regular_givings_churn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Churn de Cancelamentos.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python_defaultSpec_1596543498296",
      "display_name": "Python 3.8.5 32-bit"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "M8Iw-GYXSRnt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7jp9XOejSoFW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dados = pd.read_csv('rg_data.csv')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hs2oTMakSrXn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "31b22f8e-5d16-4047-e088-f70fcef44b55"
      },
      "source": [
        "dados.columns"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Unnamed: 0', 'Unnamed: 0.1', 'welcome_call', 'welcome_pack', 'idade',\n",
              "       'genero', 'doacoes', 'valor', 'meio_pagamento', 'churn'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wbapymbLWAmB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dados = dados.dropna()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kdzbylFSry8",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dados.drop(['Unnamed: 0', 'Unnamed: 0.1'], axis=1, inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrWyCtjzTDaw",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "outputId": "1d0a2e4c-d447-4475-cf1e-5a9873eb4ab0"
      },
      "source": [
        "sns.countplot(dados['churn'])"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fcbfd7668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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J6jbMhw3fkM751zvmewp6HbrvP22c7ylI88IjEUlSN0NEktTNEJEkdTNEJEndDBFJUjdD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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LJkZ8hqwTJDh",
        "colab_type": "code",
        "colab": {},
        "tags": []
      },
      "source": [
        "!pip install imblearn"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": "Collecting imblearnWARNING: You are using pip version 20.1.1; however, version 20.2.1 is available.\nYou should consider upgrading via the 'c:\\users\\ham\\appdata\\local\\programs\\python\\python38-32\\python.exe -m pip install --upgrade pip' command.\n\n  Downloading imblearn-0.0-py2.py3-none-any.whl (1.9 kB)\nCollecting imbalanced-learn\n  Downloading imbalanced_learn-0.7.0-py3-none-any.whl (167 kB)\nCollecting numpy>=1.13.3\n  Using cached numpy-1.19.1-cp38-cp38-win32.whl (10.9 MB)\nCollecting scipy>=0.19.1\n  Downloading scipy-1.5.2-cp38-cp38-win32.whl (28.4 MB)\nCollecting joblib>=0.11\n  Downloading joblib-0.16.0-py3-none-any.whl (300 kB)\nCollecting scikit-learn>=0.23\n  Downloading scikit_learn-0.23.1-cp38-cp38-win32.whl (6.0 MB)\nCollecting threadpoolctl>=2.0.0\n  Downloading threadpoolctl-2.1.0-py3-none-any.whl (12 kB)\nInstalling collected packages: numpy, scipy, joblib, threadpoolctl, scikit-learn, imbalanced-learn, imblearn\nSuccessfully installed imbalanced-learn-0.7.0 imblearn-0.0 joblib-0.16.0 numpy-1.19.1 scikit-learn-0.23.1 scipy-1.5.2 threadpoolctl-2.1.0\n"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "at-MDN4KTSMk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from imblearn.over_sampling import ADASYN\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "\"\"\"\n",
        "With this we can perform over-sampling using Adaptive Synthetic (ADASYN) sampling approach for imbalanced datasets.\n",
        "\"\"\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hWA9_NhbUTQO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X =  pd.get_dummies(data=dados.drop('churn', axis=1),\n",
        "                    columns=['welcome_call', 'welcome_pack', 'genero', 'meio_pagamento']\n",
        "                    )\n",
        "y= dados['churn']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yVUvMjDJVtEX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "X_treino, X_teste, y_treino, y_teste = train_test_split(X,y)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEELs7S6VSR3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "ada = ADASYN(sampling_strategy='minority')\n",
        "X_res, y_res = ada.fit_resample(X_treino, y_treino)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WRPEPFDCVXOP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "9f9b21ca-0cc8-47f3-c7e5-522be594fe88"
      },
      "source": [
        "sns.countplot(y_res)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f6fcbf96e80>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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WE08R6mTm3+cEpKrmuwdJ0gK10E9nSZLmkSEiSepmiOi4jvdqmSQvSfKZtv7+JCsn36UW\noyRbkuxP8tU51ifJre1v8+Ekb5h0j6c6Q0THNOKrZTYCh6rq1cAtwEcm26UWsduBdcdYfxmwqk2b\ngE9OoKdFxRDR8Yzyapn1wNY2/1ng4iQn/IisdDxV9SXg4DGGrAe21cB9wFlJzp1Md4uDIaLjme3V\nMsvnGlNVh4FngVdOpDvp2Eb5+9UJMEQkSd0MER3PKK+W+eGYJEuBlwPPTKQ76dh8NdKYGSI6nlFe\nLbMDOPIa2yuBe8qnWHVy2AFc2+7SWgs8W1VPz3dTp5IF/doTjd9cr5ZJchMwXVU7gNuAO5LMMLjI\nefX8dazFJMmngbcAy5LsBW4ETgeoqr8GdgKXAzPA94Dr5qfTU5evPZEkdfN0liSpmyEiSepmiEiS\nuhkikqRuhogkqZshIknqZohIkrr9Lxrc2tFdiRtmAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l7wlysFPWbue",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.neural_network import MLPClassifier\n",
        "modelo = MLPClassifier()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-ggEpAmW2b1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "modelo.fit(X_res, y_res)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WLM7V_eBW_Dc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDLA_y2pXtOf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print(classification_report(y_teste, y_pred))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c-M5Ua9qXyX1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "y_pred = modelo.predict(X_teste)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aF9Uc7lDX2Z3",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}