48.GaussianNB.ipynb

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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.naive_bayes import GaussianNB\n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "df = pd.read_csv(\"logisticRegressionSubmit.csv\")\n",
        "df.head()"
      ],
      "execution_count": 2,
      "outputs": [
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              "    X     Y\n",
              "0  12  Fail\n",
              "1  36  Pass\n",
              "2  37  Pass\n",
              "3  47  Pass\n",
              "4  10  Fail"
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        "df"
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              "      <td>Pass</td>\n",
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              "      <th>7</th>\n",
              "      <td>23</td>\n",
              "      <td>Fail</td>\n",
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              "      <th>8</th>\n",
              "      <td>51</td>\n",
              "      <td>Pass</td>\n",
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              "      <th>9</th>\n",
              "      <td>72</td>\n",
              "      <td>Pass</td>\n",
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              "      <td>Pass</td>\n",
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              "     X     Y\n",
              "0   12  Fail\n",
              "1   36  Pass\n",
              "2   37  Pass\n",
              "3   47  Pass\n",
              "4   10  Fail\n",
              "5   10  Fail\n",
              "6   96  Pass\n",
              "7   23  Fail\n",
              "8   51  Pass\n",
              "9   72  Pass\n",
              "10  65  Pass\n",
              "11  15  Fail\n",
              "12  29  Fail\n",
              "13  45  Pass\n",
              "14  50  Pass"
            ]
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      },
      "source": [
        "X=df.iloc[:,:-1].values\n",
        "X"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12],\n",
              "       [36],\n",
              "       [37],\n",
              "       [47],\n",
              "       [10],\n",
              "       [10],\n",
              "       [96],\n",
              "       [23],\n",
              "       [51],\n",
              "       [72],\n",
              "       [65],\n",
              "       [15],\n",
              "       [29],\n",
              "       [45],\n",
              "       [50]])"
            ]
          },
          "metadata": {
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          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6vqbyYApc0Gj",
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        },
        "outputId": "92a9c4bb-10ae-4c06-84cf-f4cd67e41f7b"
      },
      "source": [
        "Y=df.iloc[:,-1].values\n",
        "Y"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Fail', 'Pass', 'Pass', 'Pass', 'Fail', 'Fail', 'Pass', 'Fail',\n",
              "       'Pass', 'Pass', 'Pass', 'Fail', 'Fail', 'Pass', 'Pass'],\n",
              "      dtype=object)"
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      "cell_type": "code",
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      "source": [
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, random_state=4,test_size = 0.25)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9qZv12gTc94_"
      },
      "source": [
        "clf = GaussianNB()"
      ],
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ua7yPRwdFEZ",
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      },
      "source": [
        "clf.fit(x_train,y_train)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GaussianNB(priors=None, var_smoothing=1e-09)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c9AzFi3EdPDu"
      },
      "source": [
        "pred=clf.predict(x_test)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
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      "source": [
        "y_test, pred"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array(['Fail', 'Fail', 'Pass', 'Pass'], dtype=object),\n",
              " array(['Pass', 'Fail', 'Pass', 'Pass'], dtype='<U4'))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
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    {
      "cell_type": "code",
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        "id": "VbRWxE3qhi_P"
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      "source": [
        ""
      ],
      "execution_count": 10,
      "outputs": []
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