Diwali_sales_analysis.ipynb

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      "source": [
        " import numpy as np\n",
        " import pandas as pd\n",
        " import matplotlib.pyplot as plt\n",
        " %matplotlib inline\n",
        " import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('/content/Diwali Sales Data.csv', encoding = 'unicode_escape')"
      ],
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        "df.shape"
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              "(11251, 15)"
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        "df.head()"
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      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   User_ID  Cust_name Product_ID Gender Age Group  Age  Marital_Status  \\\n",
              "0  1002903  Sanskriti  P00125942      F     26-35   28               0   \n",
              "1  1000732     Kartik  P00110942      F     26-35   35               1   \n",
              "2  1001990      Bindu  P00118542      F     26-35   35               1   \n",
              "3  1001425     Sudevi  P00237842      M      0-17   16               0   \n",
              "4  1000588       Joni  P00057942      M     26-35   28               1   \n",
              "\n",
              "            State      Zone       Occupation Product_Category  Orders  \\\n",
              "0     Maharashtra   Western       Healthcare             Auto       1   \n",
              "1  Andhra Pradesh  Southern             Govt             Auto       3   \n",
              "2   Uttar Pradesh   Central       Automobile             Auto       3   \n",
              "3       Karnataka  Southern     Construction             Auto       2   \n",
              "4         Gujarat   Western  Food Processing             Auto       2   \n",
              "\n",
              "    Amount  Status  unnamed1  \n",
              "0  23952.0     NaN       NaN  \n",
              "1  23934.0     NaN       NaN  \n",
              "2  23924.0     NaN       NaN  \n",
              "3  23912.0     NaN       NaN  \n",
              "4  23877.0     NaN       NaN  "
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              "      <td>Karnataka</td>\n",
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              "      <td>23912.0</td>\n",
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              "      <td>NaN</td>\n",
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              "      <th>4</th>\n",
              "      <td>1000588</td>\n",
              "      <td>Joni</td>\n",
              "      <td>P00057942</td>\n",
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              "      <td>26-35</td>\n",
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              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 11251,\n  \"fields\": [\n    {\n      \"column\": \"User_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1716,\n        \"min\": 1000001,\n        \"max\": 1006040,\n        \"num_unique_values\": 3755,\n        \"samples\": [\n          1005905,\n          1003730,\n          1005326\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cust_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1250,\n        \"samples\": [\n          \"Nida\",\n          \"Lacy\",\n          \"Caudle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2351,\n        \"samples\": [\n          \"P00224442\",\n          \"P00205242\",\n          \"P00347442\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"M\",\n          \"F\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age Group\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"26-35\",\n          \"0-17\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12,\n        \"min\": 12,\n        \"max\": 92,\n        \"num_unique_values\": 81,\n        \"samples\": [\n          18,\n          28\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marital_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          1,\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 16,\n        \"samples\": [\n          \"Maharashtra\",\n          \"Andhra\\u00a0Pradesh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zone\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"Southern\",\n          \"Eastern\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Occupation\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 15,\n        \"samples\": [\n          \"Retail\",\n          \"Aviation\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product_Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Auto\",\n          \"Hand & Power Tools\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Orders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 1,\n        \"max\": 4,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3,\n          4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5222.355869186455,\n        \"min\": 188.0,\n        \"max\": 23952.0,\n        \"num_unique_values\": 6584,\n        \"samples\": [\n          19249.0,\n          13184.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"unnamed1\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": null,\n        \"min\": null,\n        \"max\": null,\n        \"num_unique_values\": 0,\n        \"samples\": [],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
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      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 11251 entries, 0 to 11250\n",
            "Data columns (total 15 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   User_ID           11251 non-null  int64  \n",
            " 1   Cust_name         11251 non-null  object \n",
            " 2   Product_ID        11251 non-null  object \n",
            " 3   Gender            11251 non-null  object \n",
            " 4   Age Group         11251 non-null  object \n",
            " 5   Age               11251 non-null  int64  \n",
            " 6   Marital_Status    11251 non-null  int64  \n",
            " 7   State             11251 non-null  object \n",
            " 8   Zone              11251 non-null  object \n",
            " 9   Occupation        11251 non-null  object \n",
            " 10  Product_Category  11251 non-null  object \n",
            " 11  Orders            11251 non-null  int64  \n",
            " 12  Amount            11239 non-null  float64\n",
            " 13  Status            0 non-null      float64\n",
            " 14  unnamed1          0 non-null      float64\n",
            "dtypes: float64(3), int64(4), object(8)\n",
            "memory usage: 1.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.drop(['Status', 'unnamed1'], axis=1, inplace=True)"
      ],
      "metadata": {
        "id": "UbHZ_ead-xDc"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.isnull(df)"
      ],
      "metadata": {
        "id": "suExT9h-__sb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "pd.isnull(df).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pfp5YkHEAPWR",
        "outputId": "7c3ccfa3-497d-458c-e679-8cfc003cc62c"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User_ID              0\n",
              "Cust_name            0\n",
              "Product_ID           0\n",
              "Gender               0\n",
              "Age Group            0\n",
              "Age                  0\n",
              "Marital_Status       0\n",
              "State                0\n",
              "Zone                 0\n",
              "Occupation           0\n",
              "Product_Category     0\n",
              "Orders               0\n",
              "Amount              12\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(inplace=True)"
      ],
      "metadata": {
        "id": "9iuaExw1AYFk"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yONVK-O7Ad9N",
        "outputId": "7c7849b4-9327-499f-c918-f2d91a57dd23"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11239, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Amount'] = df['Amount'].astype('int')"
      ],
      "metadata": {
        "id": "Hp1XidrZCXop"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D-LGUoFwEWbg",
        "outputId": "352947b0-7e77-47a5-db3e-b43941456f69"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['User_ID', 'Cust_name', 'Product_ID', 'Gender', 'Age Group', 'Age',\n",
              "       'Marital_Status', 'State', 'Zone', 'Occupation', 'Product_Category',\n",
              "       'Orders', 'Amount'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.rename(columns = {'Marital_Status':'Shadi'})"
      ],
      "metadata": {
        "id": "PJBsPsiEEbPp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "5_zoRBC1ExmY",
        "outputId": "25a5b53d-2664-4189-e239-2d8e8aa1b325"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            User_ID           Age  Marital_Status        Orders        Amount\n",
              "count  1.123900e+04  11239.000000    11239.000000  11239.000000  11239.000000\n",
              "mean   1.003004e+06     35.410357        0.420055      2.489634   9453.610553\n",
              "std    1.716039e+03     12.753866        0.493589      1.114967   5222.355168\n",
              "min    1.000001e+06     12.000000        0.000000      1.000000    188.000000\n",
              "25%    1.001492e+06     27.000000        0.000000      2.000000   5443.000000\n",
              "50%    1.003064e+06     33.000000        0.000000      2.000000   8109.000000\n",
              "75%    1.004426e+06     43.000000        1.000000      3.000000  12675.000000\n",
              "max    1.006040e+06     92.000000        1.000000      4.000000  23952.000000"
            ],
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              "      <th>count</th>\n",
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              "      <td>11239.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.003004e+06</td>\n",
              "      <td>35.410357</td>\n",
              "      <td>0.420055</td>\n",
              "      <td>2.489634</td>\n",
              "      <td>9453.610553</td>\n",
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              "      <th>min</th>\n",
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              "      <td>2.000000</td>\n",
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              "      <td>33.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>8109.000000</td>\n",
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              "      <td>1.004426e+06</td>\n",
              "      <td>43.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "    async function quickchart(key) {\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"User_ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 461312.82997958694,\n        \"min\": 1716.0388257054724,\n        \"max\": 1006040.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1003003.5217546045,\n          1003064.0,\n          11239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3960.7779927819724,\n        \"min\": 12.0,\n        \"max\": 11239.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.41035679330901,\n          33.0,\n          11239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Marital_Status\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3973.439417307323,\n        \"min\": 0.0,\n        \"max\": 11239.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          0.42005516505027135,\n          1.0,\n          0.4935894048750261\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Orders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3972.7985251347,\n        \"min\": 1.0,\n        \"max\": 11239.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11239.0,\n          2.4896343091022333,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7024.070687950829,\n        \"min\": 188.0,\n        \"max\": 23952.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9453.610552540262,\n          8109.0,\n          11239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['Age', 'Orders', 'Amount']].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "ILaVTKWQE8Vq",
        "outputId": "eb3e4fd9-4510-407e-fbbe-c12731a8aa57"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Age        Orders        Amount\n",
              "count  11239.000000  11239.000000  11239.000000\n",
              "mean      35.410357      2.489634   9453.610553\n",
              "std       12.753866      1.114967   5222.355168\n",
              "min       12.000000      1.000000    188.000000\n",
              "25%       27.000000      2.000000   5443.000000\n",
              "50%       33.000000      2.000000   8109.000000\n",
              "75%       43.000000      3.000000  12675.000000\n",
              "max       92.000000      4.000000  23952.000000"
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>Orders</th>\n",
              "      <th>Amount</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>11239.000000</td>\n",
              "      <td>11239.000000</td>\n",
              "      <td>11239.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>35.410357</td>\n",
              "      <td>2.489634</td>\n",
              "      <td>9453.610553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.753866</td>\n",
              "      <td>1.114967</td>\n",
              "      <td>5222.355168</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>188.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5443.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>33.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>8109.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>43.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>12675.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>4.000000</td>\n",
              "      <td>23952.000000</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"df[['Age', 'Orders', 'Amount']]\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3960.7779927819724,\n        \"min\": 12.0,\n        \"max\": 11239.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          35.41035679330901,\n          33.0,\n          11239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Orders\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3972.7985251347,\n        \"min\": 1.0,\n        \"max\": 11239.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          11239.0,\n          2.4896343091022333,\n          3.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Amount\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7024.070687950829,\n        \"min\": 188.0,\n        \"max\": 23952.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          9453.610552540262,\n          8109.0,\n          11239.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Exploratory Data Analysis**\n"
      ],
      "metadata": {
        "id": "If3_FrX5FQVX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# GENDER\n",
        "\n",
        "ax = sns.countplot(x = 'Gender', data= df , color = 'Red')\n",
        "\n",
        "for bars in ax.containers:\n",
        "  ax.bar_label(bars)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "T5OsB337FXwY",
        "outputId": "dfac5679-3bb5-4925-a07f-19673fc91b73"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sales_gen = df.groupby(['Gender'], as_index=False)['Amount'].sum().sort_values(by='Amount', ascending=False)"
      ],
      "metadata": {
        "id": "uPEQtt8iGTov"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x = 'Gender', y = 'Amount', data=sales_gen)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "kpoac9d_HA2w",
        "outputId": "8319e680-c5cb-4275-cd64-bacccc4de8c5"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Gender', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 34
        },
        {
          "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": [
        "from above graphs we can see most of the most of the buyers are females and even the purchasing power of females are greater than men."
      ],
      "metadata": {
        "id": "AytBzHhnTiba"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Age\n",
        "ax = sns.countplot(data = df, x = 'Age Group', hue = 'Gender')\n",
        "\n",
        "for bars in ax.containers:\n",
        "  ax.bar_label(bars)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 340
        },
        "id": "-mIUv3plT5bf",
        "outputId": "8511c1fa-dc73-43d8-8ef7-fa28c0a43cfe"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x900 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# total Amount vs Age group\n",
        "\n",
        "sales_age = df.groupby(['Age Group'], as_index=False)['Amount'].sum().sort_values(by = 'Amount', ascending=False)\n",
        "\n",
        "sns.barplot(x = 'Age Group', y = 'Amount', data = sales_age, color = 'Green')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "SWX22rRZVv7r",
        "outputId": "b0ff2ed9-d7da-4794-8460-4a27406596af"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Age Group', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "from above graphs we can see most of the buyers are of age group between 26-35 yrs Female\n"
      ],
      "metadata": {
        "id": "QMOq0UM0XCh7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#State\n",
        "\n",
        "sales_state = df.groupby(['State'], as_index=False)['Orders'].sum().sort_values(by = 'Orders', ascending=False).head(10)\n",
        "sns.set(rc={'figure.figsize':(18,9)})\n",
        "sns.barplot(data = sales_state, x = 'State', y = 'Orders', color = 'Blue')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "9UD4sBpqXNMg",
        "outputId": "8ec23545-9d8a-4e1b-e8ad-a0bdac3caae2"
      },
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='State', ylabel='Orders'>"
            ]
          },
          "metadata": {},
          "execution_count": 62
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x900 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#total amount/sales from top 10 states\n",
        "\n",
        "sales_state = df.groupby(['State'], as_index = False)['Amount'].sum().sort_values(by = 'Amount', ascending=False).head(10)\n",
        "sns.barplot(data= sales_state, x = 'State', y = 'Amount')\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 365
        },
        "id": "qMTqFfzNao1h",
        "outputId": "dfe6e5fd-c06a-4068-8d2e-3fc043951445"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='State', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 71
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x900 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "from above graphs we can see that most of the orders are from Uttarpradesh, maharashtra and karnataka respectively but total sales/amount is from UP, Karnataka then Maharashtra"
      ],
      "metadata": {
        "id": "2f-yUuD7Z9ca"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Marital Status\n",
        "ax = sns.countplot(data= df, x = 'Marital_Status')\n",
        "\n",
        "sns.set(rc={'figure.figsize':(7,5)})\n",
        "\n",
        "for bars in ax.containers:\n",
        "  ax.bar_label(bars)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 468
        },
        "id": "-04SzJoaai-Y",
        "outputId": "32f17940-02d2-41c3-c648-8f7fcf790647"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sales_state = df.groupby(['Marital_Status', 'Gender'], as_index = False)['Amount'].sum().sort_values(by = 'Amount', ascending=False)\n",
        "\n",
        "sns.barplot(data = sales_state, x = 'Marital_Status', y = 'Amount', hue = 'Gender')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "F-ZdSgGLfB88",
        "outputId": "738b2194-fad7-420b-d8c1-1dd976f1055a"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Marital_Status', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 88
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 700x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From above graphs we can see most of the buyers are married (females) and they have high purchasing power"
      ],
      "metadata": {
        "id": "JogqOOHzgE1B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set(rc = {'figure.figsize':(25,10)})\n",
        "ax = sns.countplot(data = df, x = 'Occupation')\n",
        "\n",
        "for bars in ax.containers:\n",
        "  ax.bar_label(bars)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "id": "3tKPH6atgQkJ",
        "outputId": "d4672d43-4ad2-42ee-be4a-fb1a01132e6e"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sales_state = df.groupby(['Occupation'], as_index = False)['Amount'].sum().sort_values(by = 'Amount', ascending=False)\n",
        "\n",
        "sns.barplot(data = sales_state, x = 'Occupation', y = 'Amount')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "cj-t8jj5i8P8",
        "outputId": "20ded6a2-d8f9-4b35-f414-77d7cdfff0cc"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Occupation', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "from above graphs we can see that most of the purchased are from IT sector followed by Healthcare and Aviation sector."
      ],
      "metadata": {
        "id": "ZCilhQOrjcHY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Product Category\n",
        "ax = sns.countplot(data = df, x = 'Product_Category')\n",
        "sns.set(rc={'figure.figsize':(20,5)})\n",
        "for bars in ax.containers:\n",
        "  ax.bar_label(bars)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 192
        },
        "id": "RSO_a9a9joDH",
        "outputId": "70a4b1b5-c14b-48dd-b71b-c0c59dea6fa5"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sales_state = df.groupby(['Product_Category'], as_index = False)['Amount'].sum().sort_values(by = 'Amount', ascending=False).head(10)\n",
        "sns.barplot(data = sales_state, x = 'Product_Category', y = 'Amount')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        },
        "id": "YFwE8-8AkcOH",
        "outputId": "67a3e1d5-bac8-4421-b7dc-336318127087"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Product_Category', ylabel='Amount'>"
            ]
          },
          "metadata": {},
          "execution_count": 103
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "from above graphs we can see that most of the sold products are from food, clothing and electronics category"
      ],
      "metadata": {
        "id": "mlhkbyJnlHEh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Conclusion:**\n",
        "**Married women age group 26-35 years from UP, Maharashtra and Karnataka working in IT, HEalthcare and aviation are more likely to buy products from Food, Clothing and Electronics category.**"
      ],
      "metadata": {
        "id": "52F7Hsjslgs1"
      }
    }
  ]
}