Atharv_Patawar_A5_Task_2.ipynb

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      "source": [
        "##Q1 Import and explore the data\n",
        "Dataset Link: https://drive.google.com/file/d/1tPHOMKD_ef-j8MuynCn2fSpbrL11-J1l/view?usp=sharing\n"
      ],
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        "id": "aHQLsCXT_yf9"
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      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.read_csv(\"pulsar.txt\", sep=\",\")"
      ],
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        "id": "MkncFSeq-3pQ"
      },
      "execution_count": 3,
      "outputs": []
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      "execution_count": 4,
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              "        Mean of the integrated profile  \\\n",
              "0                           121.156250   \n",
              "1                            76.968750   \n",
              "2                           130.585938   \n",
              "3                           156.398438   \n",
              "4                            84.804688   \n",
              "...                                ...   \n",
              "12523                       124.312500   \n",
              "12524                       115.617188   \n",
              "12525                       116.031250   \n",
              "12526                       135.664062   \n",
              "12527                       120.726562   \n",
              "\n",
              "        Standard deviation of the integrated profile  \\\n",
              "0                                          48.372971   \n",
              "1                                          36.175557   \n",
              "2                                          53.229534   \n",
              "3                                          48.865942   \n",
              "4                                          36.117659   \n",
              "...                                              ...   \n",
              "12523                                      53.179053   \n",
              "12524                                      46.784600   \n",
              "12525                                      43.213846   \n",
              "12526                                      49.933749   \n",
              "12527                                      50.472256   \n",
              "\n",
              "        Excess kurtosis of the integrated profile  \\\n",
              "0                                        0.375485   \n",
              "1                                        0.712898   \n",
              "2                                        0.133408   \n",
              "3                                       -0.215989   \n",
              "4                                        0.825013   \n",
              "...                                           ...   \n",
              "12523                                   -0.012418   \n",
              "12524                                    0.218177   \n",
              "12525                                    0.663456   \n",
              "12526                                   -0.089940   \n",
              "12527                                    0.346178   \n",
              "\n",
              "        Skewness of the integrated profile   Mean of the DM-SNR curve  \\\n",
              "0                                -0.013165                   3.168896   \n",
              "1                                 3.388719                   2.399666   \n",
              "2                                -0.297242                   2.743311   \n",
              "3                                -0.171294                  17.471572   \n",
              "4                                 3.274125                   2.790134   \n",
              "...                                    ...                        ...   \n",
              "12523                            -0.556021                   7.186455   \n",
              "12524                             0.226757                   6.140468   \n",
              "12525                             0.433088                   0.785117   \n",
              "12526                            -0.226726                   3.859532   \n",
              "12527                             0.184797                   0.769231   \n",
              "\n",
              "        Standard deviation of the DM-SNR curve  \\\n",
              "0                                    18.399367   \n",
              "1                                    17.570997   \n",
              "2                                    22.362553   \n",
              "3                                          NaN   \n",
              "4                                    20.618009   \n",
              "...                                        ...   \n",
              "12523                                29.308266   \n",
              "12524                                      NaN   \n",
              "12525                                11.628149   \n",
              "12526                                21.501505   \n",
              "12527                                11.792603   \n",
              "\n",
              "        Excess kurtosis of the DM-SNR curve   Skewness of the DM-SNR curve  \\\n",
              "0                                  7.449874                      65.159298   \n",
              "1                                  9.414652                     102.722975   \n",
              "2                                  8.508364                      74.031324   \n",
              "3                                  2.958066                       7.197842   \n",
              "4                                  8.405008                      76.291128   \n",
              "...                                     ...                            ...   \n",
              "12523                              4.531382                      21.725143   \n",
              "12524                              5.732201                      34.357283   \n",
              "12525                             17.055215                     312.204325   \n",
              "12526                              7.398395                      62.334018   \n",
              "12527                             17.662222                     329.548016   \n",
              "\n",
              "       target_class  \n",
              "0               0.0  \n",
              "1               0.0  \n",
              "2               0.0  \n",
              "3               0.0  \n",
              "4               0.0  \n",
              "...             ...  \n",
              "12523           0.0  \n",
              "12524           0.0  \n",
              "12525           0.0  \n",
              "12526           0.0  \n",
              "12527           0.0  \n",
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              "      <th></th>\n",
              "      <th>Mean of the integrated profile</th>\n",
              "      <th>Standard deviation of the integrated profile</th>\n",
              "      <th>Excess kurtosis of the integrated profile</th>\n",
              "      <th>Skewness of the integrated profile</th>\n",
              "      <th>Mean of the DM-SNR curve</th>\n",
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              "      <th>Excess kurtosis of the DM-SNR curve</th>\n",
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              "      <th>0</th>\n",
              "      <td>121.156250</td>\n",
              "      <td>48.372971</td>\n",
              "      <td>0.375485</td>\n",
              "      <td>-0.013165</td>\n",
              "      <td>3.168896</td>\n",
              "      <td>18.399367</td>\n",
              "      <td>7.449874</td>\n",
              "      <td>65.159298</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>1</th>\n",
              "      <td>76.968750</td>\n",
              "      <td>36.175557</td>\n",
              "      <td>0.712898</td>\n",
              "      <td>3.388719</td>\n",
              "      <td>2.399666</td>\n",
              "      <td>17.570997</td>\n",
              "      <td>9.414652</td>\n",
              "      <td>102.722975</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>130.585938</td>\n",
              "      <td>53.229534</td>\n",
              "      <td>0.133408</td>\n",
              "      <td>-0.297242</td>\n",
              "      <td>2.743311</td>\n",
              "      <td>22.362553</td>\n",
              "      <td>8.508364</td>\n",
              "      <td>74.031324</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>156.398438</td>\n",
              "      <td>48.865942</td>\n",
              "      <td>-0.215989</td>\n",
              "      <td>-0.171294</td>\n",
              "      <td>17.471572</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.958066</td>\n",
              "      <td>7.197842</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>84.804688</td>\n",
              "      <td>36.117659</td>\n",
              "      <td>0.825013</td>\n",
              "      <td>3.274125</td>\n",
              "      <td>2.790134</td>\n",
              "      <td>20.618009</td>\n",
              "      <td>8.405008</td>\n",
              "      <td>76.291128</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>12523</th>\n",
              "      <td>124.312500</td>\n",
              "      <td>53.179053</td>\n",
              "      <td>-0.012418</td>\n",
              "      <td>-0.556021</td>\n",
              "      <td>7.186455</td>\n",
              "      <td>29.308266</td>\n",
              "      <td>4.531382</td>\n",
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              "      <td>115.617188</td>\n",
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              "      <td>135.664062</td>\n",
              "      <td>49.933749</td>\n",
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              "      <td>-0.226726</td>\n",
              "      <td>3.859532</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fx5MoWR0_LkZ",
        "outputId": "104ec20e-601e-4be0-f509-3ca42e0b2b8b"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              " Mean of the integrated profile                     0\n",
              " Standard deviation of the integrated profile       0\n",
              " Excess kurtosis of the integrated profile       1735\n",
              " Skewness of the integrated profile                 0\n",
              " Mean of the DM-SNR curve                           0\n",
              " Standard deviation of the DM-SNR curve          1178\n",
              " Excess kurtosis of the DM-SNR curve                0\n",
              " Skewness of the DM-SNR curve                     625\n",
              "target_class                                        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FZ4gacFQ_Pki",
        "outputId": "0812ce3e-7d92-4cf0-f256-5eb2be75869b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12528 entries, 0 to 12527\n",
            "Data columns (total 9 columns):\n",
            " #   Column                                         Non-Null Count  Dtype  \n",
            "---  ------                                         --------------  -----  \n",
            " 0    Mean of the integrated profile                12528 non-null  float64\n",
            " 1    Standard deviation of the integrated profile  12528 non-null  float64\n",
            " 2    Excess kurtosis of the integrated profile     10793 non-null  float64\n",
            " 3    Skewness of the integrated profile            12528 non-null  float64\n",
            " 4    Mean of the DM-SNR curve                      12528 non-null  float64\n",
            " 5    Standard deviation of the DM-SNR curve        11350 non-null  float64\n",
            " 6    Excess kurtosis of the DM-SNR curve           12528 non-null  float64\n",
            " 7    Skewness of the DM-SNR curve                  11903 non-null  float64\n",
            " 8   target_class                                   12528 non-null  float64\n",
            "dtypes: float64(9)\n",
            "memory usage: 881.0 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Q2 Carry out data pre-processing"
      ],
      "metadata": {
        "id": "naklkC17ALnZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(axis=0,inplace=True)"
      ],
      "metadata": {
        "id": "SSm6_aEU_pkL"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Q4 Split the data in traina and test set"
      ],
      "metadata": {
        "id": "ris4X7S3Bg3E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = df.iloc[:, :-1].values\n",
        "y = df.iloc[:, -1].values"
      ],
      "metadata": {
        "id": "mRHZIsfmA9V6"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)"
      ],
      "metadata": {
        "id": "6tRph5J18UcR"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Q3 Scale the data using Standard Scaler"
      ],
      "metadata": {
        "id": "qfCEvxCTAWMV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "####Q3a According to you, why is scaling important in SVM? What are the different methods for scaling the data available in sklearn.preprocessing?"
      ],
      "metadata": {
        "id": "9TiEh5yjAvmi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ans: MinMaxScaler and MaxAbsScaler"
      ],
      "metadata": {
        "id": "76fHawYYP8U3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "metadata": {
        "id": "xNxZBv9iAvck"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Q5 Implement SVM"
      ],
      "metadata": {
        "id": "fmrpw-xPAT5i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Different types of kernels in SVM:\n",
        "https://www.kaggle.com/discussions/questions-and-answers/326336\n"
      ],
      "metadata": {
        "id": "hnSv4K9cFBnk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q5a Linear SVM\n",
        "\n",
        "Apply Linear SVM for costs (C) = [0.1, 1, 10, 100, 1000], predict for the test set and check f1 score for each of the costs. State the best cost "
      ],
      "metadata": {
        "id": "QXZCpBvDAZIQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'linear', random_state = 0)\n",
        "param_grid = {'C': [0.1,1, 10, 100]} \n",
        "grid_search = GridSearchCV(estimator = classifier,\n",
        "                           scoring='f1_micro',\n",
        "                           param_grid = param_grid,\n",
        "                           refit=2,\n",
        "                           verbose=2)\n",
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "XQ_H9e8Z8WRY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bbe7caa4-984c-4176-f166-367ad1cb4c03"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 4 candidates, totalling 20 fits\n",
            "[CV] END ..............................................C=0.1; total time=   0.4s\n",
            "[CV] END ..............................................C=0.1; total time=   0.2s\n",
            "[CV] END ..............................................C=0.1; total time=   0.1s\n",
            "[CV] END ..............................................C=0.1; total time=   0.1s\n",
            "[CV] END ..............................................C=0.1; total time=   0.1s\n",
            "[CV] END ................................................C=1; total time=   0.1s\n",
            "[CV] END ................................................C=1; total time=   0.1s\n",
            "[CV] END ................................................C=1; total time=   0.1s\n",
            "[CV] END ................................................C=1; total time=   0.1s\n",
            "[CV] END ................................................C=1; total time=   0.1s\n",
            "[CV] END ...............................................C=10; total time=   0.3s\n",
            "[CV] END ...............................................C=10; total time=   0.2s\n",
            "[CV] END ...............................................C=10; total time=   0.2s\n",
            "[CV] END ...............................................C=10; total time=   0.2s\n",
            "[CV] END ...............................................C=10; total time=   0.2s\n",
            "[CV] END ..............................................C=100; total time=   1.0s\n",
            "[CV] END ..............................................C=100; total time=   1.0s\n",
            "[CV] END ..............................................C=100; total time=   0.9s\n",
            "[CV] END ..............................................C=100; total time=   0.7s\n",
            "[CV] END ..............................................C=100; total time=   0.9s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=SVC(kernel='linear', random_state=0),\n",
              "             param_grid={'C': [0.1, 1, 10, 100]}, refit=2, scoring='f1_micro',\n",
              "             verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(grid_search.cv_results_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273
        },
        "id": "ziwiAgWgCSSO",
        "outputId": "19402b66-eba1-4407-c15f-6b680c76adb2"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
              "0       0.151359      0.115843         0.020865        0.010866     0.1   \n",
              "1       0.078607      0.002772         0.012064        0.000817       1   \n",
              "2       0.203979      0.024578         0.012117        0.001268      10   \n",
              "3       0.903651      0.097746         0.011395        0.000303     100   \n",
              "\n",
              "       params  split0_test_score  split1_test_score  split2_test_score  \\\n",
              "0  {'C': 0.1}           0.977714           0.982746           0.978433   \n",
              "1    {'C': 1}           0.979152           0.984184           0.977714   \n",
              "2   {'C': 10}           0.979152           0.983465           0.978433   \n",
              "3  {'C': 100}           0.979871           0.983465           0.978433   \n",
              "\n",
              "   split3_test_score  split4_test_score  mean_test_score  std_test_score  \\\n",
              "0           0.974119           0.974101         0.977423        0.003206   \n",
              "1           0.976995           0.975540         0.978717        0.002971   \n",
              "2           0.976995           0.975540         0.978717        0.002679   \n",
              "3           0.976995           0.975540         0.978861        0.002717   \n",
              "\n",
              "   rank_test_score  \n",
              "0                4  \n",
              "1                3  \n",
              "2                2  \n",
              "3                1  "
            ],
            "text/html": [
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_C</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.151359</td>\n",
              "      <td>0.115843</td>\n",
              "      <td>0.020865</td>\n",
              "      <td>0.010866</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'C': 0.1}</td>\n",
              "      <td>0.977714</td>\n",
              "      <td>0.982746</td>\n",
              "      <td>0.978433</td>\n",
              "      <td>0.974119</td>\n",
              "      <td>0.974101</td>\n",
              "      <td>0.977423</td>\n",
              "      <td>0.003206</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.078607</td>\n",
              "      <td>0.002772</td>\n",
              "      <td>0.012064</td>\n",
              "      <td>0.000817</td>\n",
              "      <td>1</td>\n",
              "      <td>{'C': 1}</td>\n",
              "      <td>0.979152</td>\n",
              "      <td>0.984184</td>\n",
              "      <td>0.977714</td>\n",
              "      <td>0.976995</td>\n",
              "      <td>0.975540</td>\n",
              "      <td>0.978717</td>\n",
              "      <td>0.002971</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.203979</td>\n",
              "      <td>0.024578</td>\n",
              "      <td>0.012117</td>\n",
              "      <td>0.001268</td>\n",
              "      <td>10</td>\n",
              "      <td>{'C': 10}</td>\n",
              "      <td>0.979152</td>\n",
              "      <td>0.983465</td>\n",
              "      <td>0.978433</td>\n",
              "      <td>0.976995</td>\n",
              "      <td>0.975540</td>\n",
              "      <td>0.978717</td>\n",
              "      <td>0.002679</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.903651</td>\n",
              "      <td>0.097746</td>\n",
              "      <td>0.011395</td>\n",
              "      <td>0.000303</td>\n",
              "      <td>100</td>\n",
              "      <td>{'C': 100}</td>\n",
              "      <td>0.979871</td>\n",
              "      <td>0.983465</td>\n",
              "      <td>0.978433</td>\n",
              "      <td>0.976995</td>\n",
              "      <td>0.975540</td>\n",
              "      <td>0.978861</td>\n",
              "      <td>0.002717</td>\n",
              "      <td>1</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ans: The Best Cost comes out to be 100"
      ],
      "metadata": {
        "id": "MNgUA27fP4Qo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q5b Polynomial Kernel"
      ],
      "metadata": {
        "id": "ZfPGIeT6EO1e"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Apply Linear SVM for degrees = [1,2,3,4,5], predict for the test set and check f1 score for each of the degrees. State the best degree. "
      ],
      "metadata": {
        "id": "3vwpxFdTEOx3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'poly', random_state = 0)\n",
        "param_grid = {'degree': [1,2,3,4,5]} \n",
        "grid_search = GridSearchCV(estimator = classifier,\n",
        "                           scoring='f1_micro',\n",
        "                           param_grid = param_grid,\n",
        "                           refit=2,\n",
        "                           verbose=2)\n",
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "doPCbujkDpaP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a7b5bd74-55b0-4b81-9169-6b53604c29b5"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n",
            "[CV] END ...........................................degree=1; total time=   0.1s\n",
            "[CV] END ...........................................degree=1; total time=   0.1s\n",
            "[CV] END ...........................................degree=1; total time=   0.1s\n",
            "[CV] END ...........................................degree=1; total time=   0.1s\n",
            "[CV] END ...........................................degree=1; total time=   0.1s\n",
            "[CV] END ...........................................degree=2; total time=   0.1s\n",
            "[CV] END ...........................................degree=2; total time=   0.1s\n",
            "[CV] END ...........................................degree=2; total time=   0.1s\n",
            "[CV] END ...........................................degree=2; total time=   0.1s\n",
            "[CV] END ...........................................degree=2; total time=   0.1s\n",
            "[CV] END ...........................................degree=3; total time=   0.2s\n",
            "[CV] END ...........................................degree=3; total time=   0.2s\n",
            "[CV] END ...........................................degree=3; total time=   0.2s\n",
            "[CV] END ...........................................degree=3; total time=   0.2s\n",
            "[CV] END ...........................................degree=3; total time=   0.2s\n",
            "[CV] END ...........................................degree=4; total time=   0.3s\n",
            "[CV] END ...........................................degree=4; total time=   0.3s\n",
            "[CV] END ...........................................degree=4; total time=   0.2s\n",
            "[CV] END ...........................................degree=4; total time=   0.2s\n",
            "[CV] END ...........................................degree=4; total time=   0.3s\n",
            "[CV] END ...........................................degree=5; total time=   0.6s\n",
            "[CV] END ...........................................degree=5; total time=   1.4s\n",
            "[CV] END ...........................................degree=5; total time=   0.5s\n",
            "[CV] END ...........................................degree=5; total time=   0.3s\n",
            "[CV] END ...........................................degree=5; total time=   0.4s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=SVC(kernel='poly', random_state=0),\n",
              "             param_grid={'degree': [1, 2, 3, 4, 5]}, refit=2,\n",
              "             scoring='f1_micro', verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(grid_search.cv_results_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "z0fvYZ5FDDpi",
        "outputId": "b2c19b76-e4cd-4061-c792-5920bcbacc5b"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_degree  \\\n",
              "0       0.080357      0.008224         0.013863        0.001184            1   \n",
              "1       0.101765      0.004286         0.014647        0.000411            2   \n",
              "2       0.173749      0.015794         0.014137        0.001415            3   \n",
              "3       0.236586      0.031382         0.015446        0.000879            4   \n",
              "4       0.609957      0.403887         0.024691        0.021127            5   \n",
              "\n",
              "          params  split0_test_score  split1_test_score  split2_test_score  \\\n",
              "0  {'degree': 1}           0.977714           0.983465           0.978433   \n",
              "1  {'degree': 2}           0.974838           0.976995           0.974119   \n",
              "2  {'degree': 3}           0.976995           0.979871           0.976276   \n",
              "3  {'degree': 4}           0.971963           0.976276           0.973400   \n",
              "4  {'degree': 5}           0.971963           0.976276           0.970525   \n",
              "\n",
              "   split3_test_score  split4_test_score  mean_test_score  std_test_score  \\\n",
              "0           0.974838           0.974820         0.977854        0.003167   \n",
              "1           0.971244           0.971942         0.973828        0.002067   \n",
              "2           0.974119           0.974820         0.976416        0.002006   \n",
              "3           0.972682           0.972662         0.973397        0.001510   \n",
              "4           0.973400           0.970504         0.972534        0.002156   \n",
              "\n",
              "   rank_test_score  \n",
              "0                1  \n",
              "1                3  \n",
              "2                2  \n",
              "3                4  \n",
              "4                5  "
            ],
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              "      <th>mean_fit_time</th>\n",
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              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.080357</td>\n",
              "      <td>0.008224</td>\n",
              "      <td>0.013863</td>\n",
              "      <td>0.001184</td>\n",
              "      <td>1</td>\n",
              "      <td>{'degree': 1}</td>\n",
              "      <td>0.977714</td>\n",
              "      <td>0.983465</td>\n",
              "      <td>0.978433</td>\n",
              "      <td>0.974838</td>\n",
              "      <td>0.974820</td>\n",
              "      <td>0.977854</td>\n",
              "      <td>0.003167</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.101765</td>\n",
              "      <td>0.004286</td>\n",
              "      <td>0.014647</td>\n",
              "      <td>0.000411</td>\n",
              "      <td>2</td>\n",
              "      <td>{'degree': 2}</td>\n",
              "      <td>0.974838</td>\n",
              "      <td>0.976995</td>\n",
              "      <td>0.974119</td>\n",
              "      <td>0.971244</td>\n",
              "      <td>0.971942</td>\n",
              "      <td>0.973828</td>\n",
              "      <td>0.002067</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.173749</td>\n",
              "      <td>0.015794</td>\n",
              "      <td>0.014137</td>\n",
              "      <td>0.001415</td>\n",
              "      <td>3</td>\n",
              "      <td>{'degree': 3}</td>\n",
              "      <td>0.976995</td>\n",
              "      <td>0.979871</td>\n",
              "      <td>0.976276</td>\n",
              "      <td>0.974119</td>\n",
              "      <td>0.974820</td>\n",
              "      <td>0.976416</td>\n",
              "      <td>0.002006</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.236586</td>\n",
              "      <td>0.031382</td>\n",
              "      <td>0.015446</td>\n",
              "      <td>0.000879</td>\n",
              "      <td>4</td>\n",
              "      <td>{'degree': 4}</td>\n",
              "      <td>0.971963</td>\n",
              "      <td>0.976276</td>\n",
              "      <td>0.973400</td>\n",
              "      <td>0.972682</td>\n",
              "      <td>0.972662</td>\n",
              "      <td>0.973397</td>\n",
              "      <td>0.001510</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.609957</td>\n",
              "      <td>0.403887</td>\n",
              "      <td>0.024691</td>\n",
              "      <td>0.021127</td>\n",
              "      <td>5</td>\n",
              "      <td>{'degree': 5}</td>\n",
              "      <td>0.971963</td>\n",
              "      <td>0.976276</td>\n",
              "      <td>0.970525</td>\n",
              "      <td>0.973400</td>\n",
              "      <td>0.970504</td>\n",
              "      <td>0.972534</td>\n",
              "      <td>0.002156</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-929c8c12-9db2-4d1c-b455-bbef1b0f364a')\"\n",
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              "\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": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ans: The Best Degree comes out to be 1"
      ],
      "metadata": {
        "id": "yZLE1JkUPxCn"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q5c Sigmoid Kernel"
      ],
      "metadata": {
        "id": "3RnH4YwDDt8_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Apply Linear SVM for costs (C) = [0.1, 1, 10, 100, 1000], predict for the test set and check f1 score for each of the costs. State the best cost "
      ],
      "metadata": {
        "id": "0xQpdX-6D5By"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'sigmoid', random_state = 0)\n",
        "param_grid = {'C': [0.1, 1, 10, 100, 1000]} \n",
        "grid_search = GridSearchCV(estimator = classifier,\n",
        "                           scoring='f1_micro',\n",
        "                           param_grid = param_grid,\n",
        "                           refit=2,\n",
        "                           verbose=2)\n",
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "mtJYyYDRDRkA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f98ab131-264a-49ff-d413-61a2a7798efe"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n",
            "[CV] END ..............................................C=0.1; total time=   0.4s\n",
            "[CV] END ..............................................C=0.1; total time=   0.4s\n",
            "[CV] END ..............................................C=0.1; total time=   0.4s\n",
            "[CV] END ..............................................C=0.1; total time=   0.4s\n",
            "[CV] END ..............................................C=0.1; total time=   0.4s\n",
            "[CV] END ................................................C=1; total time=   0.4s\n",
            "[CV] END ................................................C=1; total time=   0.4s\n",
            "[CV] END ................................................C=1; total time=   0.4s\n",
            "[CV] END ................................................C=1; total time=   0.4s\n",
            "[CV] END ................................................C=1; total time=   0.4s\n",
            "[CV] END ...............................................C=10; total time=   0.4s\n",
            "[CV] END ...............................................C=10; total time=   0.4s\n",
            "[CV] END ...............................................C=10; total time=   0.4s\n",
            "[CV] END ...............................................C=10; total time=   0.4s\n",
            "[CV] END ...............................................C=10; total time=   0.4s\n",
            "[CV] END ..............................................C=100; total time=   0.4s\n",
            "[CV] END ..............................................C=100; total time=   0.4s\n",
            "[CV] END ..............................................C=100; total time=   0.4s\n",
            "[CV] END ..............................................C=100; total time=   0.4s\n",
            "[CV] END ..............................................C=100; total time=   0.4s\n",
            "[CV] END .............................................C=1000; total time=   0.4s\n",
            "[CV] END .............................................C=1000; total time=   0.4s\n",
            "[CV] END .............................................C=1000; total time=   0.4s\n",
            "[CV] END .............................................C=1000; total time=   0.4s\n",
            "[CV] END .............................................C=1000; total time=   0.4s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=SVC(kernel='sigmoid', random_state=0),\n",
              "             param_grid={'C': [0.1, 1, 10, 100, 1000]}, refit=2,\n",
              "             scoring='f1_micro', verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(grid_search.cv_results_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "8qiRGQ8HDyl0",
        "outputId": "3b808466-4fdc-4fdb-d18b-1110596ff9be"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
              "0       0.352181      0.010508         0.076017        0.002613     0.1   \n",
              "1       0.341135      0.014646         0.070055        0.001557       1   \n",
              "2       0.334204      0.020239         0.070215        0.001257      10   \n",
              "3       0.338605      0.008985         0.073295        0.003316     100   \n",
              "4       0.329366      0.007951         0.070202        0.002012    1000   \n",
              "\n",
              "        params  split0_test_score  split1_test_score  split2_test_score  \\\n",
              "0   {'C': 0.1}           0.879224           0.882818           0.867721   \n",
              "1     {'C': 1}           0.874191           0.882099           0.864845   \n",
              "2    {'C': 10}           0.874191           0.881380           0.863408   \n",
              "3   {'C': 100}           0.873472           0.881380           0.863408   \n",
              "4  {'C': 1000}           0.873472           0.881380           0.863408   \n",
              "\n",
              "   split3_test_score  split4_test_score  mean_test_score  std_test_score  \\\n",
              "0           0.872753           0.869065         0.874316        0.005829   \n",
              "1           0.865564           0.862590         0.869858        0.007280   \n",
              "2           0.865564           0.861151         0.869139        0.007547   \n",
              "3           0.865564           0.861151         0.868995        0.007456   \n",
              "4           0.865564           0.861151         0.868995        0.007456   \n",
              "\n",
              "   rank_test_score  \n",
              "0                1  \n",
              "1                2  \n",
              "2                3  \n",
              "3                4  \n",
              "4                4  "
            ],
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              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_C</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.352181</td>\n",
              "      <td>0.010508</td>\n",
              "      <td>0.076017</td>\n",
              "      <td>0.002613</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'C': 0.1}</td>\n",
              "      <td>0.879224</td>\n",
              "      <td>0.882818</td>\n",
              "      <td>0.867721</td>\n",
              "      <td>0.872753</td>\n",
              "      <td>0.869065</td>\n",
              "      <td>0.874316</td>\n",
              "      <td>0.005829</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.341135</td>\n",
              "      <td>0.014646</td>\n",
              "      <td>0.070055</td>\n",
              "      <td>0.001557</td>\n",
              "      <td>1</td>\n",
              "      <td>{'C': 1}</td>\n",
              "      <td>0.874191</td>\n",
              "      <td>0.882099</td>\n",
              "      <td>0.864845</td>\n",
              "      <td>0.865564</td>\n",
              "      <td>0.862590</td>\n",
              "      <td>0.869858</td>\n",
              "      <td>0.007280</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.334204</td>\n",
              "      <td>0.020239</td>\n",
              "      <td>0.070215</td>\n",
              "      <td>0.001257</td>\n",
              "      <td>10</td>\n",
              "      <td>{'C': 10}</td>\n",
              "      <td>0.874191</td>\n",
              "      <td>0.881380</td>\n",
              "      <td>0.863408</td>\n",
              "      <td>0.865564</td>\n",
              "      <td>0.861151</td>\n",
              "      <td>0.869139</td>\n",
              "      <td>0.007547</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.338605</td>\n",
              "      <td>0.008985</td>\n",
              "      <td>0.073295</td>\n",
              "      <td>0.003316</td>\n",
              "      <td>100</td>\n",
              "      <td>{'C': 100}</td>\n",
              "      <td>0.873472</td>\n",
              "      <td>0.881380</td>\n",
              "      <td>0.863408</td>\n",
              "      <td>0.865564</td>\n",
              "      <td>0.861151</td>\n",
              "      <td>0.868995</td>\n",
              "      <td>0.007456</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.329366</td>\n",
              "      <td>0.007951</td>\n",
              "      <td>0.070202</td>\n",
              "      <td>0.002012</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'C': 1000}</td>\n",
              "      <td>0.873472</td>\n",
              "      <td>0.881380</td>\n",
              "      <td>0.863408</td>\n",
              "      <td>0.865564</td>\n",
              "      <td>0.861151</td>\n",
              "      <td>0.868995</td>\n",
              "      <td>0.007456</td>\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2dd0662a-bae4-48ee-97f0-871e59948657 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2dd0662a-bae4-48ee-97f0-871e59948657');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [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",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ans: The Best Cost comes out to be 0.1"
      ],
      "metadata": {
        "id": "l-JOwqWXPr2Z"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Q5d RBF Kernel"
      ],
      "metadata": {
        "id": "xtz9KvY6AcE9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Apply Linear SVM for costs (C) = [0.1, 1, 10, 100, 1000], predict for the test set and check f1 score for each of the costs. State the best cost "
      ],
      "metadata": {
        "id": "S85NYKGkDj0D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "classifier = SVC(kernel = 'rbf', random_state = 0)\n",
        "param_grid = {'C': [0.1, 1, 10, 100, 1000]} \n",
        "grid_search = GridSearchCV(estimator = classifier,\n",
        "                           scoring='f1_micro',\n",
        "                           param_grid = param_grid,\n",
        "                           refit=2,\n",
        "                           verbose=2)\n",
        "grid_search.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "oeA1eR798Z7f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98e7e8d8-2238-4a1c-9b79-44ede2171c5c"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n",
            "[CV] END ..............................................C=0.1; total time=   0.2s\n",
            "[CV] END ..............................................C=0.1; total time=   0.2s\n",
            "[CV] END ..............................................C=0.1; total time=   0.2s\n",
            "[CV] END ..............................................C=0.1; total time=   0.2s\n",
            "[CV] END ..............................................C=0.1; total time=   0.2s\n",
            "[CV] END ................................................C=1; total time=   0.2s\n",
            "[CV] END ................................................C=1; total time=   0.2s\n",
            "[CV] END ................................................C=1; total time=   0.2s\n",
            "[CV] END ................................................C=1; total time=   0.2s\n",
            "[CV] END ................................................C=1; total time=   0.2s\n",
            "[CV] END ...............................................C=10; total time=   0.2s\n",
            "[CV] END ...............................................C=10; total time=   0.2s\n",
            "[CV] END ...............................................C=10; total time=   0.2s\n",
            "[CV] END ...............................................C=10; total time=   0.2s\n",
            "[CV] END ...............................................C=10; total time=   0.2s\n",
            "[CV] END ..............................................C=100; total time=   0.4s\n",
            "[CV] END ..............................................C=100; total time=   0.4s\n",
            "[CV] END ..............................................C=100; total time=   0.4s\n",
            "[CV] END ..............................................C=100; total time=   0.4s\n",
            "[CV] END ..............................................C=100; total time=   0.4s\n",
            "[CV] END .............................................C=1000; total time=   1.1s\n",
            "[CV] END .............................................C=1000; total time=   1.0s\n",
            "[CV] END .............................................C=1000; total time=   1.8s\n",
            "[CV] END .............................................C=1000; total time=   1.5s\n",
            "[CV] END .............................................C=1000; total time=   1.3s\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "GridSearchCV(estimator=SVC(random_state=0),\n",
              "             param_grid={'C': [0.1, 1, 10, 100, 1000]}, refit=2,\n",
              "             scoring='f1_micro', verbose=2)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame(grid_search.cv_results_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "62A7aRY8D9Bo",
        "outputId": "c0d31a34-33d9-4a91-ab7a-b7bb6a74071c"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_C  \\\n",
              "0       0.167003      0.011127         0.049315        0.002512     0.1   \n",
              "1       0.144586      0.008736         0.037290        0.004190       1   \n",
              "2       0.171304      0.006793         0.033908        0.004074      10   \n",
              "3       0.338804      0.014868         0.031324        0.002922     100   \n",
              "4       1.322414      0.260155         0.036569        0.009727    1000   \n",
              "\n",
              "        params  split0_test_score  split1_test_score  split2_test_score  \\\n",
              "0   {'C': 0.1}           0.976276           0.979152           0.977714   \n",
              "1     {'C': 1}           0.976995           0.982746           0.978433   \n",
              "2    {'C': 10}           0.979871           0.982027           0.977714   \n",
              "3   {'C': 100}           0.979871           0.982027           0.974838   \n",
              "4  {'C': 1000}           0.976276           0.982027           0.971244   \n",
              "\n",
              "   split3_test_score  split4_test_score  mean_test_score  std_test_score  \\\n",
              "0           0.971244           0.970504         0.974978        0.003480   \n",
              "1           0.974838           0.973381         0.977279        0.003239   \n",
              "2           0.976276           0.974820         0.978142        0.002561   \n",
              "3           0.975557           0.975540         0.977567        0.002856   \n",
              "4           0.976276           0.974820         0.976129        0.003476   \n",
              "\n",
              "   rank_test_score  \n",
              "0                5  \n",
              "1                3  \n",
              "2                1  \n",
              "3                2  \n",
              "4                4  "
            ],
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mean_fit_time</th>\n",
              "      <th>std_fit_time</th>\n",
              "      <th>mean_score_time</th>\n",
              "      <th>std_score_time</th>\n",
              "      <th>param_C</th>\n",
              "      <th>params</th>\n",
              "      <th>split0_test_score</th>\n",
              "      <th>split1_test_score</th>\n",
              "      <th>split2_test_score</th>\n",
              "      <th>split3_test_score</th>\n",
              "      <th>split4_test_score</th>\n",
              "      <th>mean_test_score</th>\n",
              "      <th>std_test_score</th>\n",
              "      <th>rank_test_score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.167003</td>\n",
              "      <td>0.011127</td>\n",
              "      <td>0.049315</td>\n",
              "      <td>0.002512</td>\n",
              "      <td>0.1</td>\n",
              "      <td>{'C': 0.1}</td>\n",
              "      <td>0.976276</td>\n",
              "      <td>0.979152</td>\n",
              "      <td>0.977714</td>\n",
              "      <td>0.971244</td>\n",
              "      <td>0.970504</td>\n",
              "      <td>0.974978</td>\n",
              "      <td>0.003480</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.144586</td>\n",
              "      <td>0.008736</td>\n",
              "      <td>0.037290</td>\n",
              "      <td>0.004190</td>\n",
              "      <td>1</td>\n",
              "      <td>{'C': 1}</td>\n",
              "      <td>0.976995</td>\n",
              "      <td>0.982746</td>\n",
              "      <td>0.978433</td>\n",
              "      <td>0.974838</td>\n",
              "      <td>0.973381</td>\n",
              "      <td>0.977279</td>\n",
              "      <td>0.003239</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.171304</td>\n",
              "      <td>0.006793</td>\n",
              "      <td>0.033908</td>\n",
              "      <td>0.004074</td>\n",
              "      <td>10</td>\n",
              "      <td>{'C': 10}</td>\n",
              "      <td>0.979871</td>\n",
              "      <td>0.982027</td>\n",
              "      <td>0.977714</td>\n",
              "      <td>0.976276</td>\n",
              "      <td>0.974820</td>\n",
              "      <td>0.978142</td>\n",
              "      <td>0.002561</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.338804</td>\n",
              "      <td>0.014868</td>\n",
              "      <td>0.031324</td>\n",
              "      <td>0.002922</td>\n",
              "      <td>100</td>\n",
              "      <td>{'C': 100}</td>\n",
              "      <td>0.979871</td>\n",
              "      <td>0.982027</td>\n",
              "      <td>0.974838</td>\n",
              "      <td>0.975557</td>\n",
              "      <td>0.975540</td>\n",
              "      <td>0.977567</td>\n",
              "      <td>0.002856</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.322414</td>\n",
              "      <td>0.260155</td>\n",
              "      <td>0.036569</td>\n",
              "      <td>0.009727</td>\n",
              "      <td>1000</td>\n",
              "      <td>{'C': 1000}</td>\n",
              "      <td>0.976276</td>\n",
              "      <td>0.982027</td>\n",
              "      <td>0.971244</td>\n",
              "      <td>0.976276</td>\n",
              "      <td>0.974820</td>\n",
              "      <td>0.976129</td>\n",
              "      <td>0.003476</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ccfef34-6bba-4057-8ff4-665e4e789a40')\"\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-8ccfef34-6bba-4057-8ff4-665e4e789a40');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [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",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ans: The Best Cost comes out to be 10"
      ],
      "metadata": {
        "id": "wG7wMX62Pho4"
      }
    }
  ]
}