WalmartCasestudy.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyP3DHQ/z36tCmHnBHUZD8Kt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divya-d14/Walmart-CaseStudy/blob/main/WalmartCasestudy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Business Case: Walmart - Confidence Interval and CLT**"
      ],
      "metadata": {
        "id": "DxCw68DgUAB0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![image.png](data:image/png;base64,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        "\n",
        "**About Walmart:**\n",
        "\n",
        "Walmart is an American multinational retail corporation that operates a chain of\n",
        "supercenters, discount departmental stores, and grocery stores in the United States.\n",
        "Walmart has more than 100 million customers worldwide.\n",
        "\n",
        "\n",
        "**Business Problem:**\n",
        "\n",
        "The Management team at Walmart Inc. wants to analyze **the customer purchase behavior (specifically, purchase amount) against the customer's gender and the various other factors** to help the business make better decisions. They want to understand if the **spending habits differ between male and female customers**."
      ],
      "metadata": {
        "id": "7MGmbTAVUdzx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#importing basic libraries\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n"
      ],
      "metadata": {
        "id": "pLXzMqQ0UYaG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#reading the data into a dataframe\n",
        "\n",
        "df_walmart = pd.read_csv('https://d2beiqkhq929f0.cloudfront.net/public_assets/assets/000/001/293/original/walmart_data.csv?1641285094')\n",
        "df_walmart.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "_iIUlRNHVrvj",
        "outputId": "566cd728-b559-4228-c9a1-8fa1aa269499"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   User_ID Product_ID Gender   Age  Occupation City_Category  \\\n",
              "0  1000001  P00069042      F  0-17          10             A   \n",
              "1  1000001  P00248942      F  0-17          10             A   \n",
              "2  1000001  P00087842      F  0-17          10             A   \n",
              "3  1000001  P00085442      F  0-17          10             A   \n",
              "4  1000002  P00285442      M   55+          16             C   \n",
              "\n",
              "  Stay_In_Current_City_Years  Marital_Status  Product_Category  Purchase  \n",
              "0                          2               0                 3      8370  \n",
              "1                          2               0                 1     15200  \n",
              "2                          2               0                12      1422  \n",
              "3                          2               0                12      1057  \n",
              "4                         4+               0                 8      7969  "
            ],
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              "      <th>User_ID</th>\n",
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              "      <th>0</th>\n",
              "      <td>1000001</td>\n",
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              "      <td>1000001</td>\n",
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              "      <th>3</th>\n",
              "      <td>1000001</td>\n",
              "      <td>P00085442</td>\n",
              "      <td>F</td>\n",
              "      <td>0-17</td>\n",
              "      <td>10</td>\n",
              "      <td>A</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>1057</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1000002</td>\n",
              "      <td>P00285442</td>\n",
              "      <td>M</td>\n",
              "      <td>55+</td>\n",
              "      <td>16</td>\n",
              "      <td>C</td>\n",
              "      <td>4+</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>7969</td>\n",
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              "        const element = document.querySelector('#df-bf2656ff-e0cf-4996-8d63-2ecb5c7f49be');\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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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_walmart"
            }
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Checking Structure and Characterestics of Data**"
      ],
      "metadata": {
        "id": "AHwWwlPgzsPk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TbeIo2JdV21q",
        "outputId": "e9464962-0381-48f2-fbd1-d5181e654340"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(550068, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6USGGX9RV5vj",
        "outputId": "b4a6a031-ad1c-423b-9420-8c9fd08713e0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User_ID                        int64\n",
              "Product_ID                    object\n",
              "Gender                        object\n",
              "Age                           object\n",
              "Occupation                     int64\n",
              "City_Category                 object\n",
              "Stay_In_Current_City_Years    object\n",
              "Marital_Status                 int64\n",
              "Product_Category               int64\n",
              "Purchase                       int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "53y73926WBwT",
        "outputId": "d8e556eb-5f29-4cee-88ca-a15026990d0b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User_ID                       0\n",
              "Product_ID                    0\n",
              "Gender                        0\n",
              "Age                           0\n",
              "Occupation                    0\n",
              "City_Category                 0\n",
              "Stay_In_Current_City_Years    0\n",
              "Marital_Status                0\n",
              "Product_Category              0\n",
              "Purchase                      0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No Null/missing values in data"
      ],
      "metadata": {
        "id": "iR_z-oMxRoSa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZgESdLz5lXK-",
        "outputId": "d6303ee9-2cf1-45a2-bbcb-ce7249c351b8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 550068 entries, 0 to 550067\n",
            "Data columns (total 10 columns):\n",
            " #   Column                      Non-Null Count   Dtype \n",
            "---  ------                      --------------   ----- \n",
            " 0   User_ID                     550068 non-null  int64 \n",
            " 1   Product_ID                  550068 non-null  object\n",
            " 2   Gender                      550068 non-null  object\n",
            " 3   Age                         550068 non-null  object\n",
            " 4   Occupation                  550068 non-null  int64 \n",
            " 5   City_Category               550068 non-null  object\n",
            " 6   Stay_In_Current_City_Years  550068 non-null  object\n",
            " 7   Marital_Status              550068 non-null  int64 \n",
            " 8   Product_Category            550068 non-null  int64 \n",
            " 9   Purchase                    550068 non-null  int64 \n",
            "dtypes: int64(5), object(5)\n",
            "memory usage: 42.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for i in df_walmart.columns[:-1]:\n",
        "  df_walmart[i]=df_walmart[i].astype('category')\n",
        "\n",
        "df_walmart.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xBt5FXo2leKZ",
        "outputId": "209c312b-b4ac-4b4d-f28a-c0f75753a23f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 550068 entries, 0 to 550067\n",
            "Data columns (total 10 columns):\n",
            " #   Column                      Non-Null Count   Dtype   \n",
            "---  ------                      --------------   -----   \n",
            " 0   User_ID                     550068 non-null  category\n",
            " 1   Product_ID                  550068 non-null  category\n",
            " 2   Gender                      550068 non-null  category\n",
            " 3   Age                         550068 non-null  category\n",
            " 4   Occupation                  550068 non-null  category\n",
            " 5   City_Category               550068 non-null  category\n",
            " 6   Stay_In_Current_City_Years  550068 non-null  category\n",
            " 7   Marital_Status              550068 non-null  category\n",
            " 8   Product_Category            550068 non-null  category\n",
            " 9   Purchase                    550068 non-null  int64   \n",
            "dtypes: category(9), int64(1)\n",
            "memory usage: 10.3 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "since all the columns in df are categorical variables except purchase, converted them into category for efficient memory usage and better analysis."
      ],
      "metadata": {
        "id": "2r1qwsQojXxV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.describe(include='category')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "Ii9ilepKi6DD",
        "outputId": "1cd6409b-c5c3-4206-ff88-81bdc52a03c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "        User_ID Product_ID  Gender     Age  Occupation City_Category  \\\n",
              "count    550068     550068  550068  550068      550068        550068   \n",
              "unique     5891       3631       2       7          21             3   \n",
              "top     1001680  P00265242       M   26-35           4             B   \n",
              "freq       1026       1880  414259  219587       72308        231173   \n",
              "\n",
              "       Stay_In_Current_City_Years  Marital_Status  Product_Category  \n",
              "count                      550068          550068            550068  \n",
              "unique                          5               2                20  \n",
              "top                             1               0                 5  \n",
              "freq                       193821          324731            150933  "
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
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              "</div>\n",
              "\n",
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              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_walmart\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"User_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 482567,\n        \"min\": 1026,\n        \"max\": 1001680,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5891,\n          1026,\n          550068\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3631,\n          \"1880\",\n          \"550068\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          \"414259\",\n          \"550068\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          7,\n          \"219587\",\n          \"550068\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 265177,\n        \"min\": 4,\n        \"max\": 550068,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          21,\n          72308,\n          550068\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City_Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          \"231173\",\n          \"550068\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Stay_In_Current_City_Years\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          5,\n          \"193821\",\n          \"550068\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marital_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 268766,\n        \"min\": 0,\n        \"max\": 550068,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2,\n          324731,\n          550068\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_Category\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 259805,\n        \"min\": 5,\n        \"max\": 550068,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          20,\n          150933,\n          550068\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 139
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lxL-5gpMS7mL",
        "outputId": "9e191551-e083-40c4-910b-872086851450"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Purchase\n",
              "count  550068.000000\n",
              "mean     9263.968713\n",
              "std      5023.065394\n",
              "min        12.000000\n",
              "25%      5823.000000\n",
              "50%      8047.000000\n",
              "75%     12054.000000\n",
              "max     23961.000000"
            ],
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              "      <th>Purchase</th>\n",
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              "      <th>count</th>\n",
              "      <td>550068.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>9263.968713</td>\n",
              "    </tr>\n",
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              "      <th>std</th>\n",
              "      <td>5023.065394</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>12.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5823.000000</td>\n",
              "    </tr>\n",
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              "      <th>50%</th>\n",
              "      <td>8047.000000</td>\n",
              "    </tr>\n",
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              "      <th>75%</th>\n",
              "      <td>12054.000000</td>\n",
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              "      <td>23961.000000</td>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      buttonEl.style.display =\n",
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              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-cb432226-acf4-4aa8-95e8-56bf0e3a0f15');\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",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df_walmart\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Purchase\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 191363.80903912007,\n        \"min\": 12.0,\n        \"max\": 550068.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9263.968712959126,\n          8047.0,\n          550068.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.boxplot(df_walmart['Purchase'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 432
        },
        "id": "mwg38mnRMObT",
        "outputId": "3df35eb1-4cfb-4d66-937f-918b09ca2a7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Purchase'>"
            ]
          },
          "metadata": {},
          "execution_count": 141
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Purchase has outliers"
      ],
      "metadata": {
        "id": "6q0GMt3KjSyk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**UNIVARIATE ANALYSIS**\n"
      ],
      "metadata": {
        "id": "0p_FjpOQUWch"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(data=df_walmart,x='Purchase',kde=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "KYcB1IsrzW-j",
        "outputId": "49f0fd4a-8f82-4d69-ba0f-7d5ee4943ee4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Purchase', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Most spent amount is between 5000 - 10000"
      ],
      "metadata": {
        "id": "CmAuL7xvRyRM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(20,20))\n",
        "\n",
        "plt.subplot(4,2,1)\n",
        "sns.pie=(df_walmart['Gender'].value_counts(normalize=True)*100).plot(kind='pie',autopct='%1.1f%%')\n",
        "sns.pie.set_title('Gender Distribution')\n",
        "\n",
        "plt.subplot(4,2,2)\n",
        "sns.pie=(df_walmart['Marital_Status'].value_counts(normalize=True)*100).plot(kind='pie',autopct='%1.1f%%')\n",
        "sns.pie.set_title('MaritalStatus Distribution')\n",
        "\n",
        "plt.subplot(4,2,3)\n",
        "sns.countplot(data=df_walmart,x='Age')\n",
        "plt.title('Age Distribution')\n",
        "\n",
        "plt.subplot(4,2,4)\n",
        "sns.countplot(data=df_walmart,x='Occupation')\n",
        "plt.title('Occupation Distribution')\n",
        "\n",
        "plt.subplot(4,2,5)\n",
        "sns.countplot(data=df_walmart,x='Product_Category')\n",
        "plt.title('Product_Category Distribution')\n",
        "\n",
        "plt.subplot(4,2,6)\n",
        "sns.countplot(data=df_walmart,x='City_Category')\n",
        "plt.title('City_Category Distribution')\n",
        "\n",
        "plt.subplot(4,2,7)\n",
        "sns.countplot(data=df_walmart,x='Stay_In_Current_City_Years')\n",
        "plt.title('Stay_In_Current_City_Years Distribution')\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mbf3bw4LVGAu",
        "outputId": "f34f46a9-9ca6-4381-b15f-8fe451cea9e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x2000 with 7 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.groupby('Product_Category')['Purchase'].mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gtMwaIPwVx2T",
        "outputId": "ed2dfe35-8665-4d1c-9d4c-625e97c305dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product_Category\n",
              "10    19675.570927\n",
              "7     16365.689600\n",
              "6     15838.478550\n",
              "9     15537.375610\n",
              "15    14780.451828\n",
              "16    14766.037037\n",
              "1     13606.218596\n",
              "14    13141.625739\n",
              "2     11251.935384\n",
              "17    10170.759516\n",
              "3     10096.705734\n",
              "8      7498.958078\n",
              "5      6240.088178\n",
              "11     4685.268456\n",
              "18     2972.864320\n",
              "4      2329.659491\n",
              "12     1350.859894\n",
              "13      722.400613\n",
              "20      370.481176\n",
              "19       37.041797\n",
              "Name: Purchase, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.groupby('Product_ID')['Purchase'].mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVifXQTaV_Jo",
        "outputId": "ffbeb58e-5854-40e0-b712-bd953f24a27d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product_ID\n",
              "P00086242    21256.505495\n",
              "P00085342    20980.268116\n",
              "P00200642    20468.773234\n",
              "P00116142    20463.791277\n",
              "P00119342    20448.756494\n",
              "                 ...     \n",
              "P00372445      374.930705\n",
              "P00375436      374.266585\n",
              "P00371644      362.911012\n",
              "P00370853       37.393643\n",
              "P00370293       36.675159\n",
              "Name: Purchase, Length: 3631, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.groupby('Occupation')['Purchase'].mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1Ez3Bh8NWDiS",
        "outputId": "3dae59db-a4c1-4a1e-e39e-f035c636a282"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Occupation\n",
              "17    9821.478236\n",
              "12    9796.640239\n",
              "15    9778.891163\n",
              "8     9532.592497\n",
              "14    9500.702772\n",
              "7     9425.728223\n",
              "16    9394.464349\n",
              "5     9333.149298\n",
              "13    9306.351061\n",
              "6     9256.535691\n",
              "4     9213.980251\n",
              "11    9213.845848\n",
              "3     9178.593088\n",
              "18    9169.655844\n",
              "0     9124.428588\n",
              "10    8959.355375\n",
              "1     8953.193270\n",
              "2     8952.481683\n",
              "20    8836.494905\n",
              "19    8710.627231\n",
              "9     8637.743761\n",
              "Name: Purchase, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.groupby('City_Category')['Purchase'].mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CXNmWP1aVWAE",
        "outputId": "d0ca55a9-ad5f-4de2-ed0a-e1164a3f5c74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "City_Category\n",
              "C    9719.920993\n",
              "B    9151.300563\n",
              "A    8911.939216\n",
              "Name: Purchase, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.groupby('Stay_In_Current_City_Years')['Purchase'].mean().sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nE_65zq8Yvyy",
        "outputId": "421bf191-f2a3-4af0-d1d7-d824134101c7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Stay_In_Current_City_Years\n",
              "2     9320.429810\n",
              "3     9286.904119\n",
              "4+    9275.598872\n",
              "1     9250.145923\n",
              "0     9180.075123\n",
              "Name: Purchase, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Bivariate analysis\n",
        "\n",
        "plt.figure(figsize=(15, 10))\n",
        "sns.set_style('ticks')\n",
        "sns.set_palette('Set2')\n",
        "\n",
        "for i, column in enumerate(['Gender', 'Age', 'City_Category', 'Stay_In_Current_City_Years', 'Marital_Status']):\n",
        "    plt.subplot(3, 3, i + 1)\n",
        "    sns.boxplot(data=df_walmart, x=column, y='Purchase',medianprops={'color': 'magenta'})\n",
        "    plt.title(column +' vs Purchase')\n",
        "    plt.xlabel(column)\n",
        "    plt.ylabel('Purchase')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "ZgGopLbNgKwa",
        "outputId": "ad586a26-ff78-4a48-dddf-08aa2cf9ed77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1000 with 5 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "- Most Users and Most Purchases are from Males(as of Gender)\n",
        "- Most people are from 26-35 age group followed by 36-45 and 18-25\n",
        "- More users bought P00265242, while P00086242 generated more amount.\n",
        "  Similarly 1,5,8 are most sold products and 9,17 are least but most amount generated are from Categories 10,7,6,9.\n",
        "- 0,4 occupation users and 1 year staying users are more\n",
        "- B city_category people are more but buying level of C city_Category is high"
      ],
      "metadata": {
        "id": "HUiZejL-Y-aI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#What Products are different age groups buying\n",
        "df_walmart.groupby('Age')['Product_Category'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tI3AiejvnMA-",
        "outputId": "91a733f1-d08f-4acb-d4b0-e11a38f5a347"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age\n",
              "0-17     20\n",
              "18-25    20\n",
              "26-35    20\n",
              "36-45    20\n",
              "46-50    20\n",
              "51-55    20\n",
              "55+      20\n",
              "Name: Product_Category, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.crosstab(df_walmart['Product_Category'],df_walmart['Age'],normalize=True,margins=True)*100"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 739
        },
        "id": "C6lyjdXsn1r9",
        "outputId": "211633e6-7277-4423-aebd-649af92f87ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Age                   0-17      18-25      26-35      36-45     46-50  \\\n",
              "Product_Category                                                        \n",
              "1                 0.651738   4.901576  10.589418   5.026288  1.904128   \n",
              "2                 0.146346   0.804991   1.623072   0.892981  0.382680   \n",
              "3                 0.218155   0.856258   1.392919   0.700641  0.250151   \n",
              "4                 0.137801   0.447763   0.762088   0.427947  0.179978   \n",
              "5                 0.787175   5.185177  11.175527   5.340612  2.176276   \n",
              "6                 0.072536   0.681552   1.542537   0.708821  0.294873   \n",
              "7                 0.009635   0.087444   0.300145   0.147073  0.059447   \n",
              "8                 0.410495   3.256143   8.045551   4.235113  1.937215   \n",
              "9                 0.002909   0.011453   0.027997   0.019452  0.005999   \n",
              "10                0.020179   0.109623   0.324869   0.224518  0.094534   \n",
              "11                0.134529   0.835715   1.795051   0.900434  0.382498   \n",
              "12                0.022724   0.079808   0.199248   0.180705  0.094534   \n",
              "13                0.020361   0.137438   0.381044   0.227245  0.100169   \n",
              "14                0.007090   0.041813   0.102533   0.056720  0.027088   \n",
              "15                0.029087   0.186159   0.431219   0.253605  0.109441   \n",
              "16                0.041631   0.290510   0.748635   0.355411  0.159798   \n",
              "17                0.001091   0.007454   0.023088   0.024542  0.017271   \n",
              "18                0.004908   0.061629   0.189431   0.127621  0.063810   \n",
              "19                0.010726   0.049994   0.102351   0.058175  0.027088   \n",
              "20                0.016362   0.085262   0.163253   0.091989  0.041268   \n",
              "All               2.745479  18.117760  39.919974  19.999891  8.308246   \n",
              "\n",
              "Age                  51-55       55+         All  \n",
              "Product_Category                                  \n",
              "1                 1.645069  0.801901   25.520118  \n",
              "2                 0.323778  0.164525    4.338373  \n",
              "3                 0.167979  0.088535    3.674637  \n",
              "4                 0.123257  0.057811    2.136645  \n",
              "5                 1.798505  0.975698   27.438971  \n",
              "6                 0.263604  0.156708    3.720631  \n",
              "7                 0.048358  0.024361    0.676462  \n",
              "8                 1.697972  1.128588   20.711076  \n",
              "9                 0.005272  0.001454    0.074536  \n",
              "10                0.094352  0.063628    0.931703  \n",
              "11                0.265058  0.101987    4.415272  \n",
              "12                0.078718  0.061811    0.717548  \n",
              "13                0.087807  0.054721    1.008784  \n",
              "14                0.027997  0.013635    0.276875  \n",
              "15                0.092352  0.041631    1.143495  \n",
              "16                0.122167  0.068537    1.786688  \n",
              "17                0.019452  0.012180    0.105078  \n",
              "18                0.076900  0.043813    0.568112  \n",
              "19                0.024361  0.018725    0.291419  \n",
              "20                0.036359  0.029087    0.463579  \n",
              "All               6.999316  3.909335  100.000000  "
            ],
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Age</th>\n",
              "      <th>0-17</th>\n",
              "      <th>18-25</th>\n",
              "      <th>26-35</th>\n",
              "      <th>36-45</th>\n",
              "      <th>46-50</th>\n",
              "      <th>51-55</th>\n",
              "      <th>55+</th>\n",
              "      <th>All</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Product_Category</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.651738</td>\n",
              "      <td>4.901576</td>\n",
              "      <td>10.589418</td>\n",
              "      <td>5.026288</td>\n",
              "      <td>1.904128</td>\n",
              "      <td>1.645069</td>\n",
              "      <td>0.801901</td>\n",
              "      <td>25.520118</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.146346</td>\n",
              "      <td>0.804991</td>\n",
              "      <td>1.623072</td>\n",
              "      <td>0.892981</td>\n",
              "      <td>0.382680</td>\n",
              "      <td>0.323778</td>\n",
              "      <td>0.164525</td>\n",
              "      <td>4.338373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.218155</td>\n",
              "      <td>0.856258</td>\n",
              "      <td>1.392919</td>\n",
              "      <td>0.700641</td>\n",
              "      <td>0.250151</td>\n",
              "      <td>0.167979</td>\n",
              "      <td>0.088535</td>\n",
              "      <td>3.674637</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.137801</td>\n",
              "      <td>0.447763</td>\n",
              "      <td>0.762088</td>\n",
              "      <td>0.427947</td>\n",
              "      <td>0.179978</td>\n",
              "      <td>0.123257</td>\n",
              "      <td>0.057811</td>\n",
              "      <td>2.136645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.787175</td>\n",
              "      <td>5.185177</td>\n",
              "      <td>11.175527</td>\n",
              "      <td>5.340612</td>\n",
              "      <td>2.176276</td>\n",
              "      <td>1.798505</td>\n",
              "      <td>0.975698</td>\n",
              "      <td>27.438971</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.072536</td>\n",
              "      <td>0.681552</td>\n",
              "      <td>1.542537</td>\n",
              "      <td>0.708821</td>\n",
              "      <td>0.294873</td>\n",
              "      <td>0.263604</td>\n",
              "      <td>0.156708</td>\n",
              "      <td>3.720631</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>0.009635</td>\n",
              "      <td>0.087444</td>\n",
              "      <td>0.300145</td>\n",
              "      <td>0.147073</td>\n",
              "      <td>0.059447</td>\n",
              "      <td>0.048358</td>\n",
              "      <td>0.024361</td>\n",
              "      <td>0.676462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.410495</td>\n",
              "      <td>3.256143</td>\n",
              "      <td>8.045551</td>\n",
              "      <td>4.235113</td>\n",
              "      <td>1.937215</td>\n",
              "      <td>1.697972</td>\n",
              "      <td>1.128588</td>\n",
              "      <td>20.711076</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>0.002909</td>\n",
              "      <td>0.011453</td>\n",
              "      <td>0.027997</td>\n",
              "      <td>0.019452</td>\n",
              "      <td>0.005999</td>\n",
              "      <td>0.005272</td>\n",
              "      <td>0.001454</td>\n",
              "      <td>0.074536</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>0.020179</td>\n",
              "      <td>0.109623</td>\n",
              "      <td>0.324869</td>\n",
              "      <td>0.224518</td>\n",
              "      <td>0.094534</td>\n",
              "      <td>0.094352</td>\n",
              "      <td>0.063628</td>\n",
              "      <td>0.931703</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>0.134529</td>\n",
              "      <td>0.835715</td>\n",
              "      <td>1.795051</td>\n",
              "      <td>0.900434</td>\n",
              "      <td>0.382498</td>\n",
              "      <td>0.265058</td>\n",
              "      <td>0.101987</td>\n",
              "      <td>4.415272</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>0.022724</td>\n",
              "      <td>0.079808</td>\n",
              "      <td>0.199248</td>\n",
              "      <td>0.180705</td>\n",
              "      <td>0.094534</td>\n",
              "      <td>0.078718</td>\n",
              "      <td>0.061811</td>\n",
              "      <td>0.717548</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>0.020361</td>\n",
              "      <td>0.137438</td>\n",
              "      <td>0.381044</td>\n",
              "      <td>0.227245</td>\n",
              "      <td>0.100169</td>\n",
              "      <td>0.087807</td>\n",
              "      <td>0.054721</td>\n",
              "      <td>1.008784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>0.007090</td>\n",
              "      <td>0.041813</td>\n",
              "      <td>0.102533</td>\n",
              "      <td>0.056720</td>\n",
              "      <td>0.027088</td>\n",
              "      <td>0.027997</td>\n",
              "      <td>0.013635</td>\n",
              "      <td>0.276875</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>0.029087</td>\n",
              "      <td>0.186159</td>\n",
              "      <td>0.431219</td>\n",
              "      <td>0.253605</td>\n",
              "      <td>0.109441</td>\n",
              "      <td>0.092352</td>\n",
              "      <td>0.041631</td>\n",
              "      <td>1.143495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>0.041631</td>\n",
              "      <td>0.290510</td>\n",
              "      <td>0.748635</td>\n",
              "      <td>0.355411</td>\n",
              "      <td>0.159798</td>\n",
              "      <td>0.122167</td>\n",
              "      <td>0.068537</td>\n",
              "      <td>1.786688</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>0.001091</td>\n",
              "      <td>0.007454</td>\n",
              "      <td>0.023088</td>\n",
              "      <td>0.024542</td>\n",
              "      <td>0.017271</td>\n",
              "      <td>0.019452</td>\n",
              "      <td>0.012180</td>\n",
              "      <td>0.105078</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>0.004908</td>\n",
              "      <td>0.061629</td>\n",
              "      <td>0.189431</td>\n",
              "      <td>0.127621</td>\n",
              "      <td>0.063810</td>\n",
              "      <td>0.076900</td>\n",
              "      <td>0.043813</td>\n",
              "      <td>0.568112</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>0.010726</td>\n",
              "      <td>0.049994</td>\n",
              "      <td>0.102351</td>\n",
              "      <td>0.058175</td>\n",
              "      <td>0.027088</td>\n",
              "      <td>0.024361</td>\n",
              "      <td>0.018725</td>\n",
              "      <td>0.291419</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>0.016362</td>\n",
              "      <td>0.085262</td>\n",
              "      <td>0.163253</td>\n",
              "      <td>0.091989</td>\n",
              "      <td>0.041268</td>\n",
              "      <td>0.036359</td>\n",
              "      <td>0.029087</td>\n",
              "      <td>0.463579</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>All</th>\n",
              "      <td>2.745479</td>\n",
              "      <td>18.117760</td>\n",
              "      <td>39.919974</td>\n",
              "      <td>19.999891</td>\n",
              "      <td>8.308246</td>\n",
              "      <td>6.999316</td>\n",
              "      <td>3.909335</td>\n",
              "      <td>100.000000</td>\n",
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              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-23da97b4-6560-4460-a503-e7d13f07c0c1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 21,\n  \"fields\": [\n    {\n      \"column\": \"Product_Category\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1,\n          18,\n          16\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"0-17\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6095349528403348,\n        \"min\": 0.0010907742315495537,\n        \"max\": 2.7454787408102272,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.6517376033508584,\n          0.004908484041972992,\n          0.041631216504141304\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"18-25\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.063046048709885,\n        \"min\": 0.007453623915588618,\n        \"max\": 18.11775998603809,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          4.901575805173178,\n          0.06162874408254979,\n          0.2905095370026979\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"26-35\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8.954854665802637,\n        \"min\": 0.023088054567798888,\n        \"max\": 39.91997353054531,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          10.589418035588327,\n          0.18943112487910585,\n          0.7486347142535105\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"36-45\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.464817110235507,\n        \"min\": 0.019452140462633712,\n        \"max\": 19.999890922576846,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          5.026287658980344,\n          0.1276205850912978,\n          0.3554106037798963\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"46-50\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8512811289638649,\n        \"min\": 0.005999258273522546,\n        \"max\": 8.308245526007694,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          1.9041282168750044,\n          0.29487263392889607,\n          0.1001694335973007\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"51-55\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.5625060978422374,\n        \"min\": 0.0052720754524895105,\n        \"max\": 6.99931644814823,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          1.6450693368819855,\n          0.07689958332424356,\n          0.12216671393355003\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"55+\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8756471702719479,\n        \"min\": 0.0014543656420660717,\n        \"max\": 3.909334845873601,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          0.8019008558941804,\n          0.04381276496724042,\n          0.06853698088236364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22.368126387900436,\n        \"min\": 0.07453623915588618,\n        \"max\": 100.0,\n        \"num_unique_values\": 21,\n        \"samples\": [\n          25.52011751274388,\n          0.5681115789320593,\n          1.7866881912781691\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        " - There is no such product that has 0 sales in any age group. All the products have been using by all age groups.\n",
        "\n",
        " - Clearly 1,5,8 has more market (about 72%) and 26-35 age buying levels is more.\n"
      ],
      "metadata": {
        "id": "F8V1I5P_al8-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Is there a relationship between age, marital status, and the amount spent?\n",
        "sns.boxplot(data=df_walmart,x='Age',y='Purchase',hue='Marital_Status')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "51w9VQYtnMF4",
        "outputId": "f894c8e9-e1e0-40fd-eb4f-9411f1b4f7c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age', ylabel='Purchase'>"
            ]
          },
          "metadata": {},
          "execution_count": 152
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There is no impact between age,gender and amount spent"
      ],
      "metadata": {
        "id": "5A_wO-3KquRe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Are there preferred product categories for different genders?"
      ],
      "metadata": {
        "id": "JhNz73MogLS2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(pd.crosstab(df_walmart['Product_Category'],df_walmart['Gender'],normalize=True,margins=True)*100,annot=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        },
        "id": "cSRafxCkpaHy",
        "outputId": "966814e6-7bcb-403b-d941-a68a9fc8b3ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Gender', ylabel='Product_Category'>"
            ]
          },
          "metadata": {},
          "execution_count": 154
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Around 66% of the males and 36% of females bought 1,5,8 product Category."
      ],
      "metadata": {
        "id": "k1xv8FqGcLZH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#How does gender affect the amount spent?\n",
        "\n",
        "df_walmart.groupby('Gender')['User_ID'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7KjXrg0dBGqc",
        "outputId": "b87bd1b9-1b69-4ed4-b983-45fe0da0ce56"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "F    1666\n",
              "M    4225\n",
              "Name: User_ID, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.groupby('Gender')['Purchase'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tcEhFWKB0CVb",
        "outputId": "dd9d7329-5e80-4faf-bf01-826725ce2692"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Gender\n",
              "F    8734.565765\n",
              "M    9437.526040\n",
              "Name: Purchase, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 156
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "female_data = df_walmart[df_walmart['Gender']=='F']\n",
        "male_data = df_walmart[df_walmart['Gender']=='M']\n",
        "\n",
        "female_avg_purchase=female_data['Purchase'].mean().round(2)\n",
        "male_avg_purchase=male_data['Purchase'].mean().round(2)\n",
        "\n",
        "print(\"Female Average Purchase: \",female_avg_purchase)\n",
        "print(\"Male Average Purchase: \",male_avg_purchase)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-JtVtYzvmpg1",
        "outputId": "c0b1ed90-3dee-4dd6-ed1b-afbd6e162f8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Female Average Purchase:  8734.57\n",
            "Male Average Purchase:  9437.53\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The average amount spent by male and female doesn't look same.\n",
        "But Does it significantly different even for actual Population? Let's check this using CLT"
      ],
      "metadata": {
        "id": "jJPMA8dOePtM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "According to Central Limit Theorem, irrespective of the shape of original population, the mean should be normal distribution with all the means of samples.\n",
        "\n"
      ],
      "metadata": {
        "id": "mynnQiy0eeR4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the mean distribution if it is following Gaussean/Normal\n",
        "\n",
        "n = 1500 #Sample_size\n",
        "male_means = []\n",
        "female_means = []\n",
        "for i in range(n):\n",
        "  female_mean = female_data.sample(n,replace=True)['Purchase'].mean()\n",
        "  male_mean = male_data.sample(n,replace=True)['Purchase'].mean()\n",
        "  male_means.append(male_mean)\n",
        "  female_means.append(female_mean)\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "plt.subplot(1,2,1)\n",
        "sns.histplot(male_means,kde=True)\n",
        "plt.title('SampleMaleMeanDistribution')\n",
        "\n",
        "plt.subplot(1,2,2)\n",
        "sns.histplot(female_means,kde=True)\n",
        "plt.title('SampleFemaleMeanDistribution')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "UPc2KQPFmlBy",
        "outputId": "500120de-f843-4a55-8abe-d23758201e38"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "\n",
        "plt.figure(figsize=(10,5))\n",
        "sm.qqplot(np.array(male_means),line='s')\n",
        "plt.title('SampleFemaleMeanDistribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "CX0xtnAP1wSU",
        "outputId": "35aa24b4-45e0-4519-e868-6f01181e13e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Since the means are actually in normal distribution finding out the Confidence intervals of mean of genders\n",
        "# using samples of 300,3000,30000 with 90,95,99 percent confidence levels.\n",
        "\n",
        "from scipy.stats import norm\n",
        "male_means = []\n",
        "female_means = []\n",
        "\n",
        "print(\"The average amount spent by female is\",female_avg_purchase)\n",
        "print(\"The average amount spent by male is\",male_avg_purchase)\n",
        "\n",
        "for n in [300,3000,30000]:\n",
        "  for i in range(n):\n",
        "    female_mean = female_data.sample(n,replace=True)['Purchase'].mean()\n",
        "    male_mean = male_data.sample(n,replace=True)['Purchase'].mean()\n",
        "    male_means.append(male_mean)\n",
        "    female_means.append(female_mean)\n",
        "\n",
        "  sample_male_mean = np.mean(male_means).round(2)\n",
        "  sample_female_mean = np.mean(female_means).round(2)\n",
        "  sample_male_stddev = pd.Series(male_means).std().round(2)\n",
        "  sample_female_stddev = pd.Series(female_means).std().round(2)\n",
        "\n",
        "  print(\"\\n\\nFOR SAMPLE : \",n)\n",
        "  print(\"sample male stddev: \",sample_male_stddev)\n",
        "  print(\"sample female stddev: \",sample_female_stddev)\n",
        "  for p in [0.90,0.95,0.99]:\n",
        "    male_avg_from = sample_male_mean - norm.ppf(p)*(sample_male_stddev)\n",
        "    male_avg_to = sample_male_mean + norm.ppf(p)*(sample_male_stddev)\n",
        "\n",
        "    female_avg_from = sample_female_mean - norm.ppf(p)*(sample_female_stddev)\n",
        "    female_avg_to = sample_female_mean + norm.ppf(p)*(sample_female_stddev)\n",
        "\n",
        "    print(\"**The Average Amount Spent Intervals Of Male and Female with confidence of\",round(p*100),\"% is**\")\n",
        "    print(\"\\tMale Average Amount Spent: \",male_avg_from,\" to \",male_avg_to)\n",
        "    print(\"\\tFemale Average Amount Spent: \",female_avg_from,\" to \",female_avg_to)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eBTMhjal76fZ",
        "outputId": "f0a98e32-e776-4f7a-b669-5bb61ec7339c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The average amount spent by female is 8734.57\n",
            "The average amount spent by male is 9437.53\n",
            "\n",
            "\n",
            "FOR SAMPLE :  300\n",
            "sample male stddev:  296.92\n",
            "sample female stddev:  276.17\n",
            "**The Average Amount Spent Intervals Of Male and Female with confidence of 90 % is**\n",
            "\tMale Average Amount Spent:  9053.671709158498  to  9814.708290841503\n",
            "\tFemale Average Amount Spent:  8390.163904143548  to  9098.016095856452\n",
            "**The Average Amount Spent Intervals Of Male and Female with confidence of 95 % is**\n",
            "\tMale Average Amount Spent:  8945.800061085569  to  9922.579938914432\n",
            "\tFemale Average Amount Spent:  8289.830773844813  to  9198.349226155187\n",
            "**The Average Amount Spent Intervals Of Male and Female with confidence of 99 % is**\n",
            "\tMale Average Amount Spent:  8743.450789239794  to  10124.929210760207\n",
            "\tFemale Average Amount Spent:  8101.622507626141  to  9386.557492373859\n",
            "\n",
            "\n",
            "FOR SAMPLE :  3000\n",
            "sample male stddev:  126.49\n",
            "sample female stddev:  117.24\n",
            "**The Average Amount Spent Intervals Of Male and Female with confidence of 90 % is**\n",
            "\tMale Average Amount Spent:  9274.716542474263  to  9598.923457525736\n",
            "\tFemale Average Amount Spent:  8587.44089445555  to  8887.93910554445\n",
            "**The Average Amount Spent Intervals Of Male and Female with confidence of 95 % is**\n",
            "\tMale Average Amount Spent:  9228.762464726908  to  9644.877535273092\n",
            "\tFemale Average Amount Spent:  8544.84736077621  to  8930.532639223791\n",
            "**The Average Amount Spent Intervals Of Male and Female with confidence of 99 % is**\n",
            "\tMale Average Amount Spent:  9142.560257412573  to  9731.079742587426\n",
            "\tFemale Average Amount Spent:  8464.948975247453  to  9010.431024752548\n",
            "\n",
            "\n",
            "FOR SAMPLE :  30000\n",
            "sample male stddev:  48.59\n",
            "sample female stddev:  45.16\n",
            "**The Average Amount Spent Intervals Of Male and Female with confidence of 90 % is**\n",
            "\tMale Average Amount Spent:  9375.17940943019  to  9499.720590569812\n",
            "\tFemale Average Amount Spent:  8676.995131300007  to  8792.744868699994\n",
            "**The Average Amount Spent Intervals Of Male and Female with confidence of 95 % is**\n",
            "\tMale Average Amount Spent:  9357.526562266428  to  9517.373437733573\n",
            "\tFemale Average Amount Spent:  8660.588410206872  to  8809.15158979313\n",
            "**The Average Amount Spent Intervals Of Male and Female with confidence of 99 % is**\n",
            "\tMale Average Amount Spent:  9324.412756800357  to  9550.487243199645\n",
            "\tFemale Average Amount Spent:  8629.812130008317  to  8839.927869991685\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Clearly, The average amount spent by male is higher than those of female**\n",
        "\n",
        "we can also see the confidence intervals of different samples do getting overlap but as the sample size increases,\n",
        "\n",
        "-  the width of the confidence interval decreases ( providing more accurate range close to actual mean)\n",
        "\n",
        "-  the lesser the spread of distribution making it more narrower bellcurve"
      ],
      "metadata": {
        "id": "g3BbiTqzh4zz"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#How does MaritalStatus affect the amount spent?\n",
        "\n",
        "single_ppl= df_walmart[df_walmart['Marital_Status']==0]\n",
        "married_ppl = df_walmart[df_walmart['Marital_Status']==1]\n",
        "\n",
        "single_ppl_avg_purchase=single_ppl['Purchase'].mean().round(2)\n",
        "married_ppl_avg_purchase=married_ppl['Purchase'].mean().round(2)\n",
        "\n"
      ],
      "metadata": {
        "id": "SH7_aSd3Ksnn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_walmart.groupby('Marital_Status')['User_ID'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-eYxVKjvhaaL",
        "outputId": "b34062ef-9c50-44f7-c7a6-5edce8999707"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Marital_Status\n",
              "0    3417\n",
              "1    2474\n",
              "Name: User_ID, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the mean distribution if it is following Gaussean/Normal\n",
        "\n",
        "n = 1500 #Sample_size\n",
        "single_ppl_means = []\n",
        "married_ppl_means = []\n",
        "for i in range(n):\n",
        "  single_ppl_mean = single_ppl.sample(n,replace=True)['Purchase'].mean()\n",
        "  married_ppl_mean = married_ppl.sample(n,replace=True)['Purchase'].mean()\n",
        "  single_ppl_means.append(single_ppl_mean)\n",
        "  married_ppl_means.append(married_ppl_mean)\n",
        "\n",
        "fig, axis = plt.subplots(nrows=1, ncols=2, figsize=(10, 5))\n",
        "sns.histplot(single_ppl_means,kde=True,ax=axis[0])\n",
        "sns.histplot(married_ppl_means,kde=True,ax=axis[1])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "gwh9RUv-326f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "outputId": "63ae9e8d-b047-49b5-a61b-fadea0358288"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The clear bellcurve shows means are actually in normal distribution finding out the Confidence intervals of mean based on maritalstatus\n",
        "# using samples of 300,3000,30000 with 90,95,99 percent confidence levels.\n",
        "\n",
        "from scipy.stats import norm\n",
        "single_ppl_means = []\n",
        "married_ppl_means = []\n",
        "\n",
        "print(\"Single People Average Purchase: \",single_ppl_avg_purchase)\n",
        "print(\"Married People Average Purchase: \",married_ppl_avg_purchase)\n",
        "\n",
        "for n in [300,3000,30000]:\n",
        "  for i in range(n):\n",
        "    single_ppl_mean = single_ppl.sample(n,replace=True)['Purchase'].mean()\n",
        "    married_ppl_mean = married_ppl.sample(n,replace=True)['Purchase'].mean()\n",
        "    single_ppl_means.append(single_ppl_mean)\n",
        "    married_ppl_means.append(married_ppl_mean)\n",
        "\n",
        "  sample_single_ppl_mean = np.mean(single_ppl_means).round(2)\n",
        "  sample_married_ppl_mean = np.mean(married_ppl_means).round(2)\n",
        "  sample_single_ppl_stddev = pd.Series(single_ppl_means).std().round(2)\n",
        "  sample_married_ppl_stddev = pd.Series(married_ppl_means).std().round(2)\n",
        "\n",
        "  print(\"\\n\\nFOR SAMPLE : \",n)\n",
        "  for p in [0.90,0.95,0.99]:\n",
        "    single_ppl_avg_from = sample_single_ppl_mean - norm.ppf(p)*(sample_single_ppl_stddev)\n",
        "    single_ppl_avg_to = sample_single_ppl_mean + norm.ppf(p)*(sample_single_ppl_stddev)\n",
        "\n",
        "    married_ppl_avg_from = sample_married_ppl_mean - norm.ppf(p)*(sample_married_ppl_stddev)\n",
        "    married_ppl_avg_to = sample_married_ppl_mean + norm.ppf(p)*(sample_married_ppl_stddev)\n",
        "\n",
        "    print(\"**The Average Amount Spent Intervals Of Single and Married People with confidence of\",round(p*100),\"% is**\")\n",
        "    print(\"\\tSingle People Average Amount Spent: \",single_ppl_avg_from,\" to \",single_ppl_avg_to)\n",
        "    print(\"\\tMarried People Average Amount Spent: \",married_ppl_avg_from,\" to \",married_ppl_avg_to)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "es2FjcV2Kq-V",
        "outputId": "a7798863-1fa9-49db-ad34-cf1e472909e5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Single People Average Purchase:  9265.91\n",
            "Married People Average Purchase:  9261.17\n",
            "\n",
            "\n",
            "FOR SAMPLE :  300\n",
            "**The Average Amount Spent Intervals Of Single and Married People with confidence of 90 % is**\n",
            "\tSingle People Average Amount Spent:  8847.401355241915  to  9656.598644758085\n",
            "\tMarried People Average Amount Spent:  8890.634507686751  to  9627.88549231325\n",
            "**The Average Amount Spent Intervals Of Single and Married People with confidence of 95 % is**\n",
            "\tSingle People Average Amount Spent:  8732.70326143515  to  9771.29673856485\n",
            "\tMarried People Average Amount Spent:  8786.13430274368  to  9732.38569725632\n",
            "**The Average Amount Spent Intervals Of Single and Married People with confidence of 99 % is**\n",
            "\tSingle People Average Amount Spent:  8517.548712686566  to  9986.451287313434\n",
            "\tMarried People Average Amount Spent:  8590.109297510893  to  9928.410702489107\n",
            "\n",
            "\n",
            "FOR SAMPLE :  3000\n",
            "**The Average Amount Spent Intervals Of Single and Married People with confidence of 90 % is**\n",
            "\tSingle People Average Amount Spent:  9099.02577046647  to  9429.79422953353\n",
            "\tMarried People Average Amount Spent:  9101.595371250149  to  9417.16462874985\n",
            "**The Average Amount Spent Intervals Of Single and Married People with confidence of 95 % is**\n",
            "\tSingle People Average Amount Spent:  9052.141639441912  to  9476.678360558088\n",
            "\tMarried People Average Amount Spent:  9056.865621449733  to  9461.894378550265\n",
            "**The Average Amount Spent Intervals Of Single and Married People with confidence of 99 % is**\n",
            "\tSingle People Average Amount Spent:  8964.19480685503  to  9564.62519314497\n",
            "\tMarried People Average Amount Spent:  8972.960049748091  to  9545.799950251907\n",
            "\n",
            "\n",
            "FOR SAMPLE :  30000\n",
            "**The Average Amount Spent Intervals Of Single and Married People with confidence of 90 % is**\n",
            "\tSingle People Average Amount Spent:  9202.657662975205  to  9328.762337024793\n",
            "\tMarried People Average Amount Spent:  9200.063776511388  to  9322.016223488614\n",
            "**The Average Amount Spent Intervals Of Single and Married People with confidence of 95 % is**\n",
            "\tSingle People Average Amount Spent:  9184.783201553986  to  9346.636798446012\n",
            "\tMarried People Average Amount Spent:  9182.77786442965  to  9339.302135570351\n",
            "**The Average Amount Spent Intervals Of Single and Married People with confidence of 99 % is**\n",
            "\tSingle People Average Amount Spent:  9151.25368459719  to  9380.166315402808\n",
            "\tMarried People Average Amount Spent:  9150.352368153137  to  9371.727631846865\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The average amount spent by single people is very much overlapping  with those of Married. **The high overlapping clearly shows there is not much impact of marital status in purchase behavior and we can not say singles avg purchase is more or viceversa**."
      ],
      "metadata": {
        "id": "oyzIzyJLOpwm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Considering the Age effect on amount spent\n",
        "\n",
        "df_walmart['Age'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DabgFhLiwEeF",
        "outputId": "5a2b2972-7e0c-46fd-d943-d908117e9e52"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['0-17', '55+', '26-35', '46-50', '51-55', '36-45', '18-25']\n",
              "Categories (7, object): ['0-17', '18-25', '26-35', '36-45', '46-50', '51-55', '55+']"
            ]
          },
          "metadata": {},
          "execution_count": 165
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Checking the mean distribution if it is following Gaussean/Normal\n",
        "\n",
        "df_age_means = {}\n",
        "for i in ['0-17', '18-25', '26-35', '36-45', '46-50', '51-55', '55+']:\n",
        "  df_age_purchase = df_walmart[df_walmart['Age']==i]\n",
        "  df_age_means[i] = []\n",
        "  n=1500\n",
        "  for s in range(n):\n",
        "    df_age_mean = df_age_purchase.sample(n,replace=True)['Purchase'].mean()\n",
        "    df_age_means[i].append(df_age_mean)\n",
        "\n",
        "\n",
        "fig, axis = plt.subplots(nrows=4, ncols=2, figsize=(20, 15))\n",
        "\n",
        "sns.histplot(df_age_means['0-17'],kde=True,ax=axis[0,0])\n",
        "sns.histplot(df_age_means['18-25'],kde=True,ax=axis[0,1])\n",
        "sns.histplot(df_age_means['26-35'],kde=True,ax=axis[1,0])\n",
        "sns.histplot(df_age_means['36-45'],kde=True,ax=axis[1,1])\n",
        "sns.histplot(df_age_means['46-50'],kde=True,ax=axis[2,0])\n",
        "sns.histplot(df_age_means['51-55'],kde=True,ax=axis[2,1])\n",
        "sns.histplot(df_age_means['55+'],kde=True,ax=axis[3,0])\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 812
        },
        "id": "1PCmdTs95KDq",
        "outputId": "128bff88-18eb-4676-d157-f00891c24fff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x1500 with 8 Axes>"
            ],
            "image/png": 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MmTNHv/jFL5STk6OysjJVVFQ0eAAAmqa23qt1JQdlyFDX8Gj1i04KdiSgSewWq67pO0KS9P6hHcpzlwU5EQAAAAAAQGhp1giZpUuXaubMmfrpT3/awnEAoPPxGX59UrhHtb56Oa0ODUlIZVF0tEuD4rtpWGJ3rS/J06u7P9e0IT+Qmb4MAAAAAAAgqZkjZLxer4YNYyoSAGgJ/9i/UYU1lbKYzBqe1F02syXYkYBmu6rPcDksVu2rdOmTI3uCHQcAAAAAACBkNKsg87Of/UxvvfVWS2cBgE5nXfFBvZu/TZI0JCFVUbawICcCzkycI0I/6ZkpSVq+f4MqPLVBTgQAAAAAABAamjVlWV1dnZYtW6acnBz1799fVmvD0/z+979vkXAA0JEdri7XK7vWSpIyYruqW0RMkBMBLeP8bunKKdynvKoyvbEvV5P7jw52JAAAAAAAgKBrVkFmx44dGjBggCRp586dDfax7gEAnFpNvVd/+mq16nz1So9J1tCENFV6GUmAjsFiMuu6fiP15MZ3tbZon8Z06aP+sV2CHQsAAAAAACComlWQWbJkSUvnAIBOwzAMLd65VoU1FYq1h+vmAdna4MoLdiygRfWOTtT3U87SqsO79NruL/TgsB/JyvpIAAAAAACgE2vWGjIAgOb7sGCn1rvyZDGZNTXjPEXbWTcGHdNPew1RlC1MR2oq9G7+9mDHAQAAAAAACKpmjZC54YYbTjo12eLFi5sdCAA6sv2VLr2+L1eS9LPeWeoTnRjkREDribDadWWfLP15R45W5G3RyKSeSgp3BjsWAAAAAABAUDRrhExGRoYGDBgQePTr109er1dfffWV0tPTWyycz+fTc889pwsuuECZmZmaMGGCXnzxRRmGEWhjGIZmz56t7OxsZWZm6qabbtL+/ftbLAMAtJTqeo/mb/9EPsOvoQlpGtet5V4vgVD1vaRe6h/TRV6/T3/b80WDv+EAAAAAAACdSbNGyNx///2Nbn/hhRdUXV19RoG+bf78+frb3/6mJ598Uv369dOWLVv0+9//XlFRUZo0aVKgzZIlSzRr1iylpaVp9uzZmjJlilasWCGHw9FiWQDgTBxbN+YzldRWKcERqRvTzznpSEOgozCZTLq230jNWL9CW8sOa31JnoYn9Qh2LAAAAAAAgDbXomvIXHbZZXrjjTda7Hy5ubkaP368zj//fKWlpemHP/yhsrOztWnTJklff8G5eLFuu+02TZgwQQMGDNBTTz2loqIivffeey2WAwDO1IcFO5X79boxt2RkK8JqD3YkoEkcZmuzR7d0jYjWRWlnS5KW7V2n6npPYB8jZgAAAAAAQGfRrBEyJ5Kbmyu7veW+ZMzKytKyZcu0b98+9e7dW9u3b9e6dev0u9/9TpKUn5+v4uJijRkzJnBMVFSUhgwZotzcXF1yySWNntfj8cjj+e+XQW63u8UyA8B37a906Y1vrRvTKyohyImAprOZLTKZTFpfclBub12Tj3faHIqyOXTUU6M5Wz/SOcm95bQ5NCyR0TIAAAAAAKBzaFZB5s4772zw3DAMFRcXa8uWLbr99ttbJJgk3XLLLXK73frRj34ki8Uin8+ne++9V5dddpkkqbi4WJKUkNDwy82EhASVlJSc8Lxz587VnDlzWiwnAJxITb1H87d/qnrWjUEH4fbWqdxT06xjB8V1U07RPu2pKFFimFP9opNaOB0AAAAAAEDoalZBJioqqsFzk8mk3r1766677lJ2dnaLBJOklStX6q233tIzzzyjfv36adu2bZo5c6aSk5N1+eWXN/u8U6dO1eTJkwPP3W63xo4d2xKRASDAMAwt3vWZSmrdSnBEatJZrBuDzi0hLFI9nfE64C7VJtch9XTGBzsSAAAAAABAm2lWQWbmzJktnaNRTz31lG655ZbA1GP9+/dXQUGB5s6dq8svv1xJScfurHW5XEpOTg4c53K5NGDAgBOe1263t+jUagDQmI8O79L6kmPrxtycca4ibbzuABmxXVRUU6kan1cbXfkan3riv9cAAAAAAAAdiflMDt6yZYvefPNNvfnmm/rqq69aKlNAbW3tcXeTWyyWwALAaWlpSkpKUk5OTmC/2+3Wxo0blZWV1eJ5AOB0Hags1et710uSrug9VL2jEoOcCAgNVrNFmfHdJEk7you0u7woyIkAAAAAAADaRrNGyLhcLt177736/PPPFR0dLUmqqKjQqFGj9Mc//lHx8S0zBcm4ceP08ssvq1u3boEpyxYtWqSJEydKOjZV2qRJk/SnP/1JPXv2VFpammbPnq3k5GRNmDChRTIAQFNV13s0b/vqwLox47v1D3YkIKQkhUepe2Ss8qqOavGuz/RA1o9ktzTrLQkAAAAAAEC70axvP2bMmKGqqiq9/fbb6tu3ryRp9+7d+u1vf6vHHntMzz77bIuEe+CBBzR79mxNnz49MC3Z1VdfrTvuuCPQ5uabb1ZNTY0eeughVVRUaPjw4VqwYIEcDkeLZACApjAMQ4t3fqaS2iolhkXqxnTWjQEac3Zcikpqq1RYU6l/HdyiK3oPDXYkAAAAAACAVtWsgszq1au1aNGiQDFGkvr166eHH35Yv/jFL1osnNPp1B/+8Af94Q9/OGEbk8mku+++W3fffXeL/V4AaK4PC3Yq1/X1ujEDshVhZd0YoDE2s0XfS+6pVYd36938bRqW2F29ohKCHQsAAAAAAKDVNGsNGb/fL5vNdtx2q9Uqv99/xqEAoD3aX+nS6/tyJUk/653Fl8vAKaRFxmlkUk8ZOjayrN7vC3YkAAAAAACAVtOsgsw555yjxx9/XIWFhYFthYWFmjlzpkaPHt1i4QCgvajyejRv2yfyGX4NS+iucd3Sgx0JaBd+3ne4omwOHao+qrcPbgl2HAAAAAAAgFbTrILMQw89JLfbrfHjx2vChAmaMGGCxo8fL7fbrQcffLClMwJASDMMQ6/sWitXXZUSw5yalD6KdWOA0+S0hemaviMlSSvzvtLeipIgJwIAAAAAAGgdzVpDJiUlRcuXL9eaNWu0d+9eSVLfvn01ZsyYFg0HAO3B+wU7tNGVL6vJrKkZ2Qpn3RigSYYn9dD3XL30efF+LdqxRg8Mu1gOS7PeogAAAAAAAISsJo2QycnJ0cUXXyy32y2TyaRzzz1XN9xwg2644QYNHjxYl1xyib788svWygoAIWdfRYne+HrdmCv7DFMPZ3yQEwHt0zX9RijOHqGiWrde37s+2HEAAAAAAABaXJMKMq+88oquuuoqOZ3O4/ZFRUXp6quv1qJFi1osHACEsipvneZt/0R+w9DwxB4am3JWsCMB7VaE1a4b08+RJH18ZLe2lBYEOREAAAAAAEDLalJBZseOHTrvvPNOuP/cc8/V1q1bzzgUAIQ6wzD0l51rVVpXreQwp244i3VjgDOVEddVF3TrL0l6Zedaub11QU4EAAAAAADQcppUkCkpKZHVeuI53a1Wq0pLS884FACEKsMwJEnvHdquTaWHZDWZdXNGtsKttiAnAzqGy3sNUUp4tCq8tXp19+eB/+cAAAAAAADauyatmNulSxft2rVLPXv2bHT/jh07lJSU1CLBACAUmUwmvX1ws946sEWSlJXYXfsrXdpf6WrW+ZLCnMqIS2nJiEC7ZrdYNbn/GM3a+G+tL8nTZ8X7dU5y72DHAgAAAAAAOGNNKsiMHTtWs2fP1nnnnSeHw9FgX21trV544QWNGzeuRQMCQCgpq6vWu/nbZMhQt4gYJYc5Ve6pafb5nFbHqRsBnUzPqHj9uMcg/fPAZi3d/aXSY5IV74gMdiwAAAAAAIAz0qSCzG233aZ3331XF110ka677jr17n3sjtW9e/fqtddek8/n06233toqQQEg2Lx+n+ZuW61aX72ibGHKjE9l3Riglfyw+0BtLi3QvkqX/rJjre4ZPE5mU5NmWgUAAAAAAAgpTSrIJCYmaunSpXrkkUf07LPPBuZ1N5lMys7O1kMPPaTExMRWCQoAwbZszzrtq3TJbrZoRGJ3Wc18OQy0FovJrMnpo/VY7krtKC/Uu/nb9MPuA4MdCwAAAAAAoNmaVJCRpNTUVM2fP1/l5eU6cOCAJKlnz56KiYlp8XAAECo+PbJHHx/ZLZOkMV36KNLGVGNAa+sSEa2r+47Qkl2f6c0Dm9Q/tot6R3HjBwB849VXX9XChQtVXFysAQMG6MEHH1RmZuYpj3v77bf1q1/9SuPHj9dLL73UBkkBAAAASFKzb++OiYlRZmamMjMzKcYA6ND2V7r02u4vJEmX9sxUamRscAMBHYTDbA2Mtj2Rc7v00fDEHvIbhhZsX6Oaeu8pz3uqcwJAR7BixQrNnDlTd9xxh5YvX64BAwZoypQpcrlcJz0uPz9fTz75pEaMGNFGSQEAAAB8o8kjZACgM6n01OrlbatVb/g1JD5VP+o+UJ8c2R3sWECHYDNbZDKZtL7koNzeuhO26xOVoO1Hj6ik1q3ntnygc7v0OWFbp82hYYk9WiMuAISURYsW6aqrrtLEiRMlSdOnT9dHH32kN954Q7fcckujx/h8Pt1333365S9/qXXr1qmioqItIwMAAACdHgUZADgBn+HX/O2fqqyuWl3CozS5/2iZTaZgxwI6HLe3TuWempO2GZKQqjWF+7S/0qVYe7jSGKkGoBPzeDzaunWrpk6dGthmNps1ZswY5ebmnvC4F198UQkJCbryyiu1bt26U/4Oj8cTeO52u888OAAAANDJUZABgBN4Y2+udpQXymG26taM7yvcag92JKDTindEKj0mWTvLi7S5tEBx9nDWcgLQaZWVlcnn8ykhIaHB9oSEBO3du7fRY7788ku9/vrr+sc//nFav2Pu3LmaM2fOmUYFAAAA8C3NXkMGADqy1Yd36/2CHZKkm/qfo26RrJUFBNtZ0UmKd0TIZ/i13pUvv+EPdiQAaBfcbremTZumGTNmKD4+/h+U0jMAADq9SURBVLSOmTp1qtatWxd4rFq1qpVTAgAAAB0fI2QA4Dt2HC3Ua3u+kCRd1nMw61EAIcJkMikrIU0fH9mjck+NdpQXKSO2a7BjAUCbi4uLk8VikcvlarDd5XIpMTHxuPZ5eXk6dOiQbrvttsA2v/9YUfvss8/WO++8ox49Gr7fsdvtstsZHQwAAAC0JAoyAPAtRTWVmrtttfyGoZFJPXVx90HBjgTgW8KtdmXGd9O6kjztqShRosOppHBnsGMBQJuy2+0aOHCgcnJyNGHCBEnHCiw5OTm6/vrrj2vfp08fvfXWWw22Pffcc6qqqtIf/vAHde1KcRsAAABoCyE/ZVlhYaHuu+8+jRo1SpmZmbr00ku1efPmwH7DMDR79mxlZ2crMzNTN910k/bv3x+8wADarep6j17cukpV9R71csZr0lmjZDKZgh0LwHekRMSohzNOkpTrylOtzxvkRADQ9iZPnqxly5Zp+fLl2rNnjx555BHV1NToiiuukCRNmzZNzzzzjCTJ4XAoPT29wSM6OlqRkZFKT09nJAwAAADQRkJ6hEx5ebmuueYajRo1SvPnz1dcXJwOHDigmJj/ruUwf/58LVmyRLNmzVJaWppmz56tKVOmaMWKFXI4WOwX6KwMw2hSMcVn+DV/+6c6UlOhWHu4bh84VnZLSL9EAp3awNgUldVVq9Jbp9ySfJ2T3IsCKoBO5eKLL1Zpaamef/55FRcXKyMjQwsWLAhMWXb48GGZzSF//x0AAADQqYT0t43z589X165dNXPmzMC27t27B/5tGIYWL16s2267LTBU/6mnntKYMWP03nvv6ZJLLmnzzABCg8lk0vqSg3J7606r/ZfFB7SjvEgWk1mju/TWRlf+cW2SwpzKiEtp6agAmsFiNmt4Yg+tPrJHrroq7aooVnpMcrBjAUCbuv766xudokySlixZctJjZ82a1RqRAAAAAJxESN8y9cEHH2jQoEG66667NHr0aP30pz/VsmXLAvvz8/NVXFysMWPGBLZFRUVpyJAhys3NPeF5PR6P3G53gweAjsftrVO5p+aUj02uQ9pRXiRJGpqQKovJ3Gi7mnqmRQJCidPm0OD4Y0XSneVFKqnl7zkAAAAAAAhdIT1CJi8vT3/72980efJk3Xrrrdq8ebMee+wx2Ww2XX755SouLpYkJSQkNDguISFBJSUlJzzv3LlzNWfOnFbNDqB9KKqp1JayAklS/5hkpUTEnOIIAKEkLTJOrtoq5VUdVa4rX6kRscGOBAAAAAAA0KiQLsgYhqFBgwbpV7/6lSTp7LPP1q5du7R06VJdfvnlzT7v1KlTNXny5MBzt9utsWPHnnFeAO3LUU+N1pXkyZCUFhmrftFJwY4EoBkGxnVTmadGbm+d1hTu1YS0DJlZTwYAAAAAAISYkJ6yLCkpSX379m2wrU+fPiooKAjslySXy9WgjcvlCixm2Ri73S6n09ngAaBzqa736IuiA/IZfiWGRSozvhsLggPtlNVs1vCE7jKbTDpcU6F3878KdiQAAAAAAIDjhHRBZtiwYdq3b1+Dbfv371dqaqokKS0tTUlJScrJyQnsd7vd2rhxo7Kysto0K4D2w+Or12dF+1Xnr1eULUzDE3vIbArpl0MApxBlD9OguG6SpDf3b9Lur9eFAgAAAAAACBUh/Q3kjTfeqI0bN+rll1/WgQMH9NZbb2nZsmW69tprJUkmk0mTJk3Sn/70J73//vvasWOHpk2bpuTkZE2YMCHI6QGEIp/h1xclB1VV71GYxabvJfWUzWwJdiwALaB7ZKx6RSXIL0MLtq+R21sX7EgAAAAAAAABIb2GTGZmpubMmaNnn31WL774otLS0nT//ffrsssuC7S5+eabVVNTo4ceekgVFRUaPny4FixYIIfDEcTkAEKRYRjKLclXWV21rCazRiX1VLjVFuxYAFqIyWTS95J6qqbeo8KaSv1lZ45uP3ss68kAAAAAAICQENIFGUkaN26cxo0bd8L9JpNJd999t+6+++42TAWgPfrq6BEdqamQWSaNSOqhKHtYsCMBaGE2s0U3D8jWrA3/1ubSAr1/aLsuTMsIdiwAAAAAAIDQnrIMAFrKnopi7at0SZKGJKQqMcwZ5EQAWkt3Z5yu7jtckvT3/Ru0t6IkyIkAAAAAAAAoyADoBA66S7XtaKEkKSO2q1IjY4MbCECrO69rP41I7CG/YWjB9k9V5fUEOxIAAAAAAOjkKMgA6NAOV5drU2mBJKlvdKL6RicGORGAtmAymXT9WaOUFOaUq65Ki3etlWEYwY4FAAAAAAA6MQoyADqsw9Xlyi3JlyT1cMZpQEyXICcC0JbCrTbdkpEtq8msDa58fViwM9iRAAAAAABAJ0ZBBkCHtK+iRB8f3i2/DKVERGtwXDeZTKZgxwLQxno44/WzPlmSpNf35Wr/12tJAQAAAAAAtDUKMgA6nIKqo3p+60eqN/xKDIvU0IQ0ijFAJ3Z+SrqyErrLZ/g1f/unqqlnPRkAAAAAAND2KMgA6FBKat2aveVDVdd7lOiI1IjEHrKYeKkDOjOTyaRJ6aOU4IhUSa1br+z8jPVkAAAAAABAm+NbSgAdRlldtf64+QMd9dSoW0SMzu+WLqvZEuxYAEJAhNUeWE8m15Wn9wt2BDsSAAAAAADoZCjIAOgQyj01+uPm91VS61ZimFN3Dxonh8Ua7FgAQkivqAT9rM8wSdIb+3K1t6IkyIkAAAAAAEBnQkEGQLtX6anVHzd/oMKaSsU7IvSrweMV64gIdiwAIej8lLM0PLGH/Iaheds/kdtbF+xIAAAAAACgk6AgA6Bdq/LW6bktH+hwdbli7eH61eDxSgiLDHYsACHKZDLphrNGKTk8SmV11Vq0Y438rCcDAAAAAADaAAUZAO1WTb1Hs7d8qPyqo4q2heneweOVFB4V7FgAQly41aZbBmTLZrZoS9lh/Tv/q2BHAgAAAAAAnQAFGQDtUm29V89v+UgH3KVyWh26d/AF6hoRHexYANqJ7s44/bzvCEnSm/s3aefRwiAnAgAAAAAAHR0FGQDtTp2vXnO2rtLeyhJFWO26Z/AF6hYZG+xYANqZc7v00ejk3jJkaP72T1XuqQl2JAAAAAAA0IFRkAHQrhwbGfOhdlUUKcxi092Dxqm7My7YsQCECIfZKuM014QxmUy6pt9IdYuIUYW3Vgu3r5Hf8J+w/emeFwAAAAAAoDHWYAcAgNNVU+/VC1s/1J6KkkAxpldUQrBjAQghNrNFJpNJ60sOyu2tO61jhiV2V1FepXaUF+rFrR9rSELqcW2cNoeGJfZo6bgAAAAAAKAToSADoF2orvfo+S0fal+lSxFWm+4edAHFGAAn5PbWNWkKssHx3ZTryteWsgJFWG1KDo9qxXQAAAAAAKAzYsoyACGvylun5zZ/oH2VLkVa7bp38HiKMQBaVGpkrHo64yVJG1z5qqn3BjkRAAAAAADoaCjIAAhpbm+d/rj5Ax1wl8ppdejewePV4+svTQGgJZ0d11XRtjB5/D6tLzl40vVkAAAAAAAAmoqCDICgO9FC2ZWeWv1x8/vKqypTlC1Mv8ocr+7OuDZOB6CzsJjMGp7YXVaTWWWeGm0tOxLsSAAAAAAAoANpV2vIzJs3T88884wmTZqkP/zhD5Kkuro6zZo1SytWrJDH41F2drYefvhhJSYmBjktgNPV2ALc1fUevX9ohyq8tQqz2DQ2pZ/2VBRrT0XxKc+XFOZURlxKa0YG0EFF2hzKSkzTF8UHdcBdqjh7uNIoBAMAAAAAgBbQbkbIbNq0SUuXLlX//v0bbH/iiSf04Ycf6rnnntOSJUtUVFSkO++8M0gpATTXNwtwl3tqVFB1VO/kffV1Mcaqc5J7SVJg/6kerP0A4Ex0CY/WWdFJkqRNZQUq99QEOREAAAAAAOgI2kVBpqqqSr/5zW/02GOPKSYmJrC9srJSb7zxhn73u99p9OjRGjRokJ544gnl5uZqw4YNwQsMoNkqPLVaU7hPNT6vIq12jenSR06bI9ixAHQy6THJSg5zym8Y+rL4oOp89cGOBAAAAAAA2rl2UZB59NFHNXbsWI0ZM6bB9i1btsjr9TbY3rdvX3Xr1u2kBRmPxyO3293gASD4yuqqlVO4V3X+ekXbwjSmSx9FWO3BjgWgEzKZTBqa2F0RVptqfF59WrhHfsMf7FgAAAAAAKAdC/k1ZN5++2199dVXev3114/bV1JSIpvNpujo6AbbExISVFx84nUm5s6dqzlz5rR4VgDNd7i6XGuL9slnGIpzROh7ST1lM1uCHQtAJ2Y3WzQisac+Kdyjw9UV+ueBzfppryHBjgUAAAAAANqpkC7IHD58WI8//rj+/Oc/y+FouSmLpk6dqsmTJweeu91ujR07tsXOD6Bp1pcc1EcFu+SXoaQwp4Yn9pDV3C4G8AHo4KLtYRoSn6pcV75W5m1Vr6gEDU1IC3YsAAAAAADQDoV0QWbr1q1yuVy64oorAtt8Pp+++OILvfrqq1q4cKG8Xq8qKioajJJxuVxKSko64XntdrvsdqZBAkLBqsO79LfdX8qQoZSIaGUlpMlsohgDIHSkRsaqpt6r7eWFWrRjjX439CKlRMSc+kAAAAAAAIBvCemCzDnnnKO33nqrwbbf//736tOnj26++WalpKTIZrMpJydHF110kSRp7969Kigo0NChQ4OQGMDpMgxDbx7YpJV5WyVJ/aKT1D8mWSaTKcjJAOB4WYlp8hmGdlUU6cWtq/S7oRfJaWu50bsAAAAAAKDjC+mCjNPpVHp6eoNtERERio2NDWyfOHGiZs2apZiYGDmdTj322GPKysqiIAOEMJ/fryW7PlNO0T5J0qU9BivK5lCFtzbIyQCgcWaTWVMzsjVr479VXOvWy1+t1j2Dx8nKWlcAAAAAAOA0tft5ge6//36df/75uuuuu3T99dcrMTFRL7zwQrBjATiBWp9Xc75apZyifTLLpBvOGqUf9xzMyBgAIS/KHqY7zh6rMItNuyqK9NruL2QYRrBjAQAAAACAdiKkR8g0ZsmSJQ2eOxwOPfzww3r44YeDlAjA6arw1OiFrat00F0qu9miWzKyNTg+NdixAOC0dYuM1c0DztWcrav0aeFedY2I0Q/SMoIdCwAAAAAAtAPtfoQMgPahsLpCT258VwfdpXJaHfpV5niKMQDapUHx3XRVn2GSpL/vy9VGV36QEwHorF599VVdcMEFGjx4sK688kpt2rTphG2XLVuma6+9ViNHjtTIkSN10003nbQ9AAAAgJZHQQZAq9txtFCzNr6rktoqJYU59duhP1DvqMRgxwKAZhvXLV3f79pPhqSF29cov6os2JEAdDIrVqzQzJkzdccdd2j58uUaMGCApkyZIpfL1Wj7zz77TJdccokWL16spUuXKiUlRb/4xS9UWFjYxskBAACAzouCDIBWtaZwr2Zv+VDV9R71jkrQtCE/UHJ4VLBjAcAZMZlM+nnfERoQ20V1/nq9uHWVKjw1wY4FoBNZtGiRrrrqKk2cOFH9+vXT9OnTFRYWpjfeeKPR9s8884yuu+46ZWRkqG/fvnrsscfk9/uVk5PTxskBAACAzouCDIBW4TcM/WP/Rr2yc618hl/DE3voV4PHK9oeFuxoANAiLGazbhlwnrqER6m0rlpztq5Src8b7FgAOgGPx6OtW7dqzJgxgW1ms1ljxoxRbm7uaZ2jpqZG9fX1iomJOeHvcLvdDR4AAAAAzgwFGQAtzuOr14Ltn2pl3lZJ0sXdB+p/Bpwru8Ua5GQA0LIibXbdMXCsIq0OHXCXat62T+Tz+4MdC0AHV1ZWJp/Pp4SEhAbbExISVFJSclrnePrpp5WcnNygqPNtc+fO1fDhwwOPsWPHnnFuAAAAoLOjIAOgSQzDOOn+Ck+Nnt38vtaVHJTFZNZN6efoJ72GyGwytVFCAGhbXcKj9cuBY2U3W7S17LAW7/rslK+VABBM8+bN04oVKzRnzhw5HI5G20ydOlXr1q0LPFatWtXGKQEAAICOh9vVATSJyWTS+pKDcnvrjttXWletVYd3qbreI7vZou+n9JPX79PHh3ed8HxJYU5lxKW0ZmQAaHW9oxN1S0a2Xtr6sdYW7VO0PUwTe2cFOxaADiouLk4Wi0Uul6vBdpfLpcTExJMeu3DhQs2bN0+LFi3SgAEDTtjObrfLbre3SF4AAAAAxzBCBkCTub11KvfUNHhsO3pE/87/StX1HkVa7RrTpY/CLLbj2n33UVPPegsAOobB8am6IX2UJOnd/G1acXBrkBMB6KjsdrsGDhyonJycwDa/36+cnBxlZZ24GDx//ny99NJLWrBggQYPHtwWUQEAAAB8CyNkAJwRwzC0s7xIuyqKJR0b8ZKV2F12syXIyQCg5TjMVhmGIdMppl8c06WPqrx1en1frt48sFEOi0XjU098B/rpnBMAGjN58mT99re/1aBBg5SZmalXXnlFNTU1uuKKKyRJ06ZNU5cuXfTrX/9a0rFpyp5//nk988wzSk1NVXHxsfduERERioyMDNp1AAAAAJ0JBRkAzVbv9ynXla/CmkpJUp+oBA2I7cp6MQA6HJvZctIpG7/NYbFqcHw3bS4t0LK963WgslT9YpKOa+e0OTQssUdrRQbQwV188cUqLS3V888/r+LiYmVkZGjBggWBKcsOHz4ss/m/EyIsXbpUXq9Xd911V4Pz3HnnnfrlL3/ZptkBAACAzoqCDIBmqfLW6cuSg6r01skskzLjuynNGRfsWADQqr6ZsvFUekTGqcrr0d7KEn1WvF81Po96OOPbICGAzuT666/X9ddf3+i+JUuWNHj+wQcftEUkAAAAACdBQQZAkx2uLtcnhXvl9fvksFg1IrGH4hwRwY4FACHDZDIpI7aL/IZf+92l2lRaIMOQekZRlAEAAAAAoLOiIAPgtPkNQyvztuqDgp2SpFh7uEYk9lCY1RbkZAAQekwmkwbGpchkMmlfpUubywrkl6HeUQnBjgYAAAAAAIKAggyA01Ll9WjRzjXaXFog6dh0PAPjU2QxmU9xJAB0XiaTSWfHdpVZJu2pLNHWssOq9/vUL/r4NWUAAAAAAEDHRkEGwCnlucv08rbVKql1y2oya0RSDyWGOYMdCwDaBZPJpAGxXWQ2mbSrolg7yotU66vXmC59gh0NAAAAAAC0IQoyAE4qp3CvXt39hbx+nxIckbr17PO0v9J1WotaAwCOMZlM6h/bRXaLVVvLDuuAu1SGYejcrn1lM1uCHQ8AAAAAALQB5hoC0CiPr15/3fW5/rJzrbx+nwbFddMfsn6oHk4WpAaA5uodlaBhCd1llkkHq8r0x83vq4ICNwAAAAAAnQIFGQDHOVR1VDM3/Furj+yWSdKlPQbrjoFjFWlzBDsaALR73SJj9L3knrKbLdpTUaIncv+tg+7SYMcCAAAAAACtjIIMgADDMLTq8C7N3PBvFVSXK9oWprsGjdOPew6W2WQKdjwA6DASw5y6KO1sdQmPVpmnWk9t/I++KNof7FgAAAAAAKAVUZABIEmq8no0b9sneu3r9WIGxqXowWEX6+y4lGBHA4AOKdoept8N/YEGxqXI6/dpwY41enXX5/L46oMdDQAAAAAAtIKQL8jMnTtXEydOVFZWlkaPHq3bb79de/fubdCmrq5O06dP16hRo5SVlaVf/vKXKikpCVJioP3ZXV6sx3JXaL0rTxaTWT/rnaU7B56vaHtYsKMBQIcWYbXrzoFj9aPuA2WS9PGR3Zq14V0dri4PdjQAAAAAANDCQr4g8/nnn+u6667TsmXLtGjRItXX12vKlCmqrq4OtHniiSf04Ycf6rnnntOSJUtUVFSkO++8M4ipgfbB6/dp+f4NembTeyqtq1ZymFPThlyoC9MymKIMANqI2WTWT3sN0d2DLlC0LUyHqo/q8dx39J/8bfIb/mDHAwAAAAAALcQa7ACnsnDhwgbPZ82apdGjR2vr1q0aOXKkKisr9cYbb+jpp5/W6NGjJR0r0Fx88cXasGGDhg4detw5PR6PPB5P4Lnb7W7VawBC0f5Kl/6yc23gLuxzknvpmr4jFWa1BTkZAHROGXFd9cCwH+kvO3L01dEjen1frtaVHNSks85Rt8iYYMcDAAAAAABnKOQLMt9VWVkpSYqJOfbFxJYtW+T1ejVmzJhAm759+6pbt24nLMjMnTtXc+bMaZO8QKjx+n3618HNejdvm/wyFG0L03X9RmpoYvdgRwOATi/GHq67Bo3Tp4V79H97c7Wv0qXHc1dqfOoAXdx9IEVzAAAAAADasXZVkPH7/XriiSc0bNgwpaenS5JKSkpks9kUHR3doG1CQoKKi4sbPc/UqVM1efLkwHO3262xY8e2XnAgRHx3VMzIpJ76ed8RctocQU4GAPiGyWRSdtd+GhjXTa/u/lybSwv07/yvtLZony7vNUSjknszrSQAAAAAAO1QuyrITJ8+Xbt27dJrr712Ruex2+2y2+0tlAoIfbX1Xv3r4Ba9f2i7/DIU9fWomCxGxQBAyIpzROiOs8dqU+kh/d/e9SqudesvO9fq3fxturTnYA1N6E5hBgAAAACAdqTdFGQeffRRffTRR/rrX/+qrl27BrYnJibK6/WqoqKiwSgZl8ulpKSkYEQFQoZhGPq8eL/e2LdB5Z4aSYyKAYD2xGQyaUhCms6OS9H7h3bonbytKqgu19xtnygtMlY/TDtbwxJ7yGI2BzsqAAAAAAA4hZAvyBiGoRkzZug///mPlixZou7dG97RP2jQINlsNuXk5Oiiiy6SJO3du1cFBQWNrh8DdBZ57jIt3fOldlccm7ovKcypq/sO1+D41CAnAwA0lc1s0Q+7n63zuvbT+4e26/2C7cqvOqoFO9Yodl+uxqak67yufRVlDwt2VAAAAAAAcAIhX5CZPn26/vWvf+mll15SZGRkYF2YqKgohYWFKSoqShMnTtSsWbMUExMjp9Opxx57TFlZWRRk0ClVeT3654FNWnV4lwwZspst+lH3QbowbYBsZkuw4wEAzkCkza7LemVqfGp/fVCwUx8f3qWjnhq9eWCj3jq4SYPjUzU6ubcGx3eTldd8AAAAAABCSsgXZP72t79Jkm644YYG22fOnKkrrrhCknT//ffLbDbrrrvuksfjUXZ2th5++OE2zwoEk9fv06rDu7Ty4Fa56+skSX2iEpUZ302RNrtyCvee8e9ICnMqIy7ljM8DADgzkTaHLu05WD/sfrbWFR/UBwU7dMBdqo2ufG105SvCaldmfDcNTeiugXEpsltC/i0fAAAAAAAdXsh/Ot+xY8cp2zgcDj388MMUYdAp+Qy/Pi/ar7cObJarrkqSlBIRo5/3Ha6imkqVe2oC68ecKaeVdWcAIJTYzBad06W3zunSW4eqjmpt0T59VrRf5Z4arS3ar7VF+2UzW3RWdJIy4lKUEdtVqZGxMptMwY4OAAAAAECnE/IFGQCN8/p9Wlu4T//O/0rFtW5JUqw9XJf2HKzRXfrIYjKrqKYyyCkBAG0lNTJWE3tn6fJeQ7SnokS5rjxtKMmXq65KXx09oq+OHpEkhVls6h2VoD7RieoTlajeUYmKtNmDnB4AAAAAgI6PggzQztT56rX6yG79J3+bjn498iXS6tBFaRka1y2daWkAoJMzm8w6KyZZZ8Uk68rew3S4ulxfHT2ibWVHtKu8SLU+r7YdPaJtXxdoJKlreLR6RyWouzNOPZzxSouMU7jVFsSrAAAAAACg4+GbW6CdqPJ69NHhnXr/0A5Vfb1GTKw9XBemZei8rv3koBADAPgOk8mkbpGx6hYZqwmpA+Qz/CqoKtfeihLtrSzW3kqXimoqdaSmQkdqKpRTtC9wbHJ4lLpHHivQ9HDGKS0yVtH28BbNZxiGTEyfBgAAAADoJPgGFwhhhmFov9uljw/v1hfFB+T1+yRJSWFO/bD72RqV3Fs2syXIKQEAoeZEhQ6Lyazuzjh1d8ZprM6SJLm9tdpb4dIBd6nyqsp00F2qsrpqFdVUqqimUutKDgaOj7TaFeeIUJw9QvFhx35GWO3NKqo4bQ4NS+zR/IsEAAAAAKCdoSADhKA6X70+L9qvj4/s0kF3WWB7WmSsLko7W8OTeshiMgcxIQAglJlMJq0vOSi3t+60j4mxhynGnqJBcSmq9XlVVlet0rpqldVVq8JTozJPjarqPaqq9yi/6mjgOJvZohh7mGLtEYp3RCjOEcHNAgAAAAAANIKCDBAiDMPQ3soSfVa0X58V7VetzytJsprMGpHUQ99POUt9ohKZ2gUAOgiH2dqqU3a5vXUq/3qtseaIsNoVYbUrLTJWqRGxOjsuRf86uFkF1eUq99So3FMrt7dWXr9PJbVVKqmtChwbbQtTvCNS8WERSnBEMq0mAAAAAACiIAME3aGqo/q8aL++LDnQ4Mus5DCnvp9ylkZ36S2nLSyICQEArcFmtjRrJMupJIU5lRGX0mLn+0aY1abk8KgGxRWf4Velp07l3prAiJrqeo8qvLWq8NZqv9slSYqyhSk5zKmkcKfiHREyM8oTAAAAANAJUZABgqCwukLrXXn6vGi/CqrLA9utJrPSnHHqE5WgruHRX39Rl9fk87fWl3EAgJZ3piNZvstpdbTYuU7FYjIr1hGuWEe4ejrjJUm19V6V1lXLVVel0rpqVXprA489lSWymMxKDItUD2e8BsZ1U0JYZJvlBQAAAAAgmCjIAG3AZ/i1p6JEm1yHtKk0X4U1lYF9VpNZg+K7yWl1KMrmkMV87K7hCm9ts39fW34ZBwDAt4VZbepmjVG3yBhJksdXr+Jat4pr3SqqqZTH71NhTaUKayr1RfEBpUbEKjMhVUPiU9UzKkFmpuYEAAAAAHRQFGSAVlLpqdX2o0e0uaxAW0oLVFXvCeyzmMxKj0nWyKSeykrsrgirXR8f3tWid0gDABAK7BarUiNjlRoZK8MwVOGtVVFNpUrrqlVSW6VD1Ud1qPqoVuZtVbQtTIPjU5WZkKqM2K6sPQMAAAAA6FD4lAu0kDpfvXaVF2n70UJtP3pEeVVlDfZHWu0aHN9NmfFpOjsuReFWW5CSAgAQHCaTSTH28MBjWGIPbSkr0CbXIW0tK1CFt1afFu7Rp4V7ZDNbNCC2izLj05QZ302xjohgxwcAAAAA4IxQkAGayDAMmUwm1dZ7tbeyRLvLi7Wrokh7KkrkM/wN2qZFxiojNkVDElLVJzpRFhYxBgAgwGlz6Jzk3jonubfq/T7tKi/WxtJ8bXIdkquuSptLC7S5tECvSurhjNeQr0fPdI+Mk4mpzQAAAAAA7QwFGeA0GIaho54a7a0o0e6KIm0qPSRXbZWM77SLsNqVEh6trhHR6hIeHRgFc7i6XIeryxs9d1KYUxlxKa18BQAAhBaH2Rq4yUGSrGaLMuK6KiOuq67uM1wF1eXa+PXaa/srXTroLtVBd6neOrhZcfYIZSakKjM+Vf1ju8hmtjQ497fPCwAAAABAqKAgAzSi0lOrA+5S7a90BX5WeGuPaxdusSneEaF4R6QSwyIVYbUHvgDy+Ovl8dSf8nc5rY4Wzw8AQKizmS0ymUxaX3JQbm9do22cNrvGdOmjrITuKqg+qvyqozpcXaEyT7VWHd6lVYd3yWoyKyUi+tg6NRGxSgx3alhijza+GgAAAAAATo2CDDo9t7dO+VVl2l9ZqgNfF2BcdVXHtTPLpNTIWPWNTpLXX68wi411YAAAOENub53KPTWnbJcY5lRimFOD4/wqqatSYU2FCmsqVeerV17VUeVVHf26XaSOVFcqPSZJPZzxslt4uwsAAAAACA18QkWnUe/36UhNhfKrjurQtx5HG/kSyCSpS3i0ekbFq5czQT2j4tU9Mi7wpc7Hh3ed1pdHAACgZVnMZnUJj1KX8CgZhqFyb60KqytUVFOpcm+tSmqr9OaBjZIks8mk7pFx6hOdqD5RieodlajEsEimMwMAAAAABAUFGXQ4PsOvkhq3DtdU6Eh1uQ5VHZvi5EhNhfzGd1d9OSYxzKmezvhAAaaHM57RLwAAhDiTyaRYe7hi7eHqH9tFNfVeVXhr5fX7tLeiRBXeY1OQHnCX6kPtlHRsutGUiBilRESr69c/UyJiFO+IlPkkhZrWWJeGtW4AAAAAoHOhIIN2q9bnVWF1pQ7XlOtIdcWxR82xO2R9hr/RY8ItNqVGxiotMlapkXFKjYxRakSswii+AADQ7oVbbeoaEa3vp5wlwzBUWletvRXF2lvp0r7KEh10l6nG59XeyhLtrSxpcKzVZFa8I0JxjsjA+nBxjgjFOSIUYw9XlM2hXRVFqqn3tkhWp83BWjcAAAAA0MlQkEFIq/J6VFxbqeJat4pr3Mf+/fXPxqYa+4bdbFGX8Gg5LFZFWu2Kc0Qo1h6uCKv9W3eiGoFpy05XUphTGXEpZ3ZRAACg1ZlMJiWERSohLFIjk3tJkrx+n4pqKnW4uvzrR4UOV5erqKZS9YZfRbVuFdW6T3pem9kiu9kih8Uqu9kqu8Uih9kqu8Uqu9kiu8Uqh9kS2Gc2mdvgagEAAAAA7QEFGQSN3zBU6a1VWV21SmurdNRTozJPtcrqqlVcU6miWreq6z0nPUeUzaGu4THqGhGtrhHRx6YfCY9RnCNCZpOpwVov9YZfFd7aM8rstDrO6HgAABA8NrNFqZGxSo2MbbDdZ/hVWlutsroqldZVq7Tuv/8uq6tWhbdWbm+dDBny+n3y+n2qOsV7lG9YTeb/FmvMVjksFtnMVsXYwxTniNDAuG4nnSoNAAAAANBxdJiCzKuvvqqFCxequLhYAwYM0IMPPqjMzMxgx+p0/Iah6nqP3N46ub/+8sJdXye3t06V3jqVe2pU9vWXG0c9NSecWuzbwiw2RdkcctocX/8MU5TNoShbmByWhl3YVVslV20VI1kAAMBps5jMSgp3KincecI2fsPQe4e2qaTWLY/Ppzp/vTy+enn8PtX56uXx18vj8zX4aejYDSH19R5VN3LOXFe+fpN5ofrFJLXataFja+pnoJUrV2r27Nk6dOiQevXqpfvuu09jx45tw8QAAABA59YhCjIrVqzQzJkzNX36dA0ZMkSvvPKKpkyZonfeeUcJCQnBjndKO48WqrjWLbPJJJNMkkkyH/uXZDLJfOxfMplMMn+9zSTJJJNMpoY/pf/+/OZuS1ODYxSYssswjt0R6jeMwE+/4Zfvm58y5PP75fHXq873rcfXzz3f+ndNvVdub52q6j0yZJz2tZtkCtwhWu/3y2o2K8xiU6TVroivH1Zz41N91Pq8qvU1Po87I1kAAEBLMptMX98kEiadxtJzhnFsNI3H/98izbeLOIakHpFx6u6Ma/Xs6Jia+hlo/fr1+vWvf61f/epXGjdunN566y3dcccd+vvf/6709PQgXAEAAADQ+XSIgsyiRYt01VVXaeLEiZKk6dOn66OPPtIbb7yhW2655bj2Ho9HHs9/p5morKyUJLndJ58zvDW4at16cv3KNv+9rS3capXT4lCEzS6nzaFIW5giLTZF28MV6whXnD1CsY5wRdvCZfm64LLBlacq73em//BJ8p1+gecbRp1HbrdbZk+97N6mH9+W520v52yt87aXc7bWedvLOVvrvO3lnK113vZyztY6b3s5Z2udt7Nnba3rN6u+1d7TNTWrQxZJFslsl75zf0mkza6hCd3lralV47eXtK5v/hsZRsv9t0fbaupnoMWLF+u8887T//zP/0iS7rnnHq1Zs0Z//etf9eijjx7XPpQ+MwEAAADB0Bqfm9p9Qcbj8Wjr1q2aOnVqYJvZbNaYMWOUm5vb6DFz587VnDlzjtvOcH0AAAB0JlVVVYqKigp2DDRRcz4DbdiwQTfddFODbdnZ2Xrvvfcabc9nJgAAAOCYo0ePttjnpnZfkCkrK5PP5ztuWH5CQoL27t3b6DFTp07V5MmTA8/9fr/Ky8sVGxsbmM4LnYvb7dbYsWO1atUqOZ0nnj8eOBH6EM4E/Qdnij6EpjIMQ1VVVUpOTg52FDRDcz4DlZSUKDEx8bj2JSUljbb/7memiooKjRs3Th999BFFPJwW/jahqegzaCr6DJqKPoOmqqys1Pnnn6+YmJgWO2e7L8g0h91ul91ub7AtOjo6SGkQSpxOJy/IOCP0IZwJ+g/OFH0ITcGX6jiZxj4zScf6Da8zaAr+NqGp6DNoKvoMmoo+g6Yyn2CN82adq8XOFCRxcXGyWCxyuVwNtrtcruPuAAMAAACA9q45n4ESExOPGw3DZyYAAACgbbX7gozdbtfAgQOVk5MT2Ob3+5WTk6OsrKwgJgMAAACAltecz0BDhw7V2rVrG2xbs2aNhg4d2ppRAQAAAHxLuy/ISNLkyZO1bNkyLV++XHv27NEjjzyimpoaXXHFFcGOhnbCbrfrzjvvbHRaBuB00IdwJug/OFP0IaDzOdVnoGnTpumZZ54JtJ80aZJWr16tP//5z9qzZ49eeOEFbdmyRddff/1p/T5eZ9BU9Bk0FX0GTUWfQVPRZ9BUrdFnTIZhGC12tiD661//qoULF6q4uFgZGRl64IEHNGTIkGDHAgAAAIBWcbLPQDfccINSU1M1a9asQPuVK1fqueee06FDh9SrVy/95je/0dixY4MVHwAAAOh0OkxBBgAAAAAAAAAAIFR1iCnLAAAAAAAAAAAAQhkFGQAAAAAAAAAAgFZGQQYAAAAAAAAAAKCVUZABAAAAAAAAAABoZRRk0KG43W49/vjjGjdunDIzM/Xzn/9cmzZtCuw3DEOzZ89Wdna2MjMzddNNN2n//v0NznH06FH9+te/1rBhwzRixAjdf//9qqqqatBm+/btuvbaazV48GCNHTtW8+fPb4vLQys7Wf/xer363//9X1166aUaOnSosrOzNW3aNBUWFjY4B/2n8zrV68+3PfTQQ+rfv7/+8pe/NNhO/+ncTqcP7dmzR7feequGDx+uoUOHauLEiSooKAjsr6ur0/Tp0zVq1ChlZWXpl7/8pUpKShqco6CgQLfccouGDBmi0aNH68knn1R9fX2bXCOA0Pbqq6/qggsu0ODBg3XllVee8O/YN1auXKk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AAAAAAAAAtLL/D5ganDGZWGhCAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#The clear bellcurve shows means are actually in normal distribution finding out the Confidence intervals of mean based on maritalstatus\n",
        "# using samples of 300,3000,30000 with 90,95,99 percent confidence levels.\n",
        "\n",
        "for i in ['0-17', '18-25', '26-35', '36-45', '46-50', '51-55', '55+']:\n",
        "  df_age_purchase = df_walmart[df_walmart['Age']==i]\n",
        "  df_age_avg_amount_spent = df_age_purchase['Purchase'].mean().round(2)\n",
        "  print(\"\\n\\nThe average amount spent by people of age\",i,\"is\",df_age_avg_amount_spent)\n",
        "  df_age_means = []\n",
        "  for n in [300,3000,30000]:\n",
        "    for s in range(n):\n",
        "      df_age_mean = df_age_purchase.sample(n,replace=True)['Purchase'].mean()\n",
        "      df_age_means.append(df_age_mean)\n",
        "    sample_df_age_mean = np.mean(df_age_means).round(2)\n",
        "    sample_df_age_stddev = pd.Series(df_age_means).std().round(2)\n",
        "    print(\"FOR SAMPLE : \",n)\n",
        "\n",
        "    for p in [0.90,0.95,0.99]:\n",
        "      df_age_means_from = sample_df_age_mean - norm.ppf(p)*(sample_df_age_stddev)\n",
        "      df_age_means_to = sample_df_age_mean + norm.ppf(p)*(sample_df_age_stddev)\n",
        "\n",
        "      print(\"People of age\",i,\"Average Amount Spent Intervals with confidence of\",round(p*100),\"% is \",df_age_means_from,\" to \",df_age_means_to)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_qrAZ8fGy3_e",
        "outputId": "9342fc7a-6a5b-4861-e8ae-1e1657405604"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "The average amount spent by people of age 0-17 is 8933.46\n",
            "FOR SAMPLE :  300\n",
            "People of age 0-17 Average Amount Spent Intervals with confidence of 90 % is  8561.887328864872  to  9307.032671135126\n",
            "People of age 0-17 Average Amount Spent Intervals with confidence of 95 % is  8456.268153572668  to  9412.65184642733\n",
            "People of age 0-17 Average Amount Spent Intervals with confidence of 99 % is  8258.144146058847  to  9610.775853941152\n",
            "FOR SAMPLE :  3000\n",
            "People of age 0-17 Average Amount Spent Intervals with confidence of 90 % is  8769.771200945876  to  9091.568799054125\n",
            "People of age 0-17 Average Amount Spent Intervals with confidence of 95 % is  8724.158627136243  to  9137.181372863757\n",
            "People of age 0-17 Average Amount Spent Intervals with confidence of 99 % is  8638.597024414172  to  9222.742975585828\n",
            "FOR SAMPLE :  30000\n",
            "People of age 0-17 Average Amount Spent Intervals with confidence of 90 % is  8871.07164216502  to  8995.22835783498\n",
            "People of age 0-17 Average Amount Spent Intervals with confidence of 95 % is  8853.47329031047  to  9012.826709689529\n",
            "People of age 0-17 Average Amount Spent Intervals with confidence of 99 % is  8820.461708981462  to  9045.838291018537\n",
            "\n",
            "\n",
            "The average amount spent by people of age 18-25 is 9169.66\n",
            "FOR SAMPLE :  300\n",
            "People of age 18-25 Average Amount Spent Intervals with confidence of 90 % is  8776.07782458217  to  9535.602175417831\n",
            "People of age 18-25 Average Amount Spent Intervals with confidence of 95 % is  8668.42052472547  to  9643.25947527453\n",
            "People of age 18-25 Average Amount Spent Intervals with confidence of 99 % is  8466.473334485478  to  9845.206665514523\n",
            "FOR SAMPLE :  3000\n",
            "People of age 18-25 Average Amount Spent Intervals with confidence of 90 % is  9006.720811167  to  9326.519188833001\n",
            "People of age 18-25 Average Amount Spent Intervals with confidence of 95 % is  8961.391612965266  to  9371.848387034735\n",
            "People of age 18-25 Average Amount Spent Intervals with confidence of 99 % is  8876.361575755926  to  9456.878424244076\n",
            "FOR SAMPLE :  30000\n",
            "People of age 18-25 Average Amount Spent Intervals with confidence of 90 % is  9107.86018521296  to  9231.11981478704\n",
            "People of age 18-25 Average Amount Spent Intervals with confidence of 95 % is  9090.388989079904  to  9248.591010920096\n",
            "People of age 18-25 Average Amount Spent Intervals with confidence of 99 % is  9057.615930737376  to  9281.364069262623\n",
            "\n",
            "\n",
            "The average amount spent by people of age 26-35 is 9252.69\n",
            "FOR SAMPLE :  300\n",
            "People of age 26-35 Average Amount Spent Intervals with confidence of 90 % is  8888.233546451987  to  9638.966453548013\n",
            "People of age 26-35 Average Amount Spent Intervals with confidence of 95 % is  8781.822372665914  to  9745.377627334086\n",
            "People of age 26-35 Average Amount Spent Intervals with confidence of 99 % is  8582.212707693438  to  9944.987292306563\n",
            "FOR SAMPLE :  3000\n",
            "People of age 26-35 Average Amount Spent Intervals with confidence of 90 % is  9094.670419500635  to  9409.829580499365\n",
            "People of age 26-35 Average Amount Spent Intervals with confidence of 95 % is  9049.998798030047  to  9454.501201969953\n",
            "People of age 26-35 Average Amount Spent Intervals with confidence of 99 % is  8966.202265407937  to  9538.297734592063\n",
            "FOR SAMPLE :  30000\n",
            "People of age 26-35 Average Amount Spent Intervals with confidence of 90 % is  9191.988533371463  to  9313.351466628537\n",
            "People of age 26-35 Average Amount Spent Intervals with confidence of 95 % is  9174.786180763847  to  9330.553819236153\n",
            "People of age 26-35 Average Amount Spent Intervals with confidence of 99 % is  9142.517428164167  to  9362.822571835834\n",
            "\n",
            "\n",
            "The average amount spent by people of age 36-45 is 9331.35\n",
            "FOR SAMPLE :  300\n",
            "People of age 36-45 Average Amount Spent Intervals with confidence of 90 % is  8969.70020879419  to  9669.11979120581\n",
            "People of age 36-45 Average Amount Spent Intervals with confidence of 95 % is  8870.562342277482  to  9768.257657722517\n",
            "People of age 36-45 Average Amount Spent Intervals with confidence of 99 % is  8684.596192131736  to  9954.223807868264\n",
            "FOR SAMPLE :  3000\n",
            "People of age 36-45 Average Amount Spent Intervals with confidence of 90 % is  9175.538860385135  to  9482.701139614866\n",
            "People of age 36-45 Average Amount Spent Intervals with confidence of 95 % is  9132.000741346137  to  9526.239258653864\n",
            "People of age 36-45 Average Amount Spent Intervals with confidence of 99 % is  9050.330470774947  to  9607.909529225055\n",
            "FOR SAMPLE :  30000\n",
            "People of age 36-45 Average Amount Spent Intervals with confidence of 90 % is  9271.134648795134  to  9390.985351204865\n",
            "People of age 36-45 Average Amount Spent Intervals with confidence of 95 % is  9254.146644403749  to  9407.97335559625\n",
            "People of age 36-45 Average Amount Spent Intervals with confidence of 99 % is  9222.27997340985  to  9439.84002659015\n",
            "\n",
            "\n",
            "The average amount spent by people of age 46-50 is 9208.63\n",
            "FOR SAMPLE :  300\n",
            "People of age 46-50 Average Amount Spent Intervals with confidence of 90 % is  8812.147160400284  to  9593.252839599718\n",
            "People of age 46-50 Average Amount Spent Intervals with confidence of 95 % is  8701.43085718654  to  9703.969142813461\n",
            "People of age 46-50 Average Amount Spent Intervals with confidence of 99 % is  8493.745485386054  to  9911.654514613947\n",
            "FOR SAMPLE :  3000\n",
            "People of age 46-50 Average Amount Spent Intervals with confidence of 90 % is  9045.371494223777  to  9369.988505776224\n",
            "People of age 46-50 Average Amount Spent Intervals with confidence of 95 % is  8999.359288146596  to  9416.000711853405\n",
            "People of age 46-50 Average Amount Spent Intervals with confidence of 99 % is  8913.048041752727  to  9502.311958247274\n",
            "FOR SAMPLE :  30000\n",
            "People of age 46-50 Average Amount Spent Intervals with confidence of 90 % is  9146.461350774609  to  9270.438649225393\n",
            "People of age 46-50 Average Amount Spent Intervals with confidence of 95 % is  9128.888430064359  to  9288.011569935643\n",
            "People of age 46-50 Average Amount Spent Intervals with confidence of 99 % is  9095.924553332645  to  9320.975446667357\n",
            "\n",
            "\n",
            "The average amount spent by people of age 51-55 is 9534.81\n",
            "FOR SAMPLE :  300\n",
            "People of age 51-55 Average Amount Spent Intervals with confidence of 90 % is  9147.465957088854  to  9875.054042911146\n",
            "People of age 51-55 Average Amount Spent Intervals with confidence of 95 % is  9044.335400917285  to  9978.184599082715\n",
            "People of age 51-55 Average Amount Spent Intervals with confidence of 99 % is  8850.879628996026  to  10171.640371003974\n",
            "FOR SAMPLE :  3000\n",
            "People of age 51-55 Average Amount Spent Intervals with confidence of 90 % is  9376.605955918461  to  9695.174044081537\n",
            "People of age 51-55 Average Amount Spent Intervals with confidence of 95 % is  9331.4511427062  to  9740.328857293798\n",
            "People of age 51-55 Average Amount Spent Intervals with confidence of 99 % is  9246.748222735463  to  9825.031777264536\n",
            "FOR SAMPLE :  30000\n",
            "People of age 51-55 Average Amount Spent Intervals with confidence of 90 % is  9473.26115588517  to  9596.23884411483\n",
            "People of age 51-55 Average Amount Spent Intervals with confidence of 95 % is  9455.829922978868  to  9613.670077021132\n",
            "People of age 51-55 Average Amount Spent Intervals with confidence of 99 % is  9423.13182900352  to  9646.36817099648\n",
            "\n",
            "\n",
            "The average amount spent by people of age 55+ is 9336.28\n",
            "FOR SAMPLE :  300\n",
            "People of age 55+ Average Amount Spent Intervals with confidence of 90 % is  8990.057616480308  to  9730.102383519692\n",
            "People of age 55+ Average Amount Spent Intervals with confidence of 95 % is  8885.161412290301  to  9834.998587709699\n",
            "People of age 55+ Average Amount Spent Intervals with confidence of 99 % is  8688.393578328189  to  10031.766421671811\n",
            "FOR SAMPLE :  3000\n",
            "People of age 55+ Average Amount Spent Intervals with confidence of 90 % is  9179.826052419437  to  9497.573947580564\n",
            "People of age 55+ Average Amount Spent Intervals with confidence of 95 % is  9134.787495866827  to  9542.612504133174\n",
            "People of age 55+ Average Amount Spent Intervals with confidence of 99 % is  9050.302654055158  to  9627.097345944843\n",
            "FOR SAMPLE :  30000\n",
            "People of age 55+ Average Amount Spent Intervals with confidence of 90 % is  9275.455621354833  to  9397.664378645166\n",
            "People of age 55+ Average Amount Spent Intervals with confidence of 95 % is  9258.133379066954  to  9414.986620933045\n",
            "People of age 55+ Average Amount Spent Intervals with confidence of 99 % is  9225.639733365731  to  9447.480266634268\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Questions:\n",
        "\n",
        "***Are women spending more money per transaction than men? Why or Why not?\n",
        "Confidence intervals and distribution of the mean of the expenses by female and male customers***\n",
        "\n",
        "No, the women are not spending more. May be due to many reasons,some of them might be using their friend's account/cards for better offers.\n",
        "Or may be less products for females comapritively.\n",
        "\n",
        "The confidence intervals of mean of female and male expenses per product with 99% confidence are\n",
        "Male :  9324.41  to  9550.49\n",
        "Female:  8629.81  to  8839.93\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "***Are confidence intervals of average male and female spending overlapping? How can Walmart leverage this conclusion to make changes or improvements?***\n",
        "\n",
        "There is a little overlap of averages when we consider the low number of samples( check above gender regarding averages). when we increase the sample size, we can clearly see the males are spending more.\n",
        "Even less in number, females are contributing to a great level in purchases.\n",
        "We can easily get more sales when we attracted large nuber of female customers.\n",
        "\n",
        "\n",
        "***Results when the same activity is performed for Married vs Unmarried***\n",
        "There is a very much overlap of married vs unmarried people amount spent per transaction.\n",
        "For 30000 samples,\n",
        "The Average Amount Spent Intervals Of Single and Married People with confidence of 90 % is\n",
        "Single People Average Amount Spent:  9202.65  to  9328.76\n",
        "Married People Average Amount Spent:  9200.06 to  9322.01\n",
        "it's almost similar.\n",
        "\n",
        "*The high overlapping clearly shows there is not much impact of marital status in purchase behavior and we can not say singles avg purchase is more or viceversa*\n",
        "\n",
        "\n",
        "***Results when the same activity is performed for Age***\n",
        "\n",
        "*Similarly for different age groups also, we can not conclude anything as their average purchase intervals are almost overlapping. Can't conclude that Age group also has any impact on avg purchase.*\n",
        "\n",
        "-People of age 0-17 Average Amount Spent Intervals with confidence of 90 % is  8871.07  to  8995.23\n",
        "-People of age 26-35 Average Amount Spent Intervals with confidence of 90 % is  9191.99  to  9313.35\n",
        "-People of age 36-45 Average Amount Spent Intervals with confidence of 90 % is  9271.13  to  9390.99\n",
        "-People of age 46-50 Average Amount Spent Intervals with confidence of 90 % is  9146.46  to  9270.44\n",
        "-People of age 51-55 Average Amount Spent Intervals with confidence of 90 % is  9473.26  to  9596.24\n",
        "-People of age 55+ Average Amount Spent Intervals with confidence of 90 % is  9275.46  to  9397.66\n",
        "\n",
        "(pls note that values mentioned above can slightly differ in the above calcualtions when the page is refreshed)\n"
      ],
      "metadata": {
        "id": "i88MDy1l-36_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Recommendations:**\n",
        "\n",
        "- Marketing efforts (Offers/Advertisements) to attract female users can be done to increase purchases\n",
        "- As male users are more, ensure to provide more types and personalized products to retain them and increase them.\n",
        "- Products related to 18-40 age group should be targeted as they make 75% of purchases.\n",
        "-The top products should be given focus in order to maintain the quality in order to further increase the sales of those products.\n",
        "-People who are staying in city for an year have contributed to 35% of the total purchase amount. Company can focus on such customer base who are neither too old nor too new residents in the city.\n",
        "-We have highest frequency of purchase order between 5k and 10k, company can focus more on these mid range products to increase the sales.\n",
        "-The top 10 users who have purchased more company should give more offers and discounts so that they can be retained and can be helpful for companies business.\n"
      ],
      "metadata": {
        "id": "LR669rD33T_-"
      }
    }
  ]
}