Food_Sales_Prediction_2.ipynb

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      "execution_count": 1,
      "metadata": {
        "id": "wNV_3sRcsFKT"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "USUN12a3sQk3"
      },
      "outputs": [],
      "source": [
        "filename = '/content/drive/MyDrive/CODING DOJO DS BOOTCAMP/02 Week 2: Pandas/Assignments/Core Assignments/sales_predictions.csv'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "k-QHRXRXsQxC"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(filename)"
      ]
    },
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            "text/plain": [
              "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
              "0           FDA15        9.300          Low Fat         0.016047   \n",
              "1           DRC01        5.920          Regular         0.019278   \n",
              "2           FDN15       17.500          Low Fat         0.016760   \n",
              "3           FDX07       19.200          Regular         0.000000   \n",
              "4           NCD19        8.930          Low Fat         0.000000   \n",
              "5           FDP36       10.395          Regular         0.000000   \n",
              "6           FDO10       13.650          Regular         0.012741   \n",
              "7           FDP10          NaN          Low Fat         0.127470   \n",
              "8           FDH17       16.200          Regular         0.016687   \n",
              "9           FDU28       19.200          Regular         0.094450   \n",
              "\n",
              "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
              "0                  Dairy  249.8092            OUT049   \n",
              "1            Soft Drinks   48.2692            OUT018   \n",
              "2                   Meat  141.6180            OUT049   \n",
              "3  Fruits and Vegetables  182.0950            OUT010   \n",
              "4              Household   53.8614            OUT013   \n",
              "5           Baking Goods   51.4008            OUT018   \n",
              "6            Snack Foods   57.6588            OUT013   \n",
              "7            Snack Foods  107.7622            OUT027   \n",
              "8           Frozen Foods   96.9726            OUT045   \n",
              "9           Frozen Foods  187.8214            OUT017   \n",
              "\n",
              "   Outlet_Establishment_Year Outlet_Size Outlet_Location_Type  \\\n",
              "0                       1999      Medium               Tier 1   \n",
              "1                       2009      Medium               Tier 3   \n",
              "2                       1999      Medium               Tier 1   \n",
              "3                       1998         NaN               Tier 3   \n",
              "4                       1987        High               Tier 3   \n",
              "5                       2009      Medium               Tier 3   \n",
              "6                       1987        High               Tier 3   \n",
              "7                       1985      Medium               Tier 3   \n",
              "8                       2002         NaN               Tier 2   \n",
              "9                       2007         NaN               Tier 2   \n",
              "\n",
              "         Outlet_Type  Item_Outlet_Sales  \n",
              "0  Supermarket Type1          3735.1380  \n",
              "1  Supermarket Type2           443.4228  \n",
              "2  Supermarket Type1          2097.2700  \n",
              "3      Grocery Store           732.3800  \n",
              "4  Supermarket Type1           994.7052  \n",
              "5  Supermarket Type2           556.6088  \n",
              "6  Supermarket Type1           343.5528  \n",
              "7  Supermarket Type3          4022.7636  \n",
              "8  Supermarket Type1          1076.5986  \n",
              "9  Supermarket Type1          4710.5350  "
            ],
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              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
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              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.300</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
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              "      <td>Supermarket Type1</td>\n",
              "      <td>3735.1380</td>\n",
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              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>5.920</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
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              "      <td>17.500</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>Meat</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.200</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Grocery Store</td>\n",
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              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>8.930</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>High</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>FDP36</td>\n",
              "      <td>10.395</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Baking Goods</td>\n",
              "      <td>51.4008</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "      <td>556.6088</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>FDO10</td>\n",
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              "      <td>Regular</td>\n",
              "      <td>0.012741</td>\n",
              "      <td>Snack Foods</td>\n",
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              "      <td>OUT013</td>\n",
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              "      <td>FDP10</td>\n",
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              "      <td>0.127470</td>\n",
              "      <td>Snack Foods</td>\n",
              "      <td>107.7622</td>\n",
              "      <td>OUT027</td>\n",
              "      <td>1985</td>\n",
              "      <td>Medium</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type3</td>\n",
              "      <td>4022.7636</td>\n",
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              "      <th>8</th>\n",
              "      <td>FDH17</td>\n",
              "      <td>16.200</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.016687</td>\n",
              "      <td>Frozen Foods</td>\n",
              "      <td>96.9726</td>\n",
              "      <td>OUT045</td>\n",
              "      <td>2002</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tier 2</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>1076.5986</td>\n",
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              "      <th>9</th>\n",
              "      <td>FDU28</td>\n",
              "      <td>19.200</td>\n",
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              "      <td>0.094450</td>\n",
              "      <td>Frozen Foods</td>\n",
              "      <td>187.8214</td>\n",
              "      <td>OUT017</td>\n",
              "      <td>2007</td>\n",
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              "      <td>Tier 2</td>\n",
              "      <td>Supermarket Type1</td>\n",
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          "metadata": {},
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      "source": [
        "df.head(10)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HmDHoPcXuGkX"
      },
      "source": [
        "1. **How many rows and columns?**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HANYeXM3uE4M",
        "outputId": "51b8d770-37bf-4b8a-dae2-04cf638ee67b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8523, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jhMO8iMkuOoz"
      },
      "source": [
        "2. **What are the datatypes of each variable?**\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KlTI2TsxuFZ2",
        "outputId": "7cbf59d4-944d-424d-a14a-71a8ea0982eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier               object\n",
              "Item_Weight                  float64\n",
              "Item_Fat_Content              object\n",
              "Item_Visibility              float64\n",
              "Item_Type                     object\n",
              "Item_MRP                     float64\n",
              "Outlet_Identifier             object\n",
              "Outlet_Establishment_Year      int64\n",
              "Outlet_Size                   object\n",
              "Outlet_Location_Type          object\n",
              "Outlet_Type                   object\n",
              "Item_Outlet_Sales            float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5fpczkeEujTo"
      },
      "source": [
        "3. **Are there duplicates? If so, drop any duplicates.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XCGbs8wCuFkU",
        "outputId": "f95027b3-c1ec-43d5-f5cf-d474bb2c65da"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "df.duplicated().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qcOrmgD2uss5"
      },
      "source": [
        "It looks like our dataset doesn't contain any duplicates which is a good thing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J7WVGYjHu6K_"
      },
      "source": [
        "4. **Identify missing values.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7vxRYtmFuFrJ",
        "outputId": "5506bb1c-d9dd-497e-911d-d2b81c3e6264"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier                 0\n",
              "Item_Weight                  1463\n",
              "Item_Fat_Content                0\n",
              "Item_Visibility                 0\n",
              "Item_Type                       0\n",
              "Item_MRP                        0\n",
              "Outlet_Identifier               0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Size                  2410\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Type                     0\n",
              "Item_Outlet_Sales               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rB6zevU1vECW"
      },
      "source": [
        "It looks like the columns Item_Weight and Outlet_Size have missing values and we are going to need to address that"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x-egpPzkvZuH"
      },
      "source": [
        "\n",
        "*   **For the Outlet_Size column**\n",
        "\n",
        "\n",
        "\n",
        "> This column represents the size of the store in terms of ground area covered.\n",
        "\n",
        "\n",
        "\n",
        "> It doesn't have any significant effect on our analysis as it doesn't serve the purpose of our analysis which is to help the retailer understand the properties of products and outlets that play crucial roles in increasing sales.\n",
        "\n",
        "\n",
        "\n",
        "> So with that said, **I will drop this column from our dataset.**\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
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          "data": {
            "text/plain": [
              "Item_Identifier                 0\n",
              "Item_Weight                  1463\n",
              "Item_Fat_Content                0\n",
              "Item_Visibility                 0\n",
              "Item_Type                       0\n",
              "Item_MRP                        0\n",
              "Outlet_Identifier               0\n",
              "Outlet_Establishment_Year       0\n",
              "Outlet_Location_Type            0\n",
              "Outlet_Type                     0\n",
              "Item_Outlet_Sales               0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# Dropping the Outlet_Size column\n",
        "df.drop(columns='Outlet_Size', inplace=True)\n",
        "\n",
        "# Checking for missing values again\n",
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AAJeDjaHxdLl"
      },
      "source": [
        "\n",
        "*   **For the Item_Weight column**\n",
        "\n",
        "\n",
        "\n",
        "> This column represents the Weight of the product which is gonna be an important thing as it will the weights of products that are having high sales\n",
        "\n",
        "\n",
        "\n",
        "> I will **fill the missing values** of the Item_Weight column if there are any with the mean weight of the Item_Identifier it has.\n",
        "\n",
        "\n",
        "> I choose to use the mean to fill the missing values for this column because mean enables us to use the average value/weight and this provides us with roughly the same values for our analysis.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpIHoExEsuQw",
        "outputId": "7b2bd31b-98e4-4943-ae0a-fe739117558b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier              0\n",
              "Item_Weight                  4\n",
              "Item_Fat_Content             0\n",
              "Item_Visibility              0\n",
              "Item_Type                    0\n",
              "Item_MRP                     0\n",
              "Outlet_Identifier            0\n",
              "Outlet_Establishment_Year    0\n",
              "Outlet_Location_Type         0\n",
              "Outlet_Type                  0\n",
              "Item_Outlet_Sales            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "# Group the DataFrame by the Item_Identifier column and calculate the mean weight for each identifier\n",
        "mean_weights = df.groupby('Item_Identifier')['Item_Weight'].transform('mean')\n",
        "mean_weights\n",
        "\n",
        "# Fill the missing values in the Item_Weight column with the mean weight for each identifier\n",
        "df['Item_Weight'].fillna(mean_weights, inplace=True)\n",
        "\n",
        "# Checking for missing values in our dataframe again\n",
        "df.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YcNK97Wv5zbz"
      },
      "source": [
        "Even after doing that. There are still 4 rows that are still missing Item_Weight values. \n",
        "\n",
        "We shall just get rid of those 4 rows that have missing weights."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YPhj0-PysuTw",
        "outputId": "a044025c-06a5-4095-8abb-d6ac23febc0f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier              0\n",
              "Item_Weight                  0\n",
              "Item_Fat_Content             0\n",
              "Item_Visibility              0\n",
              "Item_Type                    0\n",
              "Item_MRP                     0\n",
              "Outlet_Identifier            0\n",
              "Outlet_Establishment_Year    0\n",
              "Outlet_Location_Type         0\n",
              "Outlet_Type                  0\n",
              "Item_Outlet_Sales            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "# Dropping the 4 rows that still have missing Item_Weight values\n",
        "df.dropna(subset=['Item_Weight'], inplace=True)\n",
        "\n",
        "# checking for missing values again\n",
        "df.isna().sum()\n",
        "\n",
        "# Our data now doesn't have any missing values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vjLazITS6vwh"
      },
      "source": [
        "**Great**. We are now **FREE** of missing values"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hB6RQiHT7_hY"
      },
      "source": [
        "7. **Find and fix any inconsistent categories of data (example: fix cat, Cat, and cats so that they are consistent)** "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WOPn-QlSsuX5",
        "outputId": "c9eb4df0-9e47-4873-b870-2d0fc4cc1467"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Column: Item_Identifier\n",
            "FDG33    10\n",
            "FDW13    10\n",
            "FDV60     9\n",
            "FDU12     9\n",
            "FDX20     9\n",
            "         ..\n",
            "DRF48     1\n",
            "FDY43     1\n",
            "FDC23     1\n",
            "FDO33     1\n",
            "FDT35     1\n",
            "Name: Item_Identifier, Length: 1555, dtype: int64 \n",
            "\n",
            "Column: Item_Weight\n",
            "12.150    105\n",
            "17.600    102\n",
            "13.650     89\n",
            "11.800     86\n",
            "15.100     86\n",
            "         ... \n",
            "6.985       1\n",
            "6.765       1\n",
            "12.300      1\n",
            "7.260       1\n",
            "7.470       1\n",
            "Name: Item_Weight, Length: 446, dtype: int64 \n",
            "\n",
            "Column: Item_Fat_Content\n",
            "Low Fat    5088\n",
            "Regular    2886\n",
            "LF          316\n",
            "reg         117\n",
            "low fat     112\n",
            "Name: Item_Fat_Content, dtype: int64 \n",
            "\n",
            "Column: Item_Visibility\n",
            "0.000000    526\n",
            "0.076975      3\n",
            "0.107037      2\n",
            "0.061271      2\n",
            "0.046825      2\n",
            "           ... \n",
            "0.056387      1\n",
            "0.013957      1\n",
            "0.110460      1\n",
            "0.124646      1\n",
            "0.044878      1\n",
            "Name: Item_Visibility, Length: 7876, dtype: int64 \n",
            "\n",
            "Column: Item_Type\n",
            "Fruits and Vegetables    1232\n",
            "Snack Foods              1199\n",
            "Household                 910\n",
            "Frozen Foods              855\n",
            "Dairy                     681\n",
            "Canned                    649\n",
            "Baking Goods              647\n",
            "Health and Hygiene        520\n",
            "Soft Drinks               445\n",
            "Meat                      425\n",
            "Breads                    251\n",
            "Hard Drinks               214\n",
            "Others                    169\n",
            "Starchy Foods             148\n",
            "Breakfast                 110\n",
            "Seafood                    64\n",
            "Name: Item_Type, dtype: int64 \n",
            "\n",
            "Column: Item_MRP\n",
            "172.0422    7\n",
            "188.1872    6\n",
            "142.0154    6\n",
            "170.5422    6\n",
            "196.5084    6\n",
            "           ..\n",
            "83.1934     1\n",
            "96.6752     1\n",
            "152.6682    1\n",
            "180.5660    1\n",
            "75.4670     1\n",
            "Name: Item_MRP, Length: 5936, dtype: int64 \n",
            "\n",
            "Column: Outlet_Identifier\n",
            "OUT013    932\n",
            "OUT027    932\n",
            "OUT049    930\n",
            "OUT046    930\n",
            "OUT035    930\n",
            "OUT045    929\n",
            "OUT018    928\n",
            "OUT017    926\n",
            "OUT010    555\n",
            "OUT019    527\n",
            "Name: Outlet_Identifier, dtype: int64 \n",
            "\n",
            "Column: Outlet_Establishment_Year\n",
            "1985    1459\n",
            "1987     932\n",
            "1999     930\n",
            "1997     930\n",
            "2004     930\n",
            "2002     929\n",
            "2009     928\n",
            "2007     926\n",
            "1998     555\n",
            "Name: Outlet_Establishment_Year, dtype: int64 \n",
            "\n",
            "Column: Outlet_Location_Type\n",
            "Tier 3    3347\n",
            "Tier 2    2785\n",
            "Tier 1    2387\n",
            "Name: Outlet_Location_Type, dtype: int64 \n",
            "\n",
            "Column: Outlet_Type\n",
            "Supermarket Type1    5577\n",
            "Grocery Store        1082\n",
            "Supermarket Type3     932\n",
            "Supermarket Type2     928\n",
            "Name: Outlet_Type, dtype: int64 \n",
            "\n",
            "Column: Item_Outlet_Sales\n",
            "958.7520     17\n",
            "1342.2528    16\n",
            "1845.5976    15\n",
            "703.0848     15\n",
            "1278.3360    14\n",
            "             ..\n",
            "3485.4630     1\n",
            "4124.6310     1\n",
            "1614.5650     1\n",
            "5602.7070     1\n",
            "2778.3834     1\n",
            "Name: Item_Outlet_Sales, Length: 3493, dtype: int64 \n",
            "\n"
          ]
        }
      ],
      "source": [
        "# Iterating through each column to find the column that has inconsistent data\n",
        "for col in df.columns:\n",
        "    print(\"Column:\", col)\n",
        "    print(df[col].value_counts(), \"\\n\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dcflT1Zk8cDk"
      },
      "source": [
        "I have noticed the **Item_Fat_Content** column has inconsistent data and I need to fix that."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4FpWgU06sujY",
        "outputId": "fd34b44f-0700-43ed-ab89-f56361b11109"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Fat    5088\n",
              "Regular    2886\n",
              "LF          316\n",
              "reg         117\n",
              "low fat     112\n",
              "Name: Item_Fat_Content, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "df['Item_Fat_Content'].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UxZ-fhPX77s6",
        "outputId": "d4f6b7ed-8f36-45e5-9234-58e2ea046919"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Low Fat    5516\n",
              "Regular    3003\n",
              "Name: Item_Fat_Content, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "# Replacing values LF, reg, low fat to their appropriate values\n",
        "df['Item_Fat_Content'] = df['Item_Fat_Content'].replace({'LF': 'Low Fat', 'reg':'Regular', 'low fat': 'Low Fat', 'regular': 'Regular'})\n",
        "df['Item_Fat_Content'].value_counts()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U0nc8PZVAG-v"
      },
      "source": [
        "8. **For any numerical columns, obtain the summary statistics of each (min, max, mean)**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "RAkd7GS89h3l",
        "outputId": "e35c1a8b-fd4f-4e7d-bf1c-3253d09f6051"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Item_Weight  Item_Visibility     Item_MRP  Outlet_Establishment_Year  \\\n",
              "count  8519.000000      8519.000000  8519.000000                8519.000000   \n",
              "mean     12.875420         0.066112   141.010019                1997.837892   \n",
              "std       4.646098         0.051586    62.283594                   8.369105   \n",
              "min       4.555000         0.000000    31.290000                1985.000000   \n",
              "25%       8.785000         0.026983    93.844900                1987.000000   \n",
              "50%      12.650000         0.053925   143.047000                1999.000000   \n",
              "75%      16.850000         0.094558   185.676600                2004.000000   \n",
              "max      21.350000         0.328391   266.888400                2009.000000   \n",
              "\n",
              "       Item_Outlet_Sales  \n",
              "count        8519.000000  \n",
              "mean         2181.188779  \n",
              "std          1706.511093  \n",
              "min            33.290000  \n",
              "25%           834.247400  \n",
              "50%          1794.331000  \n",
              "75%          3100.630600  \n",
              "max         13086.964800  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
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              "      <td>8519.000000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.875420</td>\n",
              "      <td>0.066112</td>\n",
              "      <td>141.010019</td>\n",
              "      <td>1997.837892</td>\n",
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              "      <th>std</th>\n",
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              "      <td>8.369105</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
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              "      <td>12.650000</td>\n",
              "      <td>0.053925</td>\n",
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              "      <td>1999.000000</td>\n",
              "      <td>1794.331000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>16.850000</td>\n",
              "      <td>0.094558</td>\n",
              "      <td>185.676600</td>\n",
              "      <td>2004.000000</td>\n",
              "      <td>3100.630600</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>21.350000</td>\n",
              "      <td>0.328391</td>\n",
              "      <td>266.888400</td>\n",
              "      <td>2009.000000</td>\n",
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          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "ZnARtlbKALX-"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-r_O3RDEs9ne"
      },
      "source": [
        "**PART 3**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "S8c8i5n8BjEE",
        "outputId": "98b862dd-1dc3-4f0c-9d1a-64fd743b36c3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Item_Identifier  Item_Weight Item_Fat_Content  Item_Visibility  \\\n",
              "0           FDA15         9.30          Low Fat         0.016047   \n",
              "1           DRC01         5.92          Regular         0.019278   \n",
              "2           FDN15        17.50          Low Fat         0.016760   \n",
              "3           FDX07        19.20          Regular         0.000000   \n",
              "4           NCD19         8.93          Low Fat         0.000000   \n",
              "\n",
              "               Item_Type  Item_MRP Outlet_Identifier  \\\n",
              "0                  Dairy  249.8092            OUT049   \n",
              "1            Soft Drinks   48.2692            OUT018   \n",
              "2                   Meat  141.6180            OUT049   \n",
              "3  Fruits and Vegetables  182.0950            OUT010   \n",
              "4              Household   53.8614            OUT013   \n",
              "\n",
              "   Outlet_Establishment_Year Outlet_Location_Type        Outlet_Type  \\\n",
              "0                       1999               Tier 1  Supermarket Type1   \n",
              "1                       2009               Tier 3  Supermarket Type2   \n",
              "2                       1999               Tier 1  Supermarket Type1   \n",
              "3                       1998               Tier 3      Grocery Store   \n",
              "4                       1987               Tier 3  Supermarket Type1   \n",
              "\n",
              "   Item_Outlet_Sales  \n",
              "0          3735.1380  \n",
              "1           443.4228  \n",
              "2          2097.2700  \n",
              "3           732.3800  \n",
              "4           994.7052  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7dd641ff-258d-4c28-ab9f-23779059ccc0\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Item_Identifier</th>\n",
              "      <th>Item_Weight</th>\n",
              "      <th>Item_Fat_Content</th>\n",
              "      <th>Item_Visibility</th>\n",
              "      <th>Item_Type</th>\n",
              "      <th>Item_MRP</th>\n",
              "      <th>Outlet_Identifier</th>\n",
              "      <th>Outlet_Establishment_Year</th>\n",
              "      <th>Outlet_Location_Type</th>\n",
              "      <th>Outlet_Type</th>\n",
              "      <th>Item_Outlet_Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FDA15</td>\n",
              "      <td>9.30</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016047</td>\n",
              "      <td>Dairy</td>\n",
              "      <td>249.8092</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>3735.1380</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>DRC01</td>\n",
              "      <td>5.92</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.019278</td>\n",
              "      <td>Soft Drinks</td>\n",
              "      <td>48.2692</td>\n",
              "      <td>OUT018</td>\n",
              "      <td>2009</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type2</td>\n",
              "      <td>443.4228</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>FDN15</td>\n",
              "      <td>17.50</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.016760</td>\n",
              "      <td>Meat</td>\n",
              "      <td>141.6180</td>\n",
              "      <td>OUT049</td>\n",
              "      <td>1999</td>\n",
              "      <td>Tier 1</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>2097.2700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>FDX07</td>\n",
              "      <td>19.20</td>\n",
              "      <td>Regular</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Fruits and Vegetables</td>\n",
              "      <td>182.0950</td>\n",
              "      <td>OUT010</td>\n",
              "      <td>1998</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Grocery Store</td>\n",
              "      <td>732.3800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>NCD19</td>\n",
              "      <td>8.93</td>\n",
              "      <td>Low Fat</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>Household</td>\n",
              "      <td>53.8614</td>\n",
              "      <td>OUT013</td>\n",
              "      <td>1987</td>\n",
              "      <td>Tier 3</td>\n",
              "      <td>Supermarket Type1</td>\n",
              "      <td>994.7052</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7dd641ff-258d-4c28-ab9f-23779059ccc0')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7dd641ff-258d-4c28-ab9f-23779059ccc0 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7dd641ff-258d-4c28-ab9f-23779059ccc0');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Histograms**"
      ],
      "metadata": {
        "id": "as6HCkguuvY8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 324
        },
        "id": "-qXVDpWVuPR3",
        "outputId": "c788af01-e163-4430-9c25-57971165307c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Making a histogram for the Item_Weight column\n",
        "sns.histplot(data=df, x='Item_Weight')\n",
        "\n",
        "plt.title('A Histogram of the count of each Item Weight in our Data', fontsize=20)\n",
        "plt.xlabel('Item_Weight', fontsize=16)\n",
        "plt.ylabel('Count', fontsize=16)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JfdnvreEH5TS"
      },
      "source": [
        "You can see from the above histogram that most of the products have a weight above 5."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 321
        },
        "id": "WEOphFc-Bxc0",
        "outputId": "25d496ba-975f-46a4-b710-b93c6feb4a88"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count of Stores')"
            ]
          },
          "metadata": {},
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Making a histogram for the Outlet_Establishment_Year\n",
        "sns.histplot(data=df, x='Outlet_Establishment_Year');\n",
        "\n",
        "# Creating a title for the plot\n",
        "plt.title('A Histogram showing how many stores were built over the years', fontsize=18)\n",
        "\n",
        "# Creating labels for the x-axis and y-axis\n",
        "plt.xlabel('Outlet Establishment Year', fontsize=15)\n",
        "plt.ylabel('Count of Stores', fontsize=15)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xyj6xt_CDedf"
      },
      "source": [
        "You can see from this histogram that there weren't any stores built between 1987 to 1996. So for around 10 years, no new stores were built.\n",
        "\n",
        "Most stores were built after 1996."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "Q6_SsV9KBZQM",
        "outputId": "fceb1123-6a74-4e32-c2f7-599fe64da061"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Count')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Creating the histogram\n",
        "sns.histplot(data=df, x='Item_Outlet_Sales');\n",
        "\n",
        "# Title for the plot\n",
        "plt.title('Count of Item Outlet Sales in all stores', fontsize=20)\n",
        "# Labels for x-axis and y-axis\n",
        "plt.xlabel('Item Outlet Sales', fontsize=15)\n",
        "plt.ylabel('Count', fontsize=15)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "LfEzQIk-CSAg"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E7CxwsKTIzrN"
      },
      "source": [
        "# **Boxplots**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "LS8OnsCJI4cc",
        "outputId": "adff0b77-b86d-4b7f-c9d1-105f4617e285"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Item Weight')"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Making a boxplot for Item_Weight column\n",
        "sns.boxplot(data=df, y='Item_Weight');\n",
        "\n",
        "# Title for our our boxplot\n",
        "plt.title('A boxplot for Item Weight in our Data', fontsize=20)  \n",
        "\n",
        "plt.ylabel('Item Weight', fontsize=16)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "From this boxplot;\n",
        "\n",
        "\n",
        "> The median Item_Weight is around 12.8\n",
        "\n",
        "\n",
        "> The minimum Item_Weight is close to 5 with the maximum Item_Weight exceeding 20.\n",
        "\n",
        "\n",
        "> We don't have outliers in Item_Weight which is a good thing\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "ohbYUnsB49mN"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "fcwi_4sXJAoj",
        "outputId": "b4fd6df8-1930-438e-e8db-52c00e2e13b2"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Item_Outlet_Sales')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Making a boxplot for Item_Outlet_Sales column\n",
        "sns.boxplot(data=df, y='Item_Outlet_Sales');\n",
        "\n",
        "# Title for our plot\n",
        "plt.title('A boxplot for Item Outle Sales in our Data', fontsize=20)\n",
        "\n",
        "plt.ylabel('Item_Outlet_Sales',fontsize=16)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o51WuYAAJ_pA"
      },
      "source": [
        "From this boxplot;\n",
        "\n",
        "\n",
        "> Our Item_Outlet_Sales have a median sales amount around 2000.\n",
        "\n",
        "\n",
        "> Our boxplot also many outliers meaning there are certain products that are having much higher sales amount than the rest.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "T7cFeeTDKadB"
      },
      "outputs": [],
      "source": [
        "# Creating a correlation variable for my dataframe\n",
        "corr = df.corr()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "ICTPab6oLSXG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "f5421e7f-5c94-449a-f1c8-73e3b29b7619"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'A Heatmap of our Data showing correlation between features')"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Creating a heatmap\n",
        "sns.heatmap(corr, cmap='Greens', annot=True);\n",
        "\n",
        "# Title\n",
        "plt.title('A Heatmap of our Data showing correlation between features', fontsize=18)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see from our heatmap that we find a moderate **positive correlation**  between Item_Outlet_Sales and Item_MRP. "
      ],
      "metadata": {
        "id": "Y8_znE8fyb7Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Finding the product with the most sales using Item_Identifier and Item_Outlet_Sales**"
      ],
      "metadata": {
        "id": "qxf-N-82xolf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# First grouping by Item_Identifier and then finding the sum of Item_Outlet_Sales \n",
        "grouped_pdcts = df.groupby('Item_Identifier')['Item_Outlet_Sales'].sum()\n",
        "\n",
        "# Sorting the resulting object in descending order\n",
        "sort_grouped_pdcts = grouped_pdcts.sort_values(ascending = False)\n",
        "\n",
        "# Showcasing the top 5 products with highest sum sales \n",
        "sort_grouped_pdcts.head()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JmYEsCHFy2Pl",
        "outputId": "249d1fcd-ae04-47bd-871a-a67136a80c22"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Item_Identifier\n",
              "FDY55    42661.8008\n",
              "FDA15    41584.5364\n",
              "FDZ20    40185.0248\n",
              "FDF05    36555.7490\n",
              "FDA04    35741.4756\n",
              "Name: Item_Outlet_Sales, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This gives us a good look at which products drive the **most business** meaning the stores should always have these goods in store."
      ],
      "metadata": {
        "id": "5LA5U92T3M1u"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "hb5BO-kGy9vT"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **PART 4**"
      ],
      "metadata": {
        "id": "fM93145qkj1S"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sales = list(sort_grouped_pdcts.head().values)\n",
        "products = list(sort_grouped_pdcts.head().index)"
      ],
      "metadata": {
        "id": "zWggy316knqF"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ploting the barplot of the top 5 products\n",
        "sns.barplot(products, sales);\n",
        "plt.title('Top 5 products with the most sales', fontsize = 25)\n",
        "plt.xlabel('Item Identifier', fontsize=16)\n",
        "plt.ylabel('Sales',fontsize=16)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "j24rO6DSkpzu",
        "outputId": "851ae7ec-7a8a-421b-eb6e-9a67254898f8"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Sales')"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Finding the stores with the most sales using Outlet_Identifier and Item_Outlet_Sales**"
      ],
      "metadata": {
        "id": "OheB2TM67TZt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# grouping our df by Outlet_Identifier and then finding the sum of sales\n",
        "grouped_stores = df.groupby('Outlet_Identifier')['Item_Outlet_Sales'].sum()\n",
        "# sorting grouped_stores in descending order \n",
        "sort_grouped_stores = grouped_stores.sort_values(ascending=False)\n",
        "\n",
        "# Breaking down our sorted stores and finding the top 3 stores\n",
        "stores_sales = sort_grouped_stores.head(3).values\n",
        "stores_identifeir = sort_grouped_stores.head(3).index\n"
      ],
      "metadata": {
        "id": "fWQ91mCty0M6"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# showcasing a barplot of the top 3 stores with the most sales\n",
        "sns.barplot(stores_identifeir, stores_sales);\n",
        "\n",
        "# title for our plot\n",
        "plt.title('Top 3 Stores with the most sales', fontsize=20)\n",
        "plt.xlabel('Store Identifier', fontsize=16)\n",
        "plt.ylabel('Store Sales in Millions', fontsize=16)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 377
        },
        "id": "yoworeZz0sCu",
        "outputId": "32c5bccf-bef8-4e7f-c545-2a279b214314"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Store Sales in Millions')"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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eU0n/lZOBYSXLNpoGM40bfXZ52XAazw5+H+k/OjWX66n8/leryrdBLmtxRvKk/Ft6f4P1Xkdq168M+a4pd9Vr9iPNPXos/1an59/u94CBVXmXzumV+TxPk/YRK1Hnv9HDZ/AO4GjSSK8ZpH3DE6QJeaOAZQq/7WNJwaSyL5qSt3cA8zi8tTLuu1/IQzgvj4iNJb0TeDQiBtXJ92vSMK/f5efXAUdGyRFBymfHjIihbSq6mdl8pd/0OVSLNN77icoYbiWV9v1Lyc0IklYmzSv4VxeKaWY2X+o3wUHSBaSOxfUlTZZ0KGkyyqF5bPuDzBndcjXwYh7KegOpXb/sWG4zs36vXzUrdYqblcysv3NwMDOzGv1iKOvKK68cQ4cO7XYxzMwWKHfdddcLETGw3rJ+ERyGDh3K+PH1zvllZmaNSGo4v6PfdEibmVn7ODiYmVmNjgYHSUtKukPSvZIelPTdOnlGSpoqaUK+fbaTZTQzs873OcwEdop0/pTFSNcMvjIibqvKd2FEHNbhspmZWdbR4BBp3Gzl4iyL5ZvH0pqZzWc63ucgaYCkCaSzCV4bEbfXyfZxSfdJuljSGg3WM0rSeEnjp06d2pdFNjNb6HQ8OETE7IjYlHTGzS0lbVyV5TLS2SM3IZ2t9OwG6xkTEcMiYtjAgXWH6ZqZ2Tzq2milSFdWu4Gq88VHxIsx5+Ixp5POm25mZh3U6dFKA/PlJclXtNqFdMGYYp7iKbY/SvlLUJqZWZt0erTSIODsfAm9RYA/RsTlko4HxkfEWOArkj5KuqDJS6SLhbTNFt84p52rswbuOumgbhfBzHqh06OV7gM2q5N+bOHxUcBRnSyXmZnNzTOkzcyshoODmZnVcHAwM7MaDg5mZlbDwcHMzGo4OJiZWQ0HBzMzq+HgYGZmNRwczMyshoODmZnVcHAwM7Mapc6tJGkRYJGImFVI+xCwMXB9RNzTR+UzM7MuKHvivQtI138+CEDS54HT8rL/SvpwRPytD8pnZmZdULZZaWvgisLzb5AuxLMccAlwTJvLZWZmXVQ2OKwCPAMgaR1gLeDUiHgV+B3wnr4pnpmZdUPZ4DAdWCk/Hg68kK/NADAbWLLN5TIzsy4q2+fwd+BISbOAI5i7iWkdYHKby2VmZl1UtubwTVLNYSypljC6sOwTwD/aWywzM+umUjWHiHgMWFfSShHxYtXiw4Hn2l4yMzPrmpauIV0nMBAR97evOGZmNj8oHRwkrQ3sBwyhtgM6IuLQdhbMzMy6p+wM6b2AP5L6KJ4nTYgripLrWRK4CVgib/viiDiuKs8SwDnAFsCLwCciYlKZ9ZuZWXuUrTmcAIwDDoyIqb3Y3kxgp4iYIWkx4BZJV0bEbYU8hwLTImIdSfsDPyJ1epuZWYeUHa20NvCTXgYGIpmRny6Wb9W1jhHA2fnxxcDOktSb7ZqZWWvKBodHmDMJrlckDZA0gdQ8dW1E3F6VZXXgaYB8or9X6m1b0ihJ4yWNnzq1VzHLzMyqtDLP4ejcKd0rETE7IjYFBgNbStp4HtczJiKGRcSwgQMH9rZYZmZWULbPYTTp6P1hSY8BL1Utj4jYsZUNR8TLkm4AdgMeKCx6BlgDmCxpUdLJ/WqG0JqZWd8pW3OYDTxKOo3G1Py8eHurzEokDZS0fH68FLALqcmqaCxwcH68D+l6EaVGQ5mZWXuUnSE9vE3bGwScLWkAKTD9MSIul3Q8MD4ixgJnAOdKmkiqoezfpm2bmVlJLc2Q7q18JtfN6qQfW3j8JrBvJ8tlZmZzK30NaUmDJP1E0p2SHs/3P5a0al8W0MzMOq9UcJC0HjAB+AowA7gj3x8OTJC0bl8V0MzMOq9ss9KPSBf82ap4KgtJawLX5OV7t710ZmbWFWWblT4AfKf6HEcR8SRpmOsH2lssMzPrprLBYXHg1QbLXs3LzcysnygbHCYAX5Y0V/58zqMv5uVmZtZPlO1zOB64nDRD+kJgCrAqacjpusCH+6Z4ZmbWDWUnwV0laU/gROAYQKSzqd4F7BkR1/RdEc3MrNNKT4KLiKuAqyQtDaxAuubC631WMjMz65qWZ0jngOCgYGbWjzUMDpKOBU6PiGfz42YiIk5ob9HMzKxbmtUcRgNXAc/mx80E6VKiZmbWDzQMDhGxSL3HZmbW/3mnb2ZmNRwczMysRrMO6bdIfQllRER09NoQZmbWd5rt0I+nfHAwM7N+pFmH9OgOlsPMzOYj7nMwM7MaPU2CK8uT4MzM+pGeJsGV5UlwZmb9SMNmpYhYpIXbgDIbk7SGpBskPSTpQUmH18kzXNIrkibkWys1GDMza4NODz+dBXwtIu6W9A7gLknXRsRDVflujog9O1w2MzPLOtohHRFTIuLu/PhV4GFg9U6WwczMetYwOEiaLWnL/Pit/LzRbVarG5Y0FNgMuL3O4m0k3SvpSkkbNXj9KEnjJY2fOnVqq5s3M7MmepoEN7nwuG0T4iQtC/wJOCIiplctvhtYMyJmSNoDuJR0KdK5RMQYYAzAsGHDPFnPzKyNmk2C+27h8eh2bVDSYqTAcH5EXFJnu9MLj6+QdJqklSPihXaVwczMmuton4MkAWcAD0fEyQ3yrJrzkZu1FgFe7Fwpzcys2SS4Q1pZUUScWSLbtsCngfslTchpRwND8jp+DewDfCH3Y7wB7B8RbjYyM+ugZn0OpzOnn0E9rCeAHoNDRNzS07oi4lTg1J7WZWZmfaeneQ4zgIuBc4En+r44ZmY2P2gWHNYCDiI1Ax0M3AqcDVyU5yiYmVk/1ez0GU9GxAkRsR6wA2nC2knAc5IukLS7JJ/V1cysHyq1c4+Iv0fE54FBpNrEMsBY4Lw+LJuZmXVJq0f+KwJDgTWBAYDnHpiZ9UM9nnhP0lLA3qS+hw8Cz5BqDPtFxKN9WzwzM+uGZvMcPkgKCB8jDVW9BNglIm7oUNnMzKxLmtUcrgGmk4ayXgK8TprkvFO9zBFxffuLZ2Zm3dBTs9I7gZGkoawVxUlskZ8HqQ/CrE89dfx7ul2Efm/Isfd3uwg2H2gWHD7QsVKYmdl8pdlZWW/sZEHMzGz+4UlsZmZWw8HBzMxq9DjPwcysXbb9xbbdLkK/d+uXb23LelxzMDOzGg4OZmZWw8HBzMxqlO5zkLQ2sB/pkp5LVi2OiDi0nQUzM7PuKRUcJO0F/JFU03gemFmVxdd4NjPrR8rWHE4AxgEHRsTUviuOmZnND8oGh7WBrzkwmJktHMp2SD8CrNTbjUlaQ9INkh6S9KCkw+vkkaRTJE2UdJ+kzXu7XTMza03Z4PBN4OjcKd0bs0g1kA2BrYEvSdqwKs/uwLr5Ngr4VS+3aWZmLSrbrDSaVHN4WNJjwEtVyyMiduxpJRExBZiSH78q6WFgdeChQrYRwDkREcBtkpaXNCi/1szMOqBscJgNtPWSoJKGApsBt1ctWh14uvB8ck5zcDAz65BSwSEihrdzo5KWBf4EHBER0+dxHaNIzU4MGTKkjaUzM7OOz5CWtBgpMJwfEZfUyfIMsEbh+eCcNpeIGBMRwyJi2MCBA/umsGZmC6mGNQdJOwB3R8SM/LipiLippzySBJwBPBwRJzfINhY4TNIfgK2AV9zfYGbWWc2alcaRRhTdkR83mgXdyjWktwU+DdwvaUJOO5p0Sg4i4tfAFcAewETgdeAzJdZrZmZt1NM1pB8qPO61iLiFFEya5QngS+3YnpmZzZtS15D29aTNzBYuPmW3mZnVcHAwM7MaDg5mZlbDwcHMzGo4OJiZWY15Dg6SNpT0cUmrtbNAZmbWfaWCg6RTJf268Hxv4F7gIuAhSe/ro/KZmVkXlK057A78vfD8u8DlwHtJM6iPa3O5zMysi8oGh0HAJABJg4GNgB9ExP3AKYBrDmZm/UjZ4PA6sGx+vCMwHRifn88A3tHmcpmZWReVvdjP3aRLej5FOu/RtRHxVl62Fr4Qj5lZv1I2OBwDXEXqhH4Z+Hxh2V6kfgczM+snyl4J7k5JQ4B3A49VXb1tDPBYXxTOzMy6o2zNgYh4DbirTvpf21oiMzPrutKT4CRtJukSSS9ImiVp85z+fUm79V0Rzcys08pOgtsO+AepWen3Va97i7n7IMzMbAFXtubwQ+Bq0vyG/61adjeweTsLZWZm3VW2z2FzYO+ICEnV15J+ARjY3mKZmVk3la05vAks3WDZIOCV9hTHzMzmB2WDwy3AEZIGFNIqNYhDgevbWiozM+uqssHhO6SmpXvz4wAOlnQDsDXpRHw9knSmpOclPdBg+XBJr0iakG/HliyfmZm1UangEBH3AjsA/ybNlhZwWF68Y0Q8WnJ7ZwE9DXu9OSI2zbfjS67XzMzaqJVJcHcDO0taElgReDkiXm9lYxFxk6ShrRXRzMw6reUrwUXEmxHxbKuBoQXbSLpX0pWSNuqjbZiZWRMNaw4ttvdHRJzQhvLcDawZETMk7QFcCqzboHyjgFEAQ4YMacOmzcysolmz0ugW1hNAr4ND8YR+EXGFpNMkrRwRL9TJO4Z00j+GDRtWPffCzMx6oWFwiIiWm5x6S9KqwL/zZLstSc1eL3a6HGZmC7vSHdLtIOkCYDiwsqTJpGtPLwYQEb8G9gG+IGkW8Aawf0S4VmBm1mEdDQ4RcUAPy08FTu1QcczMrIFWTtk9StI9kl6XNLv61peFNDOzzip7yu6DgF8AdwJLAr8DzgOmA48DnqxmZtaPlK05HAH8APhCfn5aRBwMrE3qG3CnsZlZP1I2OKwL3ES6sM9bwOIAETEN+B5weJ+UzszMuqJscHgDWCSPHHqOVGOomAGs1u6CmZlZ95QdrXQ/sA7wN+Bm4GhJTwCzSJPlHumT0pmZWVeUDQ5jmFNb+A4pSNySn78K7NXeYpmZWTeVCg4RcWHh8cR8Qrz3A0sBf693egszM1twzdMkuIh4Dbi2zWUxM7P5RLOzsi4GLFU8GV5OHwh8A9gQeJY0rHVCXxbSzMw6q1nN4SRgT1JHNACSliOdVnt1YBqwHHCgpG0dIMzM+o9mQ1m3A86vSjuCFBgOi4iVgDWAp4Cj+qR0ZmbWFc2Cw5rAhKq0PYEnIuI0gIiYApxMCiRmZtZPNAsOywAvVZ5IWhrYFBhXle9RYOV2F8zMzLqnWXB4Gli/8Hw7YABz5jdULE06AZ+ZmfUTzYLD1cA3JW2cRygdCfwH+GtVvi1J/Q5mZtZPNAsOJ5JGM91LOp/ScODEiHi+kkGSgAOpbWoyM7MFWLNrSD8v6T3AvsAKwB0RcXNVtoGkU2tc3ndFNDOzTms6QzoiXgXObLL8eeCn7S6UmZl1V+nLhJqZ2cLDwcHMzGo4OJiZWY2OBgdJZ0p6XtIDDZZL0imSJkq6T9LmnSyfmZklna45nAXs1mT57qTrVa8LjAJ+1YEymZlZlZaCg6RNJB0m6ThJq+a0dSS9o8zrI+ImCqfkqGMEcE4ktwHLSxrUShnNzKz3Sl3sR9ISwHnA3oCAAC4jTY77MfBP0gzq3lqddNqOisk5bUqdMo0i1S4YMmRIGzZtZmYVZWsO3wM+CHwaeBcpQFRcCXyozeXqUUSMiYhhETFs4MCBnd68mVm/VvYyoQcA346I30saULXsCWBom8rzDOkaERWDc5qZmXVQ2ZrDSsDDTdaxRHuKw1jgoDxqaWvglXzNCDMz66CyNYcngG2A6+ss25J0TYceSbqAdAK/lSVNBo4DFgOIiF8DVwB7ABOB14HPlCyfmZm1UdngcA5wtKRJwJ9yWkj6APBVYHSZlUTEAT0sD+BLJctkZmZ9pGyz0o9J13E4F5iW024B/gZcFRG/6IOymZlZl5SqOUTEbGB/Sb8kjUxaBXiRFBhu7MPymZlZF/QYHCQtDtwGHBkR1wDV13QwM7N+psdmpYj4D7AWMKvvi2NmZvODsn0O1wK79mVBzMxs/lF2tNIvgPMkLQpcSjqdRRQzRMS/2ls0MzPrlrLBodLp/L+koav1VM+cNjOzBVTZ4ODJaGZmC5GyQ1nP7uuCmJnZ/KNszQFIV2oDNgRWJF2X4aE8q9nMzPqR0hf7kfRZUkf0fcC4fP+spEP7pmhmZtYtZS/2cyAwBriOdNGf54BVgQOBMZJej4gL+qyUZmbWUWWblb4JnB8Rn65KP1vSucC3AAcHM7N+omyz0vqkGkM95+XlZmbWT5QNDq+SrspWz+C83MzM+omyweFK4PuSti8mStoGODEvNzOzfqKVPoetgXGSniGNWlqVVGuYmJebmVk/UXYS3HOSNgUOAbYnzXOYRDqtxlkR8XpfFdDMzDqv9CS4HABOzTczM+vHSvU5SJotacsGy7aQNLu9xTIzs24q2yGtJssGUHX6bjMzW7A1DQ6SFpFUORX3Ivl58bYMsDvwQtkNStpN0qOSJko6ss7ykZKmSpqQb59t6R2ZmVmvNexzkHQccGx+GsCtTdZzWpmN5UDzS2AXYDJwp6SxEfFQVdYLI+KwMus0M7P2a9YhPS7fixQkziDt0ItmAg8Bl5fc3pbAxMpV4yT9ARiR12FmZvOJhsEhIm4kXwFOUgC/jYhne7m91YGnC88nA1vVyfdxSTsA/wS+GhFPV2eQNAoYBTBkyJBeFsvMzIpKdUhHxHeLgUHScpKGSWp0So3euAwYGhGbANcCdS80FBFjImJYRAwbOHBgHxTDzGzh1TA4SPqQpB/WST8GeB64HXhS0u8llZ0v8QywRuH54Jz2toh4MSJm5qenA1uUXLeZmbVJs5rD54H1igmSdgFOAB4BjgB+A3wCOLzk9u4E1pW0lqTFgf2BsVXbGFR4+lHg4ZLrNjOzNml2xL8ZKRAUfQZ4E/hQRDwHkK4cyieBn/a0sYiYJekw4GrS/IgzI+JBSccD4yNiLPAVSR8FZpEuRTqypXdkZma91iw4rAI8XpW2C3BLJTBkfwWqLwLUUERcAVxRlXZs4fFRwFFl12dmZu3XrFnpVWCZyhNJ6wIrAbdV5ZtOqgWYmVk/0Sw4PEKag1AxgjQZ7pqqfGsB/25zuczMrIuaNSv9DLhE0oqknf9I4H5qZ0rvAdzbJ6UzM7OuaFhziIhLSSOS3gccRGpO2jci3j7JnqRVgQ9S1YdgZmYLtqbzEyLiFOCUJsufA1Zud6HMzKy7yp6y28zMFiIODmZmVsPBwczMajg4mJlZDQcHMzOr4eBgZmY1HBzMzKyGg4OZmdVwcDAzsxoODmZmVsPBwczMajg4mJlZDQcHMzOr4eBgZmY1HBzMzKyGg4OZmdXoeHCQtJukRyVNlHRkneVLSLowL79d0tBOl9HMbGHX0eAgaQDwS2B3YEPgAEkbVmU7FJgWEeuQrmP9o06W0czMOl9z2BKYGBH/ioj/AH8ARlTlGQGcnR9fDOwsSR0so5nZQq/pNaT7wOrA04Xnk4GtGuWJiFmSXgFWAl4oZpI0ChiVn86Q9GiflHj+sDJV739+p58c3O0izE8WrO/vOB+LFSxY3x2gr7T0/a3ZaEGng0PbRMQYYEy3y9EJksZHxLBul8Pmjb+/BdfC/N11ulnpGWCNwvPBOa1uHkmLAssBL3akdGZmBnQ+ONwJrCtpLUmLA/sDY6vyjAUqbRL7ANdHRHSwjGZmC72ONivlPoTDgKuBAcCZEfGgpOOB8RExFjgDOFfSROAlUgBZ2C0UzWf9mL+/BddC+93JB+VmZlbNM6TNzKyGg4OZmdVwcOgjkgZL+oukxyQ9Lun/JC0uaaSkU6vyjpM0LJ8uZIKkpyRNzY8nSBoqaQtJ9+fTipxSmRgo6SRJj0i6T9KfJS2f0w8svH6CpLckbdr5T2LB1MHv74T83U2QdI2k1XL6cEmvFNZxbDc+hwVVp76/wjq+JikkrZyfr5D/j/dJukPSxp18/+3g4NAH8g/nEuDSiFgXWA9YFvhes9dFxFYRsSlwLHBhRGyab5OAXwH/D1g333bLL7sW2DgiNgH+CRyV13V+5fXAp4EnImJCO99nf9Xh7++kiNgkv+7y/NqKmwvrOL5tb7Cf6/D3h6Q1gF2BpwqrOxqYkP+XBwH/15531zkODn1jJ+DNiPgdQETMBr4KHAIs3erKJA0C3hkRt+VhvecAe+V1XxMRs3LW20hzR6odQDpViZXTye9veiHrMoBHiPRex76/7GfAN5n7u9sQuD5v/xFgqKR3tf5WusfBoW9sBNxVTMg7gaeYt+HDq5NONVIxOadVOwS4sk76J4AL5mG7C6uOfn+SvifpaeBA5q45bCPpXklXStpoHra7sOrY9ydpBPBMRNxb9Zp7gb1zni1Jp6mod+A233Jw6LwVGqT36ohR0jHALOD8qvStgNcj4oHerN/e1vbvLyKOiYg1SN/dYTn5bmDNiHgv8Avg0nldv82lbd+fpKVJzUf1+oN+CCwvaQLwZeAeYHar2+gmB4e+8RCwRTFB0juBIaQfSfUPdEWan9zrGeY+6pjrtCOSRgJ7AgfWmU2+P641tKqj31/B+cDHIR3pRsSM/PgKYLFKZ6f1qFPf3/8AawH3SpqU0++WtGr+/j6T+zAOAgYC/5rXN9QNDg594zpgaUkHwdvXsfgpcBZwO7CtpFXzsmHAEsx9ttq5RMQUYLqkrXNn20HAX/LrdyO1d340Il4vvk7SIsB+uL+hVZ38/tYtZB0BPJLTVy2MaNqS9F/1OcbK6cj3FxH3R8QqETE0IoaSmps2j4jnJC2vdIoggM8CN1X1L833Ftizss7PIiIkfQw4TdJ3SH/sK4CjI2KmpMOBK/LOewZwQES81cNqv0j6cS9F6leo9C2cSvpxX5v3JbdFxOfzsh2ApyNigTpi6bYOf38/lLQ+8BbwJFD57vYBviBpFvAGsL/PMVZOh7+/RjYAzpYUwIOki5gtUHz6DDMzq+FmJTMzq+HgYGZmNRwczMyshoODmZnVcHAwM7MaDg7WNZL2knSTpOclvSHpSUmX5rkblTzDJY3Oww7nG5ImSTqvRL7ReThjX5ZltKSd6qSflSdnFdPeLel6SdPzWUT36kQZbcEzX/3hbOEh6SvAn4HHSGPAPwycmBcXd3TDgePwb7WZ45j7M6s4AfhYVdrJwNqkyZHbADcCp+fHZm/zJDjrlq+TTqlcnBx0PfDbvq4lSFoiImb25TbmBxHxeJ3kDUizda8qpE1j7hPLzTNJiwGzPGFvweejMeuWFYHn6i2ozFaVNJp0VAzw39wM8vZOR9IgSedIekHSzHxhlU8V16V0cZeQtIOkiyS9TDqFApIWlXSU0sWSZkp6VtJPJS05L29I0maSbpb0pqRn8uxc1cnX43aVLjATkj4n6XhJUyS9LOkySYML+SqfxzGVzyd/bnM1K+XmuQCGAp8ufpb1mpVaLOMXJf1Y0rPATGD5efn8bP7imoN1yx3AwZL+RTpPzT/r5DmddDKzQ4HtKJzVUtIypCaRFUhnxnwa+BRwrqSlI2JM1brOJ52AcB/m/O7PAz4C/Aj4O+mo+gTSDvTjrbwZpZPiXU8KeAeTdpLfIJ3srVor2z0q5zkEWIV0jqDzSM1tkJqD/kE6tcNvclq9WsDdOe9Y4M68vWZaKeMxeZ2jgAHAmz2s2xYEEeGbbx2/ka7OdR/pVMlBOivmBcCuVflG5+WLVqUfltOHV6X/DXgeGJCfj8z5flaVb/ucflBV+oE5fdMeyj8JOK/w/HvAf4A1CmnL5PcVrW6XtBMOYFxVvq/n9NUKaQGcWKeMZwGTqtImA2fV+4x7Uca7yafi8a3/3NysZF0RqaawGbAjacc6gdR5erWkb5dYxQ6ki6yMq0o/j3R65A2r0v9c9Xw30s784tyEsqikRYFrCutvxTakkx6+fXbPiHgNuKyX272i6vn9+b5ejaRdWi3jpZGjhfUfblayrol0+cab8g1JqwFXAcdJ+mVETGvy8hWBKXXSnyssL6rOuwqwOPBag/Wv1GTb9QwC6l1Q6d+93O5LVc8rHenz1C9SUqtlrPc92ALOwcHmGxHxrKTTSRdjX5fUL9HIS8D6ddJXLSyfa/VVz18ktY1v32D9zzYvbY0pQL1rBFentXu7faHVMrrW0A85OFhXSBoU6SIq1d6d7ys1gMqR8lLAq4V8NwL7Sto2Im4tpH+S1OfwUA9FuAr4FrBcRFzXUuHr+wfwDUlrVJqWcqf5R/p4u5CagJZq07qgb8poCxgHB+uWByT9jdSm/gTwTmAP0sVu/hgRT+V8lZ381yRdCcyOiPGkztbDgUuUrp89mdRhugvwudxk1VBEjJN0Aald/WRSLeUtUifrHsC3ov4IqkZ+RrogzDV5KGlltNIbfbxdSJ/RhyVdRZqz8GxEzHMNpI/KaAsYBwfrlmNIO5rjSU0vs4F/AkcCPy/kuxw4jbTjPZY0b0AR8ZqkHYEfky7m/g7gUeDTEdHjaS2yT5Eu/n5ILs9M0iikq6ntK2gqIl6QtDOpSexsUtPMr0n/seoL0Ldtu9lhwCmkzu8lgO+SRiD1RrvLaAsYXwnOzMxqeCirmZnVcHAwM7MaDg5mZlbDwcHMzGo4OJiZWQ0HBzMzq+HgYGZmNRwczMysxv8H3b2+WftmsPoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can clearly from see from this plot that;\n",
        "\n",
        "\n",
        "> Store **OUT027** out-performs all other stores. \n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "LO3tQUwb2ryo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's further explore store OUT027\n",
        "# Let's see the percentage of sales store OUT027 brings in compared to the total sales of all stores\n",
        "\n",
        "# Getting total sales for all stores\n",
        "total_sales = grouped_stores.sum()\n",
        "\n",
        "# Getting sales made my store OUT027\n",
        "store_OUT027 = df[df['Outlet_Identifier'] == 'OUT027']['Item_Outlet_Sales'].sum()\n",
        "\n",
        "# Percentage comparison\n",
        "(store_OUT027/total_sales)*100\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQWgMAAR1Wtw",
        "outputId": "bf4bc48d-8386-413c-b4bc-2df48bba7d6f"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18.537037433834918"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that store **OUT027** brings in close to 20% of our sales.\n",
        "\n",
        "This means that we should make sure that this store should have the most focus and should always be fully stocked.\n"
      ],
      "metadata": {
        "id": "CIAyUQll5ojH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a boxplot for Outlet sales\n",
        "sns.boxplot(y=grouped_stores.values);\n",
        "# Title\n",
        "plt.title('A boxplot showing the Item sales made by stores', fontsize=18)\n",
        "\n",
        "plt.ylabel('Stores sales in Millions', fontsize=15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "GDuxr_WFYI0w",
        "outputId": "810c638d-9edb-4579-8182-ccbc06e911d2"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Stores sales in Millions')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can see from the above boxplot that;\n",
        "\n",
        "\n",
        "> The median sales value in Millions made by the stores is around 2.2\n",
        "\n",
        "\n",
        "> We can also see that, most of our stores make sales between 1.8 Million to 2.3 Million.\n",
        "\n",
        "\n",
        "> And obviously, there are some stores that make way above and below our Median sales value which creates some outliers in the data.\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "YehrkmB1ZUaD"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "vZVbs8w7YZBJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1OtdpaaUoXOuUc1L6Jj0V88MVf_uWNggz",
      "authorship_tag": "ABX9TyPuk3Tv7encDDtXjFDMo5Ux",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}