Compute A6 Bagging.ipynb

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        "import pandas as pd\n",
        "df=pd.read_csv('churndata_processed.csv')"
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        "df"
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              "      months  multiple    gb_mon  security  backup  protection  support  \\\n",
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              "\n",
              "      payment_Mailed Check  internet_type_DSL  internet_type_Fiber Optic  \\\n",
              "0                        0                  1                          0   \n",
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        "df['churn_value'].value_counts()"
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              "0    5174\n",
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              "Name: churn_value, dtype: int64"
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        "X=(df.drop('churn_value',axis=1)).iloc[:,:]\n",
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      "metadata": {
        "id": "3icWQyi91Htr"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from imblearn.over_sampling import RandomOverSampler\n",
        "ros = RandomOverSampler(random_state=0)\n",
        "X_resampled, y_resampled = ros.fit_resample(X, y)\n",
        "from collections import Counter\n",
        "print(sorted(Counter(y_resampled).items()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "L4hCwD2-0nJQ",
        "outputId": "8dc661a3-281e-4e09-f1e5-33b07e60cf81"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(0, 5174), (1, 5174)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=X_resampled\n",
        "y=y_resampled"
      ],
      "metadata": {
        "id": "ehsmOqBg1yJ1"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import model_selection\n",
        "from sklearn.ensemble import BaggingClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "\n",
        "kfold = model_selection.KFold(n_splits = 3)\n",
        " \n",
        "classifier = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)\n",
        " \n",
        "# no. of base classifier\n",
        "num_trees = 500\n",
        " \n",
        "# bagging classifier\n",
        "model = BaggingClassifier(base_estimator = classifier,\n",
        "                          n_estimators = num_trees,\n",
        "                          random_state = seed)\n",
        " \n",
        "results = model_selection.cross_val_score(model, X, y, cv = kfold)\n",
        "print(\"accuracy :\")\n",
        "print(results.mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tPn0gKkb33P9",
        "outputId": "8625d188-9daa-4eb8-8774-622276b077a8"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "accuracy :\n",
            "0.943367971952943\n"
          ]
        }
      ]
    }
  ]
}