Our_Dataset_Implementation_with_statistics.ipynb

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        "<a href=\"https://colab.research.google.com/github/omarmoh26/BE-Alert/blob/main/Our_Dataset_Implementation_with_statistics.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive/')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4JKZxJ7Lavgn"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import seaborn as sns\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import pandas_profiling as pp\n",
        "\n",
        "# data = pd.read_csv('/content/gdrive/My Drive/adhamsleepy.csv')\n",
        "# data1 = pd.read_csv('/content/gdrive/My Drive/anything.csv')\n",
        "# data2 = pd.read_csv('/content/gdrive/My Drive/momsleepy.csv')\n",
        "# data3 = pd.read_csv('/content/gdrive/My Drive/meawake.csv')\n",
        "# data4 = pd.read_csv('/content/gdrive/My Drive/submission2.csv')\n",
        "dataset=pd.read_csv('/content/gdrive/My Drive/acquiredDatasetStatistics.csv')"
      ]
    },
    {
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      "execution_count": null,
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              "   Attention_mean  Attention_median  Attention_mode  Attention_range  \\\n",
              "0       31.700000              29.5       32.000000             80.0   \n",
              "1       43.900000              47.0       40.666667             92.0   \n",
              "2       41.733333              40.0       40.000000             73.0   \n",
              "3       41.816667              41.0       28.666667             67.0   \n",
              "4       51.116667              50.0       45.200000             67.0   \n",
              "\n",
              "   Attention_std  Attention_var  Attention_IQR     Theta_mean  Theta_median  \\\n",
              "0      17.373563     301.840678          20.75   72647.333333       44971.5   \n",
              "1      19.731846     389.345763          26.50   48285.483333       36717.0   \n",
              "2      15.941489     254.131073          20.00   67913.450000       41240.0   \n",
              "3      17.417595     303.372599          26.00   89957.316667       49925.0   \n",
              "4      14.845066     220.375989          19.25  124874.066667       69879.5   \n",
              "\n",
              "      Theta_mode  ...    Alpha1_var  Alpha1_IQR   Alpha2_mean  Alpha2_median  \\\n",
              "0   72647.333333  ...  6.193477e+08    28910.25  30511.250000        28166.5   \n",
              "1   48285.483333  ...  5.307118e+08    15023.25  21963.583333        17712.5   \n",
              "2   67913.450000  ...  4.488679e+08    19689.75  28482.483333        22845.5   \n",
              "3   89957.316667  ...  9.062865e+08    23600.00  24876.283333        12718.0   \n",
              "4  124874.066667  ...  2.762353e+09    34400.50  33726.050000        21353.5   \n",
              "\n",
              "    Alpha2_mode  Alpha2_range    Alpha2_std    Alpha2_var  Alpha2_IQR  \\\n",
              "0  30511.250000      126304.0  23364.910194  5.459190e+08    27869.25   \n",
              "1  21963.583333       76285.0  16999.893442  2.889964e+08    19321.00   \n",
              "2  28482.483333      134396.0  23611.017878  5.574802e+08    24742.25   \n",
              "3  24876.283333      132706.0  28208.102450  7.956970e+08    28127.75   \n",
              "4  33726.050000      195119.0  37984.086632  1.442791e+09    34557.00   \n",
              "\n",
              "   classification  \n",
              "0             0.0  \n",
              "1             0.0  \n",
              "2             0.0  \n",
              "3             0.0  \n",
              "4             0.0  \n",
              "\n",
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              "      <td>41240.0</td>\n",
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        "dataset['classification'] = dataset['classification'].astype(int)"
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              "   Attention_mean  Attention_median  Attention_mode  Attention_range  \\\n",
              "0       31.700000              29.5       32.000000             80.0   \n",
              "1       43.900000              47.0       40.666667             92.0   \n",
              "2       41.733333              40.0       40.000000             73.0   \n",
              "3       41.816667              41.0       28.666667             67.0   \n",
              "4       51.116667              50.0       45.200000             67.0   \n",
              "\n",
              "   Attention_std  Attention_var  Attention_IQR     Theta_mean  Theta_median  \\\n",
              "0      17.373563     301.840678          20.75   72647.333333       44971.5   \n",
              "1      19.731846     389.345763          26.50   48285.483333       36717.0   \n",
              "2      15.941489     254.131073          20.00   67913.450000       41240.0   \n",
              "3      17.417595     303.372599          26.00   89957.316667       49925.0   \n",
              "4      14.845066     220.375989          19.25  124874.066667       69879.5   \n",
              "\n",
              "      Theta_mode  ...    Alpha1_var  Alpha1_IQR   Alpha2_mean  Alpha2_median  \\\n",
              "0   72647.333333  ...  6.193477e+08    28910.25  30511.250000        28166.5   \n",
              "1   48285.483333  ...  5.307118e+08    15023.25  21963.583333        17712.5   \n",
              "2   67913.450000  ...  4.488679e+08    19689.75  28482.483333        22845.5   \n",
              "3   89957.316667  ...  9.062865e+08    23600.00  24876.283333        12718.0   \n",
              "4  124874.066667  ...  2.762353e+09    34400.50  33726.050000        21353.5   \n",
              "\n",
              "    Alpha2_mode  Alpha2_range    Alpha2_std    Alpha2_var  Alpha2_IQR  \\\n",
              "0  30511.250000      126304.0  23364.910194  5.459190e+08    27869.25   \n",
              "1  21963.583333       76285.0  16999.893442  2.889964e+08    19321.00   \n",
              "2  28482.483333      134396.0  23611.017878  5.574802e+08    24742.25   \n",
              "3  24876.283333      132706.0  28208.102450  7.956970e+08    28127.75   \n",
              "4  33726.050000      195119.0  37984.086632  1.442791e+09    34557.00   \n",
              "\n",
              "   classification  \n",
              "0               0  \n",
              "1               0  \n",
              "2               0  \n",
              "3               0  \n",
              "4               0  \n",
              "\n",
              "[5 rows x 29 columns]"
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              "      <td>32.000000</td>\n",
              "      <td>80.0</td>\n",
              "      <td>17.373563</td>\n",
              "      <td>301.840678</td>\n",
              "      <td>20.75</td>\n",
              "      <td>72647.333333</td>\n",
              "      <td>44971.5</td>\n",
              "      <td>72647.333333</td>\n",
              "      <td>...</td>\n",
              "      <td>6.193477e+08</td>\n",
              "      <td>28910.25</td>\n",
              "      <td>30511.250000</td>\n",
              "      <td>28166.5</td>\n",
              "      <td>30511.250000</td>\n",
              "      <td>126304.0</td>\n",
              "      <td>23364.910194</td>\n",
              "      <td>5.459190e+08</td>\n",
              "      <td>27869.25</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>43.900000</td>\n",
              "      <td>47.0</td>\n",
              "      <td>40.666667</td>\n",
              "      <td>92.0</td>\n",
              "      <td>19.731846</td>\n",
              "      <td>389.345763</td>\n",
              "      <td>26.50</td>\n",
              "      <td>48285.483333</td>\n",
              "      <td>36717.0</td>\n",
              "      <td>48285.483333</td>\n",
              "      <td>...</td>\n",
              "      <td>5.307118e+08</td>\n",
              "      <td>15023.25</td>\n",
              "      <td>21963.583333</td>\n",
              "      <td>17712.5</td>\n",
              "      <td>21963.583333</td>\n",
              "      <td>76285.0</td>\n",
              "      <td>16999.893442</td>\n",
              "      <td>2.889964e+08</td>\n",
              "      <td>19321.00</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>41.733333</td>\n",
              "      <td>40.0</td>\n",
              "      <td>40.000000</td>\n",
              "      <td>73.0</td>\n",
              "      <td>15.941489</td>\n",
              "      <td>254.131073</td>\n",
              "      <td>20.00</td>\n",
              "      <td>67913.450000</td>\n",
              "      <td>41240.0</td>\n",
              "      <td>67913.450000</td>\n",
              "      <td>...</td>\n",
              "      <td>4.488679e+08</td>\n",
              "      <td>19689.75</td>\n",
              "      <td>28482.483333</td>\n",
              "      <td>22845.5</td>\n",
              "      <td>28482.483333</td>\n",
              "      <td>134396.0</td>\n",
              "      <td>23611.017878</td>\n",
              "      <td>5.574802e+08</td>\n",
              "      <td>24742.25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>41.816667</td>\n",
              "      <td>41.0</td>\n",
              "      <td>28.666667</td>\n",
              "      <td>67.0</td>\n",
              "      <td>17.417595</td>\n",
              "      <td>303.372599</td>\n",
              "      <td>26.00</td>\n",
              "      <td>89957.316667</td>\n",
              "      <td>49925.0</td>\n",
              "      <td>89957.316667</td>\n",
              "      <td>...</td>\n",
              "      <td>9.062865e+08</td>\n",
              "      <td>23600.00</td>\n",
              "      <td>24876.283333</td>\n",
              "      <td>12718.0</td>\n",
              "      <td>24876.283333</td>\n",
              "      <td>132706.0</td>\n",
              "      <td>28208.102450</td>\n",
              "      <td>7.956970e+08</td>\n",
              "      <td>28127.75</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>51.116667</td>\n",
              "      <td>50.0</td>\n",
              "      <td>45.200000</td>\n",
              "      <td>67.0</td>\n",
              "      <td>14.845066</td>\n",
              "      <td>220.375989</td>\n",
              "      <td>19.25</td>\n",
              "      <td>124874.066667</td>\n",
              "      <td>69879.5</td>\n",
              "      <td>124874.066667</td>\n",
              "      <td>...</td>\n",
              "      <td>2.762353e+09</td>\n",
              "      <td>34400.50</td>\n",
              "      <td>33726.050000</td>\n",
              "      <td>21353.5</td>\n",
              "      <td>33726.050000</td>\n",
              "      <td>195119.0</td>\n",
              "      <td>37984.086632</td>\n",
              "      <td>1.442791e+09</td>\n",
              "      <td>34557.00</td>\n",
              "      <td>0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 29 columns</p>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ohS_xZuZ80ry"
      },
      "outputs": [],
      "source": [
        "data1 = dataset[['Theta_IQR','Alpha1_IQR','Alpha2_mean']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
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              "   Attention_median     Theta_mean  Theta_IQR     Theta_mode   Alpha1_mean  \\\n",
              "0              29.5   72647.333333   72656.25   72647.333333  27699.650000   \n",
              "1              47.0   48285.483333   38973.00   48285.483333  18966.533333   \n",
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              "3              41.0   89957.316667   73236.25   89957.316667  22844.183333   \n",
              "4              50.0  124874.066667  128886.75  124874.066667  36195.600000   \n",
              "\n",
              "   Alpha1_median  Alpha1_IQR   Alpha2_mean  Alpha2_median   Alpha2_mode  \n",
              "0        23318.5    28910.25  30511.250000        28166.5  30511.250000  \n",
              "1        13990.0    15023.25  21963.583333        17712.5  21963.583333  \n",
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              "      <th>Attention_median</th>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "data1.head()"
      ]
    },
    {
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      "execution_count": null,
      "metadata": {
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          "height": 206
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        "outputId": "bea29944-2adb-4efd-a34d-521d5b80afcb"
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          "data": {
            "text/plain": [
              "   Attention_median     Theta_mean  Theta_IQR     Theta_mode   Alpha1_mean  \\\n",
              "0              29.5   72647.333333   72656.25   72647.333333  27699.650000   \n",
              "1              47.0   48285.483333   38973.00   48285.483333  18966.533333   \n",
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              "4              50.0  124874.066667  128886.75  124874.066667  36195.600000   \n",
              "\n",
              "   Alpha1_median  Alpha1_IQR   Alpha2_mean  Alpha2_median   Alpha2_mode  \n",
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              "    .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>Attention_median</th>\n",
              "      <th>Theta_mean</th>\n",
              "      <th>Theta_IQR</th>\n",
              "      <th>Theta_mode</th>\n",
              "      <th>Alpha1_mean</th>\n",
              "      <th>Alpha1_median</th>\n",
              "      <th>Alpha1_IQR</th>\n",
              "      <th>Alpha2_mean</th>\n",
              "      <th>Alpha2_median</th>\n",
              "      <th>Alpha2_mode</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29.5</td>\n",
              "      <td>72647.333333</td>\n",
              "      <td>72656.25</td>\n",
              "      <td>72647.333333</td>\n",
              "      <td>27699.650000</td>\n",
              "      <td>23318.5</td>\n",
              "      <td>28910.25</td>\n",
              "      <td>30511.250000</td>\n",
              "      <td>28166.5</td>\n",
              "      <td>30511.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>47.0</td>\n",
              "      <td>48285.483333</td>\n",
              "      <td>38973.00</td>\n",
              "      <td>48285.483333</td>\n",
              "      <td>18966.533333</td>\n",
              "      <td>13990.0</td>\n",
              "      <td>15023.25</td>\n",
              "      <td>21963.583333</td>\n",
              "      <td>17712.5</td>\n",
              "      <td>21963.583333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>40.0</td>\n",
              "      <td>67913.450000</td>\n",
              "      <td>71587.25</td>\n",
              "      <td>67913.450000</td>\n",
              "      <td>19579.083333</td>\n",
              "      <td>10750.5</td>\n",
              "      <td>19689.75</td>\n",
              "      <td>28482.483333</td>\n",
              "      <td>22845.5</td>\n",
              "      <td>28482.483333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>41.0</td>\n",
              "      <td>89957.316667</td>\n",
              "      <td>73236.25</td>\n",
              "      <td>89957.316667</td>\n",
              "      <td>22844.183333</td>\n",
              "      <td>13700.5</td>\n",
              "      <td>23600.00</td>\n",
              "      <td>24876.283333</td>\n",
              "      <td>12718.0</td>\n",
              "      <td>24876.283333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>50.0</td>\n",
              "      <td>124874.066667</td>\n",
              "      <td>128886.75</td>\n",
              "      <td>124874.066667</td>\n",
              "      <td>36195.600000</td>\n",
              "      <td>18721.0</td>\n",
              "      <td>34400.50</td>\n",
              "      <td>33726.050000</td>\n",
              "      <td>21353.5</td>\n",
              "      <td>33726.050000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ],
      "source": [
        "y=dataset['classification']\n",
        "# data1=data1.drop(columns=['attention'])\n",
        "data1.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2_zQyrfZFZzK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "60ea17f9-ced1-4c61-ed4c-f9c944752844"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-5.35923494e-01 -1.64102415e-01 -4.63369618e-03]\n",
            " [-8.29042850e-01 -8.42175386e-01 -2.98620759e-01]\n",
            " [-5.45226177e-01 -6.14320016e-01 -7.44107683e-02]\n",
            " [-5.30876201e-01 -4.23390742e-01 -1.98441827e-01]\n",
            " [-4.65928047e-02  1.03974932e-01  1.05935614e-01]\n",
            " [ 2.73736246e-01  5.00066812e-01 -1.78747506e-02]\n",
            " [-6.78371214e-02  3.09808921e-01 -6.69283888e-02]\n",
            " [-8.97940573e-01 -1.06374416e+00 -2.03952457e-02]\n",
            " [-9.81843114e-01 -1.09428601e+00 -1.35713772e-01]\n",
            " [-8.85681308e-01 -1.05464997e+00 -9.69073683e-03]\n",
            " [-3.55208655e-01 -6.10902064e-01 -5.13785743e-01]\n",
            " [-7.13814460e-01 -1.03724282e+00 -3.15847486e-01]\n",
            " [-6.54463083e-01 -8.11047603e-01 -6.57140407e-01]\n",
            " [-5.56793615e-01 -9.77294377e-01 -5.10518329e-01]\n",
            " [-3.38527414e-02 -2.20462012e-01  2.34872790e-02]\n",
            " [ 2.57834974e+00  5.12749858e-01  3.54023371e+00]\n",
            " [ 4.52563882e+00 -6.38721756e-01  6.51019608e+00]\n",
            " [ 7.01476909e-01  3.00490118e+00  4.97902696e-01]\n",
            " [ 4.95421179e-01  6.99577589e-01  1.37624213e-02]\n",
            " [ 1.10955271e+00  1.00705905e+00  1.00735839e-01]\n",
            " [ 3.44505694e+00  4.42811223e+00  1.18921008e-01]\n",
            " [-2.00831112e-01  5.47246765e-01 -2.89495500e-01]\n",
            " [-6.95855235e-01 -3.31557679e-01 -4.81119049e-01]\n",
            " [-3.49676213e-01 -4.37086966e-01 -1.77326303e-01]\n",
            " [-4.69116482e-01 -6.35486908e-01 -4.70471864e-01]\n",
            " [ 5.32708055e-01  6.04595127e-01  3.35492127e-01]\n",
            " [ 1.67269571e+00  3.41842462e+00  5.53308139e-02]\n",
            " [ 8.20266687e-01  1.00112646e+00  3.43120680e-01]\n",
            " [ 1.10364607e+00  8.38090118e-01  1.99416920e-01]\n",
            " [ 4.28603289e-01  2.09223459e-01  2.00018238e-01]\n",
            " [-7.14417089e-01 -8.31897114e-01 -5.89307733e-01]\n",
            " [ 5.20068689e-03 -4.95033470e-01 -5.89582884e-01]\n",
            " [ 4.08255302e-01 -3.52199672e-01 -3.02784133e-01]\n",
            " [ 1.23396537e-01 -4.54249971e-01 -3.73388950e-01]\n",
            " [-5.03079106e-01 -5.82581885e-01 -5.98177331e-01]\n",
            " [-6.59858465e-01 -6.94580868e-01 -5.84905322e-01]\n",
            " [ 9.14658833e-02 -2.06350751e-01 -3.02309498e-01]\n",
            " [-2.32104748e-01 -4.08327336e-01 -4.04406460e-01]\n",
            " [ 6.10102572e-02  1.78791474e-02 -2.07013345e-01]\n",
            " [-1.04801921e+00  5.56231098e-02 -4.73265790e-01]\n",
            " [-8.01881018e-01 -5.36549387e-01 -4.38286683e-01]\n",
            " [-1.01092814e+00 -6.39466382e-01 -6.09068713e-01]\n",
            " [-9.75738501e-01 -4.83107257e-01 -3.84888530e-01]\n",
            " [-8.77216215e-01 -3.20888784e-01 -4.60084924e-01]\n",
            " [-1.00375750e+00 -8.97692702e-01 -6.53343900e-01]\n",
            " [-9.67752030e-01 -7.65356901e-01 -6.86494401e-01]\n",
            " [-5.58257765e-01 -1.55594155e-01 -4.29190659e-01]\n",
            " [-3.03489132e-01 -4.49953116e-01 -2.72488894e-01]\n",
            " [ 5.87040422e-01  1.21030516e+00  8.91824495e-01]\n",
            " [ 6.74824157e-01  3.13641911e-01  3.60514217e-01]\n",
            " [ 4.50591646e-01  3.88751420e-01  1.90195358e-01]\n",
            " [-3.33433502e-01 -5.70850983e-01 -2.98069311e-01]\n",
            " [-1.72368296e-01  1.30341995e-01 -5.25437393e-02]\n",
            " [ 1.75805711e-01  9.64932781e-01  3.56257407e-01]\n",
            " [ 5.38129544e-01  1.84955995e-01  7.57980137e-02]\n",
            " [-4.50400163e-01 -6.44385792e-01 -1.25265497e-01]\n",
            " [-4.48896853e-01  8.76297946e-02 -1.12663595e-01]\n",
            " [-3.71342588e-01 -2.75124840e-01 -3.32409265e-01]\n",
            " [-2.24592548e-01 -3.90370878e-01 -2.22546748e-01]\n",
            " [ 1.71239217e-01 -2.99453339e-01 -1.65213531e-02]\n",
            " [-2.09568150e-01 -2.41006349e-02 -2.40169004e-01]\n",
            " [ 1.75190029e-01  2.68647006e-01 -1.03721770e-01]\n",
            " [ 6.83745185e-02  6.26921682e-01  1.86170133e-01]]\n"
          ]
        }
      ],
      "source": [
        "scaler = StandardScaler()\n",
        "X=scaler.fit_transform(data1)\n",
        "print(X)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "l1Nh87BBewW6"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn import preprocessing\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "z5lVU6ZQK0EE"
      },
      "outputs": [],
      "source": [
        "x_train, x_test, y_train, y_test=train_test_split(X,y,test_size=0.3)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV"
      ],
      "metadata": {
        "id": "y1UPp3PBR0rF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jsjVsdaVK5Bw",
        "outputId": "5bbf070e-f052-411f-c41a-8a544372f1a7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "70 fits failed out of a total of 140.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "70 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py\", line 1461, in fit\n",
            "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/linear_model/_logistic.py\", line 447, in _check_solver\n",
            "    raise ValueError(\n",
            "ValueError: Solver lbfgs supports only 'l2' or 'none' penalties, got l1 penalty.\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_search.py:969: UserWarning: One or more of the test scores are non-finite: [  nan 0.565   nan 0.565   nan 0.73    nan 0.66    nan 0.66    nan 0.66\n",
            "   nan 0.66 ]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=LogisticRegression(),\n",
              "             param_grid={'C': array([1.e-03, 1.e-02, 1.e-01, 1.e+00, 1.e+01, 1.e+02, 1.e+03]),\n",
              "                         'penalty': ['l1', 'l2']})"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "\n",
        "# from sklearn.linear_model import LogisticRegression\n",
        "grid={\"C\":np.logspace(-3,3,7), \"penalty\":[\"l1\",\"l2\"]}# l1 lasso l2 ridge\n",
        "logreg=LogisticRegression()\n",
        "logreg_cv=GridSearchCV(logreg,grid,cv=10)\n",
        "logreg_cv.fit(x_train,y_train)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "mIrGVdPZRM_d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8fab1ca8-1059-4cf6-d139-6100163b4097"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "tuned hpyerparameters :(best parameters)  {'C': 0.1, 'penalty': 'l2'}\n",
            "Accuracy for our training dataset with tuning is : 73.00%\n"
          ]
        }
      ],
      "source": [
        "print(\"tuned hpyerparameters :(best parameters) \",logreg_cv.best_params_)\n",
        "logregAccuracy = logreg_cv.best_score_ *100\n",
        "print(\"Accuracy for our training dataset with tuning is : {:.2f}%\".format(logregAccuracy) )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tYtd0sYUL-XV"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score, plot_confusion_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2hOb9O8RMAza",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "78147665-8af4-4f75-8c25-459eb7afa53e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 10 folds for each of 16 candidates, totalling 160 fits\n"
          ]
        }
      ],
      "source": [
        "knn = KNeighborsClassifier()\n",
        "# from sklearn.model_selection import GridSearchCV\n",
        "k_range = [1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31]\n",
        "param_grid = dict(n_neighbors=k_range)\n",
        "  \n",
        "# defining parameter range\n",
        "grid = GridSearchCV(knn, param_grid, cv=10, scoring='accuracy', return_train_score=False,verbose=1)\n",
        "  \n",
        "# fitting the model for grid search\n",
        "grid_search=grid.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3nlFnxyrMocQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "727db560-abdf-4fbf-a972-db47734ce302"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'n_neighbors': 9}\n"
          ]
        }
      ],
      "source": [
        "print(grid_search.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "flwbUpRhM05r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "94c3dbe9-c3fe-4aa4-aace-e251047ebd0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for our training dataset with tuning is : 77.50%\n"
          ]
        }
      ],
      "source": [
        "accuracy = grid_search.best_score_ *100\n",
        "print(\"Accuracy for our training dataset with tuning is : {:.2f}%\".format(accuracy) )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GkGEjMKoOzDE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9ca8d33c-85da-45c5-9119-491446d6c632"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=5, estimator=RandomForestClassifier(random_state=42),\n",
              "             param_grid={'criterion': ['gini', 'entropy'],\n",
              "                         'max_depth': [4, 5, 6, 7, 8],\n",
              "                         'max_features': ['auto', 'sqrt', 'log2'],\n",
              "                         'n_estimators': [200, 500]})"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "rfc=RandomForestClassifier(random_state=42)\n",
        "param_grid = { \n",
        "    'n_estimators': [200, 500],\n",
        "    'max_features': ['auto', 'sqrt', 'log2'],\n",
        "    'max_depth' : [4,5,6,7,8],\n",
        "    'criterion' :['gini', 'entropy']\n",
        "}\n",
        "CV_rfc = GridSearchCV(estimator=rfc, param_grid=param_grid, cv= 5)\n",
        "CV_rfc.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TXjgx7hyO9Ia",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8bd18226-a653-48d2-f752-cbbe4c1a8ff9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'criterion': 'gini', 'max_depth': 4, 'max_features': 'auto', 'n_estimators': 200}\n",
            "Accuracy for our training dataset with tuning is : 72.22%\n"
          ]
        }
      ],
      "source": [
        "print(CV_rfc.best_params_)\n",
        "accuracy = CV_rfc.best_score_ *100\n",
        "print(\"Accuracy for our training dataset with tuning is : {:.2f}%\".format(accuracy) )"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC\n"
      ],
      "metadata": {
        "id": "Cl2HjqSKSFLO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wp187ogeNIe_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4db79b1-15d2-4621-fe03-7824d48220f2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 50 candidates, totalling 250 fits\n",
            "[CV 1/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 3/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 4/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 5/5] END ........C=0.1, gamma=1, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 1/5] END ...C=0.1, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END ...C=0.1, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END ...C=0.1, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END ...C=0.1, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END ...C=0.1, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 3/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 4/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 5/5] END ......C=0.1, gamma=0.1, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 1/5] END .C=0.1, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END .C=0.1, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END .C=0.1, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END .C=0.1, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END .C=0.1, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 3/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 4/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 5/5] END .....C=0.1, gamma=0.01, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 1/5] END C=0.1, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=0.1, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END C=0.1, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END C=0.1, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END C=0.1, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 3/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 4/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 5/5] END ....C=0.1, gamma=0.001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 1/5] END C=0.1, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=0.1, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END C=0.1, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END C=0.1, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END C=0.1, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 3/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 4/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 5/5] END ...C=0.1, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 1/5] END C=0.1, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=0.1, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END C=0.1, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END C=0.1, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END C=0.1, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 3/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.889 total time=   0.0s\n",
            "[CV 4/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 5/5] END ..........C=1, gamma=1, kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 1/5] END .....C=1, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END .....C=1, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END .....C=1, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END .....C=1, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END .....C=1, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 2/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.889 total time=   0.0s\n",
            "[CV 3/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 4/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ........C=1, gamma=0.1, kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 1/5] END ...C=1, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END ...C=1, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END ...C=1, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
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            "[CV 5/5] END ...C=1, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 3/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 4/5] END .......C=1, gamma=0.01, kernel=rbf;, score=0.556 total time=   0.0s\n",
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            "[CV 1/5] END ......C=1, gamma=0.001, kernel=rbf;, score=0.556 total time=   0.0s\n",
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            "[CV 1/5] END .C=1, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
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            "[CV 1/5] END .....C=1, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
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            "[CV 1/5] END C=1, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
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            "[CV 1/5] END .........C=10, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
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            "[CV 1/5] END ....C=10, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
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            "[CV 1/5] END ......C=10, gamma=0.01, kernel=rbf;, score=0.556 total time=   0.0s\n",
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            "[CV 1/5] END .C=10, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
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            "[CV 1/5] END .....C=10, gamma=0.001, kernel=rbf;, score=0.556 total time=   0.0s\n",
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            "[CV 1/5] END C=10, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=10, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
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            "[CV 1/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
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            "[CV 3/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 4/5] END ....C=10, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
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            "[CV 1/5] END C=10, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
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            "[CV 5/5] END C=10, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ........C=100, gamma=1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ........C=100, gamma=1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 3/5] END ........C=100, gamma=1, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 4/5] END ........C=100, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ........C=100, gamma=1, kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 1/5] END ...C=100, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END ...C=100, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END ...C=100, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END ...C=100, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END ...C=100, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 3/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.889 total time=   0.0s\n",
            "[CV 5/5] END ......C=100, gamma=0.1, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 1/5] END .C=100, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END .C=100, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END .C=100, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END .C=100, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END .C=100, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 3/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 4/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END .....C=100, gamma=0.01, kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 1/5] END C=100, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=100, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END C=100, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END C=100, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END C=100, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 3/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 4/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ....C=100, gamma=0.001, kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 1/5] END C=100, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=100, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END C=100, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END C=100, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END C=100, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 3/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 4/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 5/5] END ...C=100, gamma=0.0001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 1/5] END C=100, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=100, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END C=100, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END C=100, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END C=100, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 2/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.444 total time=   0.0s\n",
            "[CV 3/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 4/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 5/5] END .......C=1000, gamma=1, kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 1/5] END ..C=1000, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END ..C=1000, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END ..C=1000, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END ..C=1000, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END ..C=1000, gamma=1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.444 total time=   0.0s\n",
            "[CV 2/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 3/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.889 total time=   0.0s\n",
            "[CV 5/5] END .....C=1000, gamma=0.1, kernel=rbf;, score=0.375 total time=   0.0s\n",
            "[CV 1/5] END C=1000, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=1000, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END C=1000, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END C=1000, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END C=1000, gamma=0.1, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.444 total time=   0.0s\n",
            "[CV 2/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 3/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ....C=1000, gamma=0.01, kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 1/5] END C=1000, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=1000, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END C=1000, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END C=1000, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END C=1000, gamma=0.01, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 3/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 4/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ...C=1000, gamma=0.001, kernel=rbf;, score=0.625 total time=   0.0s\n",
            "[CV 1/5] END C=1000, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=1000, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END C=1000, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END C=1000, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END C=1000, gamma=0.001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 1/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.556 total time=   0.0s\n",
            "[CV 2/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 3/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.778 total time=   0.0s\n",
            "[CV 4/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.667 total time=   0.0s\n",
            "[CV 5/5] END ..C=1000, gamma=0.0001, kernel=rbf;, score=0.500 total time=   0.0s\n",
            "[CV 1/5] END C=1000, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 2/5] END C=1000, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 3/5] END C=1000, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 4/5] END C=1000, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n",
            "[CV 5/5] END C=1000, gamma=0.0001, kernel=polynomial;, score=nan total time=   0.0s\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py:372: FitFailedWarning: \n",
            "125 fits failed out of a total of 250.\n",
            "The score on these train-test partitions for these parameters will be set to nan.\n",
            "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
            "\n",
            "Below are more details about the failures:\n",
            "--------------------------------------------------------------------------------\n",
            "125 fits failed with the following error:\n",
            "Traceback (most recent call last):\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_validation.py\", line 680, in _fit_and_score\n",
            "    estimator.fit(X_train, y_train, **fit_params)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py\", line 255, in fit\n",
            "    fit(X, y, sample_weight, solver_type, kernel, random_seed=seed)\n",
            "  File \"/usr/local/lib/python3.8/dist-packages/sklearn/svm/_base.py\", line 315, in _dense_fit\n",
            "    ) = libsvm.fit(\n",
            "  File \"sklearn/svm/_libsvm.pyx\", line 173, in sklearn.svm._libsvm.fit\n",
            "ValueError: 'polynomial' is not in list\n",
            "\n",
            "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
            "/usr/local/lib/python3.8/dist-packages/sklearn/model_selection/_search.py:969: UserWarning: One or more of the test scores are non-finite: [0.56944444        nan 0.56944444        nan 0.56944444        nan\n",
            " 0.56944444        nan 0.56944444        nan 0.67777778        nan\n",
            " 0.7               nan 0.56944444        nan 0.56944444        nan\n",
            " 0.56944444        nan 0.63333333        nan 0.63333333        nan\n",
            " 0.67777778        nan 0.56944444        nan 0.56944444        nan\n",
            " 0.61111111        nan 0.68055556        nan 0.65555556        nan\n",
            " 0.65555556        nan 0.56944444        nan 0.58888889        nan\n",
            " 0.60833333        nan 0.58888889        nan 0.65833333        nan\n",
            " 0.65555556        nan]\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=SVC(),\n",
              "             param_grid={'C': [0.1, 1, 10, 100, 1000],\n",
              "                         'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                         'kernel': ['rbf', 'polynomial']},\n",
              "             verbose=3)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ],
      "source": [
        "param_grid = {'C': [0.1, 1, 10, 100, 1000], \n",
        "              'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
        "              'kernel': ['rbf','polynomial']} \n",
        "              # ,'linear','sigmoid'\n",
        "  \n",
        "grid = GridSearchCV(SVC(), param_grid, refit = True, verbose = 3)\n",
        "  \n",
        "# fitting the model for grid search\n",
        "grid.fit(x_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "um4Dfqj1NvDn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "127f834d-cbab-422d-89fa-ed0e1fc282e8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'C': 1, 'gamma': 0.1, 'kernel': 'rbf'}\n"
          ]
        }
      ],
      "source": [
        "print(grid.best_params_)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Htp4Z1AGNwGR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "327c8ec4-2303-4ff5-959b-1a08c82fca2e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy for our training dataset with tuning is : 70.00%\n"
          ]
        }
      ],
      "source": [
        "accuracy = grid.best_score_ *100\n",
        "print(\"Accuracy for our training dataset with tuning is : {:.2f}%\".format(accuracy) )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vrgxJkW3c39o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8cd8e025-7ddd-4e47-f857-239cd67fbf14"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(cv=10, estimator=SVC(),\n",
              "             param_grid=[{'C': [0.1, 1, 10, 100, 1000], 'kernel': ['linear']},\n",
              "                         {'C': [0.1, 1, 10, 100, 1000],\n",
              "                          'gamma': [1, 0.1, 0.01, 0.001, 0.0001],\n",
              "                          'kernel': ['sigmoid']}],\n",
              "             scoring='accuracy')"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ],
      "source": [
        "param_grid_full = [\n",
        "  {'kernel': ['linear'], 'C': [0.1, 1, 10, 100, 1000]},\n",
        "  {'kernel': ['sigmoid'], 'C': [0.1, 1, 10, 100, 1000], \n",
        "    'gamma': [1, 0.1, 0.01, 0.001, 0.0001]},\n",
        " ]\n",
        "\n",
        "grid_full = GridSearchCV(SVC(), param_grid_full, cv = 10, scoring='accuracy') \n",
        "grid_full.fit(x_train, y_train) "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "892_GjzfdFqF",
        "outputId": "4ebcedf1-0c38-493d-d826-fd866f7ffa9e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'C': 10, 'gamma': 0.1, 'kernel': 'sigmoid'}\n"
          ]
        }
      ],
      "source": [
        "print(grid_full.best_params_)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ksHCPKC6dNRi",
        "outputId": "0126885d-eb25-46ae-937c-fbf87037c911",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy: 0.79\n"
          ]
        }
      ],
      "source": [
        "grid_predictions = grid_full.predict(x_test) \n",
        "print('Accuracy: {:.2f}'.format(accuracy_score(y_test, grid_predictions)))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}