Age\n",
            "Male    25     6\n",
            "        27     3\n",
            "        22     3\n",
            "        24     3\n",
            "        23     2\n",
            "        28     2\n",
            "        29     2\n",
            "        30     2\n",
            "        31     1\n",
            "        47     1\n",
            "dtype: int64\n"
          ]
        }
      ],
      "source": [
        "# Top 10 users of KP781 by gender & by Age\n",
        "count_by_gender = aero_df[aero_df['Product']=='KP781']['Gender'].value_counts()\n",
        "count_by_gender_by_age = aero_df[aero_df['Product']==\"KP781\"][['Gender', 'Age']].sort_values(by='Gender').value_counts()\n",
        "print(\"*** KP281 Count by Gender ***\\n\", count_by_gender)\n",
        "print()\n",
        "print(\"*** KP281 Count by gender & age ***\\n\", count_by_gender_by_age.head(10))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d9UnJ5mMTn27"
      },
      "source": [
        "#### *Interestingly the product KP781 seems to be highly popular with 75% of Males buying it. Only 25% of females have bought KP781.* "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "ILhYaY5w_nj_",
        "outputId": "260d10bd-e69c-4bac-e3cd-6875df401c0f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Fitness impact due to Usage amongst the Gender\n",
        "sns.barplot(data=aero_df, x='Usage', y='Fitness', hue='Gender')\n",
        "plt.title(\"Usage Vs Fitness (Gender)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LxYj3H9U_q2b"
      },
      "source": [
        "#### *This correleation indicates high usage & fitness is amongst Male community.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "OMIik6LpN7rL",
        "outputId": "8fe842a8-cbd1-474e-e3f6-d794e14eb27b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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rVy+WLl1a+S6i6nYnnXQSXbt2pXv37owfP56+ffsCUFJSwuDBg+nduzcXXXQRd9555z6eMRGJq0b1TNZ+/fp51Qd+LFmyhGOPPTZNEcWPzmd6jBkzhpKSEjp37syjj0Z3+2EqNYbbJDPxvDX0bZJmVljTLAGNfrIxEYmfZJNg+aYOQHPKN32e1D51vU887mJXohERkYASvIhITCnBi4jElGrwIhls4sSJrFmzhkMOOYR77rkn3eFII6MEL5LBMnEyLmk4GZfgU33rVjK3a91+++088cQTZGVl0axZMx566CGmT5/OddddR/fu3evcZ3FxMSNHjqx2CgMRkVTJuATf0N5//31eeukl5s+fT8uWLdmwYQNlZWU8/PDD6Q5NGoFkBxxtN5SSBazcUJrUPsneJy6yN7rIWovVq1fTsWNHWrZsCUDHjh3p1KkTgwcPpuJDWW3atOEXv/gFvXr1YsCAAaxduxaA5cuXM2DAAHr06MHNN99MmzZt9mh/586d3HDDDZWfdn3ooYca7uBEJNaU4GsxbNgwvvjiC4455hiuuuoq3n777T222bZtGwMGDGDBggWccsopTJ8+HYAJEyYwYcIEPv74493miE/0yCOP0K5dO+bNm8e8efOYPn06//rXvyI9JhFpGpTga9GmTRsKCwuZNm0aubm5jB49eo+Jxlq0aFH5yL7jjz++ck75999/n/POOw+gxic0vf766zz66KP07t2b/v37s3HjRpYtWxbZ8YhI06EafBKysrIYPHgwgwcPpkePHsycOXO39dnZ2ZhZ5bbJTmYG4O784Q9/4IwzzkhpzCIiGsHX4pNPPtltRF1UVMThhx+e1L4DBgyofOLTU089Ve02Z5xxBg888EDlw0M+/fRTtm3bVs+oRUQycATf0HcXbN26lWuuuYbNmzfTvHlzjjrqKKZNm8a5555b676//e1vueiii7j99tsZPnw47dq122ObsWPHUlxcTN++fXF3cnNzeeGFF6I4FBFpYjIuwTe0448/vvKB2IkSHyaydevWyp/PPffcyuTfuXNnPvjgA8yMp556qvIh3fn5+ZX3wDdr1ow77riDO+64I8KjEJGmSAk+QoWFhYwbNw53p3379vzxj39Md0gi0oQowUdo0KBBLFiwIN1hiEgTpYusIiIxpRG8iDRaHXN2AeXhd6krJXgRabSu77k53SFkNJVoRERiKuNG8Kl+YnkyD+nNysqiR48elJeXc+yxxzJz5kz222+/pNovKiriyy+/ZMSIEfUNtU5mzJhBQUEBU6dObdB+RaTx0Ag+Ca1ataKoqIhFixbRokULHnzwwaT2Ky8vp6ioiFdeeSVlsezcuTNlbYlIvGXcCD7dBg0axMKFC3nxxRe57bbbKCsr48ADD+Txxx/n4IMPZvLkySxfvpwVK1bQpUsX3n33XbZv387cuXOZNGkSS5YsYeXKlaxYsYKVK1dy7bXXMn78eAAee+wxfv/731NWVkb//v25//77ycrKok2bNlxxxRW8+eab3HfffRQXF1e73Z/+9CfuvPNO2rdvT69evSqnOBaRpkkJvg7Ky8t59dVXGT58OCeffHLlp1Qffvhh7rnnHu69914AFi9ezNy5c2nVqtUepZLJkyezdOlSZs2aRWlpKd26dePKK6/ks88+4+mnn+bdd98lOzubq666iscff5wxY8awbds2+vfvz7333suSJUu4++6799ju9NNP55e//CWFhYW0a9eOIUOG0KdPn3SeLkmwq0Xr3b7XJtlSZPmmDkBzyjd9ntQ+yZQkJT6U4JOwfft2evfuDQQj+J/85Cd88sknjB49mtWrV1NWVkbXrl0rtx81ahStWrWqsb2zzjqLli1b0rJlSw466CDWrl3LP/7xDwoLCznhhBMq+zzooIOA4BrAOeecA1Djdh9++CGDBw8mNzcXgNGjR/Ppp5+m/mTIPtl29LB0hyBNkBJ8Eipq8ImuueYarrvuOkaNGsXs2bOZPHly5brWrfc+SkssnVRML+zuXHzxxdx55517bJ+Tk0NWVhZAjdtpgjIRqUoXWffR119/TefOnQH2mB8+Udu2bSktLa21vVNPPZVnn32WdevWAbBp0yY+//zzpLfr378/b7/9Nhs3bmTHjh0888wz+3JYIhIjGTeCbyw1xMmTJ3PeeedxwAEHMHTo0BofszdkyBDuuusuevfuzaRJk2psr3v37tx2220MGzaMXbt2kZ2dzX333bfH3PM1bTdgwAAmT57MwIEDad++fWVJSUSaroxL8OmQOB1whbPPPpuzzz57j+WJpRqADh06MG/evBrbrpg2GIK6+ejRo2vtv6btLr30Ui699NIa+2qKJk6cyJo1azjkkEO455570h2OSIOKPMGbWRZQAJS4+8io+xNJtGbNGkpKStIdhkhaNEQNfgKwpAH6ERGRBJEmeDPLA84CHq5PO+6emoCaOJ1HkaYl6hH8b4GJQI1zfZrZ5WZWYGYF69ev32N9Tk4OGzduVHKqJ3dn48aN5OTkpDsUEWkgkdXgzWwksM7dC81scE3bufs0YBpAv3799sjieXl5rFq1iuqSv9RNTk4OeXl56Q5DRBpIlBdZTwJGmdkIIAfY38wec/eL6tJIdnb2bp8SFRGR5ERWonH3Se6e5+75wPnAW3VN7iIisu/0SVYRkZhqkA86uftsYHZD9CUiIgF9klUaBX3iVCT1lOClUdAnTkVSTzV4EZGYUoIXEYkpJXgRkZhSghcRiSldZJWMpIdSi9ROI3gRkZhSghcRiSmVaEQyWMecXUB5+F1kd0rwIhns+p6b0x2CNGIq0YiIxJQSvIhITCnBi4jElBK8iEhMKcGLiMSU7qLJEJovXUTqSgk+Q2i+dBGpKyV4idTxNzya1HZtN5SSBazcUJrUPs+3rWdgIk2AavAiIjGlBC8iElMq0Uisaa4WacqU4CXWNFeLNGUq0YiIxJQSvIhITCnBi4jElBK8iEhM6SJrmkX1QaDCX42pZ2Qikuk0ghcRiSkleBGRmFKCFxGJKdXgpVHY1aL1bt9FpP4iS/BmlgO8A7QM+3nW3X8ZVX+S2bYdPSzdIYjETpQj+H8DQ919q5llA3PN7FV3/yDCPkVEJBRZgnd3B7aGv2aHXx5VfyIisrtIa/BmlgUUAkcB97n7h1H2J3WnRwGKxFed76IxswPMrGcy27r7TnfvDeQBJ5rZcdW0d7mZFZhZwfr16+sajtRTxaMA16xZk+5QRCTFkkrwZjbbzPY3sw7AfGC6mf0m2U7cfTMwCxhezbpp7t7P3fvl5uYm22STs6tFa3a23F93mYhI0pIt0bRz9y1mNhZ41N1/aWYL97aDmeUCO9x9s5m1Ak4H7q5nvE2W7jIRkbpKtkTT3MwOBX4IvJTkPocCs8IXgnnAG+6e7L4iIlJPyY7gbwX+Dsx193lmdgSwbG87uPtCoE894xMRkX2UVIJ392eAZxJ+XwGcE1VQIiJSf0kleDO7B7gN2A68BvQE/o+7PxZhbHWi2/12t/LWHkltV76pA9Cc8k2fJ71Pl1s+rkdkItJQkq3BD3P3LcBIoJjgvvYbogpqX+h2PxGR3SV9kTX8fhbwjLt/HVE8IiKSIsleZH3JzJYSlGiuDG+B/Da6sEREpL6Svch6Y1iH/9rdd5rZN8DZ0YbWeKi+LyKZKNlPsu4HXAU8EC7qBPSLKqjGRvV9EclEydbg/wSUAd8Pfy8huKtGREQaqWQT/JHufg+wA8DdvwEssqhERKTekk3wZeF8Mg5gZkcSPNBDREQaqWTvovklwQecDjOzx4GTgEuiCkoaTsecXUB5+F1E4iTZu2jeMLP5wACC0swEd98QaWTSIK7vuTndIYhIROryRKcc4Ktwn+5mhru/E01YIiJSX8nORXM3MBr4J1DxXt6BjE3wyc67AnWfr0VztYhIY5DsCP6/gG7urgurIiIZItm7aFYA2VEGIiIiqZXsCP4boMjM/kHC7ZHuPj6SqEREpN6STfB/C78SeYpjqdbxNzya1HZtN5SSBazcUJrUPs+3rWdgIiKNXLIJvr27/y5xgZlNiCAeERFJkWRr8BdXs+ySFMYhIiIpttcRvJldAFwIdDWzxBJNW2BTlIGJiEj91FaieQ9YDXQE7k1YXgosjCooERGpv70meHf/HPgcGNgw4YiISKrUVqKZ6+4nm1kpu981Y4C7+/6RRiciIvusthLNjwDcvUnfVKgZF0UkE9WW4J8H+gKY2XPufk70ITU+mnFRRDJRbbdJJj616YgoAxERkdSqLcF7DT+LiEgjV1uJppeZbSEYybcKfwZdZBURafRqu00yq6ECERGR1Ep2qgIREckwSvAiIjGlBC8iElORJXgzO8zMZpnZYjP7Z9TTC+9q0ZqdLfdnV4vWUXYjIpIxkp0Pfl+UAz9z9/lm1hYoNLM33H1xFJ1tO3pYFM2KiGSsyEbw7r7a3eeHP5cCS4DOUfUnIiK7a5AavJnlA32AD6tZd7mZFZhZwfr16xsiHBGRJiHyBG9mbYDngGvdfUvV9e4+zd37uXu/3NzcqMMREWkyIk3wZpZNkNwfd/e/RtmXiIjsLsq7aAx4BFji7r+Jqh8REalelCP4k4AfA0PNrCj8GhFhfyIikiCy2yTdfS67TzcsIiINSJ9kFRGJKSV4EZGYUoIXEYkpJXgRkZhSghcRiSkleBGRmFKCFxGJKSV4EZGYUoIXEYkpJXgRkZhSghcRiSkleBGRmFKCFxGJKSV4EZGYUoIXEYkpJXgRkZhSghcRiSkleBGRmFKCFxGJKSV4EZGYUoIXEYkpJXgRkZhSghcRiSkleBGRmFKCFxGJKSV4EZGYUoIXEYkpJXgRkZhSghcRiSkleBGRmFKCFxGJKSV4EZGYiizBm9kfzWydmS2Kqg8REalZlCP4GcDwCNsXEZG9iCzBu/s7wKao2hcRkb1Lew3ezC43swIzK1i/fn26wxERiY20J3h3n+bu/dy9X25ubrrDERGJjbQneBERiYYSvIhITEV5m+STwPtANzNbZWY/iaovERHZU/OoGnb3C6JqW0REaqcSjYhITCnBi4jElBK8iEhMKcGLiMSUEryISEwpwYuIxJQSvIhITCnBi4jElBK8iEhMKcGLiMSUEryISEwpwYuIxJQSvIhITCnBi4jElBK8iEhMKcGLiMSUEryISEwpwYuIxJQSvIhITCnBi4jElBK8iEhMKcGLiMSUEryISEwpwYuIxJQSvIhITCnBi4jElBK8iEhMKcGLiMSUEryISEwpwYuIxJQSvIhITCnBi4jElBK8iEhMRZrgzWy4mX1iZp+Z2Y1R9iUiIruLLMGbWRZwH3Am0B24wMy6R9WfiIjsLsoR/InAZ+6+wt3LgKeAsyPsT0REEpi7R9Ow2bnAcHcfG/7+Y6C/u4+rst3lwOXhr92ATyIJaE8dgQ0N1Fc66Pgym44vczX0sR3u7rnVrWjegEFUy92nAdMaul8zK3D3fg3db0PR8WU2HV/makzHFmWJpgQ4LOH3vHCZiIg0gCgT/DzgaDPramYtgPOBv0XYn4iIJIisROPu5WY2Dvg7kAX80d3/GVV/+6DBy0INTMeX2XR8mavRHFtkF1lFRCS99ElWEZGYUoIXEYmpJpfgzewwM5tlZovN7J9mNiHdMaWSmeWY2UdmtiA8vv+b7phSzcyyzOx/zOyldMeSamZWbGYfm1mRmRWkO55UM7P2ZvasmS01syVmNjDdMaWKmXUL/24VX1vM7Nq0xtTUavBmdihwqLvPN7O2QCHwX+6+OM2hpYSZGdDa3beaWTYwF5jg7h+kObSUMbPrgH7A/u4+Mt3xpJKZFQP93D2WHwIys5nAHHd/OLy7bj9335zuuFItnKqlhODDnZ+nK44mN4J399XuPj/8uRRYAnROb1Sp44Gt4a/Z4VdsXsXNLA84C3g43bFI3ZhZO+AU4BEAdy+LY3IPnQosT2dyhyaY4BOZWT7QB/gwvZGkVljCKALWAW+4e5yO77fARGBXugOJiAOvm1lhOI1HnHQF1gN/CktsD5tZ63QHFZHzgSfTHUSTTfBm1gZ4DrjW3bekO55Ucved7t6b4NPDJ5rZcemOKRXMbCSwzt0L0x1LhE52974Es7BebWanpDluSxcAAAMISURBVDugFGoO9AUecPc+wDYgdtOIh6WnUcAz6Y6lSSb4sDb9HPC4u/813fFEJXz7OwsYnu5YUuQkYFRYp34KGGpmj6U3pNRy95Lw+zrgeYJZWeNiFbAq4R3lswQJP27OBOa7+9p0B9LkEnx4EfIRYIm7/ybd8aSameWaWfvw51bA6cDS9EaVGu4+yd3z3D2f4C3wW+5+UZrDShkzax1e+CcsXQwDFqU3qtRx9zXAF2bWLVx0KhCLmxuquIBGUJ6BRjCbZBqcBPwY+DisUwPc5O6vpDGmVDoUmBlexW8G/MXdY3c7YUwdDDwfjEFoDjzh7q+lN6SUuwZ4PCxjrAAuTXM8KRW+MJ8OXJHuWKAJ3iYpItJUNLkSjYhIU6EELyISU0rwIiIxpQQvIhJTSvAiIjGlBC9Nhpnlm9miKssmm9n16YpJJEpK8CIiMaUELwKY2fjwGQELzeypcNmJZvZ+ODHWexWfwDSz/czsL+H2z5vZh2bWL1w3LNxnvpk9E855JJIWTfGTrCLVuRHo6u7/rpjqgWCKh0HhA+RPA+4AzgGuAr5y9+7hRG5FAGbWEbgZOM3dt5nZz4HrgFsb+mBEQAlempaaPrbtwEKCj9C/ALwQLm9HMO3D0eE22eHyk4HfAbj7IjNbGC4fAHQH3g2nG2gBvJ/qgxBJlko00pRsBA6osqwDsIHgISL3EcxuOM/MmgP/D5jl7scB/wnk1NK+Ecy/3zv86u7uP0npEYjUgRK8NBnhk65Wm9lQADPrQDCV8lzgMHefBfycYOTeJvxeEu5+SUJT7wI/DNvoDvQIl38AnGRmR4XrWpvZMVEek8jeaLIxaVLChHwf343kfwX8hWDe/HYEo/DH3P2u8IHQMwkeTPEycJG754czBs4kKMcsBY4AznP3ZeGLx91Ay7D9m939bw1zdCK7U4IXqaNwKuZsd//WzI4E3gS6uXtZmkMT2Y0usorU3X7ArPDJYAZcpeQujZFG8CIiMaWLrCIiMaUELyISU0rwIiIxpQQvIhJTSvAiIjH1/wGairwbzVUAQAAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Fitness impact due to Usage based on Marital Status\n",
        "sns.barplot(data=aero_df, x='Usage', y='Fitness', hue='MaritalStatus')\n",
        "plt.title(\"Usage Vs Fitness (MaritalStatus)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FzvtQl3pAdmL"
      },
      "source": [
        "#### *This correleation indicates high usage & fitness is amongst those who are 'Single'; at the same time, interestingly the top Usage & high Fitness is recorded by 'Partenered'.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "LHcg3JYB-9lQ",
        "outputId": "9db92322-81f0-404f-b874-80743290ac35"
      },
      "outputs": [
        {
          "data": {
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              "                                                     [key], {});\n",
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              "          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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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Gender  Miles\n",
              "23  Female    188\n",
              "61    Male    169\n",
              "8     Male    141\n",
              "53    Male    141\n",
              "39    Male    132\n",
              "72    Male    132\n",
              "34  Female    113\n",
              "57  Female    113\n",
              "31    Male    113\n",
              "48    Male    113"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# cust_mask = aero_df['Age']\n",
        "cust_list = aero_df[aero_df['Product']=='KP281'][['Gender','Miles']].sort_values(by='Miles', ascending=False).head(10)\n",
        "cust_list\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tbxP8qt0sZDl"
      },
      "source": [
        "#### *this indicates that for the product KP281, the more miles are run predominantly by Males.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "jEfJP8BkR-Iu",
        "outputId": "fb898d73-1f77-4436-fdd7-985727ac770f"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Fitness impact due to Usage based on Marital Status\n",
        "sns.boxplot(data=aero_df, x='Gender', y='Miles', hue='MaritalStatus')\n",
        "plt.title(\"Gender Vs Miles\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_pfOU89EggI"
      },
      "source": [
        "#### *Miles run seems to be quite stable amongst Males irrespective of their Marital Status. But, there are good number of outliers in female partnered segment.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "gKPHuYtUTcyR",
        "outputId": "82c094d5-0af3-4ac3-f2ae-7f0a04ba42ed"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Usage based on Marital Status\n",
        "sns.boxplot(data=aero_df, x='Gender', y='Usage', hue='MaritalStatus')\n",
        "plt.title(\"Gender Vs Usage\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zgcSKkgiFHJF"
      },
      "source": [
        "#### *On Usage, there is a stable usage of 3 to 4 times a day by Females. But there are outliers amongst Male and that too Male Partnered segment.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "eXqfc9-r839T",
        "outputId": "45613e58-3954-4082-aac1-1dcb792faff7"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        " sns.boxplot(data=aero_df, x='Gender', y='Age')\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WgicJMTyXkgh"
      },
      "source": [
        "#### *Amongst the purchasers age group the outlier lies in female category.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "pwOZxhKR-BLs",
        "outputId": "d07b3824-60a7-41aa-92f1-bf6f394119a5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        " sns.boxplot(data=aero_df, x='Product', y='Usage')\n",
        " plt.title('Products by Usage')\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FpwQ5Fpcr8wV"
      },
      "source": [
        "#### *The third product KP781 seem to have more frequent usage than others; and the second product KP481 is less used*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "bhllEz4tmFKG",
        "outputId": "58cb226b-74d0-471a-8485-aae921086af4"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-0cc9905f-8258-44e2-94d8-76bca4f011e8\">\n",
              "    <div 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>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>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>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>171</th>\n",
              "      <td>KP781</td>\n",
              "      <td>33</td>\n",
              "      <td>Female</td>\n",
              "      <td>18</td>\n",
              "      <td>Partnered</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "      <td>95866</td>\n",
              "      <td>200</td>\n",
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              "    <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",
              "    <tr>\n",
              "      <th>148</th>\n",
              "      <td>KP781</td>\n",
              "      <td>24</td>\n",
              "      <td>Female</td>\n",
              "      <td>16</td>\n",
              "      <td>Single</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>52291</td>\n",
              "      <td>200</td>\n",
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              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>KP781</td>\n",
              "      <td>22</td>\n",
              "      <td>Male</td>\n",
              "      <td>18</td>\n",
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              "      <td>4</td>\n",
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              "      <td>48556</td>\n",
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              "    <tr>\n",
              "      <th>152</th>\n",
              "      <td>KP781</td>\n",
              "      <td>25</td>\n",
              "      <td>Female</td>\n",
              "      <td>18</td>\n",
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              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>61006</td>\n",
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              "            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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              "        }\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "173   KP781   35    Male         16     Partnered      4        5   92131   \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",
              "155   KP781   25    Male         18     Partnered      6        5   75946   \n",
              "171   KP781   33  Female         18     Partnered      4        5   95866   \n",
              "175   KP781   40    Male         21        Single      6        5   83416   \n",
              "148   KP781   24  Female         16        Single      5        5   52291   \n",
              "142   KP781   22    Male         18        Single      4        5   48556   \n",
              "152   KP781   25  Female         18     Partnered      5        5   61006   \n",
              "\n",
              "     Miles  \n",
              "173    360  \n",
              "166    300  \n",
              "167    280  \n",
              "170    260  \n",
              "155    240  \n",
              "171    200  \n",
              "175    200  \n",
              "148    200  \n",
              "142    200  \n",
              "152    200  "
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prod_by_age = aero_df[aero_df['Product']=='KP781'].sort_values('Miles', ascending=False)\n",
        "prod_by_age.head(10)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "ZRlPJju2rlzf",
        "outputId": "0a02326c-58f7-48f1-9f18-ef71bc58a796"
      },
      "outputs": [
        {
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              "\n",
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              "          const element = document.querySelector('#df-e113a96f-3f97-4ca8-a7a0-121c4e0bb3e1');\n",
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              "            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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            ],
            "text/plain": [
              "    Product  Age  Gender  Education MaritalStatus  Usage  Fitness  Income  \\\n",
              "150   KP781   25    Male         16     Partnered      4        5   49801   \n",
              "140   KP781   22    Male         14        Single      4        3   48658   \n",
              "161   KP781   27    Male         21     Partnered      4        4   90886   \n",
              "157   KP781   26  Female         21        Single      4        3   69721   \n",
              "153   KP781   25    Male         18     Partnered      4        3   64741   \n",
              "146   KP781   24    Male         16        Single      4        5   61006   \n",
              "145   KP781   23    Male         16        Single      4        5   48556   \n",
              "144   KP781   23  Female         18        Single      5        4   53536   \n",
              "160   KP781   27    Male         18        Single      4        3   88396   \n",
              "147   KP781   24    Male         18     Partnered      4        5   57271   \n",
              "\n",
              "     Miles  \n",
              "150    120  \n",
              "140    106  \n",
              "161    100  \n",
              "157    100  \n",
              "153    100  \n",
              "146    100  \n",
              "145    100  \n",
              "144    100  \n",
              "160    100  \n",
              "147     80  "
            ]
          },
          "execution_count": 131,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "prod_by_age = aero_df[aero_df['Product']=='KP781'].sort_values('Miles', ascending=False)\n",
        "prod_by_age.tail(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TtAc9cWcrohv"
      },
      "source": [
        "#### *The above result set indicate that the product KP781 has got more miles run by 'Partnered' and less miles by 'Singles'*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 295
        },
        "id": "VZB43P2NX3zi",
        "outputId": "403c14f5-c9f7-4f58-ace4-9eb1fdf7dcb2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        " sns.boxplot(data=aero_df, x='Product', y='Age')\n",
        " plt.title(\"Products by Age group\")\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ONu1wRU2lyFb"
      },
      "source": [
        "#### *The first two products seem to be quite popular amongst age groups b/w 25 to 35; whereas the third product attracts higher age group* "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "DFuelVd3YWjd",
        "outputId": "f3553246-83d9-403d-92ab-5c3bbbcb0d97"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        " sns.boxplot(data=aero_df, x='Product', y='Miles')\n",
        " plt.title(\"Products by Miles Run\")\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lO5tj7stliEr"
      },
      "source": [
        "#### *Interestingly, though the third product KP781 is bought by less number of people, the miles run in it is higher than the other two products*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "g2GUHR1gYkIc",
        "outputId": "92b7d602-b101-4d5f-d055-98e2ac827d97"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        " sns.boxplot(data=aero_df, x='Gender', y='Miles')\n",
        " plt.title(\"Miles run by Gender\")\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sm4KqAc8lVAQ"
      },
      "source": [
        "#### *this graph shows Males run more miles in the threadmill than Females*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "VURetSBuYMIV",
        "outputId": "822603e6-56f5-4884-8480-904c9f8e63ff"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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A9evXU+D/ggOOJHbeeedSa5gwYQLTpk0rbP99DYKvAAuAcZLmAUfRh4Hm08D1i6n87uDKiHikS5MxwLMAEbFR0jpgJPBil/1MBaYCjB8/vo8l1zZixAg2bNiwTfsYLDpeh+E7vavkSvqDdzFixIiyizArhfqa/JJGAkdQOST0cES82Msm1duOAG4HpkXEE1XLnwAmRMRzaf7XwOE97bu5uTlaW1v7+tTWgxkzZgBw+eWXl1yJmRVN0uKIqHmRz9YMTLMT8DLwKrC/pD/t64YR8QpwHzChy6rVwLhU5FBgD2DtVtRkZmbbqE+HhiRdDPwVsBzYnBYHcH8P2zQAv4+IV9L5hWPZ8mTwfGAy8BBwKnBvkecHzMxsS309R/AJYL+I6PEEcRejgbnpPMEOwPcj4k5JFwKtETEfuBb4jqQ24CVg4lbs38zMtoO+BsHTVK766XMQRMQy4JAayy+omn4TOK2v+zQzs+2vr0GwHlgq6adUhUFETC+kKjMzq5u+BsH89KjmY/lmZoNAX4NgRER0usZQ0owC6jEzszrr6+Wjk2ssm7Id6zAzs5L02COQdDrw18AfSqo+NLQblat8zMxsgOvt0NCDwAvAKODfqpa/BiwrqigzM6ufHoMgIp4BngGOrE85ZmZWb70dGnogIj4i6TU6XyUkKjcY3b3Q6szMrHC9HRqaBBARu9WhFjMzK0FvVw3d3jEh6QcF12JmZiXoLQiqRyF7X5GFmJlZOXoLguhm2szMBonezhEcJOlVKj2D4WkafLLYzGzQ6O3yUQ9ma2Y2yG3NCGVmZjYIOQjMzDJXWBBIGifpPklPSlpe626lklokrZO0ND0uqLUvMzMrTl9vQ/1ObAS+EBFLJO0GLJa0MCKe7NLuZxFxQoF1mJlZDwrrEUTECxGxJE2/BqwAxhT1fGZm9s7U5RyBpEYq4xc/UmP1kZIek/RjSQd0s/1USa2SWtvb2wus1MwsP4UHgaRdgR8An4+IV7usXgLsHREHAbOAO2rtIyLmRERzRDQ3NDQUW7CZWWYKDQJJw6iEwLyIuK3r+oh4NSJeT9N3AcMkjSqyJjMz66zIq4YEXAusiIhLu2nzB6kdkg5L9awtqiYzM9tSkVcNHQV8Cnhc0tK07EvAeICImA2cCpwjaSOwAZgYEb6nkZlZHRUWBBHxAJ3vXlqrzRXAFUXVYGZmvfMvi83MMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzPqdKVOm0NLSwllnnVV2KVkocoSycZLuk/SkpOWSZtRoI0kzJbVJWibpg0XVY2YDx6pVqwBoa2srt5BMFNkj2Ah8ISL2B44AzpW0f5c2xwH7psdU4NsF1mNmA8CUKVM6zbtXULwiRyh7AXghTb8maQUwBniyqtlJwA1peMqHJY2QNDptO2jNmjWrX3zT6ahhxowtOmt11dTUxLRp00qtwfqPjt5Ah/7w/8pgV5dzBJIagUOAR7qsGgM8WzX/XFrWdfupkloltba3txdVZnaGDx/O8OHDyy7DzEpW5OD1AEjaFfgB8PmIePWd7CMi5gBzAJqbmwf84Pb+9mtm/UmhPQJJw6iEwLyIuK1Gk9XAuKr5sWmZmWWqsbGx03xTU1M5hWSkyKuGBFwLrIiIS7tpNh84M109dASwbrCfHzCznl1//fWd5q+55ppyCslIkYeGjgI+BTwuaWla9iVgPEBEzAbuAj4OtAHrgb8psB4zGyAaGxtZtWqVewN1osoFOwNHc3NztLa2ll2GmdmAImlxRDTXWudfFpuZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZpkrcqjK6yStkfREN+tbJK2TtDQ9LiiqFjMz616RQ1VeD1wB3NBDm59FxAkF1mBmZr0orEcQEfcDLxW1fzMz2z7KPkdwpKTHJP1Y0gHdNZI0VVKrpNb29vZ61mdmNuiVGQRLgL0j4iBgFnBHdw0jYk5ENEdEc0NDQ90KNDPLQWlBEBGvRsTrafouYJikUWXVY2aWq9KCQNIfSFKaPizVsraseszMclXYVUOSvge0AKMkPQd8BRgGEBGzgVOBcyRtBDYAEyMiiqrHzMxqKywIIuL0XtZfQeXyUjMzK1HZVw2ZmVnJHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BYGaWOQeBmVnmHARmZplzEJiZZc5BkLG1a9cyffp01q71wHDWv/i9WV+FBYGk6yStkfREN+slaaakNknLJH2wqFqstrlz5/L4449zww03lF2KWSd+b9ZXkT2C64EJPaw/Dtg3PaYC3y6wFuti7dq1LFiwgIhgwYIF/uZl/Ybfm/VXWBBExP3ASz00OQm4ISoeBkZIGl1UPdbZ3Llz2bx5MwCbNm3yNy/rN/zerL8yzxGMAZ6tmn8uLduCpKmSWiW1tre316W4we6ee+5h48aNAGzcuJGFCxeWXJFZhd+b9TcgThZHxJyIaI6I5oaGhrLLGRSOOeYYhg4dCsDQoUM59thjS67IrMLvzforMwhWA+Oq5semZVYHkydPZocdKv/5hwwZwplnnllyRWYVfm/WX5lBMB84M109dASwLiJeKLGerIwcOZIJEyYgiQkTJjBy5MiySzID/N4sw9Cidizpe0ALMErSc8BXgGEAETEbuAv4ONAGrAf+pqharLbJkyezatUqf+OyfsfvzfpSRJRdw1Zpbm6O1tbWssswMxtQJC2OiOZa6wbEyWIzMyuOg8DMLHMOAjOzzDkIzMwyN+BOFktqB54pu45BZBTwYtlFmNXg9+b2tXdE1PxF7oALAtu+JLV2dyWBWZn83qwfHxoyM8ucg8DMLHMOAptTdgFm3fB7s058jsDMLHPuEZiZZc5BYGaWOQfBICQpJN1YNT9UUrukO3vZrqW3NmZ9IWmTpKVVj8YCn2uVpFFF7T8Hhd2G2kr1BvABScMjYgNwLB70x+prQ0QcXHYR1jfuEQxedwHHp+nTge91rJB0mKSHJP1S0oOS9uu6saRdJF0n6dHU7qQ61W2DlKRDJf2npMWS7pY0Oi1fJOmyNC75CkkfknSbpKckXVS1/R1p2+WSpnbzHGek9+xSSVdJGlKvv28gcxAMXjcBEyXtBBwIPFK1biXwJxFxCHAB8M81tv/fwL0RcRjwZ8C/SNql4Jpt8BhedVjodknDgFnAqRFxKHAd8PWq9m+nXxHPBv4DOBf4ADBFUscQZZ9O2zYD06uWAyDpj4G/Ao5KvZFNwKQC/8ZBw4eGBqmIWJaOy55OpXdQbQ9grqR9gSCNHNfFx4ATJf1Dmt8JGA+sKKRgG2w6HRqS9AEqH+wLJQEMAaqHpp2f/n0cWN4xbK2kp6mMbb6Wyof/X6Z244B90/IOfw4cCvwiPcdwYM32/bMGJwfB4DYf+FcqQ4ZWf3v6J+C+iPjLFBaLamwr4JSI+FWxJVomROUD/shu1r+V/t1cNd0xP1RSC3AMcGRErJe0iMqXk67PMTcizt9uVWfCh4YGt+uAr0XE412W78F/nzye0s22dwPTlL5aSTqkkAotF78CGiQdCSBpmKQDtmL7PYCXUwi8HziiRpufAqdKek96jj0l7b2thefAQTCIRcRzETGzxqpLgG9I+iXd9wr/icoho2WSlqd5s3ckIt4GTgUulvQYsBT48FbsYgGVnsEK4JvAwzWe40ngy8BPJC0DFgKjt7X2HPgWE2ZmmXOPwMwscw4CM7PMOQjMzDLnIDAzy5yDwMwscw4Cs0TSeyV9V9LT6Z42D1X9knVb9uu7ulq/5iAwA9IP5+4A7o+I96V72kwExpZQi3/xb3XlIDCrOJrKjc9mdyyIiGciYpakIZL+RdIvJC2T9Dn4/9/0F0m6VdJKSfOqfok9IS1bApzcsc/u7uoqaYqk+ZLupfILWbO68TcPs4oDgCXdrPsMsC4iPiRpR+Dnkn6S1h2Stn0e+DlwlKRW4Goq4dIG3Fy1r467un5a0gjgUUn3pHUfBA6MiJe25x9m1hsHgVkNkq4EPgK8DTwDHCjp1LR6Dyp3vnwbeDQinkvbLAUagdeB/4qIp9LyG4GO++d3d1dXgIUOASuDg8CsYjlwSsdMRJybhj9sBX4DTIuIu6s3SHfErL5T5iZ6/3+q5l1dJR1OZWQ5s7rzOQKzinuBnSSdU7Vs5/Tv3cA5aXAVJP1RL4P0rAQaJe2T5k+vWue7ulq/4yAwA6Jy98VPAB+V9F+SHgXmAv8IXAM8CSyR9ARwFT1884+IN6kcCvpROllcPTiK7+pq/Y7vPmpmljn3CMzMMucgMDPLnIPAzCxzDgIzs8w5CMzMMucgMDPLnIPAzCxz/w+mtslvVAH36QAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        " sns.boxplot(data=aero_df, x='Gender', y='Fitness')\n",
        " plt.title(\"Fitness by Gender\")\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5g5GeDAVlKEg"
      },
      "source": [
        "#### *Amongst the gender, Males are of more Fitness freak than Females*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "nnQhTWnuYup6",
        "outputId": "7c0a2168-efb9-4185-a80d-f5ce495cbf18"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        " sns.boxplot(data=aero_df, x='MaritalStatus', y='Fitness')\n",
        " plt.title(\"Fitness by Marital Status\")\n",
        " plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1w0pNIOuk-N8"
      },
      "source": [
        "#### *this indicates that both Singles & Partnered seem to be equally Fitness freak*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "9pKoY14u-62O",
        "outputId": "26156584-7fd4-464c-a91e-33bdc0c367a2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Usage based on Marital Status\n",
        "sns.barplot(data=aero_df, x='Product', y='Usage', hue='MaritalStatus')\n",
        "plt.title(\"Product Usage by MaritalStatus\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "Z5uW0s-sVixj",
        "outputId": "adcc6569-815f-47a3-dea8-3cef5c29a487"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Fitness based on Marital Status\n",
        "sns.barplot(data=aero_df, x='Gender', y='Fitness', hue='Product')\n",
        "plt.title(\"Fitness by Gender\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mb8qhfHbkfSn"
      },
      "source": [
        "#### *the above graphs indicate that both from Fitness and Usage point of view the third product KP781 seems to be popular amongst both the genders irrespective of marital status*\n",
        "\n",
        "#### *the other two products are equally used*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "7YCoerXICdNE",
        "outputId": "1aad826c-32ab-4fbf-8d10-831ed2239140"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Count based on Marital Status & Age\n",
        "prod_cnt = aero_df['Product'].value_counts()\n",
        "sns.barplot(data=aero_df, x='MaritalStatus', y='Age', hue='Gender')\n",
        "plt.title(\"Age by Marital Status\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tal8SM2rChOh"
      },
      "source": [
        "#### *Male Singles seem to be younger compared Female Singles from those who purchased Threadmills. On the otherhand Partnered Males are of elder as compared to Partnered Females. This indicates Young females purchase threadmills more and male community buy them after they become partnered.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "8nmW2MdECe4i",
        "outputId": "6385b695-2dda-47aa-e7ad-56dcb6dd9322"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Count based on Marital Status & Fitness\n",
        "prod_cnt = aero_df['Product'].value_counts()\n",
        "sns.barplot(data=aero_df, x='MaritalStatus', y='Fitness', hue='Gender')\n",
        "plt.title(\"Fitness by Marital Status\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3fx1UArYewwk"
      },
      "source": [
        "#### *Clearly males are more of Fitness oriented. Irrespective of Marital Status females seem to be at average fitness of 3.0*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "JhvssWTGaEKe",
        "outputId": "ff6f3e0d-73fa-4a46-c930-de52c23f5165"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Education Vs Gender Vs Marital Status\n",
        "sns.barplot(data=aero_df, x='Gender', y='Education', hue='MaritalStatus')\n",
        "plt.title(\"Usage Vs Fitness (MaritalStatus)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qepp-K72abJr"
      },
      "source": [
        "#### *Amongst the buyers, hightly educated ones are 'Male-Partnered' and 'Female-Singles'.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "EOmLdde2aHv3",
        "outputId": "e6f5e056-7284-497d-e1c7-848227de1163"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Education Vs Gender Vs Marital Status\n",
        "sns.barplot(data=aero_df, x='Gender', y='Income', hue='MaritalStatus')\n",
        "plt.title(\"Usage Vs Fitness (MaritalStatus)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PvcXBL7Ga5QI"
      },
      "source": [
        "#### *Amongst the buyers, the top earning segment is Male.*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "OfkTnTdhazDv",
        "outputId": "ce3e77cb-a2ba-496f-9c24-795b5d8ec919"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Education Vs Gender Vs Marital Status\n",
        "sns.barplot(data=aero_df, x='Education', y='Income', hue='Gender')\n",
        "plt.title(\"Usage Vs Fitness (MaritalStatus)\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YyXfu2kubVFw"
      },
      "source": [
        "#### *this graph clearly indicates Male community topping both in Education & Income amongst the buyers.*\n",
        "\n",
        "#### *while Male community is present at all Education levels, Females don't seem to have the lower Education (12) and one of the higher education (20)*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ugbdyF5uZxC"
      },
      "source": [
        "# **5. Business Insights**\n",
        "## From the given dataset and based on the above analysis the following insights are drawn:\n",
        "\n",
        "*   As can be seen from the data, KP281 is sold more and KP781 is sold less amongst three products\n",
        "*   The age group who purchased high number of threadmills is between 23 & 26\n",
        "*   From the buyers list the Male community is large in number than Females\n",
        "*   Interestingly 'Partnered' category seem to have purchased the Threadmill more than the Singles\n",
        "*   About 61% females who are partnered have purchased Threadmills. Similarly amongst partnered males percentage is: 59. Amongst the Singles - Male Vs Female percentages are 41% and 39%. This indicates that purchase of Threadmill is higher among male singles compared to female singles; wherease it becomes reverse amongnst 'partnered' community\n",
        "*   Assuming the given Usage numbers are of number of times usage per day, majority of the customers use 3 times; Usage frequency of 3 or 4 times per day is more than others\n",
        "*   Interestingly the product KP781 is used 4 or more times a day, but that's the lowest sold among 3 threadmills. The other two products are sold in high numbers but mostly used 3 times a day\n",
        "*   There is a stable usage of 3 to 4 times a day by Females. But there are outliers amongst Male and that too Male Partnered segment\n",
        "*   The first product KP281 seems to be popular amongst the age groups 23 to 28\n",
        "*   The second product KP481 seems to be popular amongst the age groups 25 and 23\n",
        "*   The third product KP781 seems to be popular amongst the age group 25 and 24\n",
        "*   This indicates that generally threadmills are mostly bought by the age groups between 23 to 28\n",
        "*   The Product KP281 is equally purchased by both Male & Females. Amongst the Age group for Male it is 23 & 26; for Female it is 25 & 28\n",
        "*   More Males have purchased the Product KP481 than Females. Amongst the Age group for both Male & Female it is 25 and 23 who purchased in high numbers\n",
        "*   Interestingly the product KP781 seems to be highly popular with 75% of Males buying it. Only 25% of females have bought KP781.\n",
        "*   Correleation indicates high usage & fitness is amongst Male community\n",
        "*   Correleation indicates high usage & fitness is amongst those who are 'Single'; at the same time, interestingly the top Usage & high Fitness is recorded by 'Partenered'\n",
        "*   For the product KP281, the more miles are run predominantly by Males\n",
        "*   Miles run seems to be quite stable amongst Males irrespective of their Marital Status. But, there are good number of outliers in female partnered segment\n",
        "*   Amongst the purchaser's age group the outlier lies in female category\n",
        "*   The third product KP781 seem to have more frequent usage than others; and the second product KP481 is less used\n",
        "*   One of the result set indicates that the product KP781 has got more miles run by 'Partnered' and less miles by 'Singles'\n",
        "*   The first two products seem to be quite popular amongst age groups b/w 25 to 35; whereas the third product attracts higher age group\n",
        "*   Interestingly, though the third product KP781 is bought by less number of people, the miles run in it is higher than the other two products\n",
        "*   this graph shows Males run more miles in the threadmill than Females\n",
        "*   Amongst the gender, Males are of more Fitness freak than Females\n",
        "*   Both Singles & Partnered seem to be equally Fitness freak\n",
        "*   From Fitness and Usage point of view the third product KP781 seems to be popular amongst both the genders irrespective of marital status. The other two products are equally less used\n",
        "*   Male Singles seem to be younger compared Female Singles from those who purchased Threadmills. On the otherhand Partnered Males are more elder than Partnered Females. This indicates Young females purchase threadmills more and male community buy them after they become partnered\n",
        "*   Clearly males are more of Fitness oriented. Irrespective of Marital Status females seem to be at average fitness of 3.0\n",
        "*   Amongst the buyers, hightly educated ones are 'Male-Partnered' and 'Female-Singles'\n",
        "*   Amongst the buyers, the top earning segment is Male\n",
        "*   Male community tops both in Education & Income amongst the buyers\n",
        "*   while Male community is present in all Education levels, Females don't seem to present in the lower Education (12) and one of the higher education (20) segments.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1MBZ5Kl0u0o9"
      },
      "source": [
        "# **6. Recommendations & Conclusion**\n",
        "## The closer look at the existing customers and the detailed analysis of given parameters have helped in deriving the below recommendations:\n",
        "\n",
        "*   **Product Type**: The product 'KP781' is more frequently used and those who \n",
        "    have used have higher % of Fitness. But it has less sales compared to other \n",
        "    two products\n",
        "    > Special features of 'KP781' can be highlighted prominently in the \n",
        "      marketing to attract more sales\n",
        "\n",
        "    > Product 'KP281' sold in more numbers. So, it's product features can be\n",
        "      more highlighted and sale numbers can be pubished to induce more buyers\n",
        "\n",
        "    > Introduce 'Friends & Family' referral discount program to increase the sales of products like 'KP481' and 'KP781'\n",
        "    \n",
        "\n",
        "*   **Age-Group**: High # of current customers fall in age group b/w 23 to 28. \n",
        "    > Special promotions like Student/Sportsman/Youth Package can be introduced \n",
        "      to attract more customers in this segment\n",
        "\n",
        "    > For the other age group customers, seasonal discounts can be given\n",
        "\n",
        "*   **Education & Income Segment**: Male community tops both in Education & \n",
        "    Income amongst the buyers\n",
        "    > Special programs like 'Graduation day' discount, 'Salary day' discount \n",
        "      can be offered to reward this particular segment\n",
        "    \n",
        "    > Uniquely designed discounts can be offered Female community to attract \n",
        "      more purchases  \n",
        "    \n",
        "    > Especially the females at the Education category (12) and (20) can be \n",
        "      given speical discounts as this segment is an untapped space\n",
        "      \n",
        "*   **Gender & Marital Status**: Male customers are higher than female.\n",
        "    > Uniquely designed products for female community will help. For eg: \n",
        "      features like, more softer handles, video screen in the center area to \n",
        "      watch their favorite programs, cell phone holders with ease of access, \n",
        "      etc., will help  \n",
        "    \n",
        "    > There should be always an added discount % of price for female\n",
        "    \n",
        "    > Special 'wedding day' discount can be given for couples\n",
        "\n",
        "    > Offer best 'Male ally' discount to those who buy product for their spouse\n",
        "    \n",
        "    > Conduct Qly/Hly/Annual events like half/full marathon with the customers and reward them with T-Shirts, goodies, etc.,\n",
        "\n",
        "*   **High Usage & Fitness Category**: From the given data it is evident that \n",
        "    males are using the threadmills higher than females and their fitness also \n",
        "    high.\n",
        "    > Introduce systems like 'Elite/Premium' membership, Credit points and \n",
        "      things like that to recognize the high usage, more miles & fitness \n",
        "    \n",
        "    > Supplement with add-ons to attract new customers\n",
        "    \n",
        "    > Free 'fitness classes' can be offered \n",
        "\n",
        "    > Reward the customers with consistent usage and those who maintain the streak \n",
        "    \n"
      ]
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