ML-Notebooks/Shopping_Intentions.ipynb

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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
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        "import os\n",
        "os.getcwd()"
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      "source": [
        "# Importing necessary libraries\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline \n",
        "from tensorflow import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense"
      ],
      "metadata": {
        "id": "z7LfgnPHh2Vi"
      },
      "execution_count": 4,
      "outputs": []
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      "source": [
        "# dataset can be found here: https://www.kaggle.com/henrysue/online-shoppers-intention\n",
        "shop = pd.read_csv(\"/content/drive/MyDrive/Dataset/online_shoppers_intention.csv\")\n",
        "shop.head()"
      ],
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          "height": 270
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        "id": "3ZcPC2YHh2ZI",
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              "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
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        "- Out target column is: Revenue - A boolean representing whether or not the user completed the purchase."
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              "       Administrative  Administrative_Duration  Informational  \\\n",
              "count    12330.000000             12330.000000   12330.000000   \n",
              "mean         2.315166                80.818611       0.503569   \n",
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              "mean                34.472398       31.731468              1194.746220   \n",
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              "       OperatingSystems       Browser        Region   TrafficType  \n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>184.137500</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.014286</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>598.936905</td>\n",
              "      <td>0.003112</td>\n",
              "      <td>0.025156</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>4.000000</td>\n",
              "      <td>93.256250</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>1464.157214</td>\n",
              "      <td>0.016813</td>\n",
              "      <td>0.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>3398.750000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2549.375000</td>\n",
              "      <td>705.000000</td>\n",
              "      <td>63973.522230</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>361.763742</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-00ac756d-bf27-42a6-a49a-43456bb56065')\"\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-00ac756d-bf27-42a6-a49a-43456bb56065 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-00ac756d-bf27-42a6-a49a-43456bb56065');\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": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shop.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vyWJUdj6h2m0",
        "outputId": "b14b709a-8c8b-415b-8b01-9d3ecf8f6ebf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12330, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shop.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mI9dsiv8h2p-",
        "outputId": "b714ad58-0c6d-4e49-b09f-ce24f242fb5f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Administrative             0\n",
              "Administrative_Duration    0\n",
              "Informational              0\n",
              "Informational_Duration     0\n",
              "ProductRelated             0\n",
              "ProductRelated_Duration    0\n",
              "BounceRates                0\n",
              "ExitRates                  0\n",
              "PageValues                 0\n",
              "SpecialDay                 0\n",
              "Month                      0\n",
              "OperatingSystems           0\n",
              "Browser                    0\n",
              "Region                     0\n",
              "TrafficType                0\n",
              "VisitorType                0\n",
              "Weekend                    0\n",
              "Revenue                    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,12))\n",
        "sns.heatmap(shop.corr(),annot=True,cmap='viridis',linewidths=.5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 682
        },
        "id": "nM_EGGQbh2sI",
        "outputId": "ac32adb8-c405-42e7-aae1-92645ad3a99b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6750be8220>"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x864 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shope = shop.drop(['Administrative', 'Informational', 'ProductRelated_Duration'], axis = 1)"
      ],
      "metadata": {
        "id": "ZUayg7QYh2u-"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Finding about categorical data"
      ],
      "metadata": {
        "id": "bITR-PKniwik"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "shope.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "_9wA7FUmh2xw",
        "outputId": "3bc656f9-d16e-4469-c76e-43e446c16cb6"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Administrative_Duration  Informational_Duration  ProductRelated  \\\n",
              "0                      0.0                     0.0               1   \n",
              "1                      0.0                     0.0               2   \n",
              "2                      0.0                     0.0               1   \n",
              "3                      0.0                     0.0               2   \n",
              "4                      0.0                     0.0              10   \n",
              "\n",
              "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
              "0         0.20       0.20         0.0         0.0   Feb                 1   \n",
              "1         0.00       0.10         0.0         0.0   Feb                 2   \n",
              "2         0.20       0.20         0.0         0.0   Feb                 4   \n",
              "3         0.05       0.14         0.0         0.0   Feb                 3   \n",
              "4         0.02       0.05         0.0         0.0   Feb                 3   \n",
              "\n",
              "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \n",
              "0        1       1            1  Returning_Visitor    False    False  \n",
              "1        2       1            2  Returning_Visitor    False    False  \n",
              "2        1       9            3  Returning_Visitor    False    False  \n",
              "3        2       2            4  Returning_Visitor    False    False  \n",
              "4        3       1            4  Returning_Visitor     True    False  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-91535fe3-298c-43eb-bea2-8b4893b8bd15\">\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>Administrative_Duration</th>\n",
              "      <th>Informational_Duration</th>\n",
              "      <th>ProductRelated</th>\n",
              "      <th>BounceRates</th>\n",
              "      <th>ExitRates</th>\n",
              "      <th>PageValues</th>\n",
              "      <th>SpecialDay</th>\n",
              "      <th>Month</th>\n",
              "      <th>OperatingSystems</th>\n",
              "      <th>Browser</th>\n",
              "      <th>Region</th>\n",
              "      <th>TrafficType</th>\n",
              "      <th>VisitorType</th>\n",
              "      <th>Weekend</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.20</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>3</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>False</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>Feb</td>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>Returning_Visitor</td>\n",
              "      <td>True</td>\n",
              "      <td>False</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91535fe3-298c-43eb-bea2-8b4893b8bd15')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-91535fe3-298c-43eb-bea2-8b4893b8bd15 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-91535fe3-298c-43eb-bea2-8b4893b8bd15');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shope['ProductRelated'].value_counts() # a categorical type but the data is too spread (ranges from 1 too 399)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9jqPwCzFi1-0",
        "outputId": "2bc360e9-94ac-42b5-8b99-3fe8bd3fcbec"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1      622\n",
              "2      465\n",
              "3      458\n",
              "4      404\n",
              "6      396\n",
              "      ... \n",
              "243      1\n",
              "409      1\n",
              "262      1\n",
              "414      1\n",
              "192      1\n",
              "Name: ProductRelated, Length: 311, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shope['Month'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SCFKfFiwi5Sz",
        "outputId": "a0d0f869-758e-43b5-c912-2289b81a2feb"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "May     3364\n",
              "Nov     2998\n",
              "Mar     1907\n",
              "Dec     1727\n",
              "Oct      549\n",
              "Sep      448\n",
              "Aug      433\n",
              "Jul      432\n",
              "June     288\n",
              "Feb      184\n",
              "Name: Month, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shope['OperatingSystems'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n69fQ7LQi8aL",
        "outputId": "a0803501-a938-4937-b264-2fcc874922a4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2    6601\n",
              "1    2585\n",
              "3    2555\n",
              "4     478\n",
              "8      79\n",
              "6      19\n",
              "7       7\n",
              "5       6\n",
              "Name: OperatingSystems, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shope['VisitorType'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8q5gDndUi_JL",
        "outputId": "7bc2f27d-d65f-4510-d745-0e94d1bb0a73"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Returning_Visitor    10551\n",
              "New_Visitor           1694\n",
              "Other                   85\n",
              "Name: VisitorType, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shope['Browser'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0rPUEH4ijBcb",
        "outputId": "b98b4638-aa78-458b-c422-5e8bc3135f0c"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2     7961\n",
              "1     2462\n",
              "4      736\n",
              "5      467\n",
              "6      174\n",
              "10     163\n",
              "8      135\n",
              "3      105\n",
              "13      61\n",
              "7       49\n",
              "12      10\n",
              "11       6\n",
              "9        1\n",
              "Name: Browser, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "shope['Region'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-kq3AP9ljD1T",
        "outputId": "b28cd131-d7e0-4806-cb9a-b768b606d0ae"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1    4780\n",
              "3    2403\n",
              "4    1182\n",
              "2    1136\n",
              "6     805\n",
              "7     761\n",
              "9     511\n",
              "8     434\n",
              "5     318\n",
              "Name: Region, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "visitor = pd.get_dummies(shope['VisitorType'])\n",
        "\n",
        "X = pd.concat([shope, visitor], axis=1)\n",
        "X.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "O-sx4ksmjHhb",
        "outputId": "4d83f7ef-4c36-4deb-c22c-f8128b0ae632"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Administrative_Duration  Informational_Duration  ProductRelated  \\\n",
              "0                      0.0                     0.0               1   \n",
              "1                      0.0                     0.0               2   \n",
              "2                      0.0                     0.0               1   \n",
              "3                      0.0                     0.0               2   \n",
              "4                      0.0                     0.0              10   \n",
              "\n",
              "   BounceRates  ExitRates  PageValues  SpecialDay Month  OperatingSystems  \\\n",
              "0         0.20       0.20         0.0         0.0   Feb                 1   \n",
              "1         0.00       0.10         0.0         0.0   Feb                 2   \n",
              "2         0.20       0.20         0.0         0.0   Feb                 4   \n",
              "3         0.05       0.14         0.0         0.0   Feb                 3   \n",
              "4         0.02       0.05         0.0         0.0   Feb                 3   \n",
              "\n",
              "   Browser  Region  TrafficType        VisitorType  Weekend  Revenue  \\\n",
              "0        1       1            1  Returning_Visitor    False    False   \n",
              "1        2       1            2  Returning_Visitor    False    False   \n",
              "2        1       9            3  Returning_Visitor    False    False   \n",
              "3        2       2            4  Returning_Visitor    False    False   \n",
              "4        3       1            4  Returning_Visitor     True    False   \n",
              "\n",
              "   New_Visitor  Other  Returning_Visitor  \n",
              "0            0      0                  1  \n",
              "1            0      0                  1  \n",
              "2            0      0                  1  \n",
              "3            0      0                  1  \n",
              "4            0      0                  1  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-106bc7c7-57bc-4b66-a57f-58276b904fdf\">\n",
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              "      <div>\n",
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              "\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
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              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-106bc7c7-57bc-4b66-a57f-58276b904fdf');\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": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now we have build some features and extracted some feature data, whats left is to remove redundant features\n",
        "X.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7t-Ueq7xjKkq",
        "outputId": "c15a72b6-70da-44ca-c2bc-58cf1e178ed2"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Administrative_Duration', 'Informational_Duration', 'ProductRelated',\n",
              "       'BounceRates', 'ExitRates', 'PageValues', 'SpecialDay', 'Month',\n",
              "       'OperatingSystems', 'Browser', 'Region', 'TrafficType', 'VisitorType',\n",
              "       'Weekend', 'Revenue', 'New_Visitor', 'Other', 'Returning_Visitor'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y = X['Revenue']\n",
        "X_new = X.drop(['Revenue', 'Month', 'VisitorType'], axis = 1)"
      ],
      "metadata": {
        "id": "I-9ANVayjM0U"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_new['Weekend'] = np.asarray(X_new['Weekend']).astype(np.float32)\n",
        "\n",
        "y = np.asarray(y).astype(np.float32)"
      ],
      "metadata": {
        "id": "l6T0PHIojP0s"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# The above code converts the boolean values to float type - actaully it converts all the data into float type. This has been done so that\n",
        "# the sequential model is able to build tensors out of them\n",
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cqXYlSEcjSUE",
        "outputId": "849b215b-60ab-4f62-b896-db02ca78cde0"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12330,)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_new.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oyUUQYjEjUHk",
        "outputId": "b82cd1e6-55a9-4922-d668-7742343c5f59"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12330, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = Sequential()\n",
        "model.add(Dense(units=18,activation=\"relu\"))\n",
        "model.add(Dense(units=32,activation=\"relu\"))\n",
        "model.add(Dense(units=16,activation=\"relu\"))\n",
        "\n",
        "# Last layer for multi-class classification of 3 species\n",
        "model.add(Dense(units=2,activation=\"softmax\"))\n",
        "model.compile(optimizer='adam',loss='sparse_categorical_crossentropy',metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "uiD1v1gajXR0"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "In the above code, we have started with creating a Sequential Keras model with the first layer as dense having 4 inputs for the 4 columns \n",
        "in our X (Sepal Length, Sepal Width, Petal Length, Petal Width).\n",
        "\n",
        "After this, we have added 2 hidden layers and using “relu” as the activation function for all the layers except the last one.\n",
        "The last layer contains 2 units for 2 respective classes of Revenue (False, True) with “softmax” activation \n",
        "function as we are dealing with Multi-Class classification problem, also while compiling the model we are using “adam” optimizer along \n",
        "with “accuracy” as metrics to maximize the same and minimizing our defined loss “categorical_crossentropy” with each epoch."
      ],
      "metadata": {
        "id": "Xw_VNwJVjb3I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_new,y,epochs=100, validation_split=0.1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXBvW9VhjZMs",
        "outputId": "9ab60326-8463-4613-92b0-1cf188372f9e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "347/347 [==============================] - 4s 4ms/step - loss: 0.4589 - accuracy: 0.8763 - val_loss: 0.4854 - val_accuracy: 0.7989\n",
            "Epoch 2/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.3400 - accuracy: 0.8841 - val_loss: 0.4327 - val_accuracy: 0.8273\n",
            "Epoch 3/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.3130 - accuracy: 0.8850 - val_loss: 0.4397 - val_accuracy: 0.8135\n",
            "Epoch 4/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2969 - accuracy: 0.8857 - val_loss: 0.4650 - val_accuracy: 0.8354\n",
            "Epoch 5/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2831 - accuracy: 0.8940 - val_loss: 0.4141 - val_accuracy: 0.8321\n",
            "Epoch 6/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2730 - accuracy: 0.8930 - val_loss: 0.4137 - val_accuracy: 0.8264\n",
            "Epoch 7/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2725 - accuracy: 0.8932 - val_loss: 0.4337 - val_accuracy: 0.8264\n",
            "Epoch 8/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2774 - accuracy: 0.8935 - val_loss: 0.4120 - val_accuracy: 0.8354\n",
            "Epoch 9/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2617 - accuracy: 0.8974 - val_loss: 0.4488 - val_accuracy: 0.8354\n",
            "Epoch 10/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2627 - accuracy: 0.8950 - val_loss: 0.3812 - val_accuracy: 0.8394\n",
            "Epoch 11/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2599 - accuracy: 0.8955 - val_loss: 0.4019 - val_accuracy: 0.8443\n",
            "Epoch 12/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2636 - accuracy: 0.8920 - val_loss: 0.3978 - val_accuracy: 0.8427\n",
            "Epoch 13/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2538 - accuracy: 0.8983 - val_loss: 0.3749 - val_accuracy: 0.8402\n",
            "Epoch 14/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2551 - accuracy: 0.8987 - val_loss: 0.3811 - val_accuracy: 0.8402\n",
            "Epoch 15/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2477 - accuracy: 0.8985 - val_loss: 0.4279 - val_accuracy: 0.8467\n",
            "Epoch 16/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2502 - accuracy: 0.8985 - val_loss: 0.3871 - val_accuracy: 0.8386\n",
            "Epoch 17/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2472 - accuracy: 0.8995 - val_loss: 0.3982 - val_accuracy: 0.8394\n",
            "Epoch 18/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2458 - accuracy: 0.8990 - val_loss: 0.3845 - val_accuracy: 0.8427\n",
            "Epoch 19/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2497 - accuracy: 0.9004 - val_loss: 0.3802 - val_accuracy: 0.8443\n",
            "Epoch 20/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2422 - accuracy: 0.8993 - val_loss: 0.3838 - val_accuracy: 0.8418\n",
            "Epoch 21/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2438 - accuracy: 0.9011 - val_loss: 0.3928 - val_accuracy: 0.8475\n",
            "Epoch 22/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2432 - accuracy: 0.9015 - val_loss: 0.3782 - val_accuracy: 0.8435\n",
            "Epoch 23/100\n",
            "347/347 [==============================] - 2s 5ms/step - loss: 0.2425 - accuracy: 0.9022 - val_loss: 0.3624 - val_accuracy: 0.8435\n",
            "Epoch 24/100\n",
            "347/347 [==============================] - 1s 4ms/step - loss: 0.2425 - accuracy: 0.9006 - val_loss: 0.3761 - val_accuracy: 0.8475\n",
            "Epoch 25/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2480 - accuracy: 0.9001 - val_loss: 0.3821 - val_accuracy: 0.8427\n",
            "Epoch 26/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2405 - accuracy: 0.9007 - val_loss: 0.3914 - val_accuracy: 0.8500\n",
            "Epoch 27/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2393 - accuracy: 0.9012 - val_loss: 0.3830 - val_accuracy: 0.8394\n",
            "Epoch 28/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2396 - accuracy: 0.8996 - val_loss: 0.4084 - val_accuracy: 0.8418\n",
            "Epoch 29/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2385 - accuracy: 0.8991 - val_loss: 0.4181 - val_accuracy: 0.8427\n",
            "Epoch 30/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2379 - accuracy: 0.9000 - val_loss: 0.3736 - val_accuracy: 0.8475\n",
            "Epoch 31/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2381 - accuracy: 0.8989 - val_loss: 0.3800 - val_accuracy: 0.8427\n",
            "Epoch 32/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2365 - accuracy: 0.9024 - val_loss: 0.3770 - val_accuracy: 0.8410\n",
            "Epoch 33/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2405 - accuracy: 0.8991 - val_loss: 0.3800 - val_accuracy: 0.8491\n",
            "Epoch 34/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2380 - accuracy: 0.8995 - val_loss: 0.3917 - val_accuracy: 0.8435\n",
            "Epoch 35/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2367 - accuracy: 0.9002 - val_loss: 0.3769 - val_accuracy: 0.8443\n",
            "Epoch 36/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2347 - accuracy: 0.9031 - val_loss: 0.3932 - val_accuracy: 0.8402\n",
            "Epoch 37/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2346 - accuracy: 0.9022 - val_loss: 0.4009 - val_accuracy: 0.8410\n",
            "Epoch 38/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2351 - accuracy: 0.9005 - val_loss: 0.3908 - val_accuracy: 0.8378\n",
            "Epoch 39/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2329 - accuracy: 0.9023 - val_loss: 0.4248 - val_accuracy: 0.8459\n",
            "Epoch 40/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2342 - accuracy: 0.9015 - val_loss: 0.3822 - val_accuracy: 0.8386\n",
            "Epoch 41/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2329 - accuracy: 0.9021 - val_loss: 0.3654 - val_accuracy: 0.8435\n",
            "Epoch 42/100\n",
            "347/347 [==============================] - 1s 4ms/step - loss: 0.2343 - accuracy: 0.9014 - val_loss: 0.3866 - val_accuracy: 0.8475\n",
            "Epoch 43/100\n",
            "347/347 [==============================] - 2s 5ms/step - loss: 0.2324 - accuracy: 0.9031 - val_loss: 0.3889 - val_accuracy: 0.8386\n",
            "Epoch 44/100\n",
            "347/347 [==============================] - 2s 5ms/step - loss: 0.2319 - accuracy: 0.9005 - val_loss: 0.4387 - val_accuracy: 0.8394\n",
            "Epoch 45/100\n",
            "347/347 [==============================] - 2s 5ms/step - loss: 0.2330 - accuracy: 0.9020 - val_loss: 0.3705 - val_accuracy: 0.8337\n",
            "Epoch 46/100\n",
            "347/347 [==============================] - 2s 5ms/step - loss: 0.2318 - accuracy: 0.9018 - val_loss: 0.3777 - val_accuracy: 0.8459\n",
            "Epoch 47/100\n",
            "347/347 [==============================] - 2s 5ms/step - loss: 0.2327 - accuracy: 0.9024 - val_loss: 0.3752 - val_accuracy: 0.8443\n",
            "Epoch 48/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2302 - accuracy: 0.9029 - val_loss: 0.3919 - val_accuracy: 0.8516\n",
            "Epoch 49/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2305 - accuracy: 0.9027 - val_loss: 0.3742 - val_accuracy: 0.8459\n",
            "Epoch 50/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2342 - accuracy: 0.9005 - val_loss: 0.3862 - val_accuracy: 0.8443\n",
            "Epoch 51/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2290 - accuracy: 0.9021 - val_loss: 0.4195 - val_accuracy: 0.8402\n",
            "Epoch 52/100\n",
            "347/347 [==============================] - 1s 4ms/step - loss: 0.2306 - accuracy: 0.9026 - val_loss: 0.3676 - val_accuracy: 0.8402\n",
            "Epoch 53/100\n",
            "347/347 [==============================] - 2s 5ms/step - loss: 0.2293 - accuracy: 0.9029 - val_loss: 0.3677 - val_accuracy: 0.8345\n",
            "Epoch 54/100\n",
            "347/347 [==============================] - 2s 5ms/step - loss: 0.2275 - accuracy: 0.9032 - val_loss: 0.3825 - val_accuracy: 0.8394\n",
            "Epoch 55/100\n",
            "347/347 [==============================] - 2s 7ms/step - loss: 0.2290 - accuracy: 0.9013 - val_loss: 0.4297 - val_accuracy: 0.8443\n",
            "Epoch 56/100\n",
            "347/347 [==============================] - 1s 4ms/step - loss: 0.2285 - accuracy: 0.9027 - val_loss: 0.4344 - val_accuracy: 0.8378\n",
            "Epoch 57/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2278 - accuracy: 0.9028 - val_loss: 0.3976 - val_accuracy: 0.8386\n",
            "Epoch 58/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2275 - accuracy: 0.9045 - val_loss: 0.3773 - val_accuracy: 0.8394\n",
            "Epoch 59/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2274 - accuracy: 0.9030 - val_loss: 0.3968 - val_accuracy: 0.8394\n",
            "Epoch 60/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2264 - accuracy: 0.9029 - val_loss: 0.4186 - val_accuracy: 0.8402\n",
            "Epoch 61/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2285 - accuracy: 0.9032 - val_loss: 0.3790 - val_accuracy: 0.8370\n",
            "Epoch 62/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2265 - accuracy: 0.9048 - val_loss: 0.3911 - val_accuracy: 0.8418\n",
            "Epoch 63/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2260 - accuracy: 0.9043 - val_loss: 0.3926 - val_accuracy: 0.8443\n",
            "Epoch 64/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2257 - accuracy: 0.9056 - val_loss: 0.3990 - val_accuracy: 0.8402\n",
            "Epoch 65/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2271 - accuracy: 0.9038 - val_loss: 0.3918 - val_accuracy: 0.8321\n",
            "Epoch 66/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2263 - accuracy: 0.9061 - val_loss: 0.4085 - val_accuracy: 0.8370\n",
            "Epoch 67/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2246 - accuracy: 0.9053 - val_loss: 0.3999 - val_accuracy: 0.8386\n",
            "Epoch 68/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2230 - accuracy: 0.9047 - val_loss: 0.3984 - val_accuracy: 0.8378\n",
            "Epoch 69/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2258 - accuracy: 0.9036 - val_loss: 0.3996 - val_accuracy: 0.8435\n",
            "Epoch 70/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2234 - accuracy: 0.9044 - val_loss: 0.4023 - val_accuracy: 0.8321\n",
            "Epoch 71/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2250 - accuracy: 0.9047 - val_loss: 0.4012 - val_accuracy: 0.8354\n",
            "Epoch 72/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2229 - accuracy: 0.9057 - val_loss: 0.4177 - val_accuracy: 0.8491\n",
            "Epoch 73/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2230 - accuracy: 0.9047 - val_loss: 0.3950 - val_accuracy: 0.8345\n",
            "Epoch 74/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2256 - accuracy: 0.9051 - val_loss: 0.4012 - val_accuracy: 0.8402\n",
            "Epoch 75/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2231 - accuracy: 0.9045 - val_loss: 0.4146 - val_accuracy: 0.8435\n",
            "Epoch 76/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2240 - accuracy: 0.9053 - val_loss: 0.4033 - val_accuracy: 0.8418\n",
            "Epoch 77/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2240 - accuracy: 0.9058 - val_loss: 0.4227 - val_accuracy: 0.8427\n",
            "Epoch 78/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2228 - accuracy: 0.9059 - val_loss: 0.4030 - val_accuracy: 0.8321\n",
            "Epoch 79/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2224 - accuracy: 0.9064 - val_loss: 0.4123 - val_accuracy: 0.8370\n",
            "Epoch 80/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2230 - accuracy: 0.9073 - val_loss: 0.4380 - val_accuracy: 0.8402\n",
            "Epoch 81/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2234 - accuracy: 0.9061 - val_loss: 0.4208 - val_accuracy: 0.8378\n",
            "Epoch 82/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2242 - accuracy: 0.9043 - val_loss: 0.4174 - val_accuracy: 0.8362\n",
            "Epoch 83/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2217 - accuracy: 0.9055 - val_loss: 0.4098 - val_accuracy: 0.8362\n",
            "Epoch 84/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2230 - accuracy: 0.9057 - val_loss: 0.3976 - val_accuracy: 0.8402\n",
            "Epoch 85/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2189 - accuracy: 0.9082 - val_loss: 0.4152 - val_accuracy: 0.8281\n",
            "Epoch 86/100\n",
            "347/347 [==============================] - 1s 4ms/step - loss: 0.2221 - accuracy: 0.9068 - val_loss: 0.3886 - val_accuracy: 0.8435\n",
            "Epoch 87/100\n",
            "347/347 [==============================] - 1s 4ms/step - loss: 0.2211 - accuracy: 0.9066 - val_loss: 0.4152 - val_accuracy: 0.8345\n",
            "Epoch 88/100\n",
            "347/347 [==============================] - 1s 4ms/step - loss: 0.2212 - accuracy: 0.9062 - val_loss: 0.3904 - val_accuracy: 0.8410\n",
            "Epoch 89/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2208 - accuracy: 0.9075 - val_loss: 0.4000 - val_accuracy: 0.8313\n",
            "Epoch 90/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2207 - accuracy: 0.9065 - val_loss: 0.4171 - val_accuracy: 0.8370\n",
            "Epoch 91/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2203 - accuracy: 0.9065 - val_loss: 0.4090 - val_accuracy: 0.8483\n",
            "Epoch 92/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2204 - accuracy: 0.9075 - val_loss: 0.4095 - val_accuracy: 0.8337\n",
            "Epoch 93/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2215 - accuracy: 0.9076 - val_loss: 0.4091 - val_accuracy: 0.8362\n",
            "Epoch 94/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2189 - accuracy: 0.9070 - val_loss: 0.4387 - val_accuracy: 0.8386\n",
            "Epoch 95/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2194 - accuracy: 0.9094 - val_loss: 0.4060 - val_accuracy: 0.8402\n",
            "Epoch 96/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2199 - accuracy: 0.9074 - val_loss: 0.4127 - val_accuracy: 0.8370\n",
            "Epoch 97/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2224 - accuracy: 0.9062 - val_loss: 0.4221 - val_accuracy: 0.8410\n",
            "Epoch 98/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2186 - accuracy: 0.9095 - val_loss: 0.3947 - val_accuracy: 0.8443\n",
            "Epoch 99/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2193 - accuracy: 0.9067 - val_loss: 0.4083 - val_accuracy: 0.8370\n",
            "Epoch 100/100\n",
            "347/347 [==============================] - 1s 3ms/step - loss: 0.2201 - accuracy: 0.9084 - val_loss: 0.4054 - val_accuracy: 0.8370\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f66e66bdac0>"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Saving the model so we can use this in out web app.\n",
        "\n",
        "model.save(\"/content/drive/MyDrive/Dataset\")"
      ],
      "metadata": {
        "id": "QZA_E3OQjjK9"
      },
      "execution_count": 26,
      "outputs": []
    }
  ]
}