fernazHW1v3.ipynb

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        "<a href=\"https://colab.research.google.com/github/Zachary-Fernandes/CSCI-6962/blob/main/fernazHW1v3.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "# Homework 1\n",
        "#####Zachary Fernandes\n",
        "#####Projects in Machine Learning and AI\n",
        "\n",
        "### The Problem\n",
        "The machine learning problem I wanted to solve involved the [NASA Nearest Earth Objects dataset](https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects?select=neo_v2.csv) found on Kaggle. The exact problem here is determining whether or not an asteroid is a hazard to humans given the dataset's features - estimated diameter, relative velocity, miss distance, and absolute magnitude.\n",
        "\n",
        "I wanted to solve this problem with logistic regression. This is the best selection for solving this problem because determining whether or not an asteroid is hazard is a binary classification task. If we used linear regression here, we would get a continuous outcome as a solution. What I want to do, however, requires a categorical, binary output, that being hazardous or non-hazardous. This binary classification is a problem that is appropriate for logistic regression to solve, which is why it is the right tool for this job."
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      "cell_type": "markdown",
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      },
      "source": [
        "### Setup"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install imbalanced-learn"
      ],
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      "execution_count": 101,
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          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.7/dist-packages (0.8.1)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.7.3)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.1.0)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.21.6)\n",
            "Requirement already satisfied: scikit-learn>=0.24 in /usr/local/lib/python3.7/dist-packages (from imbalanced-learn) (1.0.2)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.24->imbalanced-learn) (3.1.0)\n"
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    {
      "cell_type": "code",
      "execution_count": 102,
      "metadata": {
        "id": "aZCA63injOt2"
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      "source": [
        "# import required libraries\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "import random\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import f1_score\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from imblearn.over_sampling import SMOTE\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Exploratory Data Analysis\n",
        "Below are the steps I take to perform Exploratory Data Analysis."
      ],
      "metadata": {
        "id": "sZhrsQnH1MKs"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qd2wLZlWjOt4"
      },
      "source": [
        "#### Getting the Data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 103,
      "metadata": {
        "id": "OyjGkMYajOt4"
      },
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      "source": [
        "# data source: https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects\n",
        "# note: we have combined Training and Testing data from Kaggle into a single data frame\n",
        "df = pd.read_csv('neo_v2.csv')"
      ]
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      "source": [
        "Data Dictionary\n",
        "\n",
        "\n",
        "| **Variable**       | **Definition**                                                |\n",
        "|--------------------|---------------------------------------------------------------|\n",
        "| id                 | Unique Identifier for each Asteroid                           |\n",
        "| name               | Name given by NASA                                            |\n",
        "| est_diameter_min   | Minimum Estimated Diameter in Kilometres                      |\n",
        "| est_diameter_max   | Maximum Estimated Diameter in Kilometres                      |\n",
        "| relative_velocity  | Velocity Relative to Earth in Kmph                            |\n",
        "| miss_distance      | Distance in Kilometres missed                                 |\n",
        "| orbiting_body      | Planet that the asteroid orbits                               |\n",
        "| sentry_object      | Included in sentry - automated collision monitoring system    |\n",
        "| absolute_magnitude | Describes intrinsic luminosity                                |\n",
        "| hazardous          | Boolean feature that shows whether asteroid is harmful or not |"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "65TzJC9WjOt5"
      },
      "source": [
        "### Exploring the Data"
      ]
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              "        id                 name  est_diameter_min  est_diameter_max  \\\n",
              "0  2162635  162635 (2000 SS164)          1.198271          2.679415   \n",
              "1  2277475    277475 (2005 WK4)          0.265800          0.594347   \n",
              "2  2512244   512244 (2015 YE18)          0.722030          1.614507   \n",
              "3  3596030          (2012 BV13)          0.096506          0.215794   \n",
              "4  3667127          (2014 GE35)          0.255009          0.570217   \n",
              "\n",
              "   relative_velocity  miss_distance orbiting_body  sentry_object  \\\n",
              "0       13569.249224   5.483974e+07         Earth          False   \n",
              "1       73588.726663   6.143813e+07         Earth          False   \n",
              "2      114258.692129   4.979872e+07         Earth          False   \n",
              "3       24764.303138   2.543497e+07         Earth          False   \n",
              "4       42737.733765   4.627557e+07         Earth          False   \n",
              "\n",
              "   absolute_magnitude  hazardous  \n",
              "0               16.73      False  \n",
              "1               20.00       True  \n",
              "2               17.83      False  \n",
              "3               22.20      False  \n",
              "4               20.09       True  "
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      "source": [
        "# viewing the first few rows of the data\n",
        "df.head()"
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            "text/plain": [
              "(90836, 10)"
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          "metadata": {},
          "execution_count": 105
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      ],
      "source": [
        "# what's the data size?\n",
        "df.shape\n",
        "# there are not many features, but there are plenty of examples"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# are there any missing values?\n",
        "df.isna().sum()\n",
        "# there are no missing values"
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            "text/plain": [
              "id                    0\n",
              "name                  0\n",
              "est_diameter_min      0\n",
              "est_diameter_max      0\n",
              "relative_velocity     0\n",
              "miss_distance         0\n",
              "orbiting_body         0\n",
              "sentry_object         0\n",
              "absolute_magnitude    0\n",
              "hazardous             0\n",
              "dtype: int64"
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          "execution_count": 106
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 90836 entries, 0 to 90835\n",
            "Data columns (total 10 columns):\n",
            " #   Column              Non-Null Count  Dtype  \n",
            "---  ------              --------------  -----  \n",
            " 0   id                  90836 non-null  int64  \n",
            " 1   name                90836 non-null  object \n",
            " 2   est_diameter_min    90836 non-null  float64\n",
            " 3   est_diameter_max    90836 non-null  float64\n",
            " 4   relative_velocity   90836 non-null  float64\n",
            " 5   miss_distance       90836 non-null  float64\n",
            " 6   orbiting_body       90836 non-null  object \n",
            " 7   sentry_object       90836 non-null  bool   \n",
            " 8   absolute_magnitude  90836 non-null  float64\n",
            " 9   hazardous           90836 non-null  bool   \n",
            "dtypes: bool(2), float64(5), int64(1), object(2)\n",
            "memory usage: 5.7+ MB\n"
          ]
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      "source": [
        "# general information about the dataset\n",
        "df.info()"
      ]
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    {
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              "                 id  est_diameter_min  est_diameter_max  relative_velocity  \\\n",
              "count  9.083600e+04      90836.000000      90836.000000       90836.000000   \n",
              "mean   1.438288e+07          0.127432          0.284947       48066.918918   \n",
              "std    2.087202e+07          0.298511          0.667491       25293.296961   \n",
              "min    2.000433e+06          0.000609          0.001362         203.346433   \n",
              "25%    3.448110e+06          0.019256          0.043057       28619.020645   \n",
              "50%    3.748362e+06          0.048368          0.108153       44190.117890   \n",
              "75%    3.884023e+06          0.143402          0.320656       62923.604633   \n",
              "max    5.427591e+07         37.892650         84.730541      236990.128088   \n",
              "\n",
              "       miss_distance  absolute_magnitude  \n",
              "count   9.083600e+04        90836.000000  \n",
              "mean    3.706655e+07           23.527103  \n",
              "std     2.235204e+07            2.894086  \n",
              "min     6.745533e+03            9.230000  \n",
              "25%     1.721082e+07           21.340000  \n",
              "50%     3.784658e+07           23.700000  \n",
              "75%     5.654900e+07           25.700000  \n",
              "max     7.479865e+07           33.200000  "
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              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>9.083600e+04</td>\n",
              "      <td>90836.000000</td>\n",
              "      <td>90836.000000</td>\n",
              "      <td>90836.000000</td>\n",
              "      <td>9.083600e+04</td>\n",
              "      <td>90836.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.438288e+07</td>\n",
              "      <td>0.127432</td>\n",
              "      <td>0.284947</td>\n",
              "      <td>48066.918918</td>\n",
              "      <td>3.706655e+07</td>\n",
              "      <td>23.527103</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.087202e+07</td>\n",
              "      <td>0.298511</td>\n",
              "      <td>0.667491</td>\n",
              "      <td>25293.296961</td>\n",
              "      <td>2.235204e+07</td>\n",
              "      <td>2.894086</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>2.000433e+06</td>\n",
              "      <td>0.000609</td>\n",
              "      <td>0.001362</td>\n",
              "      <td>203.346433</td>\n",
              "      <td>6.745533e+03</td>\n",
              "      <td>9.230000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>3.448110e+06</td>\n",
              "      <td>0.019256</td>\n",
              "      <td>0.043057</td>\n",
              "      <td>28619.020645</td>\n",
              "      <td>1.721082e+07</td>\n",
              "      <td>21.340000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>3.748362e+06</td>\n",
              "      <td>0.048368</td>\n",
              "      <td>0.108153</td>\n",
              "      <td>44190.117890</td>\n",
              "      <td>3.784658e+07</td>\n",
              "      <td>23.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3.884023e+06</td>\n",
              "      <td>0.143402</td>\n",
              "      <td>0.320656</td>\n",
              "      <td>62923.604633</td>\n",
              "      <td>5.654900e+07</td>\n",
              "      <td>25.700000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>5.427591e+07</td>\n",
              "      <td>37.892650</td>\n",
              "      <td>84.730541</td>\n",
              "      <td>236990.128088</td>\n",
              "      <td>7.479865e+07</td>\n",
              "      <td>33.200000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b06bc5dd-37b8-4568-840b-a9696b2f5161')\"\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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
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              "      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-b06bc5dd-37b8-4568-840b-a9696b2f5161 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-b06bc5dd-37b8-4568-840b-a9696b2f5161');\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": 108
        }
      ],
      "source": [
        "# statistics of the dataset (numeric columns)\n",
        "df.describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 109,
      "metadata": {
        "id": "oU6xVUw2jOt9",
        "outputId": "0fc47aed-59c0-41e1-edb8-abf36df0883b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 name orbiting_body\n",
              "count                           90836         90836\n",
              "unique                          27423             1\n",
              "top     469219 Kamo`oalewa (2016 HO3)         Earth\n",
              "freq                               43         90836"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7f075a21-92e9-49a2-95ba-beb5727fb93e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe thead th {\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>name</th>\n",
              "      <th>orbiting_body</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>90836</td>\n",
              "      <td>90836</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>27423</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>469219 Kamo`oalewa (2016 HO3)</td>\n",
              "      <td>Earth</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>43</td>\n",
              "      <td>90836</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7f075a21-92e9-49a2-95ba-beb5727fb93e')\"\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",
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              "      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-7f075a21-92e9-49a2-95ba-beb5727fb93e 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-7f075a21-92e9-49a2-95ba-beb5727fb93e');\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": 109
        }
      ],
      "source": [
        "# statistics of the dataset (categorical columns - name and orbiting-body)\n",
        "df.describe(include=['O'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 110,
      "metadata": {
        "id": "aeBX2qsDjOt9",
        "outputId": "ce4e193c-b129-4301-854d-406c79f0aeab",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    0.902682\n",
              "True     0.097318\n",
              "Name: hazardous, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ],
      "source": [
        "# is the data imbalanced?\n",
        "df.hazardous.value_counts(normalize=True) #normalizes the result\n",
        "# at about 90% false and 10% true, this dataset is imbalanced"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eWlzL_a3jOt-"
      },
      "source": [
        "#### Pairplot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lp4KzTwujOt-",
        "outputId": "23f0d76a-883c-4a31-deb7-6038b9586e3c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 694
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 161
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1321.25x1260 with 56 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# ploting pairwise relationships in the dataset\n",
        "sns.pairplot(df, hue='hazardous')\n",
        ","
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Uxqt_OdMjOt-"
      },
      "source": [
        "#### Scatter Plot"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 111,
      "metadata": {
        "id": "JxZnSp4djOt_",
        "outputId": "e33e9d08-5ffd-4623-c87e-a887a92c8032",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 111
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# useful for identifying relationship (i.e. correlation) between two parameters\n",
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "ax.scatter(df.miss_distance, df.relative_velocity)\n",
        "ax.set_xlabel('Miss Distance')\n",
        "ax.set_ylabel('Relative Velocity')\n",
        ","
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "ax.scatter(df.est_diameter_min, df.est_diameter_max)\n",
        "ax.set_xlabel('Minimum Estimated Diameter')\n",
        "ax.set_ylabel('Maximum Estimated Diameter')\n",
        ","
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "_CVHglD6sQ98",
        "outputId": "7aab822d-fb61-4666-fe3b-e50417662b21"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 112
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "ax.scatter(df.est_diameter_min, df.absolute_magnitude)\n",
        "ax.set_xlabel('Minimum Estimated Diameter')\n",
        "ax.set_ylabel('Absolute Magnitude')\n",
        ","
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "f3M_UA9WsyZy",
        "outputId": "d128e1f7-d0e1-43d8-d1b3-ec86cdb0a95d"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 113
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rfE-nrRajOt_"
      },
      "source": [
        "#### Pie Chart"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 114,
      "metadata": {
        "id": "qBhd4nyrjOt_",
        "outputId": "5c0d3462-f67c-4101-b876-85ff42e6bcfe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 338
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 114
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 360x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# useful for depicting proportional data with each slice of pie representing a\n",
        "# specific category\n",
        "fig, ax = plt.subplots(figsize=(5,5))\n",
        "ax.pie(x=df.hazardous.value_counts(), \n",
        "       labels=df.hazardous.value_counts().index,\n",
        "       autopct='%1.1f%%')\n",
        "ax.set_title('Hazardous')\n",
        ","
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fb6bL_hajOuA"
      },
      "source": [
        "#### Histogram"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 115,
      "metadata": {
        "id": "FTaTBL7-jOuA",
        "outputId": "2b3ccc6f-c040-438f-ee2d-f9e11d4aa280",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# useful for visualizing the distribution of the data\n",
        "test = df[df['hazardous'] == True]\n",
        "test.shape\n",
        "testCol = test['est_diameter_min'].values\n",
        "testCol\n",
        "testHist = np.asarray(testCol, dtype='float')\n",
        "plt.hist(testHist, bins = 20)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nlM3sfisuqCI",
        "papermill": {
          "duration": 0.069519,
          "end_time": "2022-01-13T11:50:59.493255",
          "exception": false,
          "start_time": "2022-01-13T11:50:59.423736",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "### Adjustments from Exploratory Data Analysis.\n",
        "\n",
        "During Exploratory Data Analysis, I noticed some of the features are not relevant for the purposes of this project. That being said, we can remove some of them."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Take, for example, the id and name features. For an asteroid, id is its unique identifier and name is its name provided by NASA. These features will not influence whether or not an asteroid is hazardous. Thus, we can drop them."
      ],
      "metadata": {
        "id": "hQR90ZVDiBZN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = df.drop(['id', 'name'], axis = 1)\n",
        "df1.head()"
      ],
      "metadata": {
        "id": "o47G_FuPjzg6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "fe3bdaaf-4f6e-4da6-abec-ebee5bb41381"
      },
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   est_diameter_min  est_diameter_max  relative_velocity  miss_distance  \\\n",
              "0          1.198271          2.679415       13569.249224   5.483974e+07   \n",
              "1          0.265800          0.594347       73588.726663   6.143813e+07   \n",
              "2          0.722030          1.614507      114258.692129   4.979872e+07   \n",
              "3          0.096506          0.215794       24764.303138   2.543497e+07   \n",
              "4          0.255009          0.570217       42737.733765   4.627557e+07   \n",
              "\n",
              "  orbiting_body  sentry_object  absolute_magnitude  hazardous  \n",
              "0         Earth          False               16.73      False  \n",
              "1         Earth          False               20.00       True  \n",
              "2         Earth          False               17.83      False  \n",
              "3         Earth          False               22.20      False  \n",
              "4         Earth          False               20.09       True  "
            ],
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next we notice the orbiting\\_body feature. When we looked at the categorical columns, we noticed orbiting\\_body had one unique value that appeared in 90836 instances. This unique value was Earth. It makes sense this appears for all 90836 rows of data as all of these potentially hazardous asteroids orbit Earth. Given this redundancy, we can drop the feature."
      ],
      "metadata": {
        "id": "UqPmJPmClGTM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df1.drop(['orbiting_body'], axis = 1)\n",
        "df2.head()"
      ],
      "metadata": {
        "id": "nPoRPx07hhft",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "outputId": "2d52e145-0d28-4ddc-89b1-8f36540d058f"
      },
      "execution_count": 117,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   est_diameter_min  est_diameter_max  relative_velocity  miss_distance  \\\n",
              "0          1.198271          2.679415       13569.249224   5.483974e+07   \n",
              "1          0.265800          0.594347       73588.726663   6.143813e+07   \n",
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              "4          0.255009          0.570217       42737.733765   4.627557e+07   \n",
              "\n",
              "   sentry_object  absolute_magnitude  hazardous  \n",
              "0          False               16.73      False  \n",
              "1          False               20.00       True  \n",
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          "metadata": {},
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      "cell_type": "markdown",
      "source": [
        "There is a further feature we can drop, that being sentry\\_object. When we look at the values for sentry\\_object, we notice every answer it gives is false. True in this section means it is included in a sentry-automated collision monitoring system. Since all of these entries are marked False, there is no meaningful data we can glean from it. Thus, we can drop it."
      ],
      "metadata": {
        "id": "Vy9ME6Hi5v6E"
      }
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    {
      "cell_type": "code",
      "source": [
        "df3 = df2.drop(['sentry_object'], axis = 1)\n",
        "df3.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
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        "id": "q8RtQ4IU83oC",
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      },
      "execution_count": 118,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   est_diameter_min  est_diameter_max  relative_velocity  miss_distance  \\\n",
              "0          1.198271          2.679415       13569.249224   5.483974e+07   \n",
              "1          0.265800          0.594347       73588.726663   6.143813e+07   \n",
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              "4          0.255009          0.570217       42737.733765   4.627557e+07   \n",
              "\n",
              "   absolute_magnitude  hazardous  \n",
              "0               16.73      False  \n",
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              "2               17.83      False  \n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I believe this dataset was a subset of a larger NASA dataset that included orbiting bodies (planets) other than Earth. Containing features like the orbiting\\_body and the sentry\\_object may not be relevant to matters purely revolving around Earth, but they would be pertinent in other situations in space. This would be why categories that seem irrelevant for my purposes still appear."
      ],
      "metadata": {
        "id": "X3HWDXuHntrP"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y_7esoLnjOuJ"
      },
      "source": [
        "Now we observe how our features correlate with each other and with the target variable, hazardous:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 119,
      "metadata": {
        "id": "1pLW346HjOuJ",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "''"
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          "metadata": {},
          "execution_count": 119
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x576 with 2 Axes>"
            ],
            "image/png": 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fG7oOYXzccnPXEWgBMBGUJElqoU/z6vp0LZIkSWrBiqAkSVILfdpQ2kRQkiSphf6kgQ4NS5IkLVhWBCVJklroUxWtT9ciSZKkFqwISpIkteC9hiVJkhYoF4tIkiRp7FkRlCRJaqFPVbQ+XYskSZJasCIoSZLUQp+qaCaCkiRJLfRp1XCfklpJkiS1YEVQkiSpBbePkSRJ0tizIihJktRCn6poJoKSJEkt9CkR7NO1SJIkqQUrgpIkSS3E7WMkSZI07qwISpIktdCnKlqfrkWSJEktWBGUJElqoU9VNBNBSZKkFrzXsCRJksaeFUFJkqQWvNewJEmSxp4VQUmSpBb6VEUzEZQkSWrBxSKSJEkaeyObCCb5wwzvb5jk74Ze3zfJifMf2Urj2SPJSav42ZOb67ndNUmSpNGTeXh0pdNEMAOrGsOGwJ+Tpqr6eVXtPzeRrV5V9cSquppJ1yRJkjQhyV5JfpjksiSHr+SYZya5NMklST4xU5+rPRFMsllzER8BLgZem+TcJBclecMUx6+b5PQk5ydZnmS/5q23AFskuTDJ25t+L24+c06SrYf6ODPJTknukuToJN9NcsFQX1PFucp9JLlbks8313ROku2GruWY5jouSvL0pv3HSe4xxTV9JMlThvr9+HQxS5Kk+beImvPHTJIsBt4P7A1sBRyQZKtJx2wJvAp4VFVtDfzjTP12tVhkS+B5wPrA/sDODCqjS5M8uqq+MXTsDcBTq+r3TbJ0TpKlwOHANlW1PQwSzKHPnAA8E3h9ko2AjapqWZJ/A86oqoOTbAh8N8lXq+qPU8TYqo9Jn30DcEFVPSXJY4GPANsDrwWuqaptm5jvOulzk69pd+BlwOeTbAA8svneJElSRzpaLLIzcFlVXQ6Q5HhgP+DSoWNeALy/qn4HUFW/mqnTroaGf1JV5wB7No8LgPOBBzNIEocF+LckFwFfBTYG7j1D/59ikGDCIJmbmDu4J3B4kguBM4G1gfvNQx+7AR8FqKozgLsnWR94PINsnua93013EVX1dWDLJPcEDgA+U1U3Tz4uyaFJliVZdtQZF0zXpSRJGk8bAz8dev2zpm3YA4EHJjm7GZHca6ZOu6oITlTgAry5qj44zbEHAvcEdqyqm5L8mEHytVJVdWWS3zZDss8CXjR0vqdX1Q9nCrBtH0lmSk5X1UeA5wLPBp6/kliXAEsArv/Yq3u0qF2SpNEzH1W0JIcChw41LWl+vrexBoOC2h7AJsA3kmzbrEOYUterhk8BDk6yLkCSjZPca9IxGwC/apLAxwD3b9qvBdabpu8TgFcAG1TVRUPne0mSNOfbYYb4VrWPsxgksCTZA/hNVf0eOA34+4mDphganuqajqUZ46+qS5EkSb1TVUuqaqehx+Qk8Epg06HXmzRtw34GLK2qm6rqCuC/WXGk9XY6TQSr6lTgE8C3kyxnMPw6ORH6OLBT8/5fAz9oPvtb4OwkFyd5+xTdn8igivapobZ/BdYELkpySfN6OqvaxxHAjs1w9lu4bV7fG4G7NjF/D3jM8Iemuqaq+iXwfeCYGWKVJEmrQUfbx5zLYLrY5knWYpCfLJ10zOcZVANp1lU8ELh8uk5X+9BwVf0Y2Gbo9buBd09x3LrNv38D7LqSvp4zqWm4318y6fqq6nrghS1inXUfVXUmgzmDVNX/AU+Z4pg/MMVij6rabOj57a4pyZ0ZZPOfnG3ckiSpX6rq5iQvZjAyuRg4uqouSXIksKyqljbv7ZnkUuAW4OVNkWmlvMXcCEvyeOAo4F1VdU3X8UiSJFhU3UzHr6qTgZMntb1u6HkBhzWPWVnwiWCSJwBvndR8RVU9tYt4hlXVV7ltTqQkSRoBXS+wmEsLPhGsqlMYlFIlSZIWlAWfCEqSJLXR5b2B51qfqpuSJElqwYqgJElSC7O5N/C4MBGUJElqoaN7Dc8Lh4YlSZIWKCuCkiRJLaRHQ8NWBCVJkhYoK4KSJEkt9KmKZiIoSZLUQp8SwT5diyRJklqwIihJktSCi0UkSZI09qwISpIktdCnKlqfrkWSJEktWBGUJElqoU9zBE0EJUmSWljUo0TQoWFJkqQFyoqgJElSC4vSdQRzx4qgJEnSAmVFUJIkqQUXi0iSJC1QfRpO7dO1SJIkqQUrgpIkSS0k/RkatiIoSZK0QFkRlCRJamFRjyqCJoKSJEkt9GgbQYeGJUmSFiorgpIkSS04NKyRlc236joE9czVL96x6xDGwobvO6/rEMbC79/25K5DGBtrPGr/rkPQAmAiKEmS1ILbx0iSJGnsWRGUJElqwTmCkiRJC1R6tH+MQ8OSJEkLlBVBSZKkFlwsIkmSpLFnRVCSJKkFF4tIkiQtUC4WkSRJ0tizIihJktSCi0UkSZI09qwISpIkteBiEUmSpAUqi/qTCDo0LEmStEBZEZQkSWrB7WMkSZI09qwISpIkteAcQUmSJI09K4KSJEkt9GlDaRNBSZKkFvq0j6BDw5IkSQuUFUFJkqQW0qMyWo8uRZIkSW1YEZQkSWrBxSKSJEkLlPsISpIkaexZEZQkSWrBew1LkiRp7FkRlCRJaqFPcwRNBCVJklroUyLYi6HhJPsmOXwe+j0iyT83z49M8vhpjn1Kkq3mOgZJkqT50ouKYFUtBZbO8zleN8MhTwFOAi6dzzgkSVK3XCyyGiXZLMkPkhyb5L+TfDzJ45OcneRHSXZOclCS9zXHPyPJxUm+l+QbTdvWSb6b5MIkFyXZcprzvbo5zzeBBw21H5tk/+b5W5Jc2vT170keCewLvL05xxZJXpDk3CaOzyS581A/70nyrSSXT/TZvPfKJMubz7yladsiyVeSnJfkrCQPnoevWZIkLUDjUhH8C+AZwMHAucBzgN0YJF//Anx+6NjXAU+oqiuTbNi0vQh4d1V9PMlawOKpTpJkR+DZwPYMvpvzgfMmHXN34KnAg6uqkmxYVVcnWQqcVFUnNsddXVUfap6/ETgEeG/TzUZN/A9mUMk8McnewH7ALlV1XZK7NccuAV5UVT9Ksgvwn8Bj23x5kiRp7vRpjuC4JIJXVNVygCSXAKc3SdhyYLNJx54NHJvkU8Bnm7ZvA69Osgnw2ar60UrO8/+Az1XVdc25phpuvga4ATgqyUkMhoOnsk2TAG4IrAucMvTe56vqVuDSJPdu2h4PHDNx7qr6vyTrAo8EPp3b6tB3Wsn5JEmSWhn5oeHGjUPPbx16fSuTktmqehHwGmBT4Lwkd6+qTzCoHl4PnJxklStqVXUzsDNwIvBk4CsrOfRY4MVVtS3wBmDtlVzPdDMNFgFXV9X2Q4+HTD4oyaFJliVZdtQXzmhxNZIkqa0smvtHV8YlEZy1JFtU1XeaxR2/BjZN8gDg8qp6D/AFYLuVfPwbwFOSrJNkPWCfKfpfF9igqk4GXgY8tHnrWmC9oUPXA65KsiZw4CxCPw14/tBcwrtV1e+BK5I8o2lLkodO/mBVLamqnapqp0P2c9RYkqT5lNScP7rSu0SQwYKN5UkuBr4FfA94JnBxkguBbYCPTPXBqjofOKH5zJcZzEecbD3gpCQXAd8EDmvajwdenuSCJFsArwW+w2Co+gczBV1VX2EwX3BZE+c/N28dCByS5HvAJQzmEUqSJN1hqerPhEfBDWd/3P+gmlM3f+aErkMYCxu+77yZDxK/f9uTuw5hbKz53Fd2HcLYWPMeD1itG7pctdtj5vxn7Ubf/Fonm9L0sSIoSZKkWRiXVcNzqtkC5vQp3npcVf12dccjSZLGh9vHjLkm2du+6zgkSdL46XKV71zr0aVIkiSpjQVZEZQkSVplHW73MtesCEqSJC1QVgQlSZJa6NMcQRNBSZKkFvqUCPboUiRJktSGFUFJkqQWrAhKkiRp7JkISpIktbFoHh6zkGSvJD9MclmSw6c57ulJKslOs7kUSZIkjbAki4H3A3sDWwEHJNlqiuPWA/4B+M5s+jURlCRJaiGL5v4xCzsDl1XV5VX1J+B4YL8pjvtX4K3ADbPp1ERQkiSpjXkYGk5yaJJlQ49DJ511Y+CnQ69/1rT9WZKHAZtW1ZdmeymuGpYkSepYVS0Blqzq55MsAt4JHNTmcyaCkiRJLXS0fcyVwKZDrzdp2iasB2wDnJkE4D7A0iT7VtWylXXq0LAkSdLoOxfYMsnmSdYCng0snXizqq6pqntU1WZVtRlwDjBtEghWBCVJktrpoIxWVTcneTFwCrAYOLqqLklyJLCsqpZO38PUTAQlSZJayKJ0ct6qOhk4eVLb61Zy7B6z6dOhYUmSpAXKiqAkSVIbPSqj9ehSJEmS1IYVQUmSpBa6miM4H0wEJUmS2ujReGqPLkWSJEltWBGUJElqo0dDw1YEJUmSFigrgpIkSS30abGIFUFJkqQFyoqgJElSGz0qo5kI9sxZT1ule05LK7XLvjd0HcJY+P3bntx1CGNh/Vec1HUIY+O3F1zWdQhjY4PjTl+9J3RoWJIkSePOiqAkSVILLhaRJEnS2LMiKEmS1EaPKoImgpIkSW30KBF0aFiSJGmBsiIoSZLUQmJFUJIkSWPOiqAkSVIbPZojaCIoSZLURo8SQYeGJUmSFigrgpIkSW0s6k8drT9XIkmSpFasCEqSJLXRozmCJoKSJEktpEeJoEPDkiRJC5QVQUmSpDasCEqSJGncWRGUJElqI/2po/XnSiRJktSKFUFJkqQ2ejRH0ERQkiSpjR4lgg4NS5IkLVBWBCVJklqI9xqWJEnSuLMiKEmS1EaP5giaCEqSJLXhPoKSJEkad1YEJUmS2ujR0LAVQUmSpAXKiqAkSVIbPdo+xkRQkiSpDYeGIckf7ujJkxyU5H0zHLNZkufc0XOtLkmOTPL45vk/JrnzKvRxh79bSZKkmYxDbXMzYGwSwap6XVV9tXn5j0DrRFCSJI2wLJr7R0dmdeYkn09yXpJLkhw61P6upu30JPds2l6a5NIkFyU5vmm7W9PHRUnOSbLdFOc4Nsn+Q68nqmJvAf5fkguTvCzJ4iRvT3Ju098Lp4l7jyRfT/KFJJcneUuSA5N8N8nyJFs0x+2T5DtJLkjy1ST3btrvmeS05ho/nOQnSe7RVCm/n+RDzXunJlln+DqSvBS4L/C1JF+bdE00xxzbPN88ybebmN446RpePnStb5jNfy9JkqTZmG0KenBV7QjsBLw0yd2BuwDLqmpr4OvA65tjDwd2qKrtgBc1bW8ALmja/gX4SIsYDwfOqqrtq+pdwCHANVX1cODhwAuSbD7N5x/axPEQ4K+AB1bVzsCHgZc0x3wTeERV7QAcD7yiaX89cEZzjScC9xvqd0vg/c17VwNPHz5pVb0H+DnwmKp6zAzX+G7gA1W1LXDVRGOSPZvz7AxsD+yY5NEz9CVJkubTosz9o6tLmeVxL03yPeAcYFMGycmtwAnN+x8DdmueXwR8PMlzgZubtt2AjwJU1RnA3ZOsv4ox7wn8dZILge8Ad2/iWZlzq+qqqroR+B/g1KZ9OYNhZ4BNgFOSLAdeDmw9FPfxTdxfAX431O8VVXVh8/y8ob5WxaOATzbPPzrUvmfzuAA4H3gwU1xrkkOTLEuy7EvX/88dCEOSJC0kM64aTrIH8Hhg16q6LsmZwNpTHFrNv58EPBrYB3h1km1nGcvNNIlpkkXAWisLCXhJVZ0yy35vHHp+69DrW7nt+t8LvLOqljbXe0TLfm8B1pnFZ2ro+eTvsFhRgDdX1Qen7bRqCbAE4LR7P2uqfiRJ0hxJj7aPmc2VbAD8rkkCHww8YuizE3P6ngN8s0ngNq2qrwGvbD67LnAWcCD8ObH8TVX9ftJ5fgzs2DzfF1izeX4tsN7QcacAf5tkzaa/Bya5yyyuY6ZrvLJ5/ryh9rOBZzbn2RO4a8t+J8f+yyQPab6np046z7Ob5wcOtZ8CHJxk3SaGjZPcq2UMkiRpLi2woeGvAGsk+T6DhRvnNO1/BHZOcjHwWOBIYDHwsWaI9QLgPVV1NYMK245JLmr6eB4r+hCwezMEvWvTPwyGmm9J8r0kL2Mwt+9S4Pzm3B/kju+HeATw6STnAb8Zan8DsGdznmcAv2CQ3M3WEuArE4tFGMx3PAn4FkNzAYF/AP6++d42nmisqlOBTwDfbt47kdsnli1Vvs4AACAASURBVJIkSassVY4krkySOwG3VNXNSXZlsKBj+67jmo5Dw5pru+z7u5kPEmtsPd2aNU1Y/xUndR3C2PjtAQ/uOoSxscFxp6/Wktp1//43c/6z9s7//OFOyoLeWWR69wM+1Qzl/gl4QcfxSJIkzZleJILNgpSPTmq+sap2uSP9VtWPgB3uSB+SJKlnenSLuV4kglW1nME+e5IkSfNrga0aliRJUg/1oiIoSZK02qQ/Q8NWBCVJkhYoK4KSJElt9GiOoImgJElSGz1KBPtzJZIkSWrFiqAkSVIbPdpH0IqgJEnSAmVFUJIkqY30p47WnyuRJElSK1YEJUmS2ujRqmETQUmSpBbiYhFJkiSNOyuCkiRJbbhYRJIkSePOiqAkSVIbLhaRJElaoHqUCPbnSiRJktSKFUFJkqQ24vYxkiRJGnNWBCVJktro0RxBE0FJkqQ23EdQkiRJ486KoCRJUhs9Ghruz5VIkiSpFRNBSZKkNhYtmvvHLCTZK8kPk1yW5PAp3j8syaVJLkpyepL7z3gpq3D5kiRJWo2SLAbeD+wNbAUckGSrSYddAOxUVdsBJwJvm6lfE0FJkqQ2krl/zGxn4LKquryq/gQcD+w3fEBVfa2qrmtengNsMlOnLhbpmd2O2qXrENQ3t9zcdQRjYY1H7d91CGPhtxdc1nUIY+Pun/xB1yGMjZuPW80n7GaxyMbAT4de/wyY7of+IcCXZ+rURFCSJKljSQ4FDh1qWlJVS1axr+cCOwG7z3SsiaAkSVIb81ARbJK+6RK/K4FNh15v0rTdTpLHA68Gdq+qG2c6r3MEJUmSRt+5wJZJNk+yFvBsYOnwAUl2AD4I7FtVv5pNp1YEJUmS2ujgFnNVdXOSFwOnAIuBo6vqkiRHAsuqainwdmBd4NMZLED536rad7p+TQQlSZLa6OjOIlV1MnDypLbXDT1/fNs+HRqWJElaoKwISpIktdHB0PB86c+VSJIkqRUrgpIkSW10NEdwPpgISpIkteHQsCRJksadFUFJkqQ2ejQ03J8rkSRJUitWBCVJktqwIihJkqRxZ0VQkiSpjR6tGjYRlCRJasOhYUmSJI07K4KSJElt9GhouD9XIkmSpFasCEqSJLXRozmCJoKSJEltODQsSZKkcWdFUJIkqY0eDQ3350okSZLUihVBSZKkFpLFXYcwZ0wEJUmS2nBoWJIkSePOiqAkSVIbVgQlSZI07qwISpIkteGG0uMnyWZJLu7o3Gcm2amLc0uSJK2MFcE5lmSNqrq56zgkSdI8cY7g2Fqc5ENJLklyapJ1krwgyblJvpfkM0nuDJDkwqHH9Ul2T7Jzkm8nuSDJt5I8qDn2oCRLk5wBnN70e3yS7yf5HLDORABJDkiyPMnFSd461P6Hoef7Jzm2ef6M5tjvJfnGavqeJEnSymTR3D86stASwS2B91fV1sDVwNOBz1bVw6vqocD3gUMAqmr7qtoeeC2wDPgW8APg/1XVDsDrgH8b6vthwP5VtTvwt8B1VfUQ4PXAjgBJ7gu8FXgssD3w8CRPmSHm1wFPaOLb945+AZIkSRMW2tDwFVV1YfP8PGAzYJskbwQ2BNYFTpk4OMmWwNuBx1TVTUnuAxzXtBew5lDfp1XV/zXPHw28B6CqLkpyUdP+cODMqvp10//Hm2M/P03MZwPHJvkU8NlVu2xJkjRnFvXnziILrSJ449DzWxgkwscCL66qbYE3AGsDJFkX+BTwgqq6qvnMvwJfq6ptgH0mjm388Q7GVkPP/9xvVb0IeA2wKXBekrtP/mCSQ5MsS7LsqK98+w6GIUmSFoqFlghOZT3gqiRrAgcOtR8NHFNVZw21bQBc2Tw/aJo+vwE8ByDJNsB2Tft3gd2T3CODGxUeAHy9ee+XSR6SZBHw1ImOkmxRVd+pqtcBv2aQEN5OVS2pqp2qaqdD9tp1VhctSZJWUY/mCC60oeGpvBb4DoMk6zvAeknuD+wPPDDJwc1xfwO8jcHQ8GuAL03T5weAY5J8n8G8w/MAquqqJIcDXwMCfKmqvtB85nDgpCaOZQyGqQHe3gxFBzgd+N4dv2RJkrTKerRqOFU181EaG9ef9E7/g2pu3eJuSLOxxqP27zqEsXDdP72g6xDGxt0/+YOuQxgbN//pyqzO891w4Ulz/rN27e2fvFqvYYIVQUmSpDa8s4gkSZLGnRVBSZKkFgbrPfvBRFCSJKmNHi0W6c+VSJIkqRUrgpIkSW24WESSJEnjzoqgJElSG84RlCRJ0rizIihJktRGj+YImghKkiS1sag/+wj2J6WVJElSK1YEJUmS2ujR0HB/rkSSJEmtWBGUJElqo0fbx5gISpIktRCHhiVJkjTurAhKkiS10aOh4f5ciSRJklqxIihJktRGj+YImghKkiS14Z1FJEmSNO6sCEqSJLXRo6Hh/lyJJEmSWrEiKEmS1Ibbx0iSJGncWRGUJElqoU+3mDMRlCRJasOhYUmSJI07K4KSJElt9GhouD9XIkmSpFasCEqSJLXRo1vMpaq6jkE9l+TQqlrSdRzjwO9qdvyeZs/vanb8nmbH76l/HBrW6nBo1wGMEb+r2fF7mj2/q9nxe5odv6eeMRGUJElaoEwEJUmSFigTQa0OzieZPb+r2fF7mj2/q9nxe5odv6eecbGIJEnSAmVFUJIkaYEyEZQkSVqgTAQlSZIWKBNBzYskd0rynCT/kuR1E4+u4xpFSQ6Z9Hpxktd3Fc8oS7JP0qObfKpzSXZL8vzm+T2TbN51TKMsyV2TbNd1HJo7/oWq+fIFYD/gZuCPQw+t6HFJTk6yUZKtgXOA9boOakQ9C/hRkrcleXDXwYyqJA9McnqSi5vX2yV5TddxjZrmF65XAq9qmtYEPtZdRKMpyZlJ1k9yN+B84ENJ3tl1XJobrhrWvEhycVVt03Uc4yLJs4D3M0iWn1NVZ3cc0shKsj5wAPB8oIBjgE9W1bWdBjZCknwdeDnwwaraoWnz/8lJklwI7ACcP/Q9XVRVVryGJLmgqnZI8jfAplX1er+n/rAiqPnyrSTbdh3EOEiyJfAPwGeAnwB/leTO3UY1uqrq98CJwPHARsBTgfOTvKTTwEbLnavqu5Pabu4kktH2pxpUQwogyV06jmdUrZFkI+CZwEldB6O5ZSKo+bIbcF6SHya5KMnyJBd1HdSI+iLw2qp6IbA78CPg3G5DGk1J9kvyOeBMBsN4O1fV3sBDgX/qMrYR85skW3BbgrM/cFW3IY2kTyX5ILBhkhcAXwU+1HFMo+hI4BTgsqo6N8kDGPw9pR5waFjzIsn9p2qvqp+s7lhGXZL1myrXcNsDq+q/u4ppVCU5Djiqqr4xxXuPq6rTOwhr5DQ/qJcAjwR+B1wBPLeqftxlXKMoyV8CewIBTqmq0zoOSVqtTAQ1pyaSmmZS8Qqq6v9Wd0zjIMk2wFbA2hNtVfWR7iIaTUneWlWvnKlNA81Q5yLnT+qOSHIMTXV5WFUd3EE4mmMmgppTSU6qqicnuYLBXxwZeruq6gEdhTaympWLezBIBE8G9ga+WVX7dxnXKEpyflU9bFKbk9YnSfJvwNuq6urm9V2Bf6oqVw4DSa5lisRmQlWtvxrDGXlJnj70cm0G83J/XlUv7SgkzSETQaljSZYzmON2QVU9NMm9gY9V1V92HNrISPK3wN8BWwCXDb21HnB2VT23k8BG1MQqz0ltKyTRC12Sf2Uwd/KjDH5pPRDYqKrc83QazV6e36yqR3Ydi+64NboOQP3VbDq6GUN/zqrqs50FNLqur6pbk9zcbI3yK2DTroMaMZ8Avgy8GTh8qP1apxtMaXGSO1XVjQBJ1gHu1HFMo2jfqnro0OsPJPkeYCI4vS2Be3UdhOaGiaDmRZKjge2AS4Bbm+YCTARXtCzJhgxWK54H/AH4drchjZyqqh8n+fvJbyS5m8ngCj4OnN7M7YLBnovHdRjPqPpjkgMZbEVUDPandOP7SYaG0tP8+xcMNuJWDzg0rHmR5NKq2qrrOMZNks2A9avKrXaGOPe0vSR7A49rXp5WVad0Gc8oav5/ezfwKAZ/rs4G/tHV1VpITAQ1L5IcBbyjqi7tOpZx4DC6pFGWZF/g0c3LM6vKjaV7wkRQ8yLJ7sBSBkMIN9IMKbi6c0UrG0Z3a4YVJXkqcEZVXdO83hDYo6o+321koyXJ04C3MpjHFW77/8/VsEPcFmV2krwFeDiDKQcwGEI/t6r+pbuoNFdMBDUvklwGHAYs57bkxg2lp+Aw+uwlubCqtp/UtsIK2YWu+f9vn6r6ftexjDK3RZmd5q5Q21fVrc3rxQx2OfAX+x5wsYjmy6+ramnXQYyJbyfZymH0WZnqtpj+PbaiX5oEzqyqPjP8OskngW92FM6o2xCYWJS1QZeBaG75F6jmywVJPsHgPro3TjQ6721KH2GQDDqMPrNlSd4JvL95/fcMVlrr9pYlOQH4PP7/14bbokztzQz+Tv8ag7+fHs3tt3HSGHNoWPNiaNuKYc57m4LD6LPX3DLttcDjm6bTgDdWlVt+DPH/v9mZ4g4jvwBeNblSKEiyEYN5ggDfrapfdBmP5o6JoDqR5FVV9eau4xgFSb5dVbt2Hcc4SbIeg8TmD13HIvVVkmnvRFNV56+uWDR/TATVCW93dZsk/8lg/o3D6DNIsi2DofS7NU2/AZ5XVRd3F9XoSbI2cAiwNYNFEICrYSdLcnpVPW6mtoWqGQqGwZ+hnYDvMRga3g5Y5i+w/eAcQXUlMx+yYKzDIAHcc6jNu7BM7YPAYVX1NYAkewBLAO95ensfBX4APAE4ksE9dF080mgS5TsD90hyV277+2h9YOPOAhsxVfUYgCSfBR5WVcub19sAR3QYmuaQiaC6Yim6UVXPn+59h9Fv5y4TSSBAVZ3ZzBvU7f1FVT0jyX5VdVyzcOusroMaIS8E/hG4LzA8vPl74H2dRDTaHjSRBAJU1cVJHtJlQJo7JoLqihXB2XsGg1V7gsuTvJZBxQvgucDlHcYzqm5q/n11U735Ba6G/bOqejfw7iQvqar3dh3PGFie5MPAx5rXBwLeBrMnTAQ155rNRl9aVe+a5rBPr654esCk+TYHA2/gtmHzs5o23d6SZsjzNQzu8LMug9XWApI8tqrOAK5s7sJyO87PXcFBwN8C/9C8/gbwgc6i0ZxysYjmRZLvVtXOXcfRBy6sUVtJNq+qK2ZqW6iSvKGqXu82OzNrfrH/6sR8QfWPiaDmRZJ3AWsCJwB/3uPN7Qba8xZqkOSLTDOvtKr2XY3hjLypfnlIcl5V7dhVTBpfSU4HnjZxj2/1i0PDmi8T94M9cqitgMd2EMvIchh91v696wDGQZIHM9gyZoNJQ57rM7SNjAaS3Al4OrAZQz8Pq+rIlX1mgfoDg3mCp3H7X+y9J3MPWBGUOuYwejtJ1gHuV1U/7DqWUZNkP+ApwL4M5gZOuBY4vqq+1UlgIyrJV4BrGNym8JaJ9qp6R2dBjaAkz5uqvaqOW92xaO6ZCGpeJLk38G/Afatq7yRbAbtW1VEdhzZyHEafvST7MKgOrlVVmyfZHjjSoeHbS7JrVX276zhGXZKLq2qbruOQurSo6wDUW8cCpzDYpwvgvxns26UVbc9gOO9I4B3Nw6HQqR0B7AxcDVBVFwKbdxnQiHpqkvWTrJnk9CS/TvLcroMaQd9q7lajaSTZMsmJSS5NcvnEo+u4NDecI6j5co+q+lSSVwFU1c1JbpnpQwuRq/Fauamqrklut6OOwxor2rOqXpHkqcCPgacx2PLjY9N+auHZDTgoyRUM7u4TBquGt+s2rJFzDPB64F3AY4DnYyGpN0wENV/+mOTuND+kkzyCwVwcTeIweiuXJHkOsDjJlsBLAee9rWjN5t9PAj49RfKsgb27DmBMrFNVpydJVf0EOCLJecDrug5Md5wZvebLYQwmq2+R5GzgIwx+aGtFx+Iw+my9hMEw+o3AJxj8cuF3taIvJvkBsCNwepJ7Ajd0HNMounaKx887jWg03ZhkEfCjJC9uKs3rdh2U5oaLRTQvmm0ZbgEexGC45YfAoqq6sdPARlCSc6vq4cP7BSa5sKq2n+mzC02Sh7mIZnaS3A24pqpuSXJnYP2q+kXXcY2SJD8GNgV+x+DvqQ0Z3I7vl8ALquq87qIbHUkeDnyfwffzrwy2I3p7VZ3TaWCaEw4Na758u9nQ9pKJhiTnA94hY0UOo8/eO5LcBzgROKGqLu46oFEyceu04T0EJw0Je+u02zsNOLGqTgFIsieDfQWPAf4T2KXD2EbJ2sD1VfUHBvMDSeLf5T1hIqg51fyQ3hhYJ8kO3Haf3PWBO3cW2GibPIx+T+AZ3YY0mqrqMc2fsWcCH0yyPoOE8I0dhzYqdgfOAPaZ4r3CRHCyR1TVCyZeVNWpSf69ql7YjGpo4BTg3CTPqKpfNW0fxl/se8GhYc2pZuPRg4CdgGVDb/0eOM6bua/IYfRV02z78QrgWVW1VtfxaPwkORU4HTi+aXoW8JfAXsC53uN7IMkFwGuBtwOHVNW3vPVlf5gIal4keXpVfabrOMbBSu4Lu0KbIMlDGPywfjrwWwabcH9mqEqxoCU5bLr3q+qdqyuWcZDkHgy2RdmtaTobeAODqRn3q6rLuoptlEz8fdSs1D8BOBo42L+j+sGhYc2Xs5MchVuirJTD6KvkaAbVmydUlas7V7Re8+8HAQ/nttvM7QN8t5OIRlhV/YbBSvSpmATeJgBV9aMkj2bw/6F7LfaEFUHNiyRfZjDh+tVV9dAkawAXVJW7+DccRp97ST5TVU/vOo6uJfkG8KSqurZ5vR7wpap6dLeRjZZmW51XMNiSaO2J9qp6bGdBjYkk96uq/+06Dt1xVgQ1X7yzyAyaG7Yf5zD6nHpA1wGMiHsDfxp6/aemTbf3cQZDnU8GXgQ8D/h1pxGNoCRrA4cwKWEGDu4mIs0lN5TWfHFLlNk7O8lRTRWVJFslOaTroMaUQxwDHwG+m+SIJEcA32Gwcblu7+7NdJWbqurrVXUwYDVwRR8F7gM8Afg6sAmDzbfVAyaCmi9T3VlkZXNxFrpj8M4imkNV9SYG+739rnk8v6rePPF+krt2FduIuan591VJntTM1b1blwGNqL+oqtcCf2xGMp6Eeyz2hkPDmhdVdX6S3RnaEqWqbprhYwuVw+hzxxvqNpo7sKzsLiyn4x5wAG9MsgHwT8B7GSzUelm3IY2kib+7r06yDYO7r9yrw3g0h0wENS+SLAaeCGzG4M/ZnkncvmJqDqO3kGQdBlt7/HCKt1+5uuMZUybMQFWd1Dy9BnhMl7GMuCVNFfk1DEZ61mWwr6B6wERQ8+WLDG5yvxy4teNYRt1UdxbZv9uQRlOSfYB/B9YCNk+yPXBkVe0LgztDdBnfGHEuJZBkcwZTVjZj6OfhxJ8n/dlHGezduRlwXNPm4qOeMBHUfNmkqtxnahYcRm/lCGBn4EyAqrqw+WEurYrPA0cx+MXVX1hX7gsMqqbnAd7xqGdMBDVfvpxkTys0M3MYvZWbquqa5HYjm1a32nNoeOCGqnpP10GMgU2qaq+ug9D8MBHUfDkH+FySRQwmGgeoqlq/27BGksPos3dJkucAi5vbXb0U+FbHMY2cJFsAP6uqG5PsweAuEB+pqqubQx7XWXCj5d1JXg+cylClq1loo9t8K8m2VbW860A097yziOZFkiuA/YDl5R+yaSW5yGH02UlyZ+DVwJ5N0ynAG6vqhu6iGj1JLmRwx5rNgJMZDO1tXVVP7DKuUZPkzcBfAf/Dbb+ElXcWGUiynEHFfQ1gS+ByBgnzxC/2/r3VAyaCmhfNLa72qCorXDNI8lbgdIfRZ5bkYVZrZpbk/Kp6WJKXMxj+fG+SC6pqh65jGyVJLgO2qqo/zXjwApTk/tO9X1U/WV2xaP44NKz5cjlwZnO3jOEhF+e9rchh9Nl7R5L7ACcCJ1TVxV0HNKJuSnIAg1um7dO0rdlhPKPqYmBD4FddBzKKTPQWBhNBzZcrmsdazUMr905gVxxGn1FVPaZJBJ8JfDDJ+gwSwjd2HNqoeT6De+e+qaquaFZWf7TjmEbRhsAPkpzL7X9hdfsYLRgODUsdcxh91STZFngF8Kyq8peNlWg2At60qi7qOpZR02zbtIKq/9/evcfIVdZhHP8+pUCxtIVyEbQGW224GgOCoJIISq0GEawGRFSwFa8JCIo2iCEQ71GjolFEhUJrogY0QpAChotcQkqrWIEakgLGQJTUAgVTAX3845ztTrdbdtvsmffsnueTTHbmzG7yZDO785v39158W7+zRJSSQjAaIWkvqjfpg4EpA9czCXtLkq4A5gBpo49A0oHAKVSb264DfgFcbTutvR6SbgXeRdX1WUnV+rzT9rklc403ku62/YbSOSKaNKl0gJiwlgFrgNnARcAjwIqSgVrsYaqzX3cCpvXcYks/A54E5ts+xvYPUwQOa4btp4EFVNvGHAkcVzjTeDRl5G+JGN8yRzCasoftn0o6u26z3FbPw4khbF9UOsN4kdGZUZssaV+quZRfKB1mHEvLLCa8FILRlIEj0h6XdDzwGDCzYJ7WSht9ZJJ+afvknn3NNj1F9jMbzsVUeyzeYXuFpDnAQ4UzRUQLZY5gNELSO4E/AK8ALgGmAxfZ/m3RYC0k6UaquW6fpVrpeTrwhO3PFw3WIpL2tf341vY1yzYX0YTsvRhdkEIwojBJK22/rveEEUkrbB9ROlvbSPr60AJ5uGtdJelztr8h6RKGaWvaPqtArFarP1zMtX2zpF2AybY31M8dkr0qY6JLazjGVN6Itkva6KM3Dxha9L1jmGtd9WD99V4yv21Eks4EPkr19/YqYBbwI+qzmFMERhekEIyx1vtGFKPzJUkzgM8w2EY/p2ykdpH0CeCTwBxJvfvhTQPuLJOqfWxfW999ADif6qzhgf/zBq4sEKvNPgW8HrgHwPZDkvYuGymiv9IajojWqwvl3YGvAot7ntpg+19lUrWXpL8C5wGrgU0blWcu5eYk3WP7yIG5gJImA6uy+Ci6JCOCMaYkXcuLtKRydNOgtNFHz/ZTwFPAqQD1qM0UYFdJu9r+W8l8LfREFmaNym2Szgd2kTSPatT52hF+JmJCSSEYY+2b9dcFwD7A0vrxqcA/iiRqr7TRt5GkE6jOZn4Z1WkZ+1H9Hg8umauFLpT0E6qNyntPq7mmXKRWWgwsoho5/Rhwve3LykaK6K+0hqMRku61ffhI1yK2haT7gLcAN9etvGOBD9heVDhaq0haChwA3M9ga9i2F5ZL1T71hvffHelaxESWEcFoylRJc2yvBZA0G5haOFOrpI2+XZ63vU7SJEmTbN8i6TulQ7XQEbb3Lx1iHDgdGFr0nTHMtYgJK4VgNOUc4FZJa6lOf9iPapuGGJQ2+rZ7UtKuwO3AMkn/BJ4tnKmN7pJ0kO0HSgdpI0mnAu8HZkvqnUs5Dcjio+iUtIajMZJ2pmpPAayx/Z+e5+bZvqlMsnZJG330JE0FNlJ9uDgNmAEss72uaLCWkfQg1b54D1PNEcxRfD3qTaRnM8wqdODPtl8oEiyigBSCUYSkVbYPK52jDeo37eOHtNGvt31g2WQxXuUovogYrbSGoxSVDtAiaaOPQNIGNp9PqfrxwEjX9CLBWioF3+gMeV3tBOwIPJvXU3RJCsEoJUPRNds3SJpL2uhbZXta6Qwx8fS+riQJOBE4qlyiiP5LaziKSGt49PK72pyko4G5ti+XtCcwzfbDpXPFxDBwykjpHBH9khHBaISknXtHtYa59kj/U41baaPXJF0IHA7sD1xO1c5bCrypZK4YnyQt6Hk4ieq1tbFQnIgiUghGU+4Gho5ibbpme8EWPxFbk2H7Qe8GDgVWAdh+TFLaxrG9Tui5/wLVB9QTy0SJKCOFYIwpSfsAL6c6u/NQBkezpgMvKRYsJornbFuSYdN2MhHbxfaHS2eIKC2FYIy1+VQ7888CvsVgIbgBOL9QplZLG3106sn810m6FNhN0pnAQiBnw8Y2kXQJL36qz1l9jBNRVBaLRCMkvcf21aVzjAfDLQbJApHhSVoNnAu8jepDxvKur6iObSfp9Bd73vaSfmWJKC0jgtGUWZKmU40EXkY1N3Cx7RvLxmqPtNG3yyrgSdvnlQ4S49fQQq8+thDbz5RJFFFORgSjEZLus/1aSfOBjwMXAFdllGtQPSpxBtVKxRVs3ka/wvY1haK1lqQ1wKuBR+k5YzhHp8X2kHQIcBUwk+rv7wngQ7bvLxosoo8yIhhNGShqjgeutH1/PccravWoxJK00bfJ/NIBYkL5MXCu7VsAJB1D1cF4Y8lQEf2UQjCaslLScmAOsLje4uN/hTO1Vdroo5Sj02KMTR0oAgFs35qV6NE1k0oHiAlrEXAncJ3tfwO7A58uG6m1Ftp+mmoBxB7AB4GvlY0U0QlrJX1R0ivr2wXA2tKhIvophWA05QfAS4G31483AN8uF6fVtmij91yLiOYsBPYCrqlve9bXIjojreFoypG2D5P0RwDb6yXtVDpUS6WNHlGA7fXAWQCSdqBqFT9dNlVEf2VEMJryfP2PdeAEiL1IcbM1aaNHFCDp55Km1/MCVwMPSMrWRNEpKQSjKd8Dfg3sLenLwB3AV8pGaq200SPKOKgeATwJ+B0wm2qObkRnpDUcjbC9TNJK4K1U891Osv1g4VhtlTZ6RBk7StqRqhD8vu3nB86xjuiKFILRGNtrgDWlc4wDaaNHlHEp1Vne9wG3S9oPyBzB6JScLBJRmKTTgFOo9g9cArwXuMD2r4oGi+ggSZNtv1A6R0S/pBCMaAFJBzDYRv992ugRzZO0B3AhcDTViPwdwMW21xUNFtFHKQQjIqKTJN0E3A4srS+dBhxj+7hyqSL6K4VgRER0kqS/2D5kyLXVtl9TKlNEv2X7mIiI6KobJb1P0qT6djKwvHSoiH7KiGBERHSKpA1UWo0VsQAAAMxJREFUcwIFTAX+Wz+1A/CM7emlskX0W7aPiYiITrE9beC+pJnAXGBKuUQR5aQQjIiITpL0EeBsYBbwJ+Ao4C6qFfwRnZA5ghER0VVnA0cAj9o+FjgUeKpspIj+SiEYERFdtdH2RgBJO9enIe1fOFNEX6U1HBERXfV3SbsBvwFukrQeeLRwpoi+yqrhiIjoPElvBmYAN9h+rnSeiH5JIRgRERHRUZkjGBEREdFRKQQjIiIiOiqFYERERERHpRCMiIiI6KgUghEREREd9X+4+eOivGRYJgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "fig, ax = plt.subplots(figsize=(10,8))\n",
        "sns.heatmap(df3.corr().abs(),annot=False, ax=ax, cmap='rocket_r')\n",
        ","
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 120,
      "metadata": {
        "id": "qPVfM3uQjOuJ",
        "outputId": "b77f2025-c320-4c49-93fb-c8ecc5e1e7a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "hazardous             1.000000\n",
              "relative_velocity     0.191185\n",
              "est_diameter_max      0.183363\n",
              "est_diameter_min      0.183363\n",
              "miss_distance         0.042302\n",
              "absolute_magnitude   -0.365267\n",
              "Name: hazardous, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 120
        }
      ],
      "source": [
        "# feature correlation with target variable\n",
        "df3.corr()['hazardous'].sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y3ydRC6ajOuJ"
      },
      "source": [
        "#### Oversampling to Counter Bias"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We recognize how imbalanced the data is, approximately 90% non-hazardous and 10% hazardous. We must resolve this with oversampling. For our purposes, we will use SMOTE ([Synthetic Minority Over-sampling Technique](https://imbalanced-learn.org/stable/references/generated/imblearn.over_sampling.SMOTE.html)) to oversample."
      ],
      "metadata": {
        "id": "RqYhNcMeHD6J"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 121,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-01-13T11:51:10.385674Z",
          "iopub.status.busy": "2022-01-13T11:51:10.385071Z",
          "iopub.status.idle": "2022-01-13T11:51:10.391059Z",
          "shell.execute_reply": "2022-01-13T11:51:10.390517Z",
          "shell.execute_reply.started": "2021-12-30T16:11:10.862071Z"
        },
        "id": "oT7G6q4pfePI",
        "papermill": {
          "duration": 0.097761,
          "end_time": "2022-01-13T11:51:10.391191",
          "exception": false,
          "start_time": "2022-01-13T11:51:10.293430",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "3d873044-d62d-47cf-f2f8-470abe9e1666"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       est_diameter_min  est_diameter_max  relative_velocity  miss_distance  \\\n",
              "0              1.198271          2.679415       13569.249224   5.483974e+07   \n",
              "1              0.265800          0.594347       73588.726663   6.143813e+07   \n",
              "2              0.722030          1.614507      114258.692129   4.979872e+07   \n",
              "3              0.096506          0.215794       24764.303138   2.543497e+07   \n",
              "4              0.255009          0.570217       42737.733765   4.627557e+07   \n",
              "...                 ...               ...                ...            ...   \n",
              "90831          0.026580          0.059435       52078.886692   1.230039e+07   \n",
              "90832          0.016771          0.037501       46114.605073   5.432121e+07   \n",
              "90833          0.031956          0.071456        7566.807732   2.840077e+07   \n",
              "90834          0.007321          0.016370       69199.154484   6.869206e+07   \n",
              "90835          0.039862          0.089133       27024.455553   5.977213e+07   \n",
              "\n",
              "       absolute_magnitude  \n",
              "0                   16.73  \n",
              "1                   20.00  \n",
              "2                   17.83  \n",
              "3                   22.20  \n",
              "4                   20.09  \n",
              "...                   ...  \n",
              "90831               25.00  \n",
              "90832               26.00  \n",
              "90833               24.60  \n",
              "90834               27.80  \n",
              "90835               24.12  \n",
              "\n",
              "[90836 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45385260-206f-49f9-87c2-06a12bcfa5ed\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>est_diameter_min</th>\n",
              "      <th>est_diameter_max</th>\n",
              "      <th>relative_velocity</th>\n",
              "      <th>miss_distance</th>\n",
              "      <th>absolute_magnitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.198271</td>\n",
              "      <td>2.679415</td>\n",
              "      <td>13569.249224</td>\n",
              "      <td>5.483974e+07</td>\n",
              "      <td>16.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.265800</td>\n",
              "      <td>0.594347</td>\n",
              "      <td>73588.726663</td>\n",
              "      <td>6.143813e+07</td>\n",
              "      <td>20.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.722030</td>\n",
              "      <td>1.614507</td>\n",
              "      <td>114258.692129</td>\n",
              "      <td>4.979872e+07</td>\n",
              "      <td>17.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.096506</td>\n",
              "      <td>0.215794</td>\n",
              "      <td>24764.303138</td>\n",
              "      <td>2.543497e+07</td>\n",
              "      <td>22.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.255009</td>\n",
              "      <td>0.570217</td>\n",
              "      <td>42737.733765</td>\n",
              "      <td>4.627557e+07</td>\n",
              "      <td>20.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90831</th>\n",
              "      <td>0.026580</td>\n",
              "      <td>0.059435</td>\n",
              "      <td>52078.886692</td>\n",
              "      <td>1.230039e+07</td>\n",
              "      <td>25.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90832</th>\n",
              "      <td>0.016771</td>\n",
              "      <td>0.037501</td>\n",
              "      <td>46114.605073</td>\n",
              "      <td>5.432121e+07</td>\n",
              "      <td>26.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90833</th>\n",
              "      <td>0.031956</td>\n",
              "      <td>0.071456</td>\n",
              "      <td>7566.807732</td>\n",
              "      <td>2.840077e+07</td>\n",
              "      <td>24.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90834</th>\n",
              "      <td>0.007321</td>\n",
              "      <td>0.016370</td>\n",
              "      <td>69199.154484</td>\n",
              "      <td>6.869206e+07</td>\n",
              "      <td>27.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>90835</th>\n",
              "      <td>0.039862</td>\n",
              "      <td>0.089133</td>\n",
              "      <td>27024.455553</td>\n",
              "      <td>5.977213e+07</td>\n",
              "      <td>24.12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>90836 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-45385260-206f-49f9-87c2-06a12bcfa5ed')\"\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-45385260-206f-49f9-87c2-06a12bcfa5ed 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-45385260-206f-49f9-87c2-06a12bcfa5ed');\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": 121
        }
      ],
      "source": [
        "# independent variables\n",
        "X = df3.drop(['hazardous'], axis=1)\n",
        "\n",
        "# dependent/target variable\n",
        "y = df3.hazardous\n",
        "\n",
        "X_resampled, y_resampled = SMOTE().fit_resample(X, y)\n",
        "\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y"
      ],
      "metadata": {
        "id": "28n4acaIHTot",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5262b702-bdb0-46ae-9861-0c1fc10bb669"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1         True\n",
              "2        False\n",
              "3        False\n",
              "4         True\n",
              "         ...  \n",
              "90831    False\n",
              "90832    False\n",
              "90833    False\n",
              "90834    False\n",
              "90835    False\n",
              "Name: hazardous, Length: 90836, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.value_counts()"
      ],
      "metadata": {
        "id": "ukYuc32OH9q_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc816ac0-82ea-4c20-c1ab-1bf796b8a694"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    81996\n",
              "True      8840\n",
              "Name: hazardous, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_resampled"
      ],
      "metadata": {
        "id": "PGSTutkIHUKu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "outputId": "06f06639-dfec-450f-ef0f-ea3586830e45"
      },
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        est_diameter_min  est_diameter_max  relative_velocity  miss_distance  \\\n",
              "0               1.198271          2.679415       13569.249224   5.483974e+07   \n",
              "1               0.265800          0.594347       73588.726663   6.143813e+07   \n",
              "2               0.722030          1.614507      114258.692129   4.979872e+07   \n",
              "3               0.096506          0.215794       24764.303138   2.543497e+07   \n",
              "4               0.255009          0.570217       42737.733765   4.627557e+07   \n",
              "...                  ...               ...                ...            ...   \n",
              "163987          0.121098          0.270783       45777.537354   1.988474e+07   \n",
              "163988          0.132144          0.295483       56772.231991   3.364944e+07   \n",
              "163989          0.291447          0.651695      149641.817441   6.008054e+07   \n",
              "163990          0.225351          0.503901       40862.612717   3.797934e+07   \n",
              "163991          0.107944          0.241371       60085.913054   5.522036e+07   \n",
              "\n",
              "        absolute_magnitude  \n",
              "0                16.730000  \n",
              "1                20.000000  \n",
              "2                17.830000  \n",
              "3                22.200000  \n",
              "4                20.090000  \n",
              "...                    ...  \n",
              "163987           21.716985  \n",
              "163988           21.520832  \n",
              "163989           20.044334  \n",
              "163990           20.380502  \n",
              "163991           21.989210  \n",
              "\n",
              "[163992 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d376f143-f0b9-43fa-8e02-3341e561e254\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>est_diameter_min</th>\n",
              "      <th>est_diameter_max</th>\n",
              "      <th>relative_velocity</th>\n",
              "      <th>miss_distance</th>\n",
              "      <th>absolute_magnitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.198271</td>\n",
              "      <td>2.679415</td>\n",
              "      <td>13569.249224</td>\n",
              "      <td>5.483974e+07</td>\n",
              "      <td>16.730000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.265800</td>\n",
              "      <td>0.594347</td>\n",
              "      <td>73588.726663</td>\n",
              "      <td>6.143813e+07</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.722030</td>\n",
              "      <td>1.614507</td>\n",
              "      <td>114258.692129</td>\n",
              "      <td>4.979872e+07</td>\n",
              "      <td>17.830000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.096506</td>\n",
              "      <td>0.215794</td>\n",
              "      <td>24764.303138</td>\n",
              "      <td>2.543497e+07</td>\n",
              "      <td>22.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.255009</td>\n",
              "      <td>0.570217</td>\n",
              "      <td>42737.733765</td>\n",
              "      <td>4.627557e+07</td>\n",
              "      <td>20.090000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163987</th>\n",
              "      <td>0.121098</td>\n",
              "      <td>0.270783</td>\n",
              "      <td>45777.537354</td>\n",
              "      <td>1.988474e+07</td>\n",
              "      <td>21.716985</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163988</th>\n",
              "      <td>0.132144</td>\n",
              "      <td>0.295483</td>\n",
              "      <td>56772.231991</td>\n",
              "      <td>3.364944e+07</td>\n",
              "      <td>21.520832</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163989</th>\n",
              "      <td>0.291447</td>\n",
              "      <td>0.651695</td>\n",
              "      <td>149641.817441</td>\n",
              "      <td>6.008054e+07</td>\n",
              "      <td>20.044334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163990</th>\n",
              "      <td>0.225351</td>\n",
              "      <td>0.503901</td>\n",
              "      <td>40862.612717</td>\n",
              "      <td>3.797934e+07</td>\n",
              "      <td>20.380502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>163991</th>\n",
              "      <td>0.107944</td>\n",
              "      <td>0.241371</td>\n",
              "      <td>60085.913054</td>\n",
              "      <td>5.522036e+07</td>\n",
              "      <td>21.989210</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>163992 rows × 5 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d376f143-f0b9-43fa-8e02-3341e561e254')\"\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-d376f143-f0b9-43fa-8e02-3341e561e254 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-d376f143-f0b9-43fa-8e02-3341e561e254');\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": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_resampled"
      ],
      "metadata": {
        "id": "zRL_noWNHVTQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8311ed53-5f24-46fe-bfea-01ef1e406697"
      },
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         False\n",
              "1          True\n",
              "2         False\n",
              "3         False\n",
              "4          True\n",
              "          ...  \n",
              "163987     True\n",
              "163988     True\n",
              "163989     True\n",
              "163990     True\n",
              "163991     True\n",
              "Name: hazardous, Length: 163992, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# SMOTE oversampled to equalize the True and False examples\n",
        "y_resampled.value_counts()"
      ],
      "metadata": {
        "id": "SX9xVpKSHg4P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e62464b-7d40-45e5-de48-3396db8747a3"
      },
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False    81996\n",
              "True     81996\n",
              "Name: hazardous, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Partitioning the Data"
      ],
      "metadata": {
        "id": "ei6K6FKiX1Sb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# divide data into train and test sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_resampled, y_resampled, test_size=0.1, random_state=42)"
      ],
      "metadata": {
        "id": "H4AWdM8xJrNQ"
      },
      "execution_count": 127,
      "outputs": []
    },
    {
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      "execution_count": 128,
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              "        est_diameter_min  est_diameter_max  relative_velocity  miss_distance  \\\n",
              "161658          0.161932          0.362090       32109.598746   4.091774e+06   \n",
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              "...                  ...               ...                ...            ...   \n",
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              "121958          0.498695          1.115116       61512.142952   4.438813e+07   \n",
              "\n",
              "        absolute_magnitude  \n",
              "161658           21.078520  \n",
              "46173            20.900000  \n",
              "124370           21.202415  \n",
              "109476           21.500754  \n",
              "106322           21.103693  \n",
              "...                    ...  \n",
              "119879           21.233575  \n",
              "103694           20.034432  \n",
              "131932           20.251889  \n",
              "146867           21.329456  \n",
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              "      <th>est_diameter_min</th>\n",
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              "      <td>5.078771e+07</td>\n",
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              "      <td>61512.142952</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train['miss_distance'].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ANui_jdgDIwA",
        "outputId": "34d9202a-c011-4a62-cd2b-750628e8c7ed"
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      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "74796547.23847735"
            ]
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          "metadata": {},
          "execution_count": 129
        }
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      "cell_type": "code",
      "source": [
        "X_train['miss_distance'].min()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afoceSx9DYPb",
        "outputId": "7ec15a36-fd09-4db1-ab97-0c14f0e78bb7"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6745.532515957"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test.shape"
      ],
      "metadata": {
        "id": "VivU9g3IKXft",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad8dce03-4551-4795-d37e-8565624fa3c3"
      },
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16400, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "id": "UYcxpZ8HKY4g",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc0207c6-3f38-4a95-ff0f-0ae54acf1866"
      },
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(147592,)"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.shape"
      ],
      "metadata": {
        "id": "osYf2TWBKaZN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02bc41a5-5b1e-4757-a3bc-bcaa553c686e"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16400,)"
            ]
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ft6jA90Aenxr",
        "papermill": {
          "duration": 0.081376,
          "end_time": "2022-01-13T11:51:10.042426",
          "exception": false,
          "start_time": "2022-01-13T11:51:09.961050",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "#### Feature Scaling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ywjx1i-9esCm",
        "papermill": {
          "duration": 0.082553,
          "end_time": "2022-01-13T11:51:10.207024",
          "exception": false,
          "start_time": "2022-01-13T11:51:10.124471",
          "status": "completed"
        },
        "tags": []
      },
      "source": [
        "In this step we scale features to appropriate values for this model to use. I initially used a Normalizer to scale the data between 0 and 1. When it did this, it normalized all of the values together. This meant features with larger values, such as miss_distance, dominated the normalization and unfairly skewed the data for features with smaller values, such as est_diameter_min and est_diameter_max.\n",
        "\n",
        "This was resolved by using a [StandardScalar](https://stackoverflow.com/questions/40758562/can-anyone-explain-me-standardscaler). It normalizes each feature individually such that each feature has μ = 0 (removed mean) and σ = 1 (unit variance). Doing this removes concerns of values in one feature influencing how other features' values are normalized."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 134,
      "metadata": {
        "id": "bPvL__JgjOuK"
      },
      "outputs": [],
      "source": [
        "# old approach\n",
        "# scale the data between 0 and 1\n",
        "# transformer = Normalizer(norm='max').fit(X_train)\n",
        "# X_train = transformer.transform(X_train)\n",
        "# X_test = transformer.transform(X_test)\n",
        "\n",
        "# current approach\n",
        "# normalize the data so each feature has μ = 0 and σ = 1\n",
        "scaler = StandardScaler()\n",
        "X_train = scaler.fit_transform(X_train)\n",
        "X_test = scaler.fit_transform(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cQWPuWwPH7Py",
        "outputId": "034da7e1-08ed-40ee-9067-40b234a9c2a3"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.13373382, -0.13373382, -0.86752425, -1.56671594, -0.36665249],\n",
              "       [-0.08616035, -0.08616035,  1.43361346, -0.07979675, -0.43093684],\n",
              "       [-0.08194655, -0.08194655,  0.0604119 ,  0.32367598, -0.32203868],\n",
              "       ...,\n",
              "       [ 0.1395615 ,  0.1395615 , -0.18460254,  0.56961267, -0.66431874],\n",
              "       [-0.19347749, -0.19347749,  0.12339309,  1.2669972 , -0.27629176],\n",
              "       [ 1.03734157,  1.03734157,  0.2933544 ,  0.27683336, -1.24633423]])"
            ]
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[:,3].max()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hVzs2humdabT",
        "outputId": "f0d7d68c-1831-4031-f889-d19cce3c9a5d"
      },
      "execution_count": 136,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.6680118334953222"
            ]
          },
          "metadata": {},
          "execution_count": 136
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train[:,3].min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YXte94YsdeKi",
        "outputId": "991435fe-988e-44b5-baba-12645a55b213"
      },
      "execution_count": 137,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "-1.753605070426833"
            ]
          },
          "metadata": {},
          "execution_count": 137
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6OIEa_yaH9Pa",
        "outputId": "d0868aa2-6a18-4ba7-b50e-1e0259ac67d5"
      },
      "execution_count": 138,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.05444063,  0.05444063,  0.06452461, -0.05443451, -0.59148494],\n",
              "       [-0.7447963 , -0.7447963 , -1.13876138, -1.39277011,  2.33340652],\n",
              "       [ 0.33658048,  0.33658048, -0.62653319, -0.46408428, -0.81071211],\n",
              "       ...,\n",
              "       [ 0.91885472,  0.91885472, -0.01625343, -0.05243348, -1.00198585],\n",
              "       [-0.22601303, -0.22601303,  0.63725163, -1.27157001, -0.26444333],\n",
              "       [ 0.54562814,  0.54562814,  0.40980735, -0.48077543, -0.52324917]])"
            ]
          },
          "metadata": {},
          "execution_count": 138
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OR0RJnFPjOuK"
      },
      "source": [
        "### Logistic Regression sklearn\n",
        "This section uses the logistic regression function implemented in sklearn. This is purely for reference to what potential performance could be obtained."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 139,
      "metadata": {
        "id": "d7-5SXSEjOuK"
      },
      "outputs": [],
      "source": [
        "# define the model\n",
        "lrg = LogisticRegression(class_weight='balanced', random_state=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 140,
      "metadata": {
        "id": "DjAmr7RFjOuK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "693d3aa6-4030-4463-9576-9c3d862e2600"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression(class_weight='balanced', random_state=0)"
            ]
          },
          "metadata": {},
          "execution_count": 140
        }
      ],
      "source": [
        "# train the model\n",
        "lrg.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 141,
      "metadata": {
        "id": "xJLtchASjOuL"
      },
      "outputs": [],
      "source": [
        "# predict the result using the trained model\n",
        "y_pred_lrg = lrg.predict(X_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 142,
      "metadata": {
        "id": "4zLOsylnjOuL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f37e86e0-f415-4c9d-b55b-c7b36dc93cc6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8616463414634147"
            ]
          },
          "metadata": {},
          "execution_count": 142
        }
      ],
      "source": [
        "# overall acuracy of the model\n",
        "accuracy_score(y_test, y_pred_lrg)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 143,
      "metadata": {
        "id": "Iu5lhYu6jOuL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "23d4c39a-870d-4903-b761-9440327ff3c1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8729634398969822"
            ]
          },
          "metadata": {},
          "execution_count": 143
        }
      ],
      "source": [
        "# f1 score of the model\n",
        "f1_score(y_test, y_pred_lrg)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train"
      ],
      "metadata": {
        "id": "P-97dmulfllX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69e795f4-7af3-4ee1-e98f-9a1ec5726371"
      },
      "execution_count": 144,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "161658     True\n",
              "46173     False\n",
              "124370     True\n",
              "109476     True\n",
              "106322     True\n",
              "          ...  \n",
              "119879     True\n",
              "103694     True\n",
              "131932     True\n",
              "146867     True\n",
              "121958     True\n",
              "Name: hazardous, Length: 147592, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 144
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "id": "x77F9--ZoPYp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5fdbab77-8a8a-4428-ab3b-6d35e7e8d4fe"
      },
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(147592, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 145
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape[0]"
      ],
      "metadata": {
        "id": "gw3MnTXDo2C1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10f0d2c4-52d9-4609-af57-81fc7b034dbd"
      },
      "execution_count": 146,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "147592"
            ]
          },
          "metadata": {},
          "execution_count": 146
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Logistic Regression Implementation"
      ],
      "metadata": {
        "id": "bboctjwUM4Aw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def sigmoid(z):\n",
        "  # activation function\n",
        "  return 1 / (1 + np.exp(-1 * z))"
      ],
      "metadata": {
        "id": "VFpS5m4APLBK"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def gradientDescent(X, Y, w, b, n):\n",
        "  # calculate A, the cost function, and the partial derivatives\n",
        "  A = sigmoid(np.dot(w.T,X.T) + b)\n",
        "  minimum = 0.00000001\n",
        "  # we clamp to prevent infinity from showing up in\n",
        "  # gradient descent's early steps\n",
        "  A = np.clip(A, minimum, 1 - minimum)\n",
        "  cost = -1/n * np.sum(Y * np.log(A) + (1-Y) * (np.log(1-A)))\n",
        "  dw = np.dot(X.T, (A-Y).T)/n\n",
        "  db = np.sum(A-Y)/n\n",
        "  return A, cost, dw, db"
      ],
      "metadata": {
        "id": "3GnwbwCjOBsZ"
      },
      "execution_count": 148,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def gradDescend(dw, db, w, b):\n",
        "  # standard update step\n",
        "  # adjust default update learning rate here\n",
        "  learning_rate = 0.01\n",
        "  \n",
        "  w = w - learning_rate * dw\n",
        "  b = b - learning_rate * db\n",
        "  return w, b"
      ],
      "metadata": {
        "id": "zJmypzxW8ss0"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def adaGrad(dw, db, v_w, v_b, w, b):\n",
        "  # Adagrad update step\n",
        "  epsilon = 1e-8\n",
        "  # adjust Adagrad learning rate here\n",
        "  learning_rate = 0.01\n",
        "\n",
        "  v_w = v_w + dw**2\n",
        "  v_b = v_b + db**2\n",
        "\n",
        "  w = w - (learning_rate / np.sqrt(v_w + epsilon)) * dw\n",
        "  b = b - (learning_rate / np.sqrt(v_b + epsilon)) * db\n",
        "  \n",
        "  return w, b, v_w, v_b"
      ],
      "metadata": {
        "id": "4m34gqUO4-s1"
      },
      "execution_count": 150,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def rmsProp(dw, db, v_w, v_b, w, b):\n",
        "  # RMSprop update step\n",
        "  epsilon = 1e-8\n",
        "  beta = 0.9\n",
        "  # adjust RMSprop learning rate here\n",
        "  learning_rate = 0.01\n",
        "\n",
        "  v_w = beta * v_w + (1 - beta) * dw**2\n",
        "  v_b = beta * v_b + (1 - beta) * db**2\n",
        "\n",
        "  w = w - (learning_rate / np.sqrt(v_w + epsilon)) * dw\n",
        "  b = b - (learning_rate / np.sqrt(v_b + epsilon)) * db\n",
        "\n",
        "  return w, b, v_w, v_b"
      ],
      "metadata": {
        "id": "9qVdnFSm3wnx"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def gradUpdate(X, y, w, b, n, t, listIters, listCost):\n",
        "  # update user with information on recent information\n",
        "  # prepare for updating\n",
        "  A, cost, dw, db = gradientDescent(X, y, w, b, n)\n",
        "  if t % 100 == 0:\n",
        "    # collect iteration and cost for display in a line graph\n",
        "    listIters.append(t)\n",
        "    listCost.append(cost)\n",
        "    print('Iteration: ', t, ', Cost: ', cost)\n",
        "  return dw, db"
      ],
      "metadata": {
        "id": "TD1poi4j8Ooe"
      },
      "execution_count": 152,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def permutate(a1, a2):\n",
        "  # Generates a shuffled combination of a1 and a2, which are two separate arrays \n",
        "  p = np.random.permutation(len(a2))\n",
        "  return a1[p], a2[p]"
      ],
      "metadata": {
        "id": "WpNdodlfenKY"
      },
      "execution_count": 153,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def logisticRegression(X, y, gdType, opType):\n",
        "  # calculate weights and bias based on aa linear model\n",
        "  # wrapped in an activation function\n",
        "  # set up key variables\n",
        "  w = np.ones((X.shape[1],1))\n",
        "  b = 0.0\n",
        "  n = X.shape[0]\n",
        "  t = 0\n",
        "  max_t = 0\n",
        "  if gdType == 1:\n",
        "    # stochastic gradient descent\n",
        "    max_t = 20000\n",
        "  else:\n",
        "    # batch gradient descent\n",
        "    max_t = 4000\n",
        "  listIters = []\n",
        "  listCost = []\n",
        "  m_w = 0.0\n",
        "  m_b = 0.0\n",
        "  v_w = 0.0\n",
        "  v_b = 0.0\n",
        "\n",
        "  while t <= max_t:\n",
        "    # 1) Calculate the cost and the gradient for the current parameters.\n",
        "    dw = 0.0\n",
        "    db = 0.0\n",
        "\n",
        "    if gdType == 0:\n",
        "      # batch gradient descent\n",
        "      dw, db = gradUpdate(X, y, w, b, n, t, listIters, listCost)\n",
        "    elif gdType == 1:\n",
        "      # stochastic gradient descent\n",
        "      X_perm, y_perm = permutate(X, y)\n",
        "      X_example = np.reshape(X_perm[0], (-1, 1))\n",
        "      y_example = np.reshape(y_perm[0], (-1, 1))\n",
        "      n = 1\n",
        "      dw, db = gradUpdate(X_example.T, y_example, w, b, n, t, listIters,\n",
        "                          listCost)\n",
        "    \n",
        "    # 2) Update the parameters using gradient descent rule for w and b.\n",
        "    if opType == 0:\n",
        "      # default update step\n",
        "      w, b = gradDescend(dw, db, w, b)\n",
        "    elif opType == 1:\n",
        "      # Adagrad optimization algorithm\n",
        "      w, b, v_w, v_b = adaGrad(dw, db, v_w, v_b, w, b)\n",
        "    elif opType == 2:\n",
        "      # RMSprop optimization algorithm\n",
        "      w, b, v_w, v_b = rmsProp(dw, db, v_w, v_b, w, b)\n",
        "\n",
        "    t += 1\n",
        "  # show cost function plot\n",
        "  plt.plot(listIters, listCost)\n",
        "  plt.show()\n",
        "  plt.close()\n",
        "  return w, b"
      ],
      "metadata": {
        "id": "CyT-o6jsXmLP"
      },
      "execution_count": 192,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def predict(w, b, X):\n",
        "  # use calculated weights and bias to predict labels for test dataset\n",
        "  Yhat = (sigmoid(np.dot(w.T, X.T) + b)).T\n",
        "  predictions = []\n",
        "  for entry in Yhat:\n",
        "    if entry <= 0.5:\n",
        "      # asteroid is not hazardous\n",
        "      predictions.append(0)\n",
        "    elif entry > 0.5:\n",
        "      # asteroid is hazardous\n",
        "      predictions.append(1)\n",
        "  \n",
        "  predictArr = np.array(predictions)\n",
        "  return predictArr"
      ],
      "metadata": {
        "id": "5KRNxzuRAYWt"
      },
      "execution_count": 155,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert to numpy-friendly format\n",
        "y_train = y_train.to_numpy().astype(int)"
      ],
      "metadata": {
        "id": "O2z9koyqV0Vj"
      },
      "execution_count": 156,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# convert to numpy-friendly format\n",
        "y_test = y_test.to_numpy().astype(int)"
      ],
      "metadata": {
        "id": "mLOXK2yRdmXL"
      },
      "execution_count": 157,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Batch Gradient Descent"
      ],
      "metadata": {
        "id": "JyjXCNFsNLml"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D7eZqsvMe7cO",
        "outputId": "0e0beeab-6ace-46eb-83c8-b1a6c016e64c"
      },
      "execution_count": 158,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(147592, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 158
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ith9EMwPe6Gs",
        "outputId": "fecf3879-a99f-484b-8500-1be61cf1297e"
      },
      "execution_count": 159,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(147592,)"
            ]
          },
          "metadata": {},
          "execution_count": 159
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cgydumCTfI5A",
        "outputId": "335b2be0-4c4b-46f8-d39e-08394acbd540"
      },
      "execution_count": 160,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.13373382, -0.13373382, -0.86752425, -1.56671594, -0.36665249],\n",
              "       [-0.08616035, -0.08616035,  1.43361346, -0.07979675, -0.43093684],\n",
              "       [-0.08194655, -0.08194655,  0.0604119 ,  0.32367598, -0.32203868],\n",
              "       ...,\n",
              "       [ 0.1395615 ,  0.1395615 , -0.18460254,  0.56961267, -0.66431874],\n",
              "       [-0.19347749, -0.19347749,  0.12339309,  1.2669972 , -0.27629176],\n",
              "       [ 1.03734157,  1.03734157,  0.2933544 ,  0.27683336, -1.24633423]])"
            ]
          },
          "metadata": {},
          "execution_count": 160
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "kj, jk = permutate(X_train, y_train)"
      ],
      "metadata": {
        "id": "hFpS8DUsfKxh"
      },
      "execution_count": 161,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "kj"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FpByY2gNfs07",
        "outputId": "c5a7452c-e63a-43b6-cfae-f11c69c7442c"
      },
      "execution_count": 162,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.52071957, -0.52071957,  0.89214091,  1.07780559,  0.54132087],\n",
              "       [ 3.66437183,  3.66437183,  0.57030989,  1.59420561, -1.96756082],\n",
              "       [ 1.04360291,  1.04360291, -0.86194918,  0.07537081, -0.66886884],\n",
              "       ...,\n",
              "       [ 0.36440115,  0.36440115, -0.50581238,  0.61943409, -0.86305137],\n",
              "       [ 0.84442275,  0.84442275, -0.61274948, -0.0708779 , -1.15068183],\n",
              "       [-0.20054412, -0.20054412, -0.45352436,  0.44681804, -0.25922209]])"
            ]
          },
          "metadata": {},
          "execution_count": 162
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_train.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XU30285Ofyva",
        "outputId": "67d85b7d-cf0e-411c-89e2-91e47f40e20f"
      },
      "execution_count": 163,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "147592"
            ]
          },
          "metadata": {},
          "execution_count": 163
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 164,
      "metadata": {
        "id": "csqR9m4WjOuO",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9b3f9770-2fe0-41a8-8356-daa076a1a9ba"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  0 , Cost:  0.8684696126057012\n",
            "Iteration:  100 , Cost:  0.7860814584466127\n",
            "Iteration:  200 , Cost:  0.7229858165437804\n",
            "Iteration:  300 , Cost:  0.675138977431102\n",
            "Iteration:  400 , Cost:  0.6389841101445474\n",
            "Iteration:  500 , Cost:  0.6115684420804196\n",
            "Iteration:  600 , Cost:  0.5905817015252219\n",
            "Iteration:  700 , Cost:  0.5742801670054205\n",
            "Iteration:  800 , Cost:  0.5613848685029748\n",
            "Iteration:  900 , Cost:  0.5509718082266092\n",
            "Iteration:  1000 , Cost:  0.5423802003442835\n",
            "Iteration:  1100 , Cost:  0.5351317015966286\n",
            "Iteration:  1200 , Cost:  0.528889081503921\n",
            "Iteration:  1300 , Cost:  0.5234082007686597\n",
            "Iteration:  1400 , Cost:  0.5185082668868783\n",
            "Iteration:  1500 , Cost:  0.5140512874144892\n",
            "Iteration:  1600 , Cost:  0.5099390252915661\n",
            "Iteration:  1700 , Cost:  0.506102421543801\n",
            "Iteration:  1800 , Cost:  0.5024902034484111\n",
            "Iteration:  1900 , Cost:  0.49905121747992287\n",
            "Iteration:  2000 , Cost:  0.4957521988492838\n",
            "Iteration:  2100 , Cost:  0.49257419198199154\n",
            "Iteration:  2200 , Cost:  0.48949044543180353\n",
            "Iteration:  2300 , Cost:  0.4864978389670406\n",
            "Iteration:  2400 , Cost:  0.483570541921696\n",
            "Iteration:  2500 , Cost:  0.48071765885984113\n",
            "Iteration:  2600 , Cost:  0.4779319006671616\n",
            "Iteration:  2700 , Cost:  0.475205487608058\n",
            "Iteration:  2800 , Cost:  0.4725225741690404\n",
            "Iteration:  2900 , Cost:  0.4698940227511929\n",
            "Iteration:  3000 , Cost:  0.46730215047357615\n",
            "Iteration:  3100 , Cost:  0.464761973728513\n",
            "Iteration:  3200 , Cost:  0.46227038558365996\n",
            "Iteration:  3300 , Cost:  0.4598232920451408\n",
            "Iteration:  3400 , Cost:  0.45741877325424907\n",
            "Iteration:  3500 , Cost:  0.4550614066951987\n",
            "Iteration:  3600 , Cost:  0.45275063428913975\n",
            "Iteration:  3700 , Cost:  0.45048607405770297\n",
            "Iteration:  3800 , Cost:  0.44825373859708517\n",
            "Iteration:  3900 , Cost:  0.4460578052751535\n",
            "Iteration:  4000 , Cost:  0.4438994872789746\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# train the model\n",
        "w, b = logisticRegression(X_train, y_train, 0, 0)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict the result using the trained model\n",
        "y_pred_lrg = predict(w, b, X_test)"
      ],
      "metadata": {
        "id": "Stb9ak1LBKGq"
      },
      "execution_count": 165,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# overall acuracy of the model\n",
        "accuracy_score(y_test, y_pred_lrg)"
      ],
      "metadata": {
        "id": "IKwXoKWkjzLL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0f603757-bcb6-4aa6-bc20-121a69759838"
      },
      "execution_count": 166,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8566463414634147"
            ]
          },
          "metadata": {},
          "execution_count": 166
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 score of the model\n",
        "f1_score(y_test, y_pred_lrg)"
      ],
      "metadata": {
        "id": "IiBZ2UcIBWNt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fc8a892b-d84b-4c4c-c1a7-92005200565e"
      },
      "execution_count": 167,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8670324076692495"
            ]
          },
          "metadata": {},
          "execution_count": 167
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Stochastic Gradient Descent"
      ],
      "metadata": {
        "id": "2WmvBUU03A70"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wQQJW-jS_boU",
        "outputId": "2d5b1b1e-6651-4736-a95d-1e16417c7c86"
      },
      "execution_count": 168,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(147592, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 168
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_perm, y_perm = permutate(X_train, y_train)\n",
        "X_example = np.reshape(X_perm[0], (-1, 1))\n",
        "y_example = np.reshape(y_perm[0], (-1, 1))"
      ],
      "metadata": {
        "id": "n6ntO9pjCY49"
      },
      "execution_count": 169,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_example.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rqIj66WjCgKD",
        "outputId": "11284531-b8e0-47ec-d871-0954891d5927"
      },
      "execution_count": 170,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 170
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# train the model\n",
        "w_sgd, b_sgd = logisticRegression(X_train, y_train, 1, 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "IcjYb97ja90S",
        "outputId": "299939da-1fe1-4980-b389-2367f90eeb14"
      },
      "execution_count": 197,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  0 , Cost:  0.19089795368053772\n",
            "Iteration:  100 , Cost:  1.9081462781125158\n",
            "Iteration:  200 , Cost:  1.7193880346338615\n",
            "Iteration:  300 , Cost:  0.008841222431959218\n",
            "Iteration:  400 , Cost:  0.6951887727904371\n",
            "Iteration:  500 , Cost:  0.00044182850335515493\n",
            "Iteration:  600 , Cost:  0.7449515539666287\n",
            "Iteration:  700 , Cost:  0.2498945287461088\n",
            "Iteration:  800 , Cost:  0.11092757698097527\n",
            "Iteration:  900 , Cost:  0.40341954036518574\n",
            "Iteration:  1000 , Cost:  0.4468234912990277\n",
            "Iteration:  1100 , Cost:  0.14239286244877833\n",
            "Iteration:  1200 , Cost:  0.7411183512868641\n",
            "Iteration:  1300 , Cost:  0.09004580686825386\n",
            "Iteration:  1400 , Cost:  0.6124455641736877\n",
            "Iteration:  1500 , Cost:  0.3560606590456311\n",
            "Iteration:  1600 , Cost:  0.8056428639572406\n",
            "Iteration:  1700 , Cost:  0.7391255736945362\n",
            "Iteration:  1800 , Cost:  0.022987194632980774\n",
            "Iteration:  1900 , Cost:  0.4134065168393073\n",
            "Iteration:  2000 , Cost:  0.6585333013136417\n",
            "Iteration:  2100 , Cost:  0.31030400332830504\n",
            "Iteration:  2200 , Cost:  0.04203088920313981\n",
            "Iteration:  2300 , Cost:  0.41996749267562844\n",
            "Iteration:  2400 , Cost:  0.13437614479985954\n",
            "Iteration:  2500 , Cost:  0.41644903570283365\n",
            "Iteration:  2600 , Cost:  0.34916282646556424\n",
            "Iteration:  2700 , Cost:  0.28350756047257974\n",
            "Iteration:  2800 , Cost:  0.21996625684099286\n",
            "Iteration:  2900 , Cost:  0.11085590860401985\n",
            "Iteration:  3000 , Cost:  0.18516208788261818\n",
            "Iteration:  3100 , Cost:  0.133933709284318\n",
            "Iteration:  3200 , Cost:  0.06703338272142567\n",
            "Iteration:  3300 , Cost:  0.45106068247369135\n",
            "Iteration:  3400 , Cost:  0.3380659492936131\n",
            "Iteration:  3500 , Cost:  0.8546986511109976\n",
            "Iteration:  3600 , Cost:  0.2106476943861624\n",
            "Iteration:  3700 , Cost:  0.6957830454364007\n",
            "Iteration:  3800 , Cost:  0.06927762628672461\n",
            "Iteration:  3900 , Cost:  0.02047413654483359\n",
            "Iteration:  4000 , Cost:  0.7116164400209408\n",
            "Iteration:  4100 , Cost:  0.17813356808118574\n",
            "Iteration:  4200 , Cost:  0.1005151945627304\n",
            "Iteration:  4300 , Cost:  0.1058793872701655\n",
            "Iteration:  4400 , Cost:  0.4347628054376606\n",
            "Iteration:  4500 , Cost:  0.25339911008362453\n",
            "Iteration:  4600 , Cost:  0.34345015251486294\n",
            "Iteration:  4700 , Cost:  0.37444441246579885\n",
            "Iteration:  4800 , Cost:  0.040448526218457764\n",
            "Iteration:  4900 , Cost:  1.2676645050158073\n",
            "Iteration:  5000 , Cost:  0.36207128138194655\n",
            "Iteration:  5100 , Cost:  2.4832455547975183\n",
            "Iteration:  5200 , Cost:  0.004085867968345462\n",
            "Iteration:  5300 , Cost:  0.28745466333198955\n",
            "Iteration:  5400 , Cost:  0.3164751566084852\n",
            "Iteration:  5500 , Cost:  0.13253847870407545\n",
            "Iteration:  5600 , Cost:  0.1790509988964061\n",
            "Iteration:  5700 , Cost:  0.15687137629972414\n",
            "Iteration:  5800 , Cost:  0.42766805409582337\n",
            "Iteration:  5900 , Cost:  0.40269816552493043\n",
            "Iteration:  6000 , Cost:  0.2199772521745126\n",
            "Iteration:  6100 , Cost:  0.1105593844545616\n",
            "Iteration:  6200 , Cost:  0.2858965263443536\n",
            "Iteration:  6300 , Cost:  0.24999281921376762\n",
            "Iteration:  6400 , Cost:  0.5011867108736943\n",
            "Iteration:  6500 , Cost:  0.27235212121503244\n",
            "Iteration:  6600 , Cost:  0.6241695822932904\n",
            "Iteration:  6700 , Cost:  0.24926507691036054\n",
            "Iteration:  6800 , Cost:  0.3041853464314984\n",
            "Iteration:  6900 , Cost:  0.3124142323095238\n",
            "Iteration:  7000 , Cost:  0.017050238528134257\n",
            "Iteration:  7100 , Cost:  0.7148341869803876\n",
            "Iteration:  7200 , Cost:  0.059368347912716404\n",
            "Iteration:  7300 , Cost:  0.1624916249141604\n",
            "Iteration:  7400 , Cost:  0.9117468275813979\n",
            "Iteration:  7500 , Cost:  0.22024355757188066\n",
            "Iteration:  7600 , Cost:  0.542715986042952\n",
            "Iteration:  7700 , Cost:  0.10107303807544865\n",
            "Iteration:  7800 , Cost:  0.46520021770705483\n",
            "Iteration:  7900 , Cost:  0.44219768145327737\n",
            "Iteration:  8000 , Cost:  0.34850908031475886\n",
            "Iteration:  8100 , Cost:  0.8564491699534946\n",
            "Iteration:  8200 , Cost:  0.214243804213917\n",
            "Iteration:  8300 , Cost:  1.2827592899198106\n",
            "Iteration:  8400 , Cost:  0.3920012678420474\n",
            "Iteration:  8500 , Cost:  0.0010467718574158965\n",
            "Iteration:  8600 , Cost:  0.25851848960969726\n",
            "Iteration:  8700 , Cost:  0.04491133309676985\n",
            "Iteration:  8800 , Cost:  0.006498232843369608\n",
            "Iteration:  8900 , Cost:  0.05728345974000735\n",
            "Iteration:  9000 , Cost:  0.39095367601562264\n",
            "Iteration:  9100 , Cost:  0.46650867878122076\n",
            "Iteration:  9200 , Cost:  0.342790415910626\n",
            "Iteration:  9300 , Cost:  0.6612403304641539\n",
            "Iteration:  9400 , Cost:  0.3831932432422241\n",
            "Iteration:  9500 , Cost:  0.03153572396910868\n",
            "Iteration:  9600 , Cost:  0.005738556242947286\n",
            "Iteration:  9700 , Cost:  0.6022601972019435\n",
            "Iteration:  9800 , Cost:  0.018459372804656503\n",
            "Iteration:  9900 , Cost:  0.5655238698562153\n",
            "Iteration:  10000 , Cost:  1.3912649576601501\n",
            "Iteration:  10100 , Cost:  0.4379190716918812\n",
            "Iteration:  10200 , Cost:  0.23728738975345479\n",
            "Iteration:  10300 , Cost:  0.2691561132849529\n",
            "Iteration:  10400 , Cost:  0.13001352433827887\n",
            "Iteration:  10500 , Cost:  0.2924829633442772\n",
            "Iteration:  10600 , Cost:  0.19423644904251536\n",
            "Iteration:  10700 , Cost:  0.2405678604889221\n",
            "Iteration:  10800 , Cost:  0.31421751227640016\n",
            "Iteration:  10900 , Cost:  0.08521572707318885\n",
            "Iteration:  11000 , Cost:  0.013480365962050501\n",
            "Iteration:  11100 , Cost:  0.8476532678513945\n",
            "Iteration:  11200 , Cost:  0.34755983713118227\n",
            "Iteration:  11300 , Cost:  0.15526705940586538\n",
            "Iteration:  11400 , Cost:  1.9872467853107618\n",
            "Iteration:  11500 , Cost:  0.004855515584048911\n",
            "Iteration:  11600 , Cost:  0.3759914520094993\n",
            "Iteration:  11700 , Cost:  0.010764373352691982\n",
            "Iteration:  11800 , Cost:  0.009067157709100635\n",
            "Iteration:  11900 , Cost:  0.06008930691624317\n",
            "Iteration:  12000 , Cost:  0.3578644097927564\n",
            "Iteration:  12100 , Cost:  2.3547890966340956\n",
            "Iteration:  12200 , Cost:  0.0070520250188012825\n",
            "Iteration:  12300 , Cost:  0.49569798585264574\n",
            "Iteration:  12400 , Cost:  0.21937282171715536\n",
            "Iteration:  12500 , Cost:  0.0036327292204385363\n",
            "Iteration:  12600 , Cost:  0.5723117690164614\n",
            "Iteration:  12700 , Cost:  0.548858048508365\n",
            "Iteration:  12800 , Cost:  0.14924154103759302\n",
            "Iteration:  12900 , Cost:  0.062431328783883064\n",
            "Iteration:  13000 , Cost:  0.002748688785246613\n",
            "Iteration:  13100 , Cost:  1.455437242971848\n",
            "Iteration:  13200 , Cost:  0.026123391089637786\n",
            "Iteration:  13300 , Cost:  0.1074752199618591\n",
            "Iteration:  13400 , Cost:  0.08031134820731924\n",
            "Iteration:  13500 , Cost:  0.009229805737883082\n",
            "Iteration:  13600 , Cost:  0.0001189437782902303\n",
            "Iteration:  13700 , Cost:  0.0810620661508016\n",
            "Iteration:  13800 , Cost:  0.508538214631885\n",
            "Iteration:  13900 , Cost:  0.28296708788579916\n",
            "Iteration:  14000 , Cost:  0.12264496271532813\n",
            "Iteration:  14100 , Cost:  0.7125879345023193\n",
            "Iteration:  14200 , Cost:  0.06348846106149164\n",
            "Iteration:  14300 , Cost:  0.015341227725377804\n",
            "Iteration:  14400 , Cost:  0.11367216436813943\n",
            "Iteration:  14500 , Cost:  0.29503450326181524\n",
            "Iteration:  14600 , Cost:  0.0025904637348195623\n",
            "Iteration:  14700 , Cost:  0.2846579552865273\n",
            "Iteration:  14800 , Cost:  0.014443285377293238\n",
            "Iteration:  14900 , Cost:  0.2708819863906608\n",
            "Iteration:  15000 , Cost:  0.24057243369908982\n",
            "Iteration:  15100 , Cost:  0.010430939069679231\n",
            "Iteration:  15200 , Cost:  0.10026418544212663\n",
            "Iteration:  15300 , Cost:  0.2230232648929256\n",
            "Iteration:  15400 , Cost:  0.2276520746074776\n",
            "Iteration:  15500 , Cost:  0.08736388961699544\n",
            "Iteration:  15600 , Cost:  0.28922285317428525\n",
            "Iteration:  15700 , Cost:  0.20639792723856915\n",
            "Iteration:  15800 , Cost:  0.11948474085227499\n",
            "Iteration:  15900 , Cost:  0.13715787323008938\n",
            "Iteration:  16000 , Cost:  0.054919291968305285\n",
            "Iteration:  16100 , Cost:  0.037811897567940414\n",
            "Iteration:  16200 , Cost:  0.0034572280159610334\n",
            "Iteration:  16300 , Cost:  0.33089826693191876\n",
            "Iteration:  16400 , Cost:  1.8060090484322697\n",
            "Iteration:  16500 , Cost:  0.3471451615629053\n",
            "Iteration:  16600 , Cost:  1.3197462931536799\n",
            "Iteration:  16700 , Cost:  0.26306084699294646\n",
            "Iteration:  16800 , Cost:  0.6364374533173759\n",
            "Iteration:  16900 , Cost:  0.19993646580458124\n",
            "Iteration:  17000 , Cost:  0.007806351142364006\n",
            "Iteration:  17100 , Cost:  0.31584375290178485\n",
            "Iteration:  17200 , Cost:  0.5799320559017356\n",
            "Iteration:  17300 , Cost:  1.9636458475643086\n",
            "Iteration:  17400 , Cost:  0.002202696466430267\n",
            "Iteration:  17500 , Cost:  0.41480291570467553\n",
            "Iteration:  17600 , Cost:  2.500908699750356\n",
            "Iteration:  17700 , Cost:  0.001218756757698846\n",
            "Iteration:  17800 , Cost:  0.702133597249914\n",
            "Iteration:  17900 , Cost:  0.3029672006784151\n",
            "Iteration:  18000 , Cost:  0.0004642964328680438\n",
            "Iteration:  18100 , Cost:  0.11693212748458484\n",
            "Iteration:  18200 , Cost:  1.813549544988863\n",
            "Iteration:  18300 , Cost:  0.2347733343065531\n",
            "Iteration:  18400 , Cost:  0.2863102748459929\n",
            "Iteration:  18500 , Cost:  0.3733988166121987\n",
            "Iteration:  18600 , Cost:  0.1349410113454607\n",
            "Iteration:  18700 , Cost:  0.2502831514012697\n",
            "Iteration:  18800 , Cost:  0.14098993214708433\n",
            "Iteration:  18900 , Cost:  0.011855295668483177\n",
            "Iteration:  19000 , Cost:  0.6921785902759032\n",
            "Iteration:  19100 , Cost:  0.3958559615082431\n",
            "Iteration:  19200 , Cost:  0.31511804330724846\n",
            "Iteration:  19300 , Cost:  0.0668344744071012\n",
            "Iteration:  19400 , Cost:  0.007735949729688377\n",
            "Iteration:  19500 , Cost:  0.15213233032514553\n",
            "Iteration:  19600 , Cost:  0.29995948816421353\n",
            "Iteration:  19700 , Cost:  0.562840824327611\n",
            "Iteration:  19800 , Cost:  0.000674580756839837\n",
            "Iteration:  19900 , Cost:  0.09415568150331964\n",
            "Iteration:  20000 , Cost:  0.002111421715892378\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict the result using the trained model\n",
        "y_pred_lrg_sgd = predict(w_sgd, b_sgd, X_test)"
      ],
      "metadata": {
        "id": "3IyymeRfa-MA"
      },
      "execution_count": 201,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# overall acuracy of the model\n",
        "accuracy_score(y_test, y_pred_lrg_sgd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cDH98PM_bMvl",
        "outputId": "d05bb949-99cc-4db2-e85d-90aeb3400639"
      },
      "execution_count": 202,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.863109756097561"
            ]
          },
          "metadata": {},
          "execution_count": 202
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 score of the model\n",
        "f1_score(y_test, y_pred_lrg_sgd)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B9p5D56LbOKE",
        "outputId": "40c02d0c-70e9-4c6c-9cfd-effe5f7c1a8a"
      },
      "execution_count": 203,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8756025932287915"
            ]
          },
          "metadata": {},
          "execution_count": 203
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Optimization Algorithms\n",
        "\n",
        "When implementing Adagrad and RMSprop, I used a [Towards Data Science](https://towardsdatascience.com/learning-parameters-part-5-65a2f3583f7d) article on learning parameters to help write these functions. These are both implemented to work with batch gradient descent."
      ],
      "metadata": {
        "id": "0zhM6-SaNFrf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Adagrad"
      ],
      "metadata": {
        "id": "WN2qQEx2Kg5R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# train the model\n",
        "w_lrg_adag, b_lrg_adag = logisticRegression(X_train, y_train, 0, 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "pMdYZ6DEKNrz",
        "outputId": "fb596fcd-9f19-49fd-a6e3-25ce662412dd"
      },
      "execution_count": 175,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  0 , Cost:  0.8684696126057012\n",
            "Iteration:  100 , Cost:  0.7722109266275101\n",
            "Iteration:  200 , Cost:  0.7384554996130844\n",
            "Iteration:  300 , Cost:  0.7158093770277051\n",
            "Iteration:  400 , Cost:  0.6987205387383004\n",
            "Iteration:  500 , Cost:  0.6850483375406382\n",
            "Iteration:  600 , Cost:  0.6736975705622538\n",
            "Iteration:  700 , Cost:  0.6640309938584633\n",
            "Iteration:  800 , Cost:  0.6556360503172586\n",
            "Iteration:  900 , Cost:  0.6482319519663171\n",
            "Iteration:  1000 , Cost:  0.6416177935491248\n",
            "Iteration:  1100 , Cost:  0.6356457855330778\n",
            "Iteration:  1200 , Cost:  0.6302050451910354\n",
            "Iteration:  1300 , Cost:  0.6252076992251357\n",
            "Iteration:  1400 , Cost:  0.6205875738521611\n",
            "Iteration:  1500 , Cost:  0.6162919181549592\n",
            "Iteration:  1600 , Cost:  0.6122758949203365\n",
            "Iteration:  1700 , Cost:  0.6085054144619768\n",
            "Iteration:  1800 , Cost:  0.6049515036999497\n",
            "Iteration:  1900 , Cost:  0.6015882342021597\n",
            "Iteration:  2000 , Cost:  0.5983980510134498\n",
            "Iteration:  2100 , Cost:  0.5953589256018341\n",
            "Iteration:  2200 , Cost:  0.592460256900111\n",
            "Iteration:  2300 , Cost:  0.5896883646866292\n",
            "Iteration:  2400 , Cost:  0.5870333284727662\n",
            "Iteration:  2500 , Cost:  0.5844854032119932\n",
            "Iteration:  2600 , Cost:  0.5820354374688036\n",
            "Iteration:  2700 , Cost:  0.5796757414252635\n",
            "Iteration:  2800 , Cost:  0.5774004142888117\n",
            "Iteration:  2900 , Cost:  0.575203174075612\n",
            "Iteration:  3000 , Cost:  0.573076550817838\n",
            "Iteration:  3100 , Cost:  0.5710139478925319\n",
            "Iteration:  3200 , Cost:  0.569014126616358\n",
            "Iteration:  3300 , Cost:  0.5670729297660444\n",
            "Iteration:  3400 , Cost:  0.565186700634924\n",
            "Iteration:  3500 , Cost:  0.5633533670293763\n",
            "Iteration:  3600 , Cost:  0.5615678617275522\n",
            "Iteration:  3700 , Cost:  0.559825005419669\n",
            "Iteration:  3800 , Cost:  0.5581232334148497\n",
            "Iteration:  3900 , Cost:  0.5564597853190699\n",
            "Iteration:  4000 , Cost:  0.5548354603955543\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict the result using the trained model\n",
        "y_pred_lrg_adag = predict(w_lrg_adag, b_lrg_adag, X_test)"
      ],
      "metadata": {
        "id": "1HScSz7hMWRT"
      },
      "execution_count": 176,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# overall acuracy of the model\n",
        "accuracy_score(y_test, y_pred_lrg_adag)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5PlwdfYhNha7",
        "outputId": "0da3340b-b5fa-4029-b8d8-8b115312b18c"
      },
      "execution_count": 177,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7777439024390244"
            ]
          },
          "metadata": {},
          "execution_count": 177
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 score of the model\n",
        "f1_score(y_test, y_pred_lrg_adag)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l9iciQmtNkkx",
        "outputId": "58d65e54-939a-49a2-8716-4b7d5e74079a"
      },
      "execution_count": 178,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7680559974546612"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### RMSprop"
      ],
      "metadata": {
        "id": "ugfJmIllyaYe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# train the model\n",
        "w_lrg_rmsp, b_lrg_rmsp = logisticRegression(X_train, y_train, 0, 2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "daa93515-eaf0-4be5-85a7-3a9dac238164",
        "id": "tXun6SqJyaYi"
      },
      "execution_count": 179,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iteration:  0 , Cost:  0.8684696126057012\n",
            "Iteration:  100 , Cost:  0.5592992072958942\n",
            "Iteration:  200 , Cost:  0.46836031163856223\n",
            "Iteration:  300 , Cost:  0.41458516177825666\n",
            "Iteration:  400 , Cost:  0.3907930826644459\n",
            "Iteration:  500 , Cost:  0.3837721860438979\n",
            "Iteration:  600 , Cost:  0.38347964713894794\n",
            "Iteration:  700 , Cost:  0.38347964703498055\n",
            "Iteration:  800 , Cost:  0.3834796470358068\n",
            "Iteration:  900 , Cost:  0.3834796470358071\n",
            "Iteration:  1000 , Cost:  0.383479647035807\n",
            "Iteration:  1100 , Cost:  0.38347964703580695\n",
            "Iteration:  1200 , Cost:  0.38347964703580695\n",
            "Iteration:  1300 , Cost:  0.38347964703580695\n",
            "Iteration:  1400 , Cost:  0.38347964703580695\n",
            "Iteration:  1500 , Cost:  0.383479647035807\n",
            "Iteration:  1600 , Cost:  0.38347964703580695\n",
            "Iteration:  1700 , Cost:  0.38347964703580695\n",
            "Iteration:  1800 , Cost:  0.38347964703580695\n",
            "Iteration:  1900 , Cost:  0.383479647035807\n",
            "Iteration:  2000 , Cost:  0.38347964703580695\n",
            "Iteration:  2100 , Cost:  0.383479647035807\n",
            "Iteration:  2200 , Cost:  0.383479647035807\n",
            "Iteration:  2300 , Cost:  0.383479647035807\n",
            "Iteration:  2400 , Cost:  0.383479647035807\n",
            "Iteration:  2500 , Cost:  0.38347964703580695\n",
            "Iteration:  2600 , Cost:  0.38347964703580706\n",
            "Iteration:  2700 , Cost:  0.38347964703580695\n",
            "Iteration:  2800 , Cost:  0.38347964703580695\n",
            "Iteration:  2900 , Cost:  0.383479647035807\n",
            "Iteration:  3000 , Cost:  0.38347964703580695\n",
            "Iteration:  3100 , Cost:  0.38347964703580695\n",
            "Iteration:  3200 , Cost:  0.38347964703580695\n",
            "Iteration:  3300 , Cost:  0.38347964703580695\n",
            "Iteration:  3400 , Cost:  0.38347964703580695\n",
            "Iteration:  3500 , Cost:  0.383479647035807\n",
            "Iteration:  3600 , Cost:  0.38347964703580695\n",
            "Iteration:  3700 , Cost:  0.38347964703580695\n",
            "Iteration:  3800 , Cost:  0.38347964703580706\n",
            "Iteration:  3900 , Cost:  0.38347964703580695\n",
            "Iteration:  4000 , Cost:  0.38347964703580695\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# predict the result using the trained model\n",
        "y_pred_lrg_rmsp = predict(w_lrg_rmsp, b_lrg_rmsp, X_test)"
      ],
      "metadata": {
        "id": "szL5W-lcyaYi"
      },
      "execution_count": 180,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# overall acuracy of the model\n",
        "accuracy_score(y_test, y_pred_lrg_rmsp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9298d36e-81e5-47ec-cb0e-691e82c66ae5",
        "id": "lC0F6sOjyaYj"
      },
      "execution_count": 181,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8619512195121951"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# f1 score of the model\n",
        "f1_score(y_test, y_pred_lrg_rmsp)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28441014-c8fb-4cc3-af7e-f6271ef6fc2d",
        "id": "reNo_YDPyaYk"
      },
      "execution_count": 182,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8733497426717387"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Conclusion\n",
        "\n",
        "To keep the playing field even, I ran the default update, the Adagrad update, and the RMSprop update at the same learning rate, that being 0.01. I chose this because I tend to use a learning rate of 0.1, but the [gradient descent paper](https://arxiv.org/pdf/1609.04747.pdf) recommended using learning rates of 0.01 and 0.001 for Adagrad and RMSprop respectively. 0.01 was in the middle of these values, and I wanted to try it in this scenario. To give these algorithms time to converge, I ran them for 4000 epochs each. Below are the recorded results.\n",
        "\n",
        "| **Update** | **Accuracy Score** | **F1 Score** |\n",
        "|------------|--------------------|--------------|\n",
        "| Original   | 0.85664            | 0.86703      |\n",
        "| Adagrad    | 0.77774            | 0.76806      |\n",
        "| RMSprop    | 0.86195            | 0.87335      |\n",
        "\n",
        "Adagrad adapts its learning rate to the parameters. Larger updates are performed for infrequent parameters, and smaller updates are performed for frequent parameters. This is what makes it work well with sparse data. It left me curious as to how well it would perform on abundant data. Of the three, this algorithm performs the worst in accuracy and f1 scores. I believe this is due to the weakness mentioned in the paper - accumulating squared digits in the denominator. We can glean this from how its slope tapers off in a similar manner to the original implementation, but it does so at an greater cost value. Its learning rate becomes too small and has little impact on updates.\n",
        "\n",
        "On the contrary, RMSprop performs the best of the three in accuracy and f1 score. It is clear why the book recommends a learning rate of 0.001 as at rate 0.01, it finds its minimum - the lowest of the three - in under 1000 epochs. RMSprop was intended to resolve Adagrad's diminishing learning rate problem while still featuring an adaptive learning rate.\n",
        "\n",
        "The original implementation of gradient descent sits between these optimizations. The original implementation's line resembles that of Adagrad tapering off, but it ends with a lower minimum than Adagrad within 4000 epochs. This demonstrates how poorly Adagrad performs as an optimization when working with non-sparse data. \n",
        "\n",
        "Generally, we would want to use optimization algorithms because they allow us to find the minimum more efficiently. RMSprop demonstrates what can go right with optimization algorithms. Considering how there are over 160000 examples to sift through after oversampling, we need to move through it as efficiently as possible. An adaptive learning rate aids in quickly finding a minimum from this wealth of data, which grants us more accurate answers in less time.\n",
        "\n",
        "That said, we must choose the right algorithm for a situation. Adagrad performing worse than the original gradient descent demonstrates what can go wrong. We found it was not suited for this application. It is then clear that RMSprop is the better optimization algorithm for this scenario."
      ],
      "metadata": {
        "id": "tCCxiLTVLCmp"
      }
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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        "version": 3
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      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
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    "colab": {
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
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    "gpuClass": "standard"
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