Parameter_Optimization.ipynb

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        "<a href=\"https://colab.research.google.com/github/gurtaransingh/Machine-Learning-Assignments/blob/main/Parameter_Optimization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "###Importing Necessary Libraries"
      ],
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        "id": "c1XfOlKiULn_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "B_2gz_a3pZa0"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "from sklearn.model_selection import train_test_split, GridSearchCV\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import learning_curve\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Uploading the Dataset from UCI Library"
      ],
      "metadata": {
        "id": "HKT0uK58UP2f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "xg75U3QTrsyk",
        "outputId": "953dea43-eaeb-435d-e294-41f920387887"
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      "outputs": [
        {
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              "\n",
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              "        style=\"border:none\" />\n",
              "     <output id=\"result-8f2c3847-9dbe-4baa-8ddf-2115d9d2cec1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
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              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
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              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
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              "// Max number of bytes which will be uploaded at a time.\n",
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              "\n",
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              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
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              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
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              "  const outputElement = document.getElementById(outputId);\n",
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              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
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              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
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              "\n",
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              "  const cancel = document.createElement('button');\n",
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              "  cancel.textContent = 'Cancel upload';\n",
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              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
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              "    li.append(span(\n",
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              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
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              "    const fileDataPromise = new Promise((resolve) => {\n",
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              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
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              "      yield {\n",
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              "          data: base64,\n",
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              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
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              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
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              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving Live_20210128.csv to Live_20210128.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = pd.read_csv('/content/Live_20210128.csv')\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "hOxdHOSbpbeo",
        "outputId": "81bcb93b-19f4-4bdc-ba58-d88f904e0285"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   status_id status_type status_published  num_reactions  num_comments  \\\n",
              "0          1       video   4/22/2018 6:00            529           512   \n",
              "1          2       photo  4/21/2018 22:45            150             0   \n",
              "2          3       video   4/21/2018 6:17            227           236   \n",
              "3          4       photo   4/21/2018 2:29            111             0   \n",
              "4          5       photo   4/18/2018 3:22            213             0   \n",
              "\n",
              "   num_shares  num_likes  num_loves  num_wows  num_hahas  num_sads  \\\n",
              "0         262        432         92         3          1         1   \n",
              "1           0        150          0         0          0         0   \n",
              "2          57        204         21         1          1         0   \n",
              "3           0        111          0         0          0         0   \n",
              "4           0        204          9         0          0         0   \n",
              "\n",
              "   num_angrys  Column1  Column2  Column3  Column4  \n",
              "0           0      NaN      NaN      NaN      NaN  \n",
              "1           0      NaN      NaN      NaN      NaN  \n",
              "2           0      NaN      NaN      NaN      NaN  \n",
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              "      <th></th>\n",
              "      <th>status_id</th>\n",
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              "      <td>4/21/2018 2:29</td>\n",
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              "      <td>111</td>\n",
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              "      <td>5</td>\n",
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              "      <td>4/18/2018 3:22</td>\n",
              "      <td>213</td>\n",
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          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Pre-Processing of Dataset"
      ],
      "metadata": {
        "id": "W4eBOX7pUVbl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZEfCiD6Ar93s",
        "outputId": "3b67939a-6e1f-4345-fdbd-2140d6850b9f"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(7050, 16)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BHQbqjjIFrg",
        "outputId": "b932fd57-f545-44cf-d2e1-e03a1960e3b8"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "status_id              0\n",
              "status_type            0\n",
              "status_published       0\n",
              "num_reactions          0\n",
              "num_comments           0\n",
              "num_shares             0\n",
              "num_likes              0\n",
              "num_loves              0\n",
              "num_wows               0\n",
              "num_hahas              0\n",
              "num_sads               0\n",
              "num_angrys             0\n",
              "Column1             7050\n",
              "Column2             7050\n",
              "Column3             7050\n",
              "Column4             7050\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.drop(['status_id','status_published','Column1','Column2','Column3','Column4'],axis=1)"
      ],
      "metadata": {
        "id": "rs9oyhLFtsB_"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(x = 'status_type', data=dataset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "daWE15YjtWDF",
        "outputId": "b4acdeba-ffed-4a51-fb53-04a0eb1d6592"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='status_type', ylabel='count'>"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.head()"
      ],
      "metadata": {
        "id": "lfccI17s4XWG",
        "outputId": "edfff47f-7e4d-4bc1-b5a0-254a1f9c13bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        }
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  status_type  num_reactions  num_comments  num_shares  num_likes  num_loves  \\\n",
              "0       video            529           512         262        432         92   \n",
              "1       photo            150             0           0        150          0   \n",
              "2       video            227           236          57        204         21   \n",
              "3       photo            111             0           0        111          0   \n",
              "4       photo            213             0           0        204          9   \n",
              "\n",
              "   num_wows  num_hahas  num_sads  num_angrys  \n",
              "0         3          1         1           0  \n",
              "1         0          0         0           0  \n",
              "2         1          1         0           0  \n",
              "3         0          0         0           0  \n",
              "4         0          0         0           0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5779c043-c9db-42a7-89ca-12bc27c06757\">\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset.iloc[:,1:]\n",
        "\n",
        "y = dataset['status_type']"
      ],
      "metadata": {
        "id": "DJrr7u6Xwbm-"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
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        "X"
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        "colab": {
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          "height": 423
        }
      },
      "execution_count": 22,
      "outputs": [
        {
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2a306194-35ab-4b30-9044-1990ea141f87');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ss = StandardScaler()\n",
        "ss.fit_transform(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C-KMtpckIYKr",
        "outputId": "4f168cad-78b1-415d-829e-ef86f3b06e9d"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.64610394,  0.32335035,  1.6868788 , ...,  0.07671305,\n",
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              "        -0.152587  , -0.15574803]])"
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          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Creation of 10 samples with 70-30 ratio of Training and Testing Set"
      ],
      "metadata": {
        "id": "s2VfCpWjUafO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "samples = []\n",
        "for i in range(10):\n",
        "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=i)\n",
        "    samples.append((X_train, X_test, y_train, y_test))"
      ],
      "metadata": {
        "id": "H7MnfxqttiXl"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "samples"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gPp8uFHkMC2D",
        "outputId": "ee5bbaca-05f9-4301-b7fa-3cab1d616dda"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  ...             ...           ...         ...        ...        ...       ...   \n",
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              "  2732            174            66           1        173          1         0   \n",
              "  \n",
              "        num_hahas  num_sads  num_angrys  \n",
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              "  \n",
              "  [4935 rows x 9 columns],\n",
              "        num_reactions  num_comments  num_shares  num_likes  num_loves  num_wows  \\\n",
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              "  5849    video\n",
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              "          ...  \n",
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              "  2423     photo\n",
              "           ...  \n",
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              "          ...  \n",
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              "  [2115 rows x 9 columns],\n",
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              "          ...  \n",
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              "  5445    photo\n",
              "  2220    photo\n",
              "  2672    photo\n",
              "  5539    video\n",
              "  Name: status_type, Length: 2115, dtype: object),\n",
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              "  [4935 rows x 9 columns],\n",
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              "  2017             25            18           0         25          0         0   \n",
              "  \n",
              "        num_hahas  num_sads  num_angrys  \n",
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              "  \n",
              "  [2115 rows x 9 columns],\n",
              "  5914     photo\n",
              "  4490     video\n",
              "  640       link\n",
              "  2320     photo\n",
              "  3328     photo\n",
              "           ...  \n",
              "  6200     photo\n",
              "  501      photo\n",
              "  6782    status\n",
              "  4444     video\n",
              "  382      photo\n",
              "  Name: status_type, Length: 4935, dtype: object,\n",
              "  1531     video\n",
              "  3861     photo\n",
              "  4571     video\n",
              "  6646     photo\n",
              "  4364     video\n",
              "           ...  \n",
              "  2718     photo\n",
              "  4773     photo\n",
              "  3180     photo\n",
              "  1164    status\n",
              "  2017     photo\n",
              "  Name: status_type, Length: 2115, dtype: object)]"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Creation of a Fitness Function"
      ],
      "metadata": {
        "id": "xbpZpTYpUuTl"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "kernels = ['linear', 'poly', 'rbf', 'sigmoid']\n",
        "result = pd.DataFrame(columns=['Sample', 'Best Accuracy', 'Best Kernel', 'Best Nu', 'Best Epsilon'])"
      ],
      "metadata": {
        "id": "UEJ2yRAeMOb2"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def fitnessFunction(kernel, C, gamma):\n",
        "  svm = SVC(kernel=kernel, C=C, gamma=gamma, max_iter=1000)\n",
        "  svm.fit(X_train, y_train)\n",
        "  y_pred = svm.predict(X_test)\n",
        "  return accuracy_score(y_pred, y_test)"
      ],
      "metadata": {
        "id": "3XHWEfctVUGH"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Calling the Function for Every Sample"
      ],
      "metadata": {
        "id": "ZWuJhdBfUzze"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for i in range(len(samples)):\n",
        "  best_accuracy = 0\n",
        "  best_C = 0\n",
        "  best_gamma = 0\n",
        "  for kernel in kernels:\n",
        "    X_train, X_test, y_train, y_test = samples[i]\n",
        "    C = np.random.uniform(0, 10)\n",
        "    gamma = np.random.uniform(0, 10)\n",
        "    score = fitnessFunction(kernel, C, gamma)\n",
        "    if score>best_accuracy:\n",
        "      best_accuracy = round(score, 2)\n",
        "      best_C = round(C, 2)\n",
        "      best_gamma = round(gamma, 2)\n",
        "      best_kernel = kernel\n",
        "  print('Best Accuracy = ', best_accuracy, 'Best Kernel = ', best_kernel, 'Best Nu = ', best_C, 'Best Epsilon = ', best_gamma)\n",
        "  result.loc[i] = [i+1, best_accuracy, best_kernel, best_C, best_gamma]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fjgJ1WVBWsxx",
        "outputId": "aa7e4c23-f775-4005-e3c5-c91a6ab293ca"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Best Accuracy =  0.63 Best Kernel =  poly Best Nu =  0.54 Best Epsilon =  0.25\n",
            "Best Accuracy =  0.62 Best Kernel =  rbf Best Nu =  7.25 Best Epsilon =  5.11\n",
            "Best Accuracy =  0.66 Best Kernel =  poly Best Nu =  0.19 Best Epsilon =  2.5\n",
            "Best Accuracy =  0.42 Best Kernel =  rbf Best Nu =  1.64 Best Epsilon =  7.45\n",
            "Best Accuracy =  0.44 Best Kernel =  rbf Best Nu =  4.52 Best Epsilon =  9.81\n",
            "Best Accuracy =  0.72 Best Kernel =  sigmoid Best Nu =  6.71 Best Epsilon =  1.0\n",
            "Best Accuracy =  0.5 Best Kernel =  rbf Best Nu =  9.24 Best Epsilon =  7.97\n",
            "Best Accuracy =  0.66 Best Kernel =  sigmoid Best Nu =  3.41 Best Epsilon =  3.43\n",
            "Best Accuracy =  0.66 Best Kernel =  poly Best Nu =  6.39 Best Epsilon =  4.73\n",
            "Best Accuracy =  0.63 Best Kernel =  rbf Best Nu =  2.99 Best Epsilon =  9.72\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Creation of a Result Table"
      ],
      "metadata": {
        "id": "JybxcLU4U6Pf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "MUpSkOBfcFbV",
        "outputId": "8e5ced52-1ad5-45d3-973a-7e490fcd5b56"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Sample  Best Accuracy Best Kernel  Best Nu  Best Epsilon\n",
              "0       1           0.63        poly     0.54          0.25\n",
              "1       2           0.62         rbf     7.25          5.11\n",
              "2       3           0.66        poly     0.19          2.50\n",
              "3       4           0.42         rbf     1.64          7.45\n",
              "4       5           0.44         rbf     4.52          9.81\n",
              "5       6           0.72     sigmoid     6.71          1.00\n",
              "6       7           0.50         rbf     9.24          7.97\n",
              "7       8           0.66     sigmoid     3.41          3.43\n",
              "8       9           0.66        poly     6.39          4.73\n",
              "9      10           0.63         rbf     2.99          9.72"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6e9762ff-d76f-4d2f-918d-4197f28413f9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Sample</th>\n",
              "      <th>Best Accuracy</th>\n",
              "      <th>Best Kernel</th>\n",
              "      <th>Best Nu</th>\n",
              "      <th>Best Epsilon</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>0.63</td>\n",
              "      <td>poly</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>0.62</td>\n",
              "      <td>rbf</td>\n",
              "      <td>7.25</td>\n",
              "      <td>5.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>0.66</td>\n",
              "      <td>poly</td>\n",
              "      <td>0.19</td>\n",
              "      <td>2.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>0.42</td>\n",
              "      <td>rbf</td>\n",
              "      <td>1.64</td>\n",
              "      <td>7.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>0.44</td>\n",
              "      <td>rbf</td>\n",
              "      <td>4.52</td>\n",
              "      <td>9.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>0.72</td>\n",
              "      <td>sigmoid</td>\n",
              "      <td>6.71</td>\n",
              "      <td>1.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>0.50</td>\n",
              "      <td>rbf</td>\n",
              "      <td>9.24</td>\n",
              "      <td>7.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>0.66</td>\n",
              "      <td>sigmoid</td>\n",
              "      <td>3.41</td>\n",
              "      <td>3.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>0.66</td>\n",
              "      <td>poly</td>\n",
              "      <td>6.39</td>\n",
              "      <td>4.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>0.63</td>\n",
              "      <td>rbf</td>\n",
              "      <td>2.99</td>\n",
              "      <td>9.72</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6e9762ff-d76f-4d2f-918d-4197f28413f9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      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",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      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-6e9762ff-d76f-4d2f-918d-4197f28413f9 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-6e9762ff-d76f-4d2f-918d-4197f28413f9');\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": 29
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plotting of the Convergence Graph/Linear Curve"
      ],
      "metadata": {
        "id": "roEQAPDjU9ux"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = samples[result['Best Accuracy'].idxmax()]"
      ],
      "metadata": {
        "id": "HN0vIrNjMHRL"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_sizes, train_scores, test_scores = learning_curve(SVC(kernel=result['Best Kernel'].iloc[result['Best Accuracy'].idxmax()], \n",
        "                                                        C=result['Best Nu'].iloc[result['Best Accuracy'].idxmax()],\n",
        "                                                        gamma=result['Best Epsilon'].iloc[result['Best Accuracy'].idxmax()],\n",
        "                                                        max_iter = 1000), X_train, y_train, cv=10, scoring='accuracy', n_jobs=-1,\n",
        "                                                        train_sizes = np.linspace(0.01, 1.0, 50))"
      ],
      "metadata": {
        "id": "DjxAGZsPNCK3"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_sizes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AdFu3OLlP9KM",
        "outputId": "48bd19aa-9afb-421e-a97d-899ebf848772"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  44,  134,  223,  313,  403,  493,  582,  672,  762,  851,  941,\n",
              "       1031, 1121, 1210, 1300, 1390, 1480, 1569, 1659, 1749, 1838, 1928,\n",
              "       2018, 2108, 2197, 2287, 2377, 2467, 2556, 2646, 2736, 2825, 2915,\n",
              "       3005, 3095, 3184, 3274, 3364, 3454, 3543, 3633, 3723, 3812, 3902,\n",
              "       3992, 4082, 4171, 4261, 4351, 4441])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_mean = np.mean(train_scores, axis=1)\n",
        "test_mean = np.mean(test_scores, axis=1)"
      ],
      "metadata": {
        "id": "jcTidkqyNRDn"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10, 6))\n",
        "plt.plot(train_sizes, train_mean, label='Training Score')\n",
        "plt.plot(train_sizes, test_mean, label='Cross-Validation Score')\n",
        "plt.xlabel('Iterations')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.title('Convergence Graph')\n",
        "plt.legend(loc=\"best\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "_2jLVvf4PEV4",
        "outputId": "a9204819-fe1c-4931-a230-df9e5ea4853e"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Ut9ediz55mdh"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}