MAGIC_Gamma_Telescope_ML_Project.ipynb

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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.over_sampling import RandomOverSampler"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset:\n",
        "Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.\n",
        "\n",
        "Donated by: P. Savicky Institute of Computer Science, AS of CR Czech Republic savicky '@' cs.cas.cz"
      ],
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        "cols=[\"fLength\", \"fWidth\", \"fSize\", \"fConc\", \"fConc1\", \"fAsym\", \"fM3Long\", \"fM3Trans\", \"fAlpha\", \"fDist\", \"class\"]\n",
        "#giving names to columns as the data doesn't have it\n",
        "\n",
        "df=pd.read_csv('magic04.data',names=cols)\n",
        "df.head() #printing the first five elements of the data"
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              "    fLength    fWidth   fSize   fConc  fConc1     fAsym  fM3Long  fM3Trans  \\\n",
              "0   28.7967   16.0021  2.6449  0.3918  0.1982   27.7004  22.0110   -8.2027   \n",
              "1   31.6036   11.7235  2.5185  0.5303  0.3773   26.2722  23.8238   -9.9574   \n",
              "2  162.0520  136.0310  4.0612  0.0374  0.0187  116.7410 -64.8580  -45.2160   \n",
              "3   23.8172    9.5728  2.3385  0.6147  0.3922   27.2107  -6.4633   -7.1513   \n",
              "4   75.1362   30.9205  3.1611  0.3168  0.1832   -5.5277  28.5525   21.8393   \n",
              "\n",
              "    fAlpha     fDist class  \n",
              "0  40.0920   81.8828     g  \n",
              "1   6.3609  205.2610     g  \n",
              "2  76.9600  256.7880     g  \n",
              "3  10.4490  116.7370     g  \n",
              "4   4.6480  356.4620     g  "
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      "cell_type": "code",
      "source": [
        "df['class'].unique() #seeing the unique values in column 'class'."
      ],
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        "id": "n4mZoaWxYaKF",
        "outputId": "b55eec62-88a0-4883-8e97-a52903897b41"
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      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['g', 'h'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "#preparing the data\n",
        "df['class']=(df['class']=='g').astype(int) #setting 1 for g and 0 for h in the 'class'column\n"
      ],
      "metadata": {
        "id": "qsHzNbOBZrHn"
      },
      "execution_count": 7,
      "outputs": []
    },
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      "cell_type": "markdown",
      "source": [
        "This datset contains 10 features that helps identify the class. The class contains g and h for gamma particles and hadrons respectively.\n",
        "We are classifying the particles that hit the telescope based on their feaures."
      ],
      "metadata": {
        "id": "uh2DAhnUalGM"
      }
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        "df.head()"
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              "    fLength    fWidth   fSize   fConc  fConc1     fAsym  fM3Long  fM3Trans  \\\n",
              "0   28.7967   16.0021  2.6449  0.3918  0.1982   27.7004  22.0110   -8.2027   \n",
              "1   31.6036   11.7235  2.5185  0.5303  0.3773   26.2722  23.8238   -9.9574   \n",
              "2  162.0520  136.0310  4.0612  0.0374  0.0187  116.7410 -64.8580  -45.2160   \n",
              "3   23.8172    9.5728  2.3385  0.6147  0.3922   27.2107  -6.4633   -7.1513   \n",
              "4   75.1362   30.9205  3.1611  0.3168  0.1832   -5.5277  28.5525   21.8393   \n",
              "\n",
              "    fAlpha     fDist  class  \n",
              "0  40.0920   81.8828      1  \n",
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              "2  76.9600  256.7880      1  \n",
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              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-efd52f7a-1c8e-4268-bf46-adaed6d3adc8')\"\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",
              "\n",
              "\n",
              "    <div id=\"df-a236675d-3b1f-4bb7-b78e-e2bf339c1285\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-a236675d-3b1f-4bb7-b78e-e2bf339c1285')\"\n",
              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\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-quickchart: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-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart: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",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-a236675d-3b1f-4bb7-b78e-e2bf339c1285 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\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-efd52f7a-1c8e-4268-bf46-adaed6d3adc8 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-efd52f7a-1c8e-4268-bf46-adaed6d3adc8');\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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for label in cols[:-1]:\n",
        "  plt.hist(df[df['class']==1][label], color='blue', label='gamma', alpha=0.7, density=True)\n",
        "  #plot everything where class is 1\n",
        "  #alpha is for transparency\n",
        "  #density being true normalizes the distributions\n",
        "  plt.hist(df[df['class']==0][label], color='yellow' , label='hadron' , alpha=0.7, density=True)\n",
        "  plt.title(label)\n",
        "  plt.xlabel(label)\n",
        "  plt.ylabel('Probability')\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "WqwlFkcxjjyp",
        "outputId": "fae34993-37e1-4c00-e8f4-48da2be76bf4"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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++ecaPny4EhMT1b9/f61bt04TJ04scowjR47ovffe08qVKxUYGCiHw+EMO1u2bNGFCxcUFxengQMHavPmzc7tjh49qlWrVmnt2rU6e/as7r33Xk2dOlVTpkzx1fBcFoEHAAA/0qZNG2dIadq0qebOnavk5GTdfvvtLguao6Ki9Nxzz2nkyJHOwDN79mzdcccdevLJJyVJ1113nbZt26Z169a5HCM/P19LlixR7dq1JUkbN27Uvn37dPz4cUVGRkqSlixZouuvv147d+5Up06dJF2cfVq8eLFCQkIkSYMHD1ZycnK5CDyc0gIAwI+0adPG5X1ERITOnDkjSfr444912223qV69egoJCdHgwYP1ww8/6KeffpIkHTx4UF26dHHZPjo6usgxGjZs6Aw7l7aLjIx0hh1JatmypapXr66DBw8626Kiopxh57e1mY3AAwCAH6lYsaLLe5vNJofDoRMnTujOO+9UmzZt9N5772n37t2aN2+eJPcXHletWtWjtZUHnNICAMACdu/eLYfDoRkzZigg4OJ8xrvvvuvSp0WLFvr8889d2rZv3/67+27RooVOnTqlU6dOOWd5Dhw4oHPnzqlly5Ye+gbexQwPAAAW0KRJExUUFGjOnDk6duyYli5dqldeecWlz2OPPaZ169Zp+vTp+vrrrzV37twi63eKExsbq9atW+v+++/Xnj17tGPHDg0ZMkTdu3dXx44dvfWVPIrAAwCABbRt21YvvfSSXnjhBbVq1Upvv/22EhMTXfp07dpVCxcu1OzZs9W2bVtt2LBBEyZM+N1922w2rV69WjVq1FC3bt0UGxurxo0b65133vHW1/E4m2EYhtlF+FJ2drbsdruysrIUGhpqdjnlEpele98abq4KmObnn3/W8ePHXe4xg/LhSv82Zf37zQwPAACwPFMDz/z589WmTRuFhoYqNDRU0dHR+uijjy7bf/HixbLZbC4v0jkAAPg9pl6lVb9+fU2dOlVNmzaVYRh688031a9fP+3du1fXX399sduEhobq8OHDzvc2m81X5QIAAD9lauDp+5vFIlOmTNH8+fO1ffv2ywYem82m8PBwX5QHAAAsotys4SksLFRSUpJyc3OLvevjJefPn1fDhg0VGRmpfv36af/+/Vfcb15enrKzs11eAABcZdfs+AVv/puYHnj27dunatWqKTg4WCNHjtT7779/2ZsYNWvWTG+88YZWr16tt956Sw6HQzExMfrmm28uu//ExETZ7Xbn69e3xQYAXH0u3Q340uMWUH5cuiN0YGCgx/dt+mXp+fn5SktLU1ZWllasWKHXXntNW7ZsKdGdGwsKCtSiRQsNGjRIkydPLrZPXl6e8vLynO+zs7MVGRnJZelXwGXp3sdl6YC50tPTde7cOdWpU0dVqlRhPWg54HA4dPr0aVWsWFENGjQo8m9S1svSTX+0RFBQkJo0aSJJ6tChg3bu3KnZs2fr1Vdf/d1tK1asqPbt2+vIkSOX7RMcHKzg4GCP1QsA8H+X1oKWlwdb4qKAgIBiw44nmB54fsvhcLjMyFxJYWGh9u3bp969e3u5KgCAldhsNkVERKhOnToqKCgwuxz8n6CgIOdzwDzN1MAzfvx49erVSw0aNFBOTo6WLVumzZs3a/369ZKkIUOGqF69es5bY0+aNEldu3ZVkyZNdO7cOU2bNk0nT57UQw89ZObXAAD4qcDAQK+sF0H5Y2rgOXPmjIYMGaL09HTZ7Xa1adNG69ev1+233y5JSktLc0l6Z8+e1YgRI5SRkaEaNWqoQ4cO2rZtm988qRUAAJjD9EXLvsaztH4fi5a9j0XLAOAenqUFAADwOwg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8gg8AADA8iqYXQBQEgkJfc0uocQmT15jdgkAgN9ghgcAAFgegQcAAFgegQcAAFgegQcAAFgegQcAAFgegQcAAFgel6Vf1Yq/1DshwcdlAADgZabO8MyfP19t2rRRaGioQkNDFR0drY8++uiK2yxfvlzNmzdXpUqV1Lp1a3344Yc+qhYAAPgrUwNP/fr1NXXqVO3evVu7du3Srbfeqn79+mn//v3F9t+2bZsGDRqk4cOHa+/everfv7/69++vL7/80seVAwAAf2IzDMMwu4hfq1mzpqZNm6bhw4cX+WzgwIHKzc3V2rVrnW1du3ZVu3bt9Morr5Ro/9nZ2bLb7crKylJoaKjH6vZPxZ/S2rHDx2VYTEnutLyGmzEDgFvK+ve73CxaLiwsVFJSknJzcxUdHV1sn5SUFMXGxrq09ezZUykpKb4oEQAA+CnTFy3v27dP0dHR+vnnn1WtWjW9//77atmyZbF9MzIyFBYW5tIWFhamjIyMy+4/Ly9PeXl5zvfZ2dmeKRwAAPgN02d4mjVrptTUVH3++ed69NFHNXToUB04cMBj+09MTJTdbne+IiMjPbZvAADgH0wPPEFBQWrSpIk6dOigxMREtW3bVrNnzy62b3h4uDIzM13aMjMzFR4eftn9jx8/XllZWc7XqVOnPFo/AAAo/0wPPL/lcDhcTkH9WnR0tJKTk13aNm7ceNk1P5IUHBzsvOz90gsAAFxdTF3DM378ePXq1UsNGjRQTk6Oli1bps2bN2v9+vWSpCFDhqhevXpKTEyUJI0dO1bdu3fXjBkz1KdPHyUlJWnXrl1asGCBmV8DAACUc6YGnjNnzmjIkCFKT0+X3W5XmzZttH79et1+++2SpLS0NAUE/DIJFRMTo2XLlmnChAl66qmn1LRpU61atUqtWrUy6ysARSQkFH+5f/nE9fEArg7l7j483sZ9eH6N+/CYpXNnsyu4hMADwD9Y5j48AAAA3kLgAQAAlkfgAQAAlkfgAQAAlkfgAQAAlkfgAQAAlkfgAQAAlmf609KBq1F5udfR5Mkl77uGW/YA8GPM8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMsj8AAAAMszNfAkJiaqU6dOCgkJUZ06ddS/f38dPnz4itssXrxYNpvN5VWpUiUfVQwAAPyRqYFny5YtiouL0/bt27Vx40YVFBToD3/4g3Jzc6+4XWhoqNLT052vkydP+qhiAADgjyqYefB169a5vF+8eLHq1Kmj3bt3q1u3bpfdzmazKTw83NvlAQAAiyhXa3iysrIkSTVr1rxiv/Pnz6thw4aKjIxUv379tH///sv2zcvLU3Z2tssLAABcXcpN4HE4HBo3bpxuvPFGtWrV6rL9mjVrpjfeeEOrV6/WW2+9JYfDoZiYGH3zzTfF9k9MTJTdbne+IiMjvfUVAABAOWUzDMMwuwhJevTRR/XRRx/p008/Vf369Uu8XUFBgVq0aKFBgwZp8uTJRT7Py8tTXl6e8312drYiIyOVlZWl0NBQj9Tuv/oW27pjh4/LgGkmT15T4r5rSt4VADwuOztbdru91H+/TV3Dc8no0aO1du1abd261a2wI0kVK1ZU+/btdeTIkWI/Dw4OVnBwsCfKBAAAfsrUU1qGYWj06NF6//339cknn6hRo0Zu76OwsFD79u1TRESEFyoEAABWYOoMT1xcnJYtW6bVq1crJCREGRkZkiS73a7KlStLkoYMGaJ69eopMTFRkjRp0iR17dpVTZo00blz5zRt2jSdPHlSDz30kGnfAwAAlG+mBp758+dLknr06OHSvmjRIg0bNkySlJaWpoCAXyaizp49qxEjRigjI0M1atRQhw4dtG3bNrVs2dJXZQMAAD9jauApyXrpzZs3u7yfOXOmZs6c6aWKAACAFZWby9IBAAC8hcADAAAsr1SBZ9OmTZ6uAwAAwGtKFXjuuOMOXXvttXruued06tQpT9cEAADgUaUKPN9++61Gjx6tFStWqHHjxurZs6feffdd5efne7o+AACAMitV4KlVq5b+8pe/KDU1VZ9//rmuu+46jRo1SnXr1tVjjz2mL774wtN1AgAAlFqZFy3fcMMNGj9+vEaPHq3z58/rjTfeUIcOHXTzzTdf8SnmAAAAvlLqwFNQUKAVK1aod+/eatiwodavX6+5c+cqMzNTR44cUcOGDTVgwABP1goAAFAqpbrx4JgxY/Svf/1LhmFo8ODBevHFF9WqVSvn51WrVtX06dNVt25djxUKAABQWqUKPAcOHNCcOXN09913X/ZJ5LVq1eLydQAAUC6U6pTWxIkTNWDAgCJh58KFC9q6daskqUKFCurevXvZKwQAACijUgWeW265RT/++GOR9qysLN1yyy1lLgoAAMCTShV4DMOQzWYr0v7DDz+oatWqZS4KAADAk9xaw3P33XdLkmw2m4YNG+ZySquwsFD//e9/FRMT49kKAQAAysitwGO32yVdnOEJCQlR5cqVnZ8FBQWpa9euGjFihGcrBAAAKCO3As+iRYskSVFRUXriiSc4fQUAAPxCqS5LnzhxoqfrAAAA8JoSB54bbrhBycnJqlGjhtq3b1/souVL9uzZ45HiAHhXQkJfs0twwxqzCwDgx0ocePr16+dcpNy/f39v1QMAAOBxNsMwDLOL8KXs7GzZ7XZlZWUpNDTU7HJMVvz/u9+xw8dlwC907mx2BczwAFezsv79LvPT0gEAAMq7Ep/SqlGjxhXX7fxacXdhBgAAMEuJA8+sWbO8WAYAAID3lDjwDB061Jt1AAAAeE2JA092drZzkVB2dvYV+7IYGAAAlCdureFJT09XnTp1VL169WLX81x6qGhhYaFHiwQAACiLEgeeTz75RDVr1pQkbdq0yWsFAQAAeFqJA0/37t2L/W8AAIDyrlTP0pKks2fP6vXXX9fBgwclSS1bttSDDz7onAUCAAAoL0p148GtW7cqKipKL7/8ss6ePauzZ8/q5ZdfVqNGjbR161ZP1wgAAFAmpZrhiYuL08CBAzV//nwFBgZKkgoLCzVq1CjFxcVp3759Hi0SAACgLEo1w3PkyBE9/vjjzrAjSYGBgYqPj9eRI0c8VhwAAIAnlCrw3HDDDc61O7928OBBtW3btsxFAQAAeFKJT2n997//df73Y489prFjx+rIkSPq2rWrJGn79u2aN2+epk6d6vkqAQAAysBmGIZRko4BAQGy2Wz6ve7u3HgwMTFRK1eu1KFDh1S5cmXFxMTohRdeULNmza643fLly5WQkKATJ06oadOmeuGFF9S7d+8SHbOsj5e3lr7Ftu7Y4eMy4Bc6dza7gjVmFwDARGX9+13iGZ7jx4+7vfPfs2XLFsXFxalTp066cOGCnnrqKf3hD3/QgQMHVLVq1WK32bZtmwYNGqTExETdeeedWrZsmfr37689e/aoVatWHq8RAAD4vxLP8PjCd999pzp16mjLli3q1q1bsX0GDhyo3NxcrV271tnWtWtXtWvXTq+88srvHoMZnl9jhgclxwwPADP5bIanOAcOHFBaWpry8/Nd2u+6665S7S8rK0uSrnjzwpSUFMXHx7u09ezZU6tWrSrVMQEAgPWVKvAcO3ZMf/zjH7Vv3z6XdT2XHihamoeHOhwOjRs3TjfeeOMVT01lZGQoLCzMpS0sLEwZGRnF9s/Ly1NeXp7z/e896R0AAFhPqS5LHzt2rBo1aqQzZ86oSpUq2r9/v7Zu3aqOHTtq8+bNpSokLi5OX375pZKSkkq1/eUkJibKbrc7X5GRkR7dPwAAKP9KFXhSUlI0adIk1apVSwEBAQoICNBNN92kxMREPfbYY27vb/To0Vq7dq02bdqk+vXrX7FveHi4MjMzXdoyMzMVHh5ebP/x48crKyvL+Tp16pTb9QEAAP9WqsBTWFiokJAQSVKtWrV0+vRpSVLDhg11+PDhEu/HMAyNHj1a77//vj755BM1atTod7eJjo5WcnKyS9vGjRsVHR1dbP/g4GCFhoa6vAAAwNWlVGt4WrVqpS+++EKNGjVSly5d9OKLLyooKEgLFixQ48aNS7yfuLg4LVu2TKtXr1ZISIhzHY7dblflypUlSUOGDFG9evWUmJgo6eLptO7du2vGjBnq06ePkpKStGvXLi1YsKA0XwUAAFwFSjXDM2HCBDkcDknSpEmTdPz4cd1888368MMP9fLLL5d4P/Pnz1dWVpZ69OihiIgI5+udd95x9klLS1N6errzfUxMjJYtW6YFCxaobdu2WrFihVatWsU9eAAAwGV57D48P/74o2rUqOG8Uqu84j48v8Z9eFBy3IcHgJlMvQ+PJOciYK5+AgAA5VWpTmlduHBBCQkJstvtioqKUlRUlOx2uyZMmKCCggJP1wgAAFAmpZrhGTNmjFauXKkXX3zReXVUSkqKnnnmGf3www+aP3++R4sEAAAoi1IFnmXLlikpKUm9evVytrVp00aRkZEaNGgQgQcAAJQrpTqlFRwcrKioqCLtjRo1UlBQUFlrAgAA8KhSBZ7Ro0dr8uTJLs+oysvL05QpUzR69GiPFQcAAOAJJT6ldffdd7u8//jjj1W/fn21bdtWkvTFF18oPz9ft912m2crBAAAKKMSBx673e7y/p577nF5z2XpAACgvCpx4Fm0aJE36wAAAPCaMt148LvvvnM+LLRZs2aqXbu2R4oCUP6YfQfuyZPd32YNN2cG8H9KtWg5NzdX/+///T9FRESoW7du6tatm+rWravhw4frp59+8nSNAAAAZVKqwBMfH68tW7ZozZo1OnfunM6dO6fVq1dry5Ytevzxxz1dIwAAQJmU6pTWe++9pxUrVqhHjx7Ott69e6ty5cq69957ufEgAAAoV0o1w/PTTz8pLCysSHudOnU4pQUAAMqdUgWe6OhoTZw4UT///LOz7X//+5+effZZ57O1AAAAyotSndKaNWuW7rjjjiI3HqxUqZLWr1/v0QIBAADKqlSBp3Xr1vr666/19ttv69ChQ5KkQYMG6f7771flypU9WiAAAEBZuR14CgoK1Lx5c61du1YjRozwRk0AAAAe5fYanooVK7qs3QEAACjvSrVoOS4uTi+88IIuXLjg6XoAAAA8rlRreHbu3Knk5GRt2LBBrVu3VtWqVV0+X7lypUeKAwAA8IRSBZ7q1asXeVo6AABAeeVW4HE4HJo2bZq++uor5efn69Zbb9UzzzzDlVkAAKBcc2sNz5QpU/TUU0+pWrVqqlevnl5++WXFxcV5qzYAAACPcCvwLFmyRP/85z+1fv16rVq1SmvWrNHbb78th8PhrfoAAADKzK3Ak5aWpt69ezvfx8bGymaz6fTp0x4vDAAAwFPcCjwXLlxQpUqVXNoqVqyogoICjxYFAADgSW4tWjYMQ8OGDVNwcLCz7eeff9bIkSNdLk3nsnQAAFCeuBV4hg4dWqTtgQce8FgxAAAA3uBW4Fm0aJG36gAAAPCaUj1aAgAAwJ8QeAAAgOUReAAAgOUReAAAgOUReAAAgOWZGni2bt2qvn37qm7durLZbFq1atUV+2/evFk2m63IKyMjwzcFAwAAv2Rq4MnNzVXbtm01b948t7Y7fPiw0tPTna86dep4qUIAAGAFbt2Hx9N69eqlXr16ub1dnTp1VL16dc8XBAAALMkv1/C0a9dOERERuv322/XZZ59dsW9eXp6ys7NdXgAA4OriV4EnIiJCr7zyit577z299957ioyMVI8ePbRnz57LbpOYmCi73e58RUZG+rBiAABQHph6SstdzZo1U7NmzZzvY2JidPToUc2cOVNLly4tdpvx48crPj7e+T47O5vQAwDAVcavAk9xOnfurE8//fSynwcHB7s83R2Af0pI6Gt2CSW0xuwCABTDr05pFSc1NVURERFmlwEAAMoxU2d4zp8/ryNHjjjfHz9+XKmpqapZs6YaNGig8ePH69tvv9WSJUskSbNmzVKjRo10/fXX6+eff9Zrr72mTz75RBs2bDDrKwAAAD9gauDZtWuXbrnlFuf7S2tthg4dqsWLFys9PV1paWnOz/Pz8/X444/r22+/VZUqVdSmTRt9/PHHLvsAAAD4LZthGIbZRfhSdna27Ha7srKyFBoaanY5Jit+TcSOHT4uA/CSzp3NOCpreABvKOvfb79fwwMAAPB7CDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyKphdgPX0NbsAAP9nxw7fH3Py5NJvu2aN5+oA4IoZHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHmmBp6tW7eqb9++qlu3rmw2m1atWvW722zevFk33HCDgoOD1aRJEy1evNjrdQIAAP9mauDJzc1V27ZtNW/evBL1P378uPr06aNbbrlFqampGjdunB566CGtX7/ey5UCAAB/VsHMg/fq1Uu9evUqcf9XXnlFjRo10owZMyRJLVq00KeffqqZM2eqZ8+e3ioTAAD4Ob9aw5OSkqLY2FiXtp49eyolJcWkigAAgD8wdYbHXRkZGQoLC3NpCwsLU3Z2tv73v/+pcuXKRbbJy8tTXl6e8312drbX6wQAAOWLX83wlEZiYqLsdrvzFRkZaXZJAADAx/wq8ISHhyszM9OlLTMzU6GhocXO7kjS+PHjlZWV5XydOnXKF6UCAIByxK9OaUVHR+vDDz90adu4caOio6Mvu01wcLCCg4O9XRoAACjHTJ3hOX/+vFJTU5Wamirp4mXnqampSktLk3RxdmbIkCHO/iNHjtSxY8f05JNP6tChQ/rnP/+pd999V3/5y1/MKB8AAPgJUwPPrl271L59e7Vv316SFB8fr/bt2+vpp5+WJKWnpzvDjyQ1atRI//73v7Vx40a1bdtWM2bM0GuvvcYl6QAA4IpMPaXVo0cPGYZx2c+Lu4tyjx49tHfvXi9WBQAArMavFi0DAACUBoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYHoEHAABYnl89LR0AyruEhL5ml+CGNWYXAPgMMzwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyCDwAAMDyKphdAADgoh07fHu8yZPLvo81a8q+D8AXCDw+4OtfYgAAwBWntAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOUReAAAgOWVi8Azb948RUVFqVKlSurSpYt2XOHGNYsXL5bNZnN5VapUyYfVAgAAf2N64HnnnXcUHx+viRMnas+ePWrbtq169uypM2fOXHab0NBQpaenO18nT570YcUAAMDfmB54XnrpJY0YMUIPPvigWrZsqVdeeUVVqlTRG2+8cdltbDabwsPDna+wsDAfVgwAAPyNqYEnPz9fu3fvVmxsrLMtICBAsbGxSklJuex258+fV8OGDRUZGal+/fpp//79l+2bl5en7OxslxcAALi6mBp4vv/+exUWFhaZoQkLC1NGRkax2zRr1kxvvPGGVq9erbfeeksOh0MxMTH65ptviu2fmJgou93ufEVGRnr8ewAAgPLN9FNa7oqOjtaQIUPUrl07de/eXStXrlTt2rX16quvFtt//PjxysrKcr5OnTrl44oBAIDZTH1aeq1atRQYGKjMzEyX9szMTIWHh5doHxUrVlT79u115MiRYj8PDg5WcHBwmWsFAAD+y9QZnqCgIHXo0EHJycnONofDoeTkZEVHR5doH4WFhdq3b58iIiK8VSYAAPBzps7wSFJ8fLyGDh2qjh07qnPnzpo1a5Zyc3P14IMPSpKGDBmievXqKTExUZI0adIkde3aVU2aNNG5c+c0bdo0nTx5Ug899JCZXwMAAJRjpgeegQMH6rvvvtPTTz+tjIwMtWvXTuvWrXMuZE5LS1NAwC8TUWfPntWIESOUkZGhGjVqqEOHDtq2bZtatmxp1lcAAADlnM0wDMPsInwpOztbdrtdWVlZCg0N9cIR+hZpucKNowHANJMnrynzPtaUfRdAiZT177ffXaUFAADgLtNPaQEAzJGQUHRGuvxiKgllwwwPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwvApmFwAA8F87dvjmOJMne2Y/a9Z4Zj/wP8zwAAAAyyPwAAAAy+OUFgCg3EtI6Gt2CW7gvFl5xAwPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwPAIPAACwvHJxWfq8efM0bdo0ZWRkqG3btpozZ446d+582f7Lly9XQkKCTpw4oaZNm+qFF15Q7969fVgxAMAf+ebO0J69hP4Kfw494Oq5hN70GZ533nlH8fHxmjhxovbs2aO2bduqZ8+eOnPmTLH9t23bpkGDBmn48OHau3ev+vfvr/79++vLL7/0ceUAAMBf2AzDMMwsoEuXLurUqZPmzp0rSXI4HIqMjNSYMWP097//vUj/gQMHKjc3V2vXrnW2de3aVe3atdMrr7zyu8fLzs6W3W5XVlaWQkNDPfdFnIome189awYAYD3M8FxU1r/fps7w5Ofna/fu3YqNjXW2BQQEKDY2VikpKcVuk5KS4tJfknr27HnZ/gAAAKau4fn+++9VWFiosLAwl/awsDAdOnSo2G0yMjKK7Z+RkVFs/7y8POXl5TnfZ2VlSbqYFL2joEjL+fNeOhQAwPI++cR7+37xRe/8LXz3Xc/v89Lf7dKemCoXi5a9KTExUc8++2yR9sjISBOqAQCgPLF7Z6/e2a0kKScnR/ZSHMDUwFOrVi0FBgYqMzPTpT0zM1Ph4eHFbhMeHu5W//Hjxys+Pt753uFw6Mcff9Q111wjm81Wxm/gKjs7W5GRkTp16pSX1gf5B8bhF4zFLxiLXzAWv2AsLmIcfnG5sTAMQzk5Oapbt26p9mtq4AkKClKHDh2UnJys/v37S7oYSJKTkzV69Ohit4mOjlZycrLGjRvnbNu4caOio6OL7R8cHKzg4GCXturVq3ui/MsKDQ296n9gJcbh1xiLXzAWv2AsfsFYXMQ4/KK4sSjNzM4lpp/Sio+P19ChQ9WxY0d17txZs2bNUm5urh588EFJ0pAhQ1SvXj0lJiZKksaOHavu3btrxowZ6tOnj5KSkrRr1y4tWLDAzK8BAADKMdMDz8CBA/Xdd9/p6aefVkZGhtq1a6d169Y5FyanpaUpIOCXi8liYmK0bNkyTZgwQU899ZSaNm2qVatWqVWrVmZ9BQAAUM6ZHngkafTo0Zc9hbV58+YibQMGDNCAAQO8XJX7goODNXHixCKn0K42jMMvGItfMBa/YCx+wVhcxDj8wltjYfqNBwEAALzN9EdLAAAAeBuBBwAAWB6BBwAAWB6BBwAAWB6Bx03z5s1TVFSUKlWqpC5dumjH7zwKffny5WrevLkqVaqk1q1b68MPP/RRpd7lzjjs379f99xzj6KiomSz2TRr1izfFeoD7ozFwoULdfPNN6tGjRqqUaOGYmNjf/dnyJ+4MxYrV65Ux44dVb16dVWtWlXt2rXT0qVLfVitd7n7u+KSpKQk2Ww2581YrcCdsVi8eLFsNpvLq1KlSj6s1nvc/Zk4d+6c4uLiFBERoeDgYF133XVX5d+QHj16FPmZsNls6tOnj3sHNVBiSUlJRlBQkPHGG28Y+/fvN0aMGGFUr17dyMzMLLb/Z599ZgQGBhovvviiceDAAWPChAlGxYoVjX379vm4cs9ydxx27NhhPPHEE8a//vUvIzw83Jg5c6ZvC/Yid8fiz3/+szFv3jxj7969xsGDB41hw4YZdrvd+Oabb3xcuee5OxabNm0yVq5caRw4cMA4cuSIMWvWLCMwMNBYt26djyv3PHfH4pLjx48b9erVM26++WajX79+vinWy9wdi0WLFhmhoaFGenq685WRkeHjqj3P3XHIy8szOnbsaPTu3dv49NNPjePHjxubN282UlNTfVy557k7Fj/88IPLz8OXX35pBAYGGosWLXLruAQeN3Tu3NmIi4tzvi8sLDTq1q1rJCYmFtv/3nvvNfr06ePS1qVLF+ORRx7xap3e5u44/FrDhg0tFXjKMhaGYRgXLlwwQkJCjDfffNNbJfpMWcfCMAyjffv2xoQJE7xRnk+VZiwuXLhgxMTEGK+99poxdOhQywQed8di0aJFht1u91F1vuPuOMyfP99o3LixkZ+f76sSfaasvytmzpxphISEGOfPn3fruJzSKqH8/Hzt3r1bsbGxzraAgADFxsYqJSWl2G1SUlJc+ktSz549L9vfH5RmHKzKE2Px008/qaCgQDVr1vRWmT5R1rEwDEPJyck6fPiwunXr5s1Sva60YzFp0iTVqVNHw4cP90WZPlHasTh//rwaNmyoyMhI9evXT/v37/dFuV5TmnH44IMPFB0drbi4OIWFhalVq1Z6/vnnVVhY6KuyvcITvzdff/113XfffapatapbxybwlND333+vwsJC5yMvLgkLC1NGRkax22RkZLjV3x+UZhysyhNj8be//U1169YtEoz9TWnHIisrS9WqVVNQUJD69OmjOXPm6Pbbb/d2uV5VmrH49NNP9frrr2vhwoW+KNFnSjMWzZo10xtvvKHVq1frrbfeksPhUExMjL755htflOwVpRmHY8eOacWKFSosLNSHH36ohIQEzZgxQ88995wvSvaasv7e3LFjh7788ks99NBDbh+7XDxaArgaTZ06VUlJSdq8ebNlFmW6KyQkRKmpqTp//rySk5MVHx+vxo0bq0ePHmaX5jM5OTkaPHiwFi5cqFq1apldjumio6MVHR3tfB8TE6MWLVro1Vdf1eTJk02szLccDofq1KmjBQsWKDAwUB06dNC3336radOmaeLEiWaXZ5rXX39drVu3VufOnd3elsBTQrVq1VJgYKAyMzNd2jMzMxUeHl7sNuHh4W719welGQerKstYTJ8+XVOnTtXHH3+sNm3aeLNMnyjtWAQEBKhJkyaSpHbt2ungwYNKTEz068Dj7lgcPXpUJ06cUN++fZ1tDodDklShQgUdPnxY1157rXeL9hJP/L6oWLGi2rdvryNHjnijRJ8ozThERESoYsWKCgwMdLa1aNFCGRkZys/PV1BQkFdr9pay/Ezk5uYqKSlJkyZNKtWxOaVVQkFBQerQoYOSk5OdbQ6HQ8nJyS7/b+TXoqOjXfpL0saNGy/b3x+UZhysqrRj8eKLL2ry5Mlat26dOnbs6ItSvc5TPxcOh0N5eXneKNFn3B2L5s2ba9++fUpNTXW+7rrrLt1yyy1KTU1VZGSkL8v3KE/8XBQWFmrfvn2KiIjwVpleV5pxuPHGG3XkyBFn+JWkr776ShEREX4bdqSy/UwsX75ceXl5euCBB0p3cLeWOF/lkpKSjODgYGPx4sXGgQMHjIcfftioXr2685LJwYMHG3//+9+d/T/77DOjQoUKxvTp042DBw8aEydOtMxl6e6MQ15enrF3715j7969RkREhPHEE08Ye/fuNb7++muzvoLHuDsWU6dONYKCgowVK1a4XGaZk5Nj1lfwGHfH4vnnnzc2bNhgHD161Dhw4IAxffp0o0KFCsbChQvN+goe4+5Y/JaVrtJydyyeffZZY/369cbRo0eN3bt3G/fdd59RqVIlY//+/WZ9BY9wdxzS0tKMkJAQY/To0cbhw4eNtWvXGnXq1DGee+45s76Cx5T2fx833XSTMXDgwFIfl8Djpjlz5hgNGjQwgoKCjM6dOxvbt293fta9e3dj6NChLv3fffdd47rrrjOCgoKM66+/3vj3v//t44q9w51xOH78uCGpyKt79+6+L9wL3BmLhg0bFjsWEydO9H3hXuDOWPzjH/8wmjRpYlSqVMmoUaOGER0dbSQlJZlQtXe4+7vi16wUeAzDvbEYN26cs29YWJjRu3dvY8+ePSZU7Xnu/kxs27bN6NKlixEcHGw0btzYmDJlinHhwgUfV+0d7o7FoUOHDEnGhg0bSn1Mm2EYRunmhgAAAPwDa3gAAIDlEXgAAIDlEXgAAIDlEXgAAIDlEXgAAIDlEXgAAIDlEXgAAIDlEXgA+JxhGHr44YdVs2ZN2Ww2paamml0SAIsj8ADwuXXr1mnx4sVau3at0tPT1apVK2VkZGjMmDFq3LixgoODFRkZqb59+xZ5Hp23TZkyRTExMapSpYqqV6/u02MD8B6elg7A544ePaqIiAjFxMRIkk6cOKEbb7xR1atX17Rp09S6dWsVFBRo/fr1iouL06FDh3xWW35+vgYMGKDo6Gi9/vrrPjsuAO9ihgeATw0bNkxjxoxRWlqabDaboqKiNGrUKNlsNu3YsUP33HOPrrvuOl1//fWKj4/X9u3bndumpaWpX79+qlatmkJDQ3XvvfcqMzPT+fkzzzyjdu3aaenSpYqKipLdbtd9992nnJwcZx+Hw6EXX3xRTZo0UXBwsBo0aKApU6Y4P3/22Wf1l7/8Ra1bt/bNgADwCQIPAJ+aPXu2Jk2apPr16ys9PV2ff/651q1bp7i4OFWtWrVI/0unlRwOh/r166cff/xRW7Zs0caNG3Xs2DENHDjQpf/Ro0e1atUqrV27VmvXrtWWLVs0depU5+fjx4/X1KlTlZCQoAMHDmjZsmUKCwvz6ncGYD5OaQHwKbvdrpCQEAUGBio8PFw7duyQYRhq3rz5FbdLTk7Wvn37dPz4cUVGRkqSlixZouuvv147d+5Up06dJF0MRosXL1ZISIgkafDgwUpOTtaUKVOUk5Oj2bNna+7cuRo6dKgk6dprr9VNN93kxW8MoDxghgeAqQzDKFG/gwcPKjIy0hl2JKlly5aqXr26Dh486GyLiopyhh1JioiI0JkzZ5z7yMvL02233eah6gH4CwIPAFM1bdpUNpvNYwuTK1as6PLeZrPJ4XBIkipXruyRYwDwPwQeAKaqWbOmevbsqXnz5ik3N7fI5+fOnZMktWjRQqdOndKpU6ecnx04cEDnzp1Ty5YtS3Sspk2bqnLlyj6/1B2A+Qg8AEw3b948FRYWqnPnznrvvff09ddf6+DBg3r55ZcVHR0tSYqNjVXr1q11//33a8+ePdqxY4eGDBmi7t27q2PHjiU6TqVKlfS3v/1NTz75pJYsWaKjR49q+/btLpefp6WlKTU1VWlpaSosLFRqaqpSU1N1/vx5r3x3AL7BomUApmvcuLH27NmjKVOm6PHHH1d6erpq166tDh06aP78+ZIunppavXq1xowZo27duikgIEB33HGH5syZ49axEhISVKFCBT399NM6ffq0IiIiNHLkSOfnTz/9tN58803n+/bt20uSNm3apB49epT9ywIwhc0o6YpBAAAAP8UpLQAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHkEHgAAYHn/HxeTuVWm/VsuAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Train, Validation , Test datasets"
      ],
      "metadata": {
        "id": "_04h6G3jlvek"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#split function will split the datset according to the parameters\n",
        "#sample will shuffle the entire dataset ; frac =1 means shuffle the whole dataset\n",
        "\n",
        "train, valid, test = np.split(df.sample(frac=1),[int(0.6*len(df)),int(0.8*len(df))])\n",
        "\n",
        "#0 to 60% will be the trauning dataset, 60% to 80% will be validation dataset and the rest will be for testing\n"
      ],
      "metadata": {
        "id": "68hwWeealhCn"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#scaling the dAtaset as some values are over 100 and some are in fractions\n",
        "\n",
        "\n",
        "def scale_dataset(dataframe, oversample=False):\n",
        "  x=dataframe[dataframe.columns[:-1]].values   #get all the features\n",
        "  y=dataframe[dataframe.columns[-1]].values     #get the labels\n",
        "\n",
        "  scaler = StandardScaler()\n",
        "  x = scaler.fit_transform(x) #take x and fit standard scaler to it and transfrom the values\n",
        "\n",
        "  if oversample:\n",
        "    ros=RandomOverSampler()\n",
        "    x, y = ros.fit_resample(x, y)  #take the less class and keep sampling from thereto increase size of dataset of smaller class so that they match\n",
        "\n",
        "\n",
        "  #uptil here x is 2 dimensional and y is 1 dimensional, so reshaping required\n",
        "  data = np.hstack((x,np.reshape(y,(-1,1))))  #making y 2 dimansional\n",
        "\n",
        "  return data, x, y\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "FznSmyQros9l"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(train[train['class']==1])) #gamma\n",
        "print(len(train[train['class']==0]))  #hadron\n",
        "\n",
        "#difference in the number of quantities will create an issue while training , so oversampler is used"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "985xYWIBE0hY",
        "outputId": "eba11361-d5e8-4fe9-ea1d-1a3dfca882d3"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7412\n",
            "4000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train, x_train, y_train =scale_dataset(train,oversample=True)"
      ],
      "metadata": {
        "id": "VTFLs3NUE19e"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(y_train))\n",
        "print()\n",
        "print(sum(y_train == 1))\n",
        "print(sum(y_train == 0)) #now both of the values are evenly rebalanced"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eT11MywMHDB2",
        "outputId": "a15e43cc-601c-40d2-a0c3-eb6305e31180"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14824\n",
            "\n",
            "7412\n",
            "7412\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#train, x_train , y_train = scale_dataset(train , oversample=True) # done above\n",
        "valid, x_valid , y_valid = scale_dataset(valid , oversample=False)\n",
        "test, x_test , y_test = scale_dataset(test , oversample=False)"
      ],
      "metadata": {
        "id": "zppTHW1WHfCW"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Data is all prepared and ready for training using different ML models:"
      ],
      "metadata": {
        "id": "9vGSLWcVJ8JH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# KNN"
      ],
      "metadata": {
        "id": "Rbmi2_aSKNI3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import classification_report"
      ],
      "metadata": {
        "id": "TXb4YWeQI01a"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "knn_model = KNeighborsClassifier(n_neighbors=5) #intializing and setting the number of neighbors =5\n",
        "knn_model.fit(x_train , y_train)"
      ],
      "metadata": {
        "id": "7ggwRjDUMqiZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "outputId": "e64ebae8-2d26-47eb-910a-a4e5ababc487"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KNeighborsClassifier()"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KNeighborsClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KNeighborsClassifier</label><div class=\"sk-toggleable__content\"><pre>KNeighborsClassifier()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = knn_model.predict(x_test)"
      ],
      "metadata": {
        "id": "FBgh92faNI6a"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzR5niZFNs0G",
        "outputId": "3646d61d-8e52-4dc1-8d04-f999a08ae773"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MetS1rWzNwXi",
        "outputId": "1e165757-9433-4858-9f19-6c0faeb4f985"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 1, 1, ..., 1, 1, 1])"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HnQcxNJmNy9v",
        "outputId": "1205f897-a4e7-41cd-8e14-88b2c9a6fbd2"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.75      0.74      0.74      1342\n",
            "           1       0.86      0.86      0.86      2462\n",
            "\n",
            "    accuracy                           0.82      3804\n",
            "   macro avg       0.80      0.80      0.80      3804\n",
            "weighted avg       0.82      0.82      0.82      3804\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "f1-score is a combination of precision and recall. since the test dataset is unbalanced , we will look at the f1-score mostly"
      ],
      "metadata": {
        "id": "dWbTVfsEPDrW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#NAIVE BAYES"
      ],
      "metadata": {
        "id": "fiDJW_wbq-0O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.naive_bayes import GaussianNB\n"
      ],
      "metadata": {
        "id": "yR1VMD-XOJp4"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "nb_model = GaussianNB()\n",
        "nb_model = nb_model.fit(x_train, y_train)"
      ],
      "metadata": {
        "id": "RjUWcN20rJT9"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = nb_model.predict(x_test)\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7UL3hUgdredL",
        "outputId": "04759eff-f2a6-483e-d184-b00eebf9db4f"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.70      0.40      0.51      1342\n",
            "           1       0.74      0.91      0.81      2462\n",
            "\n",
            "    accuracy                           0.73      3804\n",
            "   macro avg       0.72      0.65      0.66      3804\n",
            "weighted avg       0.72      0.73      0.71      3804\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "As compared to KNN model NB model here has a slightly worse results."
      ],
      "metadata": {
        "id": "cmTa4SZRsA7P"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#LOGISTIC REGRESSION"
      ],
      "metadata": {
        "id": "mnt2GP92sSVV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "fDGm1A8yrqIz"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lg_model = LogisticRegression()\n",
        "lg_model = lg_model.fit(x_train , y_train)"
      ],
      "metadata": {
        "id": "xVPPgnNOsco4"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = lg_model.predict(x_test)\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ZxKl3MGs3w4",
        "outputId": "106273e4-a98a-4e79-d04f-ec6c03b5a955"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.69      0.72      0.70      1342\n",
            "           1       0.84      0.82      0.83      2462\n",
            "\n",
            "    accuracy                           0.79      3804\n",
            "   macro avg       0.77      0.77      0.77      3804\n",
            "weighted avg       0.79      0.79      0.79      3804\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "To conclude the above, Log Reg model performs better than Naive Bayes but still less than KNN."
      ],
      "metadata": {
        "id": "j9TUTFBptOaS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Support Vector Machines [SVM]"
      ],
      "metadata": {
        "id": "cyVck1M5t0xX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.svm import SVC   #svc is support vector classifier"
      ],
      "metadata": {
        "id": "OQBlNzYKtEh9"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "svm_model = SVC()\n",
        "svm_model = svm_model.fit(x_train , y_train)\n"
      ],
      "metadata": {
        "id": "3RCQlRvJuLpD"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = svm_model.predict(x_test)\n",
        "print(classification_report(y_test ,  y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m-2NepVcuRJb",
        "outputId": "2045e7be-8242-41cd-e829-bacf38a2bbc9"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.82      0.79      0.80      1342\n",
            "           1       0.89      0.90      0.90      2462\n",
            "\n",
            "    accuracy                           0.86      3804\n",
            "   macro avg       0.85      0.85      0.85      3804\n",
            "weighted avg       0.86      0.86      0.86      3804\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Among the above four classification models, SVM has the best accuracy\n"
      ],
      "metadata": {
        "id": "n0ta8_32u6RF"
      }
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "85Fy4531gIYP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}