EDA_&&_Class2.ipynb

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      "name": "EDA && Class2.ipynb",
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      "name": "python3",
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        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/gpriya32/EDA-and-Classification/blob/master/EDA_%26%26_Class2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zg0XgPYqSlc4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#viz\n",
        "import seaborn as sns \n",
        "import matplotlib.gridspec as gridspec \n",
        "import plotly.express as px\n",
        "\n",
        "\n",
        "# # graph viz\n",
        "# import plotly.offline as pyo\n",
        "# from plotly.graph_objs import *\n",
        "# import plotly.graph_objs as go\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "#settings\n",
        "color = sns.color_palette()\n",
        "sns.set_style(\"dark\")\n",
        "# pyo.init_notebook_mode()\n",
        "\n"
      ],
      "execution_count": 629,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xMHP6qeXS1WI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "%matplotlib inline "
      ],
      "execution_count": 630,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QYJqYDXfS7Nk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df=pd.read_csv('/content/drive/My Drive/DATASETS/Customers2.csv')"
      ],
      "execution_count": 631,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7ASQedIZzy4G",
        "colab_type": "text"
      },
      "source": [
        "#EDA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H_atxgRyWxpn",
        "colab_type": "code",
        "colab": {
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          "height": 443
        },
        "outputId": "502c3e7c-d77e-4d15-8d08-1b177aa58e59"
      },
      "source": [
        "df"
      ],
      "execution_count": 632,
      "outputs": [
        {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>NumCalls</th>\n",
              "      <th>NumEmails</th>\n",
              "      <th>NumDownloads</th>\n",
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              "      <th>PageVisits</th>\n",
              "      <th>PriorClient</th>\n",
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              "      <td>USA</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>No</td>\n",
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              "      <td>2</td>\n",
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              "      <td>Spanish</td>\n",
              "      <td>Mexico</td>\n",
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              "      <td>0</td>\n",
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              "      <td>English</td>\n",
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              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>No</td>\n",
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              "      <td>5</td>\n",
              "      <td>7</td>\n",
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              "      <th>748</th>\n",
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              "      <td>Yes</td>\n",
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              "      <td>India</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>No</td>\n",
              "      <td>Yes</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>Yes</td>\n",
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              "<p>753 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CustomerID  NumCalls  NumEmails  ...  PageVisits  PriorClient  MadeAPurchase\n",
              "0             1        33         19  ...         0.0          Yes             No\n",
              "1             2         0          1  ...         0.0           No             No\n",
              "2             3         4          6  ...         0.0           No             No\n",
              "3             4         0         10  ...         0.0           No             No\n",
              "4             5         7          0  ...         0.0           No            Yes\n",
              "..          ...       ...        ...  ...         ...          ...            ...\n",
              "748         749         3          0  ...         0.0           No            Yes\n",
              "749         750         0          4  ...         0.0           No            Yes\n",
              "750         751        15          0  ...         0.0           No            Yes\n",
              "751         752         0          1  ...         0.0          Yes            Yes\n",
              "752         753        20          0  ...         0.0          Yes            Yes\n",
              "\n",
              "[753 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 632
        }
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          "height": 370
        },
        "outputId": "37abe0b0-b3df-4da2-94b2-f950d6a328c5"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 633,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 753 entries, 0 to 752\n",
            "Data columns (total 12 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   CustomerID     753 non-null    int64  \n",
            " 1   NumCalls       753 non-null    int64  \n",
            " 2   NumEmails      753 non-null    int64  \n",
            " 3   NumDownloads   753 non-null    int64  \n",
            " 4   NumEvents      753 non-null    int64  \n",
            " 5   NumForms       753 non-null    int64  \n",
            " 6   Language       753 non-null    object \n",
            " 7   Country        753 non-null    object \n",
            " 8   WebVisits      739 non-null    float64\n",
            " 9   PageVisits     739 non-null    float64\n",
            " 10  PriorClient    753 non-null    object \n",
            " 11  MadeAPurchase  753 non-null    object \n",
            "dtypes: float64(2), int64(6), object(4)\n",
            "memory usage: 70.7+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aON16NoiXMDP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "af5623f7-d2e1-47a7-a3dd-0c8d7882ef38"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 634,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID        0\n",
              "NumCalls          0\n",
              "NumEmails         0\n",
              "NumDownloads      0\n",
              "NumEvents         0\n",
              "NumForms          0\n",
              "Language          0\n",
              "Country           0\n",
              "WebVisits        14\n",
              "PageVisits       14\n",
              "PriorClient       0\n",
              "MadeAPurchase     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 634
        }
      ]
    },
    {
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          "height": 36
        },
        "outputId": "73dccf71-3689-4ec3-b89b-695faa2145eb"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": 635,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(753, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 635
        }
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    {
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          "height": 508
        },
        "outputId": "427e535e-6bcf-4320-c0cd-3a28db7bee60"
      },
      "source": [
        "df.loc[df['PageVisits'].isnull()]"
      ],
      "execution_count": 636,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>Chile</td>\n",
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              "      <td>USA</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>65</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>Yes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>66</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>Canada</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>67</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>USA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>68</td>\n",
              "      <td>0</td>\n",
              "      <td>6</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Yes</td>\n",
              "      <td>No</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    CustomerID  NumCalls  NumEmails  ...  PageVisits  PriorClient  MadeAPurchase\n",
              "54          55         0          3  ...         NaN          Yes            Yes\n",
              "55          56         0          0  ...         NaN          Yes             No\n",
              "56          57         3          0  ...         NaN           No            Yes\n",
              "57          58        24          0  ...         NaN          Yes            Yes\n",
              "58          59         0          0  ...         NaN          Yes             No\n",
              "59          60         0          8  ...         NaN          Yes            Yes\n",
              "60          61         1          0  ...         NaN          Yes            Yes\n",
              "61          62         0          0  ...         NaN          Yes             No\n",
              "62          63        10          6  ...         NaN          Yes            Yes\n",
              "63          64        32          0  ...         NaN          Yes            Yes\n",
              "64          65        10          5  ...         NaN          Yes            Yes\n",
              "65          66         0          0  ...         NaN          Yes             No\n",
              "66          67         0          1  ...         NaN          Yes             No\n",
              "67          68         0          6  ...         NaN          Yes             No\n",
              "\n",
              "[14 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 636
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Km3lLbkcX1mn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.fillna(0,inplace=True)"
      ],
      "execution_count": 637,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fWADUzTYMqI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "738af85f-a336-4570-fc43-f0344942b71a"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": 638,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CustomerID       0\n",
              "NumCalls         0\n",
              "NumEmails        0\n",
              "NumDownloads     0\n",
              "NumEvents        0\n",
              "NumForms         0\n",
              "Language         0\n",
              "Country          0\n",
              "WebVisits        0\n",
              "PageVisits       0\n",
              "PriorClient      0\n",
              "MadeAPurchase    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 638
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SJHsd6A8ZedT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "e967a960-91bb-4ca9-9c15-88d6c0ec7965"
      },
      "source": [
        "df.corr()"
      ],
      "execution_count": 639,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>CustomerID</th>\n",
              "      <th>NumCalls</th>\n",
              "      <th>NumEmails</th>\n",
              "      <th>NumDownloads</th>\n",
              "      <th>NumEvents</th>\n",
              "      <th>NumForms</th>\n",
              "      <th>WebVisits</th>\n",
              "      <th>PageVisits</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.009318</td>\n",
              "      <td>-0.060885</td>\n",
              "      <td>-0.043302</td>\n",
              "      <td>-0.018588</td>\n",
              "      <td>-0.027367</td>\n",
              "      <td>-0.322985</td>\n",
              "      <td>-0.241026</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumCalls</th>\n",
              "      <td>0.009318</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.024605</td>\n",
              "      <td>-0.076742</td>\n",
              "      <td>-0.160997</td>\n",
              "      <td>0.020944</td>\n",
              "      <td>0.028609</td>\n",
              "      <td>-0.022931</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumEmails</th>\n",
              "      <td>-0.060885</td>\n",
              "      <td>-0.024605</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101145</td>\n",
              "      <td>0.144024</td>\n",
              "      <td>0.139552</td>\n",
              "      <td>0.050507</td>\n",
              "      <td>-0.012449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumDownloads</th>\n",
              "      <td>-0.043302</td>\n",
              "      <td>-0.076742</td>\n",
              "      <td>0.101145</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.268271</td>\n",
              "      <td>0.099275</td>\n",
              "      <td>-0.000883</td>\n",
              "      <td>-0.037804</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumEvents</th>\n",
              "      <td>-0.018588</td>\n",
              "      <td>-0.160997</td>\n",
              "      <td>0.144024</td>\n",
              "      <td>0.268271</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.314650</td>\n",
              "      <td>-0.016813</td>\n",
              "      <td>-0.014074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NumForms</th>\n",
              "      <td>-0.027367</td>\n",
              "      <td>0.020944</td>\n",
              "      <td>0.139552</td>\n",
              "      <td>0.099275</td>\n",
              "      <td>0.314650</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.040293</td>\n",
              "      <td>-0.005587</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>WebVisits</th>\n",
              "      <td>-0.322985</td>\n",
              "      <td>0.028609</td>\n",
              "      <td>0.050507</td>\n",
              "      <td>-0.000883</td>\n",
              "      <td>-0.016813</td>\n",
              "      <td>-0.040293</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.091758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PageVisits</th>\n",
              "      <td>-0.241026</td>\n",
              "      <td>-0.022931</td>\n",
              "      <td>-0.012449</td>\n",
              "      <td>-0.037804</td>\n",
              "      <td>-0.014074</td>\n",
              "      <td>-0.005587</td>\n",
              "      <td>0.091758</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              CustomerID  NumCalls  NumEmails  ...  NumForms  WebVisits  PageVisits\n",
              "CustomerID      1.000000  0.009318  -0.060885  ... -0.027367  -0.322985   -0.241026\n",
              "NumCalls        0.009318  1.000000  -0.024605  ...  0.020944   0.028609   -0.022931\n",
              "NumEmails      -0.060885 -0.024605   1.000000  ...  0.139552   0.050507   -0.012449\n",
              "NumDownloads   -0.043302 -0.076742   0.101145  ...  0.099275  -0.000883   -0.037804\n",
              "NumEvents      -0.018588 -0.160997   0.144024  ...  0.314650  -0.016813   -0.014074\n",
              "NumForms       -0.027367  0.020944   0.139552  ...  1.000000  -0.040293   -0.005587\n",
              "WebVisits      -0.322985  0.028609   0.050507  ... -0.040293   1.000000    0.091758\n",
              "PageVisits     -0.241026 -0.022931  -0.012449  ... -0.005587   0.091758    1.000000\n",
              "\n",
              "[8 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 639
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "am-19IpBYf69",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "28fe7c8f-75b2-409f-c45b-130dc18a21a4"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "le=LabelEncoder()\n",
        "df['PriorClient']=le.fit_transform(df['PriorClient'])\n",
        "df['MadeAPurchase']=le.fit_transform(df['MadeAPurchase'])\n",
        "df"
      ],
      "execution_count": 640,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>CustomerID</th>\n",
              "      <th>NumCalls</th>\n",
              "      <th>NumEmails</th>\n",
              "      <th>NumDownloads</th>\n",
              "      <th>NumEvents</th>\n",
              "      <th>NumForms</th>\n",
              "      <th>Language</th>\n",
              "      <th>Country</th>\n",
              "      <th>WebVisits</th>\n",
              "      <th>PageVisits</th>\n",
              "      <th>PriorClient</th>\n",
              "      <th>MadeAPurchase</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>German</td>\n",
              "      <td>Germany</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>English</td>\n",
              "      <td>India</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>749</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>750</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>751</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>India</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>752</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>753</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>753 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CustomerID  NumCalls  NumEmails  ...  PageVisits  PriorClient  MadeAPurchase\n",
              "0             1        33         19  ...         0.0            1              0\n",
              "1             2         0          1  ...         0.0            0              0\n",
              "2             3         4          6  ...         0.0            0              0\n",
              "3             4         0         10  ...         0.0            0              0\n",
              "4             5         7          0  ...         0.0            0              1\n",
              "..          ...       ...        ...  ...         ...          ...            ...\n",
              "748         749         3          0  ...         0.0            0              1\n",
              "749         750         0          4  ...         0.0            0              1\n",
              "750         751        15          0  ...         0.0            0              1\n",
              "751         752         0          1  ...         0.0            1              1\n",
              "752         753        20          0  ...         0.0            1              1\n",
              "\n",
              "[753 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 640
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gDDrGbXMZybo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df=df.iloc[:, :-1]"
      ],
      "execution_count": 641,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5JLqQkAIbkt8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "test_df=df.iloc[:,11]"
      ],
      "execution_count": 642,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f0BNPiZqbw9z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "d1787678-0923-4bfa-8f5b-c6fa57e6ebbf"
      },
      "source": [
        "\n",
        "print('train shape : ',train_df.shape,'\\n','test shape',test_df.shape)\n"
      ],
      "execution_count": 643,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train shape :  (753, 11) \n",
            " test shape (753,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "58x6hMKlbzGO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "171e37a6-83c7-4fee-9dd1-b80b30a2cafe"
      },
      "source": [
        "dataframe=train_df\n",
        "train_df"
      ],
      "execution_count": 644,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>CustomerID</th>\n",
              "      <th>NumCalls</th>\n",
              "      <th>NumEmails</th>\n",
              "      <th>NumDownloads</th>\n",
              "      <th>NumEvents</th>\n",
              "      <th>NumForms</th>\n",
              "      <th>Language</th>\n",
              "      <th>Country</th>\n",
              "      <th>WebVisits</th>\n",
              "      <th>PageVisits</th>\n",
              "      <th>PriorClient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>German</td>\n",
              "      <td>Germany</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>English</td>\n",
              "      <td>India</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>749</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>750</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>751</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>India</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>752</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>753</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>753 rows × 11 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CustomerID  NumCalls  NumEmails  ...  WebVisits  PageVisits  PriorClient\n",
              "0             1        33         19  ...       10.0         0.0            1\n",
              "1             2         0          1  ...       61.0         0.0            0\n",
              "2             3         4          6  ...        1.0         0.0            0\n",
              "3             4         0         10  ...       26.0         0.0            0\n",
              "4             5         7          0  ...       11.0         0.0            0\n",
              "..          ...       ...        ...  ...        ...         ...          ...\n",
              "748         749         3          0  ...        0.0         0.0            0\n",
              "749         750         0          4  ...        0.0         0.0            0\n",
              "750         751        15          0  ...        0.0         0.0            0\n",
              "751         752         0          1  ...        0.0         0.0            1\n",
              "752         753        20          0  ...        0.0         0.0            1\n",
              "\n",
              "[753 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 644
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WJAhrmYscXNc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "outputId": "a3877d71-9631-4fbd-dec5-c26154f0e5e5"
      },
      "source": [
        "# to check correlation between different columns\n",
        "sns.heatmap(train_df.corr(),annot=True)"
      ],
      "execution_count": 645,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f3321892940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 645
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jl2Bs4lTdjGC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "f2896e1b-9dda-435a-c4a5-90046a637f4d"
      },
      "source": [
        "ls=train_df.groupby('Country').size().nlargest(8).index.values\n",
        "ls"
      ],
      "execution_count": 646,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['USA', 'Canada', 'Japan', 'France', 'China', 'Mexico', 'Colombia',\n",
              "       'Spain'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 646
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B0c41t_0miOQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def func(val):\n",
        "  if val not in ls:\n",
        "    return 'Others'\n",
        "  return val\n",
        "\n",
        "train_df['Country']=train_df['Country'].apply(func)\n"
      ],
      "execution_count": 647,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IbYhcNujnj6L",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "b681d132-569e-471c-ba56-51d87d628726"
      },
      "source": [
        "train_df.iloc[:,1:]"
      ],
      "execution_count": 648,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>NumCalls</th>\n",
              "      <th>NumEmails</th>\n",
              "      <th>NumDownloads</th>\n",
              "      <th>NumEvents</th>\n",
              "      <th>NumForms</th>\n",
              "      <th>Language</th>\n",
              "      <th>Country</th>\n",
              "      <th>WebVisits</th>\n",
              "      <th>PageVisits</th>\n",
              "      <th>PriorClient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>33</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>German</td>\n",
              "      <td>Others</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>English</td>\n",
              "      <td>Others</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>Others</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Others</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>753 rows × 10 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     NumCalls  NumEmails  NumDownloads  ...  WebVisits  PageVisits PriorClient\n",
              "0          33         19             1  ...       10.0         0.0           1\n",
              "1           0          1             0  ...       61.0         0.0           0\n",
              "2           4          6             2  ...        1.0         0.0           0\n",
              "3           0         10             0  ...       26.0         0.0           0\n",
              "4           7          0             0  ...       11.0         0.0           0\n",
              "..        ...        ...           ...  ...        ...         ...         ...\n",
              "748         3          0             0  ...        0.0         0.0           0\n",
              "749         0          4             0  ...        0.0         0.0           0\n",
              "750        15          0             0  ...        0.0         0.0           0\n",
              "751         0          1             0  ...        0.0         0.0           1\n",
              "752        20          0             0  ...        0.0         0.0           1\n",
              "\n",
              "[753 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 648
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SckZYFnbpJ8T",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "3cc7dce0-fb10-4ec2-8c0d-99e145e8bf28"
      },
      "source": [
        "#plotting no of calls from each country\n",
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "a = sns.barplot(x = 'NumCalls', y = 'Country', estimator = np.sum , data = train_df)\n",
        "plt.title(\"Number of Calls per country\")\n",
        "plt.ylabel('Country', fontsize=12)\n",
        "plt.xlabel('Number of Calls', fontsize=12)\n",
        "\n",
        "for p in a.patches:\n",
        "  val = p.get_width()\n",
        "  x = p.get_x() + p.get_width() + 10\n",
        "  y = p.get_y() + p.get_height()/2\n",
        "  a.annotate(round(val,2),(x,y))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 649,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CWN2wBN8rurB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "outputId": "ce060138-0c22-45d7-9c44-f5a2b1bbc0e5"
      },
      "source": [
        "#plotting number of emails per country\n",
        "plt.figure(figsize=(10,5))\n",
        "a1=sns.barplot(x='NumEmails',y='Country',data=train_df, estimator=np.sum)\n",
        "plt.title('Number of Emails per Country')\n",
        "plt.xlabel('Number of Emails',fontsize=12)\n",
        "plt.ylabel('Country',fontsize=12)\n",
        "\n",
        "for p in a1.patches:\n",
        "  val1=p.get_width()\n",
        "  x1=p.get_x()+p.get_width()+10\n",
        "  y1=p.get_y()+p.get_height()/2\n",
        "  a1.annotate(round(val1,2),(x1,y1))\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": 650,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jJmSfGFvxAhJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "bdb0d8f3-f59c-4645-d79e-fcf1c7e5ba78"
      },
      "source": [
        "#plotting no of down from each country\n",
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "a3 = sns.barplot(x = 'NumDownloads', y = 'Country', estimator = np.sum , data = train_df)\n",
        "plt.title(\"Number of Downloads per country\")\n",
        "\n",
        "for p in a3.patches:\n",
        "  val3 = p.get_width()\n",
        "  x3 = p.get_x() + p.get_width() + 10\n",
        "  y3 = p.get_y() + p.get_height()/2\n",
        "  a3.annotate(round(val3,2),(x3,y3))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 651,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hCtxOct_yCvU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "6b9ca864-6981-4d6f-ab06-d3883d2fb41d"
      },
      "source": [
        "#plotting no of forms from each country\n",
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "a4 = sns.barplot(x = 'NumForms', y = 'Country', estimator = np.sum , data = train_df)\n",
        "plt.title(\"Number of Forms per country\")\n",
        "\n",
        "\n",
        "for p in a4.patches:\n",
        "  val = p.get_width()\n",
        "  x4 = p.get_x() + p.get_width() + 10\n",
        "  y4 = p.get_y() + p.get_height()/2\n",
        "  a4.annotate(round(val,2),(x4,y4))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 652,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7QDJV9-tyTFv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "e044f955-3d16-4406-c9b4-59442dd64179"
      },
      "source": [
        "#plotting no of events from each country\n",
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "a5 = sns.barplot(x = 'NumEvents', y = 'Country', estimator = np.sum , data = train_df)\n",
        "plt.title(\"Number of Events per country\")\n",
        "\n",
        "\n",
        "for p in a5.patches:\n",
        "  val = p.get_width()\n",
        "  x5 = p.get_x() + p.get_width() + 10\n",
        "  y5 = p.get_y() + p.get_height()/2\n",
        "  a5.annotate(round(val,2),(x5,y5))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 653,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UcmJ9uP2zCxI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "fe58c355-2896-4675-8a42-2a48cda362d9"
      },
      "source": [
        "#plotting no of webvisits from each country\n",
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "a6 = sns.barplot(x = 'WebVisits', y = 'Country', estimator = np.sum , data = train_df)\n",
        "plt.title(\"Number of Webvisits per country\")\n",
        "\n",
        "\n",
        "for p in a6.patches:\n",
        "  val = p.get_width()\n",
        "  x6 = p.get_x() + p.get_width() + 10\n",
        "  y6= p.get_y() + p.get_height()/2\n",
        "  a6.annotate(round(val,2),(x6,y6))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 654,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7tKUz9izVXN",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "outputId": "92a663be-b1a9-4fff-fb52-f3b09b561172"
      },
      "source": [
        "#plotting no of pgvisits from each country\n",
        "plt.figure(figsize=(10,5))\n",
        "\n",
        "a7 = sns.barplot(x = 'PageVisits', y = 'Country', estimator = np.sum , data = train_df)\n",
        "plt.title(\"Number of Page visits per country\")\n",
        "\n",
        "\n",
        "for p in a7.patches:\n",
        "  val = p.get_width()\n",
        "  x7 = p.get_x() + p.get_width() + 10\n",
        "  y7 = p.get_y() + p.get_height()/2\n",
        "  a7.annotate(round(val,2),(x7,y7))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 655,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8sTtolb15VF",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "866fa2dd-f333-49c0-93c7-8776ce696c8c"
      },
      "source": [
        "tot=train_df.groupby('Country')['PriorClient'].sum()\n",
        "country=tot.index.to_list()\n",
        "prior_clients=tot.to_list()\n",
        "print (country,'\\n',prior_clients,'\\n',tot,'\\n')"
      ],
      "execution_count": 656,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Canada', 'China', 'Colombia', 'France', 'Japan', 'Mexico', 'Others', 'Spain', 'USA'] \n",
            " [92, 30, 21, 25, 69, 21, 99, 9, 117] \n",
            " Country\n",
            "Canada       92\n",
            "China        30\n",
            "Colombia     21\n",
            "France       25\n",
            "Japan        69\n",
            "Mexico       21\n",
            "Others       99\n",
            "Spain         9\n",
            "USA         117\n",
            "Name: PriorClient, dtype: int64 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TaVR5EDyzjog",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "7364b7cb-88b3-4864-b029-cb91f631309f"
      },
      "source": [
        "# no of prior clientsin each country who previously made a purchase\n",
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "ax.axis('equal')\n",
        "ax.pie(prior_clients, labels =country,autopct='%1.2f%%')\n",
        "plt.show()\n"
      ],
      "execution_count": 657,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0uOjdBJU0kBD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import plotly.graph_objects as go\n",
        "\n",
        "# fig = go.Figure(data=[go.Pie(labels=country, values=prior_clients)])\n",
        "# fig.show()"
      ],
      "execution_count": 658,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UUqwZ2GU7Nd9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 464
        },
        "outputId": "ad9c8d49-fce8-4a2f-ffe9-0f32501fedea"
      },
      "source": [
        "# language of clients who made purchase \n",
        "mod=train_df.groupby('Language')['PriorClient'].sum()\n",
        "lag=mod.index.to_list()\n",
        "prior_clients_lang=mod.to_list()\n",
        "print (lag,'\\n',prior_clients_lang,'\\n',mod,'\\n')"
      ],
      "execution_count": 659,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['Arabic', 'Chinese', 'Chinese trad.', 'Czech', 'Dutch', 'English', 'Finnish', 'French', 'German', 'Hungarian', 'Italian', 'Japanese', 'Korean', 'Norwegian', 'Portuguese', 'Russian', 'Spanish', 'Turkish'] \n",
            " [0, 24, 3, 3, 6, 255, 0, 3, 57, 3, 9, 69, 3, 0, 12, 3, 33, 0] \n",
            " Language\n",
            "Arabic             0\n",
            "Chinese           24\n",
            "Chinese trad.      3\n",
            "Czech              3\n",
            "Dutch              6\n",
            "English          255\n",
            "Finnish            0\n",
            "French             3\n",
            "German            57\n",
            "Hungarian          3\n",
            "Italian            9\n",
            "Japanese          69\n",
            "Korean             3\n",
            "Norwegian          0\n",
            "Portuguese        12\n",
            "Russian            3\n",
            "Spanish           33\n",
            "Turkish            0\n",
            "Name: PriorClient, dtype: int64 \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bXaf1giD8Tji",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "eceb69de-ee6f-4ff2-cc5e-2c6f76ff3b6f"
      },
      "source": [
        "# no of prior clients in diff languages who previously made a purchase\n",
        "\n",
        "plt.figure(figsize=(10,20))\n",
        "\n",
        "a8=sns.barplot(prior_clients_lang,lag)\n",
        "plt.title(\"Language of Clients\")\n",
        "\n",
        "\n",
        "for p in a8.patches:\n",
        "  val = p.get_width()\n",
        "  x8 = p.get_x() + p.get_width() + 10\n",
        "  y8= p.get_y() + p.get_height()/2\n",
        "  a8.annotate(round(val,2),(x8,y8))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 660,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ezSfTZg59mTo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "6f9e6319-dee5-4891-d1d7-89b41af9da02"
      },
      "source": [
        "plt.figure(figsize=(10,10))\n",
        "\n",
        "plt.plot(prior_clients_lang,lag)\n",
        "plt.title(\"Language of Clients\")\n",
        "plt.show()"
      ],
      "execution_count": 661,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6lcvZk2IshUv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "col=['NumCalls',    'NumEmails', 'NumDownloads',\n",
        "          'NumEvents',     'NumForms',   'WebVisits',   'PageVisits' ]\n",
        "def func(data):\n",
        "  val=data[col].sum()\n",
        "  return val\n",
        "  \n",
        "train_df['Total_contribution']=train_df.apply(func,axis=1)"
      ],
      "execution_count": 662,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3dFkmp1IBXyC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "2e64f629-fcba-42c0-f340-d0ebb6ef72d7"
      },
      "source": [
        "ls1=train_df.groupby('Language').size().nlargest(5).index.values\n",
        "ls1"
      ],
      "execution_count": 663,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['English', 'German', 'Spanish', 'Japanese', 'Chinese'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 663
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "egPrCjdhBlqV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def func(val):\n",
        "  if val not in ls1:\n",
        "    return 'Oth'\n",
        "  return val\n",
        "\n",
        "train_df['Language']=train_df['Language'].apply(func)"
      ],
      "execution_count": 664,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOMTk3ovvIVr",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "18f41ddd-7411-4b4e-916d-eb8371f896ec"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 665,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>CustomerID</th>\n",
              "      <th>NumCalls</th>\n",
              "      <th>NumEmails</th>\n",
              "      <th>NumDownloads</th>\n",
              "      <th>NumEvents</th>\n",
              "      <th>NumForms</th>\n",
              "      <th>Language</th>\n",
              "      <th>Country</th>\n",
              "      <th>WebVisits</th>\n",
              "      <th>PageVisits</th>\n",
              "      <th>PriorClient</th>\n",
              "      <th>Total_contribution</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>33</td>\n",
              "      <td>19</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>German</td>\n",
              "      <td>Others</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>64.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>63.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Mexico</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>English</td>\n",
              "      <td>Others</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>USA</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>18.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>749</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>750</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Canada</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>751</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>English</td>\n",
              "      <td>Others</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>752</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>Spanish</td>\n",
              "      <td>Colombia</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>753</td>\n",
              "      <td>20</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>German</td>\n",
              "      <td>Others</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>753 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CustomerID  NumCalls  ...  PriorClient  Total_contribution\n",
              "0             1        33  ...            1                64.0\n",
              "1             2         0  ...            0                63.0\n",
              "2             3         4  ...            0                13.0\n",
              "3             4         0  ...            0                37.0\n",
              "4             5         7  ...            0                18.0\n",
              "..          ...       ...  ...          ...                 ...\n",
              "748         749         3  ...            0                 3.0\n",
              "749         750         0  ...            0                 4.0\n",
              "750         751        15  ...            0                15.0\n",
              "751         752         0  ...            1                 1.0\n",
              "752         753        20  ...            1                20.0\n",
              "\n",
              "[753 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 665
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TrPpxXB4_BdB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder,OneHotEncoder\n",
        "le=LabelEncoder()\n",
        "train_df['Country']=le.fit_transform(train_df['Country'])\n",
        "train_df['Language']=le.fit_transform(train_df['Language'])\n"
      ],
      "execution_count": 666,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k3jXZ-uMNhIl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "10f581a2-55a2-4b09-b270-8f7b1b9d5eb1"
      },
      "source": [
        "# creating instance of one-hot-encoder\n",
        "one = OneHotEncoder(handle_unknown='ignore')\n",
        "one_df = pd.DataFrame(one.fit_transform(train_df[['Country']]).toarray())\n",
        "# merge with \n",
        "train_df = train_df.join(one_df)\n",
        "train_df.columns"
      ],
      "execution_count": 667,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index([        'CustomerID',           'NumCalls',          'NumEmails',\n",
              "             'NumDownloads',          'NumEvents',           'NumForms',\n",
              "                 'Language',            'Country',          'WebVisits',\n",
              "               'PageVisits',        'PriorClient', 'Total_contribution',\n",
              "                          0,                    1,                    2,\n",
              "                          3,                    4,                    5,\n",
              "                          6,                    7,                    8],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 667
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "irMVzT7_JHn_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df.columns=[  'CustomerID',     'NumCalls',    'NumEmails', 'NumDownloads',\n",
        "          'NumEvents',     'NumForms',     'Language',            'Country',\n",
        "          'WebVisits',   'PageVisits',  'PriorClient',       'Total_contribution',       'Country0',\n",
        "                    'Country1',              'Country2',              'Country3',      'Country4',\n",
        "                    'Country5', 'Country6',                    'Country7',                    'Country8',\n",
        "                    ]\n"
      ],
      "execution_count": 668,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ikxPmTUAv0U",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "00462c03-7fe5-4ceb-bb24-3eeac843225d"
      },
      "source": [
        "one_df2 = pd.DataFrame(one.fit_transform(train_df[['Language']]).toarray())\n",
        "# merge with \n",
        "train_df = train_df.join(one_df2)\n",
        "train_df.columns"
      ],
      "execution_count": 669,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index([        'CustomerID',           'NumCalls',          'NumEmails',\n",
              "             'NumDownloads',          'NumEvents',           'NumForms',\n",
              "                 'Language',            'Country',          'WebVisits',\n",
              "               'PageVisits',        'PriorClient', 'Total_contribution',\n",
              "                 'Country0',           'Country1',           'Country2',\n",
              "                 'Country3',           'Country4',           'Country5',\n",
              "                 'Country6',           'Country7',           'Country8',\n",
              "                          0,                    1,                    2,\n",
              "                          3,                    4,                    5],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 669
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PcBMtoRAez1J",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_df=train_df.drop(columns=['CustomerID','NumEvents','NumForms','NumEmails','PageVisits','NumCalls','Language','Country'])"
      ],
      "execution_count": 670,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wfkdhj97NRvS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "8dc6cb37-2efb-4322-da88-81abac54cf03"
      },
      "source": [
        "train_df"
      ],
      "execution_count": 671,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>NumDownloads</th>\n",
              "      <th>WebVisits</th>\n",
              "      <th>PriorClient</th>\n",
              "      <th>Total_contribution</th>\n",
              "      <th>Country0</th>\n",
              "      <th>Country1</th>\n",
              "      <th>Country2</th>\n",
              "      <th>Country3</th>\n",
              "      <th>Country4</th>\n",
              "      <th>Country5</th>\n",
              "      <th>Country6</th>\n",
              "      <th>Country7</th>\n",
              "      <th>Country8</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
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              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>61.0</td>\n",
              "      <td>0</td>\n",
              "      <td>63.0</td>\n",
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              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0</td>\n",
              "      <td>13.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>0</td>\n",
              "      <td>37.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>753 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     NumDownloads  WebVisits  PriorClient  ...    3    4    5\n",
              "0               1       10.0            1  ...  0.0  0.0  0.0\n",
              "1               0       61.0            0  ...  0.0  0.0  0.0\n",
              "2               2        1.0            0  ...  0.0  0.0  1.0\n",
              "3               0       26.0            0  ...  0.0  0.0  0.0\n",
              "4               0       11.0            0  ...  0.0  0.0  0.0\n",
              "..            ...        ...          ...  ...  ...  ...  ...\n",
              "748             0        0.0            0  ...  0.0  0.0  0.0\n",
              "749             0        0.0            0  ...  0.0  0.0  0.0\n",
              "750             0        0.0            0  ...  0.0  0.0  0.0\n",
              "751             0        0.0            1  ...  0.0  0.0  1.0\n",
              "752             0        0.0            1  ...  0.0  0.0  0.0\n",
              "\n",
              "[753 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 671
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AVEQfjJwC12l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "col=train_df.columns.to_list()"
      ],
      "execution_count": 672,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XSenWJBDCPgo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "48b61218-1732-4c37-f735-dacd35762cb2"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "min_max_scaler = preprocessing.MinMaxScaler()\n",
        "train_df = pd.DataFrame(min_max_scaler.fit_transform(train_df))\n",
        "train_df.columns=col\n",
        "train_df"
      ],
      "execution_count": 673,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>NumDownloads</th>\n",
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              "      <th>Country6</th>\n",
              "      <th>Country7</th>\n",
              "      <th>Country8</th>\n",
              "      <th>0</th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.090909</td>\n",
              "      <td>0.075188</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.347826</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.458647</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.342391</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.007519</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.070652</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.195489</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.201087</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.082707</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.097826</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.016304</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.021739</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.081522</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.005435</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.108696</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>753 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     NumDownloads  WebVisits  PriorClient  ...    3    4    5\n",
              "0        0.090909   0.075188          1.0  ...  0.0  0.0  0.0\n",
              "1        0.000000   0.458647          0.0  ...  0.0  0.0  0.0\n",
              "2        0.181818   0.007519          0.0  ...  0.0  0.0  1.0\n",
              "3        0.000000   0.195489          0.0  ...  0.0  0.0  0.0\n",
              "4        0.000000   0.082707          0.0  ...  0.0  0.0  0.0\n",
              "..            ...        ...          ...  ...  ...  ...  ...\n",
              "748      0.000000   0.000000          0.0  ...  0.0  0.0  0.0\n",
              "749      0.000000   0.000000          0.0  ...  0.0  0.0  0.0\n",
              "750      0.000000   0.000000          0.0  ...  0.0  0.0  0.0\n",
              "751      0.000000   0.000000          1.0  ...  0.0  0.0  1.0\n",
              "752      0.000000   0.000000          1.0  ...  0.0  0.0  0.0\n",
              "\n",
              "[753 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 673
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ApppW3vOUYc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test=train_test_split(train_df,test_df,test_size=0.2,random_state=0)"
      ],
      "execution_count": 674,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "w9ITJwP7joew",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "outputId": "fff4faba-35a6-4361-9634-265ff28c86f3"
      },
      "source": [
        "y_train"
      ],
      "execution_count": 675,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "181    1\n",
              "354    1\n",
              "252    1\n",
              "97     1\n",
              "521    0\n",
              "      ..\n",
              "707    0\n",
              "192    1\n",
              "629    1\n",
              "559    1\n",
              "684    1\n",
              "Name: MadeAPurchase, Length: 602, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 675
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bPbvWlUDjoQ6",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "outputId": "cccce6e2-1607-456c-84c3-59e608d22248"
      },
      "source": [
        "x_train"
      ],
      "execution_count": 676,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>NumDownloads</th>\n",
              "      <th>WebVisits</th>\n",
              "      <th>PriorClient</th>\n",
              "      <th>Total_contribution</th>\n",
              "      <th>Country0</th>\n",
              "      <th>Country1</th>\n",
              "      <th>Country2</th>\n",
              "      <th>Country3</th>\n",
              "      <th>Country4</th>\n",
              "      <th>Country5</th>\n",
              "      <th>Country6</th>\n",
              "      <th>Country7</th>\n",
              "      <th>Country8</th>\n",
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              "      <th>5</th>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>97</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.187970</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.163043</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>521</th>\n",
              "      <td>0.000000</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>0.016304</td>\n",
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              "      <td>...</td>\n",
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              "      <th>707</th>\n",
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              "    <tr>\n",
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              "      <td>0.000000</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>629</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.054348</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>559</th>\n",
              "      <td>0.181818</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.163043</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.005435</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>602 rows × 19 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     NumDownloads  WebVisits  PriorClient  ...    3    4    5\n",
              "181      0.000000   0.127820          1.0  ...  0.0  0.0  0.0\n",
              "354      0.000000   0.030075          1.0  ...  0.0  0.0  0.0\n",
              "252      0.000000   0.045113          0.0  ...  0.0  0.0  0.0\n",
              "97       0.000000   0.187970          0.0  ...  0.0  0.0  0.0\n",
              "521      0.000000   0.007519          1.0  ...  0.0  0.0  0.0\n",
              "..            ...        ...          ...  ...  ...  ...  ...\n",
              "707      0.000000   0.022556          0.0  ...  0.0  1.0  0.0\n",
              "192      0.000000   0.127820          1.0  ...  0.0  0.0  0.0\n",
              "629      0.000000   0.000000          0.0  ...  0.0  1.0  0.0\n",
              "559      0.181818   0.000000          1.0  ...  0.0  1.0  0.0\n",
              "684      0.000000   0.000000          0.0  ...  0.0  0.0  0.0\n",
              "\n",
              "[602 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 676
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h-Tr89SslEfJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "0f23c19b-77b5-4619-e826-0050410cca70"
      },
      "source": [
        "x_train.shape"
      ],
      "execution_count": 677,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(602, 19)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 677
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nnvkn2wxd3wT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow as tf\n",
        "ann = tf.keras.models.Sequential()\n",
        "# Adding the input layer and the first hidden layer\n",
        "ann.add(tf.keras.layers.Dense(units=7, kernel_initializer='normal',activation='relu',input_dim=19))\n",
        "\n",
        "# Adding the second hidden layer\n",
        "ann.add(tf.keras.layers.Dense(units=5,kernel_initializer='normal', activation='relu'))\n",
        "\n",
        "# Adding the third hidden layer\n",
        "ann.add(tf.keras.layers.Dense(units=3,kernel_initializer='normal', activation='relu'))\n",
        "\n",
        "# Adding the output layer\n",
        "ann.add(tf.keras.layers.Dense(units=1, kernel_initializer='normal',activation='sigmoid'))\n"
      ],
      "execution_count": 678,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OG_Kye98hBZ5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# compiling network\n",
        "ann.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])"
      ],
      "execution_count": 679,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHriugTuhOu8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "2f1b70ed-b6be-4db4-aa16-7e14eb9366ea"
      },
      "source": [
        "ann.summary()"
      ],
      "execution_count": 680,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_16\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_54 (Dense)             (None, 7)                 140       \n",
            "_________________________________________________________________\n",
            "dense_55 (Dense)             (None, 5)                 40        \n",
            "_________________________________________________________________\n",
            "dense_56 (Dense)             (None, 3)                 18        \n",
            "_________________________________________________________________\n",
            "dense_57 (Dense)             (None, 1)                 4         \n",
            "=================================================================\n",
            "Total params: 202\n",
            "Trainable params: 202\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CnL6awOThhpi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "outputId": "8e46260e-44e2-4b7d-8154-47a21bdfcc7f"
      },
      "source": [
        "hist=ann.fit(x_train, y_train, validation_split=0.15, batch_size = 32, epochs = 10)\n"
      ],
      "execution_count": 681,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "16/16 [==============================] - 0s 8ms/step - loss: 0.6918 - accuracy: 0.6751 - val_loss: 0.6885 - val_accuracy: 0.7802\n",
            "Epoch 2/10\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6888 - accuracy: 0.6751 - val_loss: 0.6836 - val_accuracy: 0.7802\n",
            "Epoch 3/10\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6858 - accuracy: 0.6751 - val_loss: 0.6780 - val_accuracy: 0.7802\n",
            "Epoch 4/10\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6818 - accuracy: 0.6751 - val_loss: 0.6711 - val_accuracy: 0.7802\n",
            "Epoch 5/10\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6773 - accuracy: 0.6751 - val_loss: 0.6609 - val_accuracy: 0.7802\n",
            "Epoch 6/10\n",
            "16/16 [==============================] - 0s 4ms/step - loss: 0.6698 - accuracy: 0.6751 - val_loss: 0.6477 - val_accuracy: 0.7802\n",
            "Epoch 7/10\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6607 - accuracy: 0.6751 - val_loss: 0.6275 - val_accuracy: 0.7802\n",
            "Epoch 8/10\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6487 - accuracy: 0.6751 - val_loss: 0.6028 - val_accuracy: 0.7802\n",
            "Epoch 9/10\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6361 - accuracy: 0.6751 - val_loss: 0.5762 - val_accuracy: 0.7802\n",
            "Epoch 10/10\n",
            "16/16 [==============================] - 0s 3ms/step - loss: 0.6236 - accuracy: 0.6751 - val_loss: 0.5562 - val_accuracy: 0.7802\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BNxRiZYZho-s",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred=ann.predict(x_test)\n",
        "y_pred = (y_pred > 0.5)"
      ],
      "execution_count": 682,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NZwP9yZ2mytE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "6ba3e207-ef24-49f5-8ff4-53ea3312dbb2"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "print(cm)\n",
        "accuracy_score(y_test, y_pred)"
      ],
      "execution_count": 683,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[ 0 58]\n",
            " [ 0 93]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6158940397350994"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 683
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y-7hGbOqEG-a",
        "colab_type": "text"
      },
      "source": [
        "NEXT\n",
        "#Random Forest\n",
        "<!--  -->"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u61Lu7TFCFdr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n"
      ],
      "execution_count": 684,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eH4-jAgKFAB5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "randomForestFinalModel = RandomForestClassifier(max_depth = 7,max_leaf_nodes=30, max_features = 'auto', n_estimators = 100,criterion='entropy')\n"
      ],
      "execution_count": 685,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zB1XE6jiFG23",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "outputId": "ed9316b6-1455-4c0c-af93-73cb7f0cd3bc"
      },
      "source": [
        "randomForestFinalModel.fit(x_train, y_train)"
      ],
      "execution_count": 686,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=True, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='entropy', max_depth=7, max_features='auto',\n",
              "                       max_leaf_nodes=30, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=100,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 686
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4Ug9x5LsFHwa",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "outputId": "b063115f-6bfb-459d-868c-be51deb065fd"
      },
      "source": [
        "pred2 = randomForestFinalModel.predict(x_test)\n",
        "pred2"
      ],
      "execution_count": 687,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1,\n",
              "       0, 0, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,\n",
              "       1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 687
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FHiKvLfSFOYw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "3c919fc6-e8d1-487b-93d7-b4df9ba0f46d"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm1 = confusion_matrix(y_test, pred2)\n",
        "print(cm1)\n",
        "accuracy_score(y_test, pred2)"
      ],
      "execution_count": 688,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[24 34]\n",
            " [ 4 89]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7483443708609272"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 688
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7G1vnlF3Hsew",
        "colab_type": "text"
      },
      "source": [
        "Logistic Regression"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VSTdUImwHsFj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 110
        },
        "outputId": "aae64840-5b59-4a8f-99c3-f15c244944a0"
      },
      "source": [
        "from sklearn.linear_model import LogisticRegression\n",
        "classifier = LogisticRegression(random_state = 0)\n",
        "classifier.fit(x_train, y_train)"
      ],
      "execution_count": 689,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
              "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
              "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
              "                   random_state=0, solver='lbfgs', tol=0.0001, verbose=0,\n",
              "                   warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 689
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CBwp_tcQFi4Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred2 = classifier.predict(x_test)"
      ],
      "execution_count": 690,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g3a9za0fH1Z9",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "outputId": "bc4ebf2a-0815-42c4-899b-03e3ddc23e49"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm2 = confusion_matrix(y_test, y_pred2)\n",
        "print(cm2)\n",
        "accuracy_score(y_test, y_pred2)"
      ],
      "execution_count": 691,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[16 42]\n",
            " [ 6 87]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6821192052980133"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 691
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VgKNlG5kH4vO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 691,
      "outputs": []
    }
  ]
}