purchasing_intention.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/majidebraa/Online-Shoppers-Purchasing-Intention/blob/main/purchasing_intention.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1-Import libraries"
      ],
      "metadata": {
        "id": "CjX4caydT_l8"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8CpA4e2fZTTq",
        "outputId": "1b4caa98-1014-4c79-ea95-db8a52a2f3d4"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Building wheels for collected packages: htmlmin, imagehash\n",
            "  Building wheel for htmlmin (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Installing collected packages: tangled-up-in-unicode, scipy, multimethod, visions, joblib, imagehash, requests, PyYAML, pydantic, phik, htmlmin, pandas-profiling\n",
            "  Attempting uninstall: scipy\n",
            "    Found existing installation: scipy 1.4.1\n",
            "    Uninstalling scipy-1.4.1:\n",
            "      Successfully uninstalled scipy-1.4.1\n",
            "  Attempting uninstall: joblib\n",
            "    Found existing installation: joblib 1.1.0\n",
            "    Uninstalling joblib-1.1.0:\n",
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            "    Found existing installation: requests 2.23.0\n",
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            "  Attempting uninstall: PyYAML\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "  Attempting uninstall: pandas-profiling\n",
            "    Found existing installation: pandas-profiling 1.4.1\n",
            "    Uninstalling pandas-profiling-1.4.1:\n",
            "      Successfully uninstalled pandas-profiling-1.4.1\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
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            "Successfully installed PyYAML-6.0 htmlmin-0.1.12 imagehash-4.2.1 joblib-1.0.1 multimethod-1.7 pandas-profiling-3.1.0 phik-0.12.0 pydantic-1.9.0 requests-2.27.1 scipy-1.7.3 tangled-up-in-unicode-0.1.0 visions-0.7.4\n"
          ]
        }
      ],
      "source": [
        "!pip install pandas_profiling==3.1.0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YReoUYtlN80D"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from pandas_profiling import ProfileReport\n",
        "from sklearn.model_selection import train_test_split,StratifiedShuffleSplit\n",
        "import tensorflow as tf\n",
        "from sklearn.metrics import classification_report\n",
        "import numpy as np\n",
        "from sklearn.model_selection import StratifiedShuffleSplit\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2- Load dataset"
      ],
      "metadata": {
        "id": "PMvmD-b_UKED"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DDV3On8vSCCN"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"/content/dataset.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Weekend = df.Weekend * 1\n",
        "df.Revenue = df.Revenue * 1"
      ],
      "metadata": {
        "id": "E2PdRTb-Yuew"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 3- Analyse dataset"
      ],
      "metadata": {
        "id": "90C3QlWqUN7c"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "QLEOFOZxSmoV",
        "outputId": "6bee3e9c-1e72-430a-f8e5-0504038c031a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2a268d74-e403-49bc-9bef-61064bb5af59')\"\n",
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              "    <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",
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              "\n",
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              "      background-color: #434B5C;\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2a268d74-e403-49bc-9bef-61064bb5af59');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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        },
        "id": "OBXXuwXfXFQL",
        "outputId": "d559de14-d754-498d-e9d8-490b7bde13f3"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12330, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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          "height": 364
        },
        "id": "M6-lLj0FUHJV",
        "outputId": "d8042a51-07d1-4399-96b5-f0574dd4b183"
      },
      "outputs": [
        {
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              "       Administrative  Administrative_Duration  Informational  \\\n",
              "count    12215.000000             12225.000000   12196.000000   \n",
              "mean         2.315759                80.741179       0.502706   \n",
              "std          3.319969               176.660783       1.271342   \n",
              "min          0.000000                 0.000000       0.000000   \n",
              "25%          0.000000                 0.000000       0.000000   \n",
              "50%          1.000000                 7.000000       0.000000   \n",
              "75%          4.000000                92.750000       0.000000   \n",
              "max         27.000000              3398.750000      24.000000   \n",
              "\n",
              "       Informational_Duration  ProductRelated  ProductRelated_Duration  \\\n",
              "count            12204.000000    12196.000000             12191.000000   \n",
              "mean                34.733067       31.814447              1192.220406   \n",
              "std                141.364602       44.607472              1905.738105   \n",
              "min                  0.000000        0.000000                 0.000000   \n",
              "25%                  0.000000        7.000000               184.050000   \n",
              "50%                  0.000000       18.000000               598.450000   \n",
              "75%                  0.000000       38.000000              1464.986002   \n",
              "max               2549.375000      705.000000             63973.522230   \n",
              "\n",
              "        BounceRates     ExitRates    PageValues    SpecialDay  \\\n",
              "count  12216.000000  12208.000000  12222.000000  12201.000000   \n",
              "mean       0.022231      0.043050      5.869171      0.061011   \n",
              "std        0.048569      0.048543     18.545277      0.198111   \n",
              "min        0.000000      0.000000      0.000000      0.000000   \n",
              "25%        0.000000      0.014286      0.000000      0.000000   \n",
              "50%        0.003098      0.025183      0.000000      0.000000   \n",
              "75%        0.016667      0.050000      0.000000      0.000000   \n",
              "max        0.200000      0.200000    361.763742      1.000000   \n",
              "\n",
              "       OperatingSystems       Browser        Region   TrafficType  \\\n",
              "count      12214.000000  12218.000000  12204.000000  12179.000000   \n",
              "mean           2.124857      2.358324      3.146673      4.069464   \n",
              "std            0.912112      1.716343      2.401164      4.030383   \n",
              "min            1.000000      1.000000      1.000000      1.000000   \n",
              "25%            2.000000      2.000000      1.000000      2.000000   \n",
              "50%            2.000000      2.000000      3.000000      2.000000   \n",
              "75%            3.000000      2.000000      4.000000      4.000000   \n",
              "max            8.000000     13.000000      9.000000     20.000000   \n",
              "\n",
              "            Revenue  \n",
              "count  12330.000000  \n",
              "mean       0.154745  \n",
              "std        0.361676  \n",
              "min        0.000000  \n",
              "25%        0.000000  \n",
              "50%        0.000000  \n",
              "75%        0.000000  \n",
              "max        1.000000  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Administrative</th>\n",
              "      <th>Administrative_Duration</th>\n",
              "      <th>Informational</th>\n",
              "      <th>Informational_Duration</th>\n",
              "      <th>ProductRelated</th>\n",
              "      <th>ProductRelated_Duration</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12215.000000</td>\n",
              "      <td>12225.000000</td>\n",
              "      <td>12196.000000</td>\n",
              "      <td>12204.000000</td>\n",
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              "      <td>12191.000000</td>\n",
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              "      <td>12179.000000</td>\n",
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              "      <th>mean</th>\n",
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              "      <td>80.741179</td>\n",
              "      <td>0.502706</td>\n",
              "      <td>34.733067</td>\n",
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              "      <td>3.146673</td>\n",
              "      <td>4.069464</td>\n",
              "      <td>0.154745</td>\n",
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              "      <td>1.271342</td>\n",
              "      <td>141.364602</td>\n",
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              "      <td>1905.738105</td>\n",
              "      <td>0.048569</td>\n",
              "      <td>0.048543</td>\n",
              "      <td>18.545277</td>\n",
              "      <td>0.198111</td>\n",
              "      <td>0.912112</td>\n",
              "      <td>1.716343</td>\n",
              "      <td>2.401164</td>\n",
              "      <td>4.030383</td>\n",
              "      <td>0.361676</td>\n",
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              "    <tr>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\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",
              "      <td>0.000000</td>\n",
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              "    <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>184.050000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.014286</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <td>2.000000</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>2.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5b6815c-3179-481c-9f24-f14d8ec0bd62')\"\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-d5b6815c-3179-481c-9f24-f14d8ec0bd62 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-d5b6815c-3179-481c-9f24-f14d8ec0bd62');\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",
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          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "df.describe()"
      ]
    },
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            "bfae771899444c6ea3d2235f34472c4e",
            "139db3f77eb1438baa18b51af6f33019",
            "7af5deb86a034ce2838b2d4407462d59",
            "e6c9154c416a41fda39d854adf032e6f",
            "3d1a6a73e0b4439287dcc6bfed3e63de",
            "9bf76c218512456388e826f7e092cee5",
            "fd6112a317b44171b43a09faab8ec08c",
            "ff2ada354edd4568bdc6b6c8b2cfd079",
            "103a93642d7b4572b54d31c2ff6f33cd",
            "7e15c6aa9a1d4465b673ec9e19281051",
            "8b649f5254fb42e7b03e81e96ecab1f7",
            "392c211f4f58411f9b596714bf212cef",
            "e2683df44a2f44b0ac4ebd98a7f09124",
            "568799656ca845dc8b615a2c4aa3b691",
            "b0dbb4ffd1b44dcaad38b88a7ff7adca"
          ]
        },
        "outputId": "d792c18d-1596-476b-e11e-25323d1fe13b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fe105f4d2e024e88b312bf8136d939a0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e89abd127cf0483b95fcf832e63a3f7d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Render HTML:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e316f9c7651e454c969a65f6a8686ad7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Export report to file:   0%|          | 0/1 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d1a6a73e0b4439287dcc6bfed3e63de"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "profile = ProfileReport(df)\n",
        "profile.to_file('/content/shop_profile.html')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 4- Select columns and Fill Nan feilds"
      ],
      "metadata": {
        "id": "TRz8iXSRUYAM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "featuresCorrelation = df\n",
        "featuresCorrelation['PageValueOverProductRelated'] = featuresCorrelation['PageValues'] / featuresCorrelation['ProductRelated']\n",
        "correlation = featuresCorrelation.corr()\n",
        "correlation['Revenue'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iBtVCqD6GCVR",
        "outputId": "a1970dc8-fb43-4da1-f55e-18f6d276ac8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Revenue                        1.000000\n",
              "PageValues                     0.491100\n",
              "PageValueOverProductRelated    0.335478\n",
              "ProductRelated                 0.158964\n",
              "ProductRelated_Duration        0.152289\n",
              "Administrative                 0.138062\n",
              "Informational                  0.095238\n",
              "Administrative_Duration        0.092366\n",
              "Informational_Duration         0.070054\n",
              "Browser                        0.023628\n",
              "TrafficType                   -0.004556\n",
              "Region                        -0.011562\n",
              "OperatingSystems              -0.014556\n",
              "SpecialDay                    -0.081262\n",
              "BounceRates                   -0.150304\n",
              "ExitRates                     -0.206250\n",
              "Name: Revenue, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(8, 10))\n",
        "sns.heatmap(correlation[['Revenue']]\n",
        "                      .sort_values(by=\"Revenue\", ascending=False), vmin=-1, vmax=1, annot=True, cmap='BrBG')\n",
        "plt.savefig('/content/Revenue_corr.png')"
      ],
      "metadata": {
        "id": "7ZKpq5ATLQW2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "st0ptrOYT0w8",
        "outputId": "e9c737dc-8b19-447b-f52a-e38e30c8f3fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Administrative                 115\n",
              "Administrative_Duration        105\n",
              "Informational                  134\n",
              "Informational_Duration         126\n",
              "ProductRelated                 134\n",
              "ProductRelated_Duration        139\n",
              "BounceRates                    114\n",
              "ExitRates                      122\n",
              "PageValues                     108\n",
              "SpecialDay                     129\n",
              "Month                          118\n",
              "OperatingSystems               116\n",
              "Browser                        112\n",
              "Region                         126\n",
              "TrafficType                    151\n",
              "VisitorType                    116\n",
              "Weekend                        131\n",
              "Revenue                          0\n",
              "PageValueOverProductRelated    279\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Administrative.fillna(df.Administrative.mean(),inplace=True)\n",
        "df.Administrative_Duration.fillna(df.Administrative_Duration.mean(),inplace=True)\n",
        "df.Informational.fillna(df.Informational.mean(),inplace=True)\n",
        "df.Informational_Duration.fillna(df.Informational_Duration.mean(),inplace=True)\n",
        "df.ProductRelated.fillna(df.ProductRelated.mean(),inplace=True)\n",
        "df.ProductRelated_Duration.fillna(df.ProductRelated_Duration.mean(),inplace=True)\n",
        "df.ExitRates.fillna(df.ExitRates.mean(),inplace=True)\n",
        "df.PageValues.fillna(df.PageValues.mean(),inplace=True)\n",
        "df.SpecialDay.fillna(df.SpecialDay.mean(),inplace=True)"
      ],
      "metadata": {
        "id": "IflHsXnqVi1O"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.Month.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h44tm470aYoI",
        "outputId": "ba0ed3e7-88e6-4cf0-f1b5-dc4b7091c7bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Month.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrB2GdyNasbC",
        "outputId": "cd127510-eb21-4daf-d421-c20fd1da9d19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "May     3329\n",
              "Nov     2969\n",
              "Mar     1889\n",
              "Dec     1710\n",
              "Oct      545\n",
              "Sep      445\n",
              "Aug      431\n",
              "Jul      428\n",
              "June     285\n",
              "Feb      181\n",
              "Name: Month, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Month.fillna('May',inplace=True)"
      ],
      "metadata": {
        "id": "_jflax6Ya_FN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(df,columns=['Month'])"
      ],
      "metadata": {
        "id": "Ww5Jep2kztvD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.Weekend.nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Xw3oNrL8R_k",
        "outputId": "a44eff0f-1ae3-4548-b462-ac2f7408044e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Weekend.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oC4U8ZII8WvF",
        "outputId": "75a523bf-75f0-4330-e326-67707c97cb1e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    9355\n",
              "1    2844\n",
              "Name: Weekend, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Weekend.fillna(0,inplace=True)"
      ],
      "metadata": {
        "id": "2V7Z4Hr88djj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VB3GjDWV4G_d",
        "outputId": "503cd0b2-6665-4f1b-f06f-760f6fd0fcd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Administrative', 'Administrative_Duration', 'Informational',\n",
              "       'Informational_Duration', 'ProductRelated', 'ProductRelated_Duration',\n",
              "       'BounceRates', 'ExitRates', 'PageValues', 'SpecialDay',\n",
              "       'OperatingSystems', 'Browser', 'Region', 'TrafficType', 'VisitorType',\n",
              "       'Weekend', 'Revenue', 'PageValueOverProductRelated', 'Month_Aug',\n",
              "       'Month_Dec', 'Month_Feb', 'Month_Jul', 'Month_June', 'Month_Mar',\n",
              "       'Month_May', 'Month_Nov', 'Month_Oct', 'Month_Sep'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "chosenFeatures = df.drop(columns=['BounceRates','SpecialDay','VisitorType','Browser','OperatingSystems','TrafficType', 'Region','PageValueOverProductRelated'])\n",
        "chosenFeatures['PageValueOverProductRelated'] = chosenFeatures['PageValues'] / chosenFeatures['ProductRelated']\n",
        "\n",
        "chosenFeatures.PageValueOverProductRelated.fillna(0,inplace=True)"
      ],
      "metadata": {
        "id": "LqijQeLt04B0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chosenFeatures.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HgUhlv4c20ll",
        "outputId": "f80c1246-8df8-47f7-a62a-e709cff7802c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Administrative', 'Administrative_Duration', 'Informational',\n",
              "       'Informational_Duration', 'ProductRelated', 'ProductRelated_Duration',\n",
              "       'ExitRates', 'PageValues', 'Weekend', 'Revenue', 'Month_Aug',\n",
              "       'Month_Dec', 'Month_Feb', 'Month_Jul', 'Month_June', 'Month_Mar',\n",
              "       'Month_May', 'Month_Nov', 'Month_Oct', 'Month_Sep',\n",
              "       'PageValueOverProductRelated'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 5- Split data to train and test and solved Imbalance"
      ],
      "metadata": {
        "id": "SBr61FkrdO7t"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42) \n",
        "\n",
        "for train_index, test_index in split.split(chosenFeatures, chosenFeatures[\"Revenue\"]):\n",
        "  strat_train_set = chosenFeatures.loc[train_index] \n",
        "  strat_test_set = chosenFeatures.loc[test_index]\n",
        "\n",
        "data = strat_train_set.drop('Revenue', axis = 1)\n",
        "data_labels = strat_train_set['Revenue'].copy()\n",
        "test = strat_test_set.drop('Revenue', axis = 1)\n",
        "test_labels = strat_test_set['Revenue'].copy()"
      ],
      "metadata": {
        "id": "gQf01g77Weln"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "chosenFeatures.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2FM1puW-71dk",
        "outputId": "f4fda9a9-9a77-4500-cab5-3a037a021960"
      },
      "execution_count": null,
      "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",
              "ExitRates                      0\n",
              "PageValues                     0\n",
              "Weekend                        0\n",
              "Revenue                        0\n",
              "Month_Aug                      0\n",
              "Month_Dec                      0\n",
              "Month_Feb                      0\n",
              "Month_Jul                      0\n",
              "Month_June                     0\n",
              "Month_Mar                      0\n",
              "Month_May                      0\n",
              "Month_Nov                      0\n",
              "Month_Oct                      0\n",
              "Month_Sep                      0\n",
              "PageValueOverProductRelated    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import ADASYN\n",
        "data_resampled, data_labels_resampled = ADASYN().fit_resample(data, data_labels)"
      ],
      "metadata": {
        "id": "NDOaEQjG7Xoz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "print(sorted(Counter(data_labels_resampled).items()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EQoc3Cuh-HmS",
        "outputId": "408f3cf0-d9eb-4c03-df1c-e6b5dbd22e35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0, 8338), (1, 8368)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_resampled = np.asarray(data_resampled)\n",
        "data_labels_resampled = np.asarray(data_labels_resampled)\n",
        "test = np.asarray(test)\n",
        "test_labels = np.asarray(test_labels)"
      ],
      "metadata": {
        "id": "oUBzc08zzfKT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 6- Creat SVM model, fit, results"
      ],
      "metadata": {
        "id": "mBm8j4Mm7vUq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n",
        "\n",
        "svmModel = SVC(kernel='rbf')\n",
        "svmModel.fit(data_resampled, data_labels_resampled)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7ldkLVq4jwUK",
        "outputId": "a0d0d912-8c2b-4006-bf7c-5923ddcc5117"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVC()"
            ]
          },
          "metadata": {},
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_predict = svmModel.predict(data_resampled)\n",
        "y_test_predict = svmModel.predict(test)"
      ],
      "metadata": {
        "id": "voKlrW3vj48s"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "print(\"Precision: \" + str(precision_score(test_labels, y_test_predict)))\n",
        "print(\"Recall: \" + str(recall_score(test_labels, y_test_predict)))\n",
        "print(\"F1 Score: \" + str(f1_score(test_labels, y_test_predict)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zgq-ed5cZ_ob",
        "outputId": "9f6a560a-02d4-485b-c2ea-d991c32ad7bd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.2846715328467153\n",
            "Recall: 0.7146596858638743\n",
            "F1 Score: 0.407158836689038\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 7- Creat KNN model, fit, results\n",
        "\n"
      ],
      "metadata": {
        "id": "zuGbmdzZuwit"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knnModel = KNeighborsClassifier(n_neighbors=3)\n",
        "knnModel.fit(data_resampled, data_labels_resampled)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vd0QDvX4u01E",
        "outputId": "656091c9-ae02-4756-e523-402eac08edc9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier(n_neighbors=3)"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train_predict = knnModel.predict(data_resampled)\n",
        "y_test_predict = knnModel.predict(test)"
      ],
      "metadata": {
        "id": "S9eoM40LvVv-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "print(\"Precision: \" + str(precision_score(test_labels, y_test_predict)))\n",
        "print(\"Recall: \" + str(recall_score(test_labels, y_test_predict)))\n",
        "print(\"F1 Score: \" + str(f1_score(test_labels, y_test_predict)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_VSh1txGvYyJ",
        "outputId": "d4e18eb7-8d33-4f3c-82ef-b9635a4cc36f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Precision: 0.3470948012232416\n",
            "Recall: 0.5942408376963351\n",
            "F1 Score: 0.43822393822393824\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 8- Creat Logistic Regression model, fit, results\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "2-Pf3LCSvzbJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "lrModel = LogisticRegression(random_state=0,solver='lbfgs', max_iter=3500)\n",
        "lrModel.fit(data_resampled, data_labels_resampled)"
      ],
      "metadata": {
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              "LogisticRegression(max_iter=3500, random_state=0)"
            ]
          },
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      },
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    },
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        "from sklearn.metrics import precision_score, recall_score, f1_score\n",
        "print(\"Precision: \" + str(precision_score(test_labels, y_test_predict)))\n",
        "print(\"Recall: \" + str(recall_score(test_labels, y_test_predict)))\n",
        "print(\"F1 Score: \" + str(f1_score(test_labels, y_test_predict)))"
      ],
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        },
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      },
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          "name": "stdout",
          "text": [
            "Precision: 0.6455331412103746\n",
            "Recall: 0.5863874345549738\n",
            "F1 Score: 0.6145404663923182\n"
          ]
        }
      ]
    }
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