earthquake_surrogate.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Earthquake Surrogate\n",
        "\n",
        "- - -"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u9JNIN_8lu3g"
      },
      "source": [
        "### Import Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "MtYs5jhAlfxE"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import torch\n",
        "import torch.nn as nn\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import numpy as np\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "\n",
        "import xgboost as xgb\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "Vgyomw5tlf29",
        "outputId": "be6ead7e-59f2-4836-b1f6-5d0b535b0037"
      },
      "outputs": [],
      "source": [
        "csv_file = \"Formatted_ETAS_Output.csv\"\n",
        "df = pd.read_csv(csv_file, sep=',', lineterminator='\\n')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gd7W0ryClf6M",
        "outputId": "6cc1154c-a219-4c9c-f89d-d5b6910a2832"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "           Date        Time          Year            X             Y  \\\n",
            "count     32001       32001  32001.000000  32001.00000  32001.000000   \n",
            "unique    19127       26803           NaN          NaN           NaN   \n",
            "top     4/26/64  0:31:11.00           NaN          NaN           NaN   \n",
            "freq         10           5           NaN          NaN           NaN   \n",
            "mean        NaN         NaN   1991.924790   -117.54676     34.810868   \n",
            "std         NaN         NaN     18.407271      2.11036      2.388448   \n",
            "min         NaN         NaN   1960.002196   -123.48700     29.080400   \n",
            "25%         NaN         NaN   1975.851679   -118.73330     33.343000   \n",
            "50%         NaN         NaN   1992.311728   -117.20600     34.587100   \n",
            "75%         NaN         NaN   2007.784510   -116.10520     36.820000   \n",
            "max         NaN         NaN   2023.487118   -113.24630     39.475100   \n",
            "\n",
            "           Magnitude           Z\\r  \n",
            "count   32001.000000  32001.000000  \n",
            "unique           NaN           NaN  \n",
            "top              NaN           NaN  \n",
            "freq             NaN           NaN  \n",
            "mean        3.452693      9.615045  \n",
            "std         0.460765      6.170866  \n",
            "min         3.000000      0.000000  \n",
            "25%         3.130000      4.236500  \n",
            "50%         3.310000      9.076000  \n",
            "75%         3.630000     15.139700  \n",
            "max         7.650000     20.000000  \n"
          ]
        }
      ],
      "source": [
        "summary_stats = df.describe(include=\"all\")\n",
        "print(summary_stats)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "x38iwIW7l-Bj",
        "outputId": "3f8d6a7b-54df-4afd-b213-40ad898a08b5"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Data Table Transformation\n",
        "\n",
        "Variables were made to be suitable for machine learning models. For example the date was put into a format where the day, month, and year were separated. Furthermore we extrapolate from the dates the quarter of the year the earthquake occurred. Feature encoding was also performed, specifically ordinal encoding."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "6rjyE18GmAMb",
        "outputId": "aa71a85d-98c3-40cd-ed0c-ca9b171ea8bd"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<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>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>Magnitude</th>\n",
              "      <th>Z\\r</th>\n",
              "      <th>Day</th>\n",
              "      <th>Month</th>\n",
              "      <th>DayOfWeek</th>\n",
              "      <th>Quarter</th>\n",
              "      <th>Hour</th>\n",
              "      <th>Minute</th>\n",
              "      <th>Second</th>\n",
              "      <th>Millisecond</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>-119.0502</td>\n",
              "      <td>33.9790</td>\n",
              "      <td>6.50</td>\n",
              "      <td>8.2474</td>\n",
              "      <td>31</td>\n",
              "      <td>12</td>\n",
              "      <td>3</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-115.6222</td>\n",
              "      <td>33.0793</td>\n",
              "      <td>4.25</td>\n",
              "      <td>7.9322</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>8</td>\n",
              "      <td>49</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-115.6323</td>\n",
              "      <td>33.1220</td>\n",
              "      <td>3.03</td>\n",
              "      <td>8.4015</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>31</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-115.5851</td>\n",
              "      <td>33.0745</td>\n",
              "      <td>3.03</td>\n",
              "      <td>7.9678</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>10</td>\n",
              "      <td>32</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-115.6256</td>\n",
              "      <td>33.0290</td>\n",
              "      <td>3.08</td>\n",
              "      <td>7.9737</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>11</td>\n",
              "      <td>7</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          X        Y  Magnitude     Z\\r  Day  Month  DayOfWeek  Quarter  Hour  \\\n",
              "0 -119.0502  33.9790       6.50  8.2474   31     12          3        4     0   \n",
              "1 -115.6222  33.0793       4.25  7.9322    2      1          5        1     0   \n",
              "2 -115.6323  33.1220       3.03  8.4015    2      1          5        1     0   \n",
              "3 -115.5851  33.0745       3.03  7.9678    2      1          5        1     0   \n",
              "4 -115.6256  33.0290       3.08  7.9737    2      1          5        1     0   \n",
              "\n",
              "   Minute  Second  Millisecond  \n",
              "0       3       9            0  \n",
              "1       8      49            0  \n",
              "2      10      31            0  \n",
              "3      10      32            0  \n",
              "4      11       7            0  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "new_df = df.copy()\n",
        "\n",
        "# Convert \"Date\" column to datetime in the new DataFrame\n",
        "new_df[\"Date\"] = pd.to_datetime(new_df[\"Date\"], errors=\"coerce\", format=\"%m/%d/%y\")\n",
        "# Adjust two-digit year values to four-digit format in the new DataFrame\n",
        "new_df.loc[new_df[\"Date\"].dt.year > pd.Timestamp.now().year, \"Date\"] -= pd.DateOffset(years=100)\n",
        "\n",
        "# Extract components from the \"Date\" column in the new DataFrame\n",
        "new_df[\"Day\"] = new_df[\"Date\"].dt.day\n",
        "new_df[\"Month\"] = new_df[\"Date\"].dt.month\n",
        "new_df[\"Year\"] = new_df[\"Date\"].dt.year\n",
        "new_df[\"DayOfWeek\"] = new_df[\"Date\"].dt.dayofweek\n",
        "new_df[\"Quarter\"] = new_df[\"Date\"].dt.quarter\n",
        "\n",
        "# Convert \"Time\" column to datetime in the new DataFrame\n",
        "new_df[\"Time\"] = pd.to_datetime(new_df[\"Time\"], format=\"%H:%M:%S.%f\")\n",
        "\n",
        "# Extract time components in the new DataFrame\n",
        "new_df[\"Hour\"] = new_df[\"Time\"].dt.hour\n",
        "new_df[\"Minute\"] = new_df[\"Time\"].dt.minute\n",
        "new_df[\"Second\"] = new_df[\"Time\"].dt.second\n",
        "new_df[\"Millisecond\"] = new_df[\"Time\"].dt.microsecond // 1000\n",
        "\n",
        "# Drop the original \"Time\" column from the new DataFrame\n",
        "new_df = new_df.drop(\"Time\", axis=1)\n",
        "\n",
        "# Drop the \"Date\" column from the new DataFrame\n",
        "new_df = new_df.drop(\"Date\", axis=1)\n",
        "new_df = new_df.drop(\"Year\", axis=1)\n",
        "\n",
        "new_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Model Training And Preprocessing\n",
        "\n",
        "Once the data was transformed and made suitable for our models, we split the data using a 70% training set to 30% testing set split.\n",
        "\n",
        "For certain models, relative influence was able to be calculated in order to see which predictor variables affected the specific model the most. This calculation and visualizations can be seen specifically for the XGBoost, Linear Regression, and Random Forest models."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IqHWNfRsmFT9",
        "outputId": "9015a26f-9f3b-4c0f-a5d6-3072500e9f3d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Scaled X_train:\n",
            "[[ 0.57704818 -1.19823102  1.49530055 ... -1.17876566 -0.83204313\n",
            "   0.        ]\n",
            " [-0.30276807  1.3833182  -0.79177252 ...  0.78103568  1.0807553\n",
            "   0.        ]\n",
            " [ 0.01798791  0.38587671 -1.28610312 ... -1.00584201 -0.89000671\n",
            "   0.        ]\n",
            " ...\n",
            " [-0.40360552 -0.5749714   1.65289841 ... -0.89055958  1.54446401\n",
            "   0.        ]\n",
            " [ 0.72015396  0.9207512   1.45023324 ... -0.37178863  0.84890095\n",
            "   0.        ]\n",
            " [-1.95810392  1.04719511  1.58157829 ...  1.70329514  1.02279171\n",
            "   0.        ]]\n",
            "y_train:\n",
            "31932    3.21\n",
            "31187    4.40\n",
            "14148    4.23\n",
            "23611    3.07\n",
            "23500    3.51\n",
            "         ... \n",
            "29802    3.24\n",
            "5390     3.05\n",
            "860      3.14\n",
            "15795    3.29\n",
            "23654    5.13\n",
            "Name: Magnitude, Length: 22400, dtype: float64\n",
            "Scaled X_test:\n",
            "[[ 1.04958158 -1.03921315  1.44085029 ...  0.95395933  1.19668248\n",
            "   0.        ]\n",
            " [-0.04205018  0.70303321  0.13277997 ... -1.3516893  -0.426298\n",
            "   0.        ]\n",
            " [-0.2884101   1.43117429 -1.3567102  ... -0.48707107  1.25464607\n",
            "   0.        ]\n",
            " ...\n",
            " [-1.14211665  0.05515471  0.55235489 ... -1.29404809 -1.64353337\n",
            "   0.        ]\n",
            " [-0.01992091  0.33081918 -0.01669897 ...  1.5880127  -1.00593389\n",
            "   0.        ]\n",
            " [-0.12080575  1.40986305 -0.00563065 ...  0.43518839  1.0807553\n",
            "   0.        ]]\n",
            "y_test:\n",
            "23100    3.02\n",
            "17362    3.26\n",
            "8993     3.26\n",
            "19566    3.00\n",
            "3798     3.79\n",
            "         ... \n",
            "1943     3.26\n",
            "29579    3.10\n",
            "5955     3.92\n",
            "18112    3.00\n",
            "13274    3.60\n",
            "Name: Magnitude, Length: 9601, dtype: float64\n"
          ]
        }
      ],
      "source": [
        "X = new_df.drop(\"Magnitude\", axis=1)  # Features\n",
        "y = new_df[\"Magnitude\"]  # Target variable\n",
        "\n",
        "# Create a StandardScaler object\n",
        "scaler = StandardScaler()\n",
        "\n",
        "# Scale the features\n",
        "X_scaled = scaler.fit_transform(X)\n",
        "\n",
        "# Split the scaled data into training and testing sets\n",
        "X_train, X_test, y_train, y_test = train_test_split(X_scaled, y, test_size=0.3, random_state=42)\n",
        "\n",
        "# Print the data to verify the changes\n",
        "print(\"Scaled X_train:\")\n",
        "print(X_train)\n",
        "\n",
        "print(\"y_train:\")\n",
        "print(y_train)\n",
        "\n",
        "print(\"Scaled X_test:\")\n",
        "print(X_test)\n",
        "\n",
        "print(\"y_test:\")\n",
        "print(y_test)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AVS3WXwfqfEV"
      },
      "source": [
        "### XGB Regressor\n",
        "\n",
        "A variation of a gradient boosting algorithm best suited for regression and classification tasks through decision trees"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "k0Aa3qUg5cuS",
        "outputId": "e3463b1b-ef3c-47bd-b725-48a165952a20"
      },
      "outputs": [
        {
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              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=42, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=42, ...)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
              "             colsample_bylevel=None, colsample_bynode=None,\n",
              "             colsample_bytree=None, early_stopping_rounds=None,\n",
              "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "             gamma=None, gpu_id=None, grow_policy=None, importance_type=None,\n",
              "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
              "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "             n_estimators=100, n_jobs=None, num_parallel_tree=None,\n",
              "             predictor=None, random_state=42, ...)"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "xgb_model = xgb.XGBRegressor(objective=\"reg:squarederror\", random_state=42)\n",
        "xgb_model.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKV52hbp50Jo",
        "outputId": "78968ca5-2e2d-4b63-b233-53b9c8717001"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "XGB Regressor R^2: -0.07775879866078106\n",
            "XGB Regressor MAE: 0.35240284276075656\n",
            "XGB Regressor MSE: 0.23095209796608107\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import r2_score\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "val_score_xgb = xgb_model.score(X_test, y_test)\n",
        "print(\"XGB Regressor R^2:\", val_score_xgb)\n",
        "\n",
        "predicted = xgb_model.predict(X_test)\n",
        "MAE_xgb = mean_absolute_error(y_test, predicted)\n",
        "print(\"XGB Regressor MAE:\", MAE_xgb)\n",
        "\n",
        "mean_squared_score_xgb = mean_squared_error(y_test, predicted)\n",
        "print(\"XGB Regressor MSE:\", mean_squared_score_xgb)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### XGB Regressor - Actual vs. Predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "NvAy05cc-aQj",
        "outputId": "0f554fb7-fd5c-4482-a2df-52a893d77433"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.scatterplot(x=y_test, y=predicted, color='#1f77b4', alpha=0.5, edgecolor='none')\n",
        "sns.lineplot(x=y_test, y=y_test, color='red', linestyle='dashed')\n",
        "\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.title('XGB Regressor - Actual vs. Predicted')\n",
        "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
        "\n",
        "sns.despine()\n",
        "plt.gca().patch.set_facecolor('none')\n",
        "plt.gca().set_frame_on(True)\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Feature Importance In XGB Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 900
        },
        "id": "lA3Ofq9D7qoz",
        "outputId": "c825b421-5f51-4345-e225-ab3f3728dee3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Relative Influence of Features:\n",
            "        Feature  Importance\n",
            "2           Z\\r    0.126057\n",
            "1             Y    0.123776\n",
            "7          Hour    0.112574\n",
            "8        Minute    0.111738\n",
            "4         Month    0.110232\n",
            "0             X    0.108772\n",
            "3           Day    0.107846\n",
            "9        Second    0.103845\n",
            "5     DayOfWeek    0.095160\n",
            "6       Quarter    0.000000\n",
            "10  Millisecond    0.000000\n"
          ]
        }
      ],
      "source": [
        "# Convert X_test to a DataFrame\n",
        "X_test_df = pd.DataFrame(X_test, columns=X.columns)\n",
        "\n",
        "# Get the feature importances\n",
        "feature_importance = xgb_model.feature_importances_\n",
        "feature_names = X_test_df.columns\n",
        "\n",
        "# Create a DataFrame to store the feature importances\n",
        "feature_importance_df_1 = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importance})\n",
        "\n",
        "# Sort the features by importance in descending order\n",
        "feature_importance_df_1 = feature_importance_df_1.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "print(\"Relative Influence of Features:\")\n",
        "print(feature_importance_df_1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ") missing from current font.l\\Programs\\Python\\Python310\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 13 (\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "data": {
            "image/png": 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mZmY55CTOzMzMLIecxJmZmZnlkBf7NTOzqlDtO0+Ad5+w6uIkzszMqkK17zwB3n3Cqou7U83MzMxyyEmcmZmZWQ45iTMzMzPLISdxZmZmZjnU40mcpMmS7pW0ML2fVOaaEyU9IGmLpMtKztVImifpaUlPSTq356I3MzMzqw5ZtMRdAcyLiMnAPODKMtc8A5wLfK3MuTOBA4BJwHRgrqQJ3ROqmZmZWXXq0SRO0mhgCnB9WnQ9MEXSqOLrIuKpiHgY2F7mac4AvhcROyJiNXAzcHqZ1xouaULxDRjXZZUxMzMzy1BPrxO3D7AsIloBIqJV0vK0fHUHn2M88GzR8XPp40tdBMwp9wTnnHPORU1NTVcBM4H6utqh/Y5YvmjyuoGD12/vU9Nav3nDiMUjRi0bt655TJ+IPotGjF62/4sr91kzaMg6gBEvbap7euSYJRPXrGrcIe1YWle/csKa1Y3Ng2vX9N3RWlPXsnnYk3s1PDvphRX7bq3pu3Xl0LrmfdY1N6waOqx50LatA2u3tAwpnG/p26+leXDtusb1L455vnb46totLw0ZsnXL4ML5Tf0HbN4wYNCmxhnTG5qamqYC04AG4LvA+cBCYHlalxtnTDly1Pjli15+/mqr06ykHrPTuE8EftFWnWbNmN7Qsmn9+jEb19X3b93ev9rq1Dhj+timpqa905gXt1WnWTOmNwxsXsFL/fq3jN64vn5JXf2KaqtTy7hG0vdlQtH7sFOdZs2Y3rB2a8sLk15Yse+GAQM3VWOdNjaM7d/U1DQXuB2Y3FadxjWMHXTgqqXjB27fNrDw+Gqq04raoVvTeqwAHgBOaatOE8Y1Djli+aLJy4aNXFm/eUNd1dXp4INGpHUpxFy2ToXP4sLn3tgNa0dVW53Sz7CZwN5p3O3Wqb3fp/Y+ywt/n4rOLwA2kvRAXQ+cDAwArgNmAw+SmArMB94DbAF+CbwbuBcYChxW9JzNwJ3AO9N716kK6zRnzpy5tEER0da5LidpKnB1RBxSVPYYcFZEPFTm+rnA0Ij4eFHZAuDsiLg/Pb4YGBcRHy157HBgeMlTjgPuBiZGxOJC4YAxDQ/lYIHJxi0rV0zZ1XXVXpeO1gN6T12qvR7gulQj/65Up0reF7Pu1tMtcUuARkk1aStcDUmWvKSC53gO2Be4Pz0ubZkDICLWAmuLyyR1ImQzMzOz6tOjY+IiYhXwMEmTIen9n9OxbR11A3CepD7pWLpTgZ90ZZxmZmZm1S6L2akfBC6UtBC4MD1G0i2SpqVfHyNpKfAx4AOSlko6KX38NSSzV58E7gMujYhFPV0JMzMzsyz1dHcqEfEEcHSZ8rcWff172phJmk6K+FC3BWhmZmaWA96xwczMzCyHnMSZmZmZ5ZCTODMzM7McchJnZmZmlkNO4szMzMxyyEmcmZmZWQ71+BIjZmZmvV2/kfU39enXf0LWcbRnx7ati7e92PyOrOOwznMSZ2Zm1sX69Os/IQf7wE7IOgbbPe5ONTMzM8shJ3FmZmZmOeQkzszMzCyHnMSZmZmZ5ZCTODMzM7McchJnZmZmlkNO4szMzMxyyEmcmZmZWQ45iTMzMzPLISdxZmZmZjnkJM7MzMwsh5zEmZmZmeWQkzgzMzOzHHISZ2ZmZpZDTuLMzMzMcshJnJmZmVkOOYkzMzMzyyEncWZmZmY55CTOzMzMLIecxJmZmZnlkJM4MzMzsxxyEmdmZmaWQ07izMzMzHLISZyZmZlZDjmJMzMzM8shJ3FmZmZmOeQkzszMzCyHOpXESeoj6VBJx0oa0tVBmZmZmVn7Kk7iJF0APA/8BbgDeE1afrOkj3ZteGZmZmZWTkVJnKTzgG8BNwNnACo6fTfwzi6LzMzMzMzaVGlL3MeAr0fE+cBPS849QdoqZ2ZmZmbdq9IkbiJwWxvnNgHDdysaMzMzM+uQSpO4F4AJbZx7DbBsV08gabKkeyUtTO8nlbmmRtI8SU9LekrSuUXnRkv6laRHJD0u6duS+lZYDzMzM7NcqzSJ+yXwOUn7FZWFpL2AfyEZK7crVwDzImIyMA+4ssw1ZwIHAJOA6cBcSRPSc5cAj0fE4cDhwFTgHRXWw8zMzCzXKk3iPgNsAR4FfgsE8J/A40ArcGl7D5Y0GpgCXJ8WXQ9MkTSq5NIzgO9FxI6IWE2SHJ6engugVlIfYADQnzItgJKGS5pQfAPGVVZdMzMzs+pUUTdkRLwgaRpwEXAS8HT6HJcD34iI9bt4in2AZRHRmj5fq6TlafnqouvGA88WHT+XXgPweeBGYAUwBLg8Iu4p81oXAXPKBXHOOedc1NTUdBUwE6ivqx3a74jliyavGzh4/fY+Na31mzeMWDxi1LJx65rH9Inos2jE6GX7v7hynzWDhqwDGPHSprqnR45ZMnHNqsYd0o6ldfUrJ6xZ3dg8uHZN3x2tNXUtm4c9uVfDs5NeWLHv1pq+W1cOrWveZ11zw6qhw5oHbds6sHZLy5DC+Za+/VqaB9eua1z/4pjna4evrt3y0pAhW7cMLpzf1H/A5g0DBm1qnDG9oampaSowDWgAvgucDywElqd1uXHGlCNHjV++6OXnr7Y6zUrqMTuN+0TgF23VadaM6Q0tm9avH7NxXX3/1u39q61OjTOmj21qato7jXlxW3WaNWN6w8DmFbzUr3/L6I3r65fU1a+otjq1jGskfV8mFL0PO9Vp1ozpDWu3trww6YUV+24YMHBTNdZpY8PY/k1NTXOB24HJbdVpXMPYQQeuWjp+4PZtAwuPr6Y6ragdujWtxwrgAeCUtuo0YVzjkCOWL5q8bNjIlfWbN9RVXZ0OPmhEWpdCzGXrVPgsLnzujd2wdlS11Sn9DJsJ7J3GXbZO0w47dOQBRZ/F1VinkVOOHNzU1HQYycoSd+6qTu39PrX3WV74+5Te1xedXwBsJOlxux44maRx5jpgNvAgianAfOA9JA1JvwTeDdwLDAUOK3rO5rQuvaZOc+bMmUsbFBFtnetykqYCV0fEIUVljwFnRcRDRWULgLMj4v70+GJgXER8VNIHgINJum9rgV8D/xERPyl5reHsPNFiHMlSKBMjYnGhcMCYhocOnDd/l+P5svTEBbMbt6xcMWVX11V7XTpaD+g9dan2eoDrUo38u1Kd9sS6WPWqdJ24yZKObePcm8pNUiixBGiUVJM+poYkS15Sct1zwL5Fx+OLrrkQ+EHa1boO+BlwXOkLRcTaiFhcfAOW7iI+MzMzs1yodEzcN0maIMs5GfhGew+OiFXAwyRNhqT3f07HvRW7ATgv3d5rFHAqUGhpWwS8GUBSf+DvSMbomZmZme0xKk3ipgF3tXHuLuCoDjzHB4ELJS0kaVX7IICkW9LxdgDXAM8ATwL3AZdGxKL03EXAG9Mu14dJ+qe/V2E9zMzMzHKt0vXVaoGWNs5tA+p29QQR8QRwdJnytxZ93Qp8qI3HPw2c0JFgzczMzHqrSlvingFmtXHueJJZHWZmZmbWzSpN4q4G/kXSBZIGAEgaIOkCkm7O73dxfGZmZmZWRqXdqZeRjHv7L+Bbkl4ERpIkgzcCX+3a8MzMzMysnEoX+20FTpN0PMm4tHqS/VRvj4g7uz48MzMzMyunUxvHR8QdwB1dHIuZmZmZdVCnkjh4eR/UgaXlEfHcbkVkZmZmZrtUURInaRjwLZIN6ge0cVnN7gZlZmZmZu2rtCVuHsmmsleRbPK6pcsjMjMzM7NdqjSJezPwiYiY1x3BmJmZmVnHVLpOHMDfujwKMzMzM6tIpUncD4FTuiMQMzMzM+u4SrtTbwe+KakWuAV4sfSCdPkRMzMzM+tGlSZxP0vvJwKzi8oDUHrv2almZmZm3azSJO64bonCzMzMzCpS6bZbv+uuQMzMzMys4zozO9XMzMzMMlbxtluSDgHOBV7DzttuRUTM6orAzMzMzKxtlW67dTTwO2AxMAl4BBgBjAeWAk91cXxmZmZmVkal3alfAm4CDiGZjXpOREwA/o5kVuoXujQ6MzMzMyur0iTucOBakqVEIF1OJF0b7gvAl7suNDMzMzNrS6VJXH9gU0TsIFnot6Ho3N+AQ7sqMDMzMzNrW6VJ3FNAY/r1I8DZkvpI6gO8H3i+K4MzMzMzs/IqnZ36C2AmcB3J+LhfAeuBVmAo8NGuDM7MzMzMyqt0sd+5RV//VtLrgXcCg4FbI+L2rg3PzMzMzMqpeJ24YhHxZ+DPXRSLmZmZmXVQRWPiJLVKel0b56ZKau2asMzMzMysPZVObFA752p4ZekRMzMzM+tGHepOTWefFhK4wmzUYoOAtwAvdGFsZmZmZtaGXSZxkuYAn0sPA7inncu/3RVBmZmZmVn7OtISd2d6L5Jk7iqSfVKLbQEeA37ZZZGZmZmZWZt2mcRFxO9INr1H0nDgaxGxrJvjMjMzM7N2dHhig6S+wEeAKd0XjpmZmZl1RIeTuIjYDqwk2Z3BzMzMzDJU6RIj1wLndkcgZmZmZtZxle7YsBh4j6T7gZ8BKyhZGy4i/qdrQjMzMzOztlSaxM1L7xuBqWXOB+AkzszMzKybVZrETeyWKMzMzMysIhUlcRHxbHcFYmZmZmYdV2lLHACSDgWOBUYCLwJ3RsRfuzIwMzMzM2tbRUlculbcfODdvLKXKkBIug6YHRFegsTMzMysm1W6xMgc4F0k229NJNn4fmJ6fAav7LFqZmZmZt2o0iTuLOALEfHFiHg2Irak918EvgC8d1dPIGmypHslLUzvJ5W5pkbSPElPS3pK0rkl598laYGkR9P7MRXWw8zMzCzXKk3i9gb+0Ma5P6Tnd+UKYF5ETCZZsuTKMtecCRwATAKmA3MlTQCQNA2YC5wQEYcCxwDrOl4FMzMzs/yrNIlbDryhjXMz0vNtkjSaZO/V69Oi64EpkkaVXHoG8L2I2BERq4GbgdPTc/8CXBYRzwNExLqIaCnzWsMlTSi+AeN2VUEzMzOzPKh0duoPgE9L2pF+vQIYC/wj8Gngq7t4/D7AssLkh4holbQ8LV9ddN14oHg5k+fSawAOBhZJugsYCtwEfDEiXrVzBHARyRi+nZxzzjkXNTU1XQXMBOrraof2O2L5osnrBg5ev71PTWv95g0jFo8YtWzcuuYxfSL6LBoxetn+L67cZ82gIesARry0qe7pkWOWTFyzqnGHtGNpXf3KCWtWNzYPrl3Td0drTV3L5mFP7tXw7KQXVuy7tabv1pVD65r3WdfcsGrosOZB27YOrN3SMqRwvqVvv5bmwbXrGte/OOb52uGra7e8NGTI1i2DC+c39R+wecOAQZsaZ0xvaGpqmgpMAxqA7wLnAwtJkueZwI0zphw5avzyRS8/f7XVaVZSj9lp3CcCv2irTrNmTG9o2bR+/ZiN6+r7t27vX211apwxfWxTU9PeacyL26rTrBnTGwY2r+Clfv1bRm9cX7+krn5FtdWpZVwj6fsyoeh92KlOs2ZMb1i7teWFSS+s2HfDgIGbqrFOGxvG9m9qapoL3A5MbqtO4xrGDjpw1dLxA7dvG1h4fDXVaUXt0K1pPVYADwCntFWnCeMahxyxfNHkZcNGrqzfvKGu6up08EEj0roUYi5bp8JnceFzb+yGtaOqrU7pZ9hMkp6nyW3Vadphh448oOizuBrrNHLKkYObmpoOA94J3LmrOrX3+9TeZ3nh71N6X190fgGwkaTH7XrgZGAAcB0wG3iQxFSSSZXvAbYAvySZYHkvSR5wWNFzNqd16TV1mjNnzlzaoJ1zn7als1OvJknaih+oNNj3RcT2dh4/Fbg6Ig4pKnsMOCsiHioqWwCcHRH3p8cXA+Mi4qOSHiH5Jp8O9AduBa6MiKtLXms4MLwkhHHA3cDEiFhcKBwwpuGhA+fNX9aBb0FmnrhgduOWlSum7Oq6aq9LR+sBvacu1V4PcF2qkX9XqtOeWBerXpUu9rudZO/UL5KsEzeCZJ24uzq4TtwSoFFSTdoKV0OSJS8pue45YF/g/vS4uGXuOeAnEbEF2CLpZ8DrSJLL4ljXAmuLy6TiVVHMzMzM8qtTi/2mCVvFi/tGxCpJD5M0GV6b3v85HfdW7AbgPEk3kTRTngq8MT13HfBWSdek8c8CftKJapiZmZnlVsVJXNp69l6S/t5GYBnJzNRrOrjQ7weB70v6HLAmfS4k3QJ8LiIeAK4BjgaeTB9zaUQsSr/+IUkf9WPADuA24KpK62FmZmaWZ5Xu2LAvSdI0GVgKrCQZfHcu8G+S3ryr/VUj4gmSBK20/K1FX7cCH2rj8TuAj6U3MzMzsz1SpUuMXA4MA46JiPERcVREjCfp6qwD/qurAzQzMzOznVWaxB0PfCoiXrXgb0TcA1ySnjczMzOzblZpErcRWNXGuVXA5t0Lx8zMzMw6otIk7lqSiQnlfICSZT7MzMzMrHtUOjv1KeD0dDHeG0kmNowBTgNqgV9LOrtwcUT8T1cFamZmZmavqDSJm5fejwMOKXP+20VfB+AkzszMzKwbVJrETeyWKMzMzMysIpVuu9XuGnBmZmZm1jM6te1WgaSdJkaki/GamZmZWTeqaHaqpEGSviLpaUlbgG0lt63dEKOZmZmZlai0Je7bwJnAL0j2MHXSZmZmZpaBSpO4vwc+HhH/2R3BmJmZmVnHVLrY7xbg8e4IxMzMzMw6rtIkbj7wj90Qh5mZmZlVoNLu1M8C35F0O3AbsKb0Au/SYGZmZtb9Kk3ippKMixsN/F2Z896lwczMzKwHVJrEXQE0A+cBT+DZqWZmZmaZqDSJOxA4LSJu6Y5gzMzMzKxjKp3Y8DdgSHcEYmZmZmYdV2kS90ngM5L27Y5gzMzMzKxjKu1O/QzJpIaFkhay8+zUiIhjuyQyMzMzM2tTpUlcK8mEBjMzMzPLUEVJXETM7KY4zMzMzKwCu0ziJO1XyRNGxDOdD8fMzMzMOqIjLXFPkSziuytKr6vZrYjMzMzMbJc6ksS9v9ujMDMzM7OK7DKJi4jv90QgZmZmZtZxla4TZ2ZmZmZVwEmcmZmZWQ45iTMzMzPLISdxZmZmZjnkJM7MzMwsh5zEmZmZmeWQkzgzMzOzHHISZ2ZmZpZDTuLMzMzMcshJnJmZmVkOOYkzMzMzyyEncWZmZmY55CTOzMzMLId6PImTNFnSvZIWpveTylxTI2mepKclPSXp3DLXvEbSZkmX9UzkZmZmZtUji5a4K4B5ETEZmAdcWeaaM4EDgEnAdGCupAmFk5Jq0sfd3N3BmpmZmVWjHk3iJI0GpgDXp0XXA1MkjSq59AzgexGxIyJWkyRrpxed/yTwS2BhO681XNKE4hswrmtqYmZmZpatvj38evsAyyKiFSAiWiUtT8tXF103Hni26Pi59BokvRY4CTgO+Gw7r3URMKfciXPOOeeipqamq4CZQH1d7dB+RyxfNHndwMHrt/epaa3fvGHE4hGjlo1b1zymT0SfRSNGL9v/xZX7rBk0ZB3AiJc21T09csySiWtWNe6Qdiytq185Yc3qxubBtWv67mitqWvZPOzJvRqenfTCin231vTdunJoXfM+65obVg0d1jxo29aBtVtahhTOt/Tt19I8uHZd4/oXxzxfO3x17ZaXhgzZumVw4fym/gM2bxgwaFPjjOkNTU1NU4FpQAPwXeB8kkR2eVqXG2dMOXLU+OWLXn7+aqvTrKQes9O4TwR+0VadZs2Y3tCyaf36MRvX1fdv3d6/2urUOGP62Kampr3TmBe3VadZM6Y3DGxewUv9+reM3ri+fkld/Ypqq1PLuEbS92VC0fuwU51mzZjesHZrywuTXlix74YBAzdVY502Nozt39TUNBe4HZjcVp3GNYwddOCqpeMHbt82sPD4aqrTitqhW9N6rAAeAE5pq04TxjUOOWL5osnLho1cWb95Q13V1engg0akdSnEXLZOhc/iwufe2A1rR1VbndLPsJnA3mncZes07bBDRx5Q9FlcjXUaOeXIwU1NTYcB7wTu3FWd2vt9au+zvPD3Kb2vLzq/ANhI0uN2PXAyMAC4DpgNPEhiKjAfeA+whaQR593AvcBQ4LCi52xO69Jr6jRnzpy5tEER0da5LidpKnB1RBxSVPYYcFZEPFRUtgA4OyLuT48vJmlF+1fg98D7I+IxSXOBoRHx8TKvNRwYXlI8DrgbmBgRiwuFA8Y0PHTgvPnLuqKO3eWJC2Y3blm5Ysqurqv2unS0HtB76lLt9QDXpRr5d6U67Yl1serV0y1xS4BGSTVpK1wNSZa8pOS654B9gfvT40LLXAOwP3CLJEiSNEkaFhHnFz9BRKwF1haXpY8xMzMzy70eHRMXEauAh0maDEnv/5yOeyt2A3CepD7peLlTgZ9ExHMRsVdETIiICcA3ScbOnY+ZmZnZHiSL2akfBC6UtBC4MD1G0i2SpqXXXAM8AzwJ3AdcGhGLMojVzMzMrCr1dHcqEfEEcHSZ8rcWfd0KfKgDzzW3S4MzMzMzywnv2GBmZmaWQ07izMzMzHLISZyZmZlZDjmJMzMzM8shJ3FmZmZmOeQkzszMzCyHnMSZmZmZ5ZCTODMzM7McchJnZmZmlkNO4szMzMxyyEmcmZmZWQ45iTMzMzPLISdxZmZmZjnkJM7MzMwsh5zEmZmZmeWQkzgzMzOzHHISZ2ZmZpZDTuLMzMzMcshJnJmZmVkOOYkzMzMzyyEncWZmZmY55CTOzMzMLIecxJmZmZnlkJM4MzMzsxxyEmdmZmaWQ07izMzMzHLISZyZmZlZDjmJMzMzM8shJ3FmZmZmOeQkzszMzCyHnMSZmZmZ5ZCTODMzM7McchJnZmZmlkNO4szMzMxyyEmcmZmZWQ45iTMzMzPLISdxZmZmZjnkJM7MzMwsh5zEmZmZmeWQkzgzMzOzHOrxJE7SZEn3SlqY3k8qc02NpHmSnpb0lKRzi859VtJfJT0i6UFJJ/VsDczMzMyyl0VL3BXAvIiYDMwDrixzzZnAAcAkYDowV9KE9NyfgKMi4nDgbOBHkgZ1e9RmZmZmVaRHkzhJo4EpwPVp0fXAFEmjSi49A/heROyIiNXAzcDpABFxW0RsTq97BBBQX+a1hkuaUHwDxnV1nczMzMyy0LeHX28fYFlEtAJERKuk5Wn56qLrxgPPFh0/l15T6r3A0xGxtMy5i4A55YI455xzLmpqaroKmAnU19UO7XfE8kWT1w0cvH57n5rW+s0bRiweMWrZuHXNY/pE9Fk0YvSy/V9cuc+aQUPWAYx4aVPd0yPHLJm4ZlXjDmnH0rr6lRPWrG5sHly7pu+O1pq6ls3Dntyr4dlJL6zYd2tN360rh9Y177OuuWHV0GHNg7ZtHVi7pWVI4XxL334tzYNr1zWuf3HM87XDV9dueWnIkK1bBhfOb+o/YPOGAYM2Nc6Y3tDU1DQVmAY0AN8FzgcWAsvTutw4Y8qRo8YvX/Ty81dbnWYl9Zidxn0i8Iu26jRrxvSGlk3r14/ZuK6+f+v2/tVWp8YZ08c2NTXtnca8uK06zZoxvWFg8wpe6te/ZfTG9fVL6upXVFudWsY1kr4vE4reh53qNGvG9Ia1W1temPTCin03DBi4qRrrtLFhbP+mpqa5wO3A5LbqNK5h7KADVy0dP3D7toGFx1dTnVbUDt2a1mMF8ABwSlt1mjCuccgRyxdNXjZs5Mr6zRvqqq5OBx80Iq1LIeaydSp8Fhc+98ZuWDuq2uqUfobNBPZO4y5bp2mHHTrygKLP4mqs08gpRw5uamo6DHgncOeu6tTe71N7n+WFv0/pfX3R+QXARpIet+uBk4EBwHXAbOBBElOB+cB7gC3AL4F3A/cCQ4HDip6zOa1Lr6nTnDlz5tIGRURb57qcpKnA1RFxSFHZY8BZEfFQUdkC4OyIuD89vhgYFxEfLbrmWOAa4ISI+FuZ1xoODC8pHgfcDUyMiMWFwgFjGh46cN78ZbtdwW70xAWzG7esXDFlV9dVe106Wg/oPXWp9nqA61KN/LtSnfbEulj16umWuCVAo6SatBWuhiRLXlJy3XPAvsD96fGrWuYkTQeuBf6hXAIHEBFrgbXFZZK6oApmZmZm2evRMXERsQp4mKTJkPT+z+m4t2I3AOdJ6pOOlzsV+AmApKOAHwGnFbfemZmZme1Jspid+kHgQkkLgQvTYyTdImlaes01wDPAk8B9wKURsSg9921gEHClpIfT22E9WgMzMzOzjPV0dyoR8QRwdJnytxZ93Qp8qI3HH9V90ZmZmZnlg3dsMDMzM8shJ3FmZmZmOeQkzszMzCyHnMSZmZmZ5ZCTODMzM7McchJnZmZmlkNO4szMzMxyyEmcmZmZWQ45iTMzMzPLISdxZmZmZjnkJM7MzMwsh5zEmZmZmeWQkzgzMzOzHHISZ2ZmZpZDTuLMzMzMcshJnJmZmVkOOYkzMzMzyyEncWZmZmY55CTOzMzMLIecxJmZmZnlkJM4MzMzsxxyEmdmZmaWQ07izMzMzHLISZyZmZlZDjmJMzMzM8shJ3FmZmZmOeQkzszMzCyHnMSZmZmZ5ZCTODMzM7McchJnZmZmlkNO4szMzMxyyEmcmZmZWQ45iTMzMzPLISdxZmZmZjnkJM7MzMwsh5zEmZmZmeWQkzgzMzOzHHISZ2ZmZpZDTuLMzMzMcshJnJmZmVkO9XgSJ2mypHslLUzvJ5W5pkbSPElPS3pK0rkdOWdmZma2p8iiJe4KYF5ETAbmAVeWueZM4ABgEjAdmCtpQgfOmZmZme0RejSJkzQamAJcnxZdD0yRNKrk0jOA70XEjohYDdwMnN6Bc2ZmZmZ7hL49/Hr7AMsiohUgIlolLU/LVxddNx54tuj4ufSaXZ17maThwPCS4n3T+3GSXi7sN3Kv/i3PLR5UYV16VGzf3r8jLY7VXpeO1gN6T12qvR7gulQj/65Upz2xLlYVlkbE9tJCRUSPRSBpKnB1RBxSVPYYcFZEPFRUtgA4OyLuT48vBsZFxEfbO1fyWnOBOd1dJzMzM7NuNjEiFpcW9nRL3BKgUVJN2gpXA+ydlhd7jqTV7P70uLj1rb1zxb4JzC8p6w/sBzwJtHa6Fu0bB9wNvBFY2k2v0VNcl+rUW+rSW+oBrku16i116S31ANels8o+f48mcRGxStLDwLuBa9P7P6dj24rdAJwn6SagHjiV5Ju0q3PFr7UWWFsmjIW7WY12FXXTLi2XNeeJ61Kdektdeks9wHWpVr2lLr2lHuC6dLUsZqd+ELhQ0kLgwvQYSbdImpZecw3wDEmL2X3ApRGxqAPnzMzMzPYIPd2dSkQ8ARxdpvytRV+3Ah9q4/FtnjMzMzPbU3jHBjMzM7McchLX9dYCTZQfj5c3a3FdqtFaekdd1tI76gGuS7VaS++oy1p6Rz3AdelSPbrEiJmZmZl1DbfEmZmZmeWQkzgzMzOzHHISZ2bWRST1+Ix/M9tzOYkzACR9IOsYzPJA0g2S6suUTwUezCAkM9tD+b/GLiDpXJLtw9qzIyK+0BPxdNJpkt5Bsi/tsqyD6Qrptm6fiwjvoWtd6c/Aw5IuiogbJfUDLiXZgeYj2YZmVn3Sz+IrIuK8rGPpbTw7tQtIuhU4G1A7l10dEbN6KKROSVvjPg18NiK+n3U8XUHSnyLidVnH0ZUkjSqzVV1uSBpNslvL/hT9IxkR78osqApJOoxkb+ZFwIHAA8BF6XZ/uSTp9RFxX9ZxdJak+4E2/6Dl8XNA0sER8VhJ2QkR8ZusYuosSX+MiJ0W+s8TSf/e3vmIuLinYilwS1zXiIhY3t4Fkrb2VDCdFRFXSroDuF/S14EdJIlpRMTobKPrtF9J+jhwNbCxUBgRm7MLqXMkHQ38mGQYxD7pNnXnR8T52UZWsRuBx4HfAq0Zx9JZTwC/A84B1gGX5TmBS10hqRWYB1wXES1ZB1Shj6f3byNJrK9Kj98P/C2TiHbfdZLeHBHPA0h6E3A58Jpsw+qUOyRdzs6fxY+1/ZCqsym93x84FrgpPX47yedBj3NLXBeQdEvxtmGdvSZrko4C/h9wF3AZRX9gI+LZrOLaHZJ2FB0GrySlNRmF1GmS7gHOA34QEUemZX+NiEOyjawykh6NiEOzjqOz0rFv3wceAj4K/B3wDeBK4Mvp1oC5JOmNwIeBmcC1wHci4plMg6qQpD8Cr4/0j1valfeHPLYCSToO+ApwPHAI8APg5IjIXVIqqdwe5xER+/V4MLspbew4PSKa0+N64IaIOL6nY3FLnAEg6SvA6cAHIuK3WcfTVSKiN03e6R8Rj0mv6rWv+hbeMh6VtPeuWq+r2M3ABRHx8/T4J5J+R9JCcj8wJavAdldE3A3cLekI4OfAv0j6NfCJdN/rPBgJDAReSo8HpGW5ExH/J+lbwK+BscDb85jAAUTExKxj6EJjCwkcQEQ0SxqbRSBO4rrGYEnfJW3lKTlX+Itb7c3fo4AjI2J91oF0JUmDy5XnsTsV2CJpKOnPmKSDgdx0eUm6gST2OmBB2rL4cvw5GhN3eESsKS5IxyieIen0jGLqEmlL3AXA0cB/p7fjSRK6yRmGVokfAfdK+lF6/C7ghxnGUzFJHy4pGkzSQ/ImSW+KiG9nENZukzQLOCgiLk/Hxg6PiIVZx9UJf5X037y6yz6TbmF3p3YBSSuACyPiJ2XOPRQRuf3PPO/S7tSXu1EL5TntTn0L8FlgP+BW4M3AWXlpOZX0vvbO95bJNHklaQGwAfgvkq6h7UXnbo2IN2cWXIUknULSJQxwR0T8KsNwKibp/7VzOiLi7B4LpotI+iTwVqAhIiZJGgf8MCKOyTi0ikkaBnwOOC4tugP4fBaNIE7iuoCk5cAWkkHnn4yib6qTuOohaSDwHmCviGh3llG1krQfcBJJUnp7RDyVcUgVk3R8RNyxqzLrWZKmRcQDWcdhvZOkvwDTgD8Vjel9JCIOzzayfHMS1wUkPQScQJLEBXBG0YBHJ3FVRtL9EXFU1nFUStLFpclnubJqV+53wr8n1UFSHcnQj4GFsoi4K7uIKifpNcBn2HkJmzwuMTIY+BSwX0ScKelA4MCIuDnbyCon6b6IeL2kPxclcX+JiNdmHVul0gaBM9n5Z8xLjORVOrDxBJKZRA9KemdEPEj7a8dZNysZE9cHOAoYnk00u+0fgdKErVxZVZJ0AMm4qmGSimdq15GM+bEMSTqDZFb6CGAZcADwF/I3UeOHwA0kM+1zO1M49R1gBXBEerwUuJ5kck3eLJF0DBCS+gCXAH/NOKbOugHoD/yRpBcuM07iulBE7AAulvQn4NeSLqGdxSetR2zklTFxrcCTJMtC5Eb6z8GJwN4li03Wka9/Et4AzAbGAJ8oKl8P/GsWAdmrXAJMBW6LiCPTn7vTMo6pM/pExJeyDqKLHB4R75N0EkBEbEwToDy6kGSNuEOBzcDdwFmZRtR5B0TEQVkHAU7iusqr/pBGxE8k/ZVkIcD9swnJoNcsMbKVV5LRTUXlK4AvZxJRJ6QTF74vaXZEzM86HtvJ9ohYJakvQET8RtJXsw6qE+6VdHhEPJJ1IF3gVa08aTdeLj/T0gWLT0x7R/pExMZdPaaKPSOpNiI2ZB2Ik7iu8bnSgoh4PF0894IM4rEi6VIchVlE/5uj9a4AiIjfAb+TdGNEPJp1PLsrIuZL2p+dx5Pckl1URrKEjYAnJV0ILAaGZhtSpxwNvF/S33j1Eja5GxMH3JX26AyQNBP4GPCzTCPqJEm/j4hjipd3KpRlGVcnrQMekHQbr/4Z6/ExcZ7YYL2apH8iGadYSBDeAvxbRPwgu6g6p619+7L44Ngdkr5EsvPE47wyZimyWO3cXiHpeOBBYDTJWKw64FN5WcKmQNKx5crTf4ZyRVI/4GLg70l6fH4OfKV4+Ze8KJ28lO6ksSAiDs4wrE6RNKdceUQ09XgsTuKsN0untZ9UtPfgWJIxP3mcEVX8wTEQOBl4ICLen1FInSLpKWBKb1tYOs/SXoNPAIU/qI8CX4+I+7OLavdIGgIQEZt2da11H0mfIElE64C1RacGk2wh+IEs4uotnMRZr1ZuCntep7WXklQL/CQiTso6lkpIujsi3ph1HJaQNJ2kpfoKktl2IpnF/UHgLRHxxwzDq1i6luJ1JDM6A/gzyaLYudoDFiDd1eA/gPER8SZJhwMzIuKKjEPrsHTZmhEk29IVDy9aX7rzSV6k4/o+S7JvMsDtwBez2AnISZz1apJuAhaQbE4OcD7w2oh4e3ZRdY10/NJj1TJLqqPSbuF9SKbpF48n8Zi4DEj6KXB1RPy0pPwfgPdHxKmZBNZJkn5DsgxHYdeD2cB7IuKEzILqJEk/I9k39cMRcbik/iSt77laIDftOv1ZRJycdSxdQdJVJON5v5sWnUtGO2k4ibNeLf1P9j9J/mMK4LfAR9O9LnOlZExcH5LlINZExDsyCqlTJP1fmWKPicuIpIURUXZf1PbOVStJD0fEEbsqywNJD0TEtJIFcvNal3uBN6RLceVa6U4T6T/Uf8kiufbsVOuVJL2p6PDbJAO1IUnkDgJyl8Tx6uVFtpN0f92UUSydFhHH7foq60HtdQH1ePdQF9gh6TUR8TcASZPJ76K/r5rAIGk4+Vobsth9wE2SriNZMgnIbQu8JA0pGm85mIzeFydx1lt9vejrg4DH0q9FksjlbrmBLGY+dZd08dKXx5NExG+yjGcP11/SQZT/I9S/p4PpApcAd0t6OD1+LfBP2YWzW26SdCVQK2k28GHgf7INqdOOSO8/VFQWvLJyQJ5cS7Ie4Q/T4zNIFjLuce5OtV6vuCsiz9Ku4QvZeX21d2UWVCeks9XeRzJuCZKtw74fEZdlF9WeS9Ji2t5ZJiJivx4Mp0tIGkWyXhzAfRHxQpbx7A5JZ1K0xEhEXJtxSAZIejOv/CP624i4NZM4nMRZb9dbNleXdDfJ2mr3UdQ9lO6EkBuSHiEZG7MhPa4F7snbYG2rTpIOBRYVurokDQX2jYjc7NMp6S7g/9LbvRGR6f6cXSWdqfoakiWSAIiIu7KLKP/cnWqWHyMi4vysg+gCKt6uJiI2pAODzbrC94HXFx1vTcumZRNOp3wLmAn8F7Bfuh/3nSRJ3X0RsTW70DpH0hnAZSTLjSwDDgD+AuTuH2xJvwdOKSyRImkkcHNEvKn9R3Y9J3HWK6VbbRUMLB3zExGP7fyoqveopL0jYnnWgeym+yX9P+B76fG5wAMZxmO9S01EbCscRMTWwn6weRERNwI3AkjaCziWJKn7H6ABGJJZcJ13CcmM+tsi4khJJwCnZRxTZw0tXuMuIl5MexR6XK5+sM0q8KuS4+LBswHkbpwPyX+wCyTdw6vXV8vVmDiScX2fJVn6pbDsy+czjch6k22S9iss7pvu05vL2alpV/BRJOP7XgdsAXK3ZWBqe0SsKiTUEfEbSV/NOqhO6iNpcGFx3/R96pdFIE7irFeKiIlZx9ANrktvuSTpw0WHz/HKYqxBsiDrt3s6JuuVmoB7JBX+kXsryV69uZEmN28imR38B+Aukm3QVmYa2O7Zkg6beFLShcBiYGi2IXXa9cBvJBWWrvoQyYzVHueJDWbWIyTtINlgfQE7L2eRyWrn1jula8MVL2HzVJbxVErSSpIk5yaSsXD3532RXEnHk/z+jyZZt7MO+FRE/DbTwDpJ0vuAt6WHv4wILzFiZjuT9M8R8S1JX6PMUhARcXEGYVUsXefqfSTjeb4PXJfXvROtukkaBhwQEQ9lHUtnpeN6C2PhpgJPkSR0v4uIe7OLzKqJu1PNql9h/NvGMudy819YRMwH5kuaSJLM/UHSAuALEfFIpsFZryHprSR7JbcCEyRNA+ZExCnZRlaZdPLVY8B30r1HzwQ+DXwRqMkyts4o2TbwZXn5J7RY2tL7P0BjREyUNAX4+4iY29Ox9OnpFzSzitWk48lWl7nlbhHTiFgEfINXllHI3e4ZVtWaSCYDrAGIiAdIFsjODUl9JB0l6RPp2L5mknrdC+R12MGmolsr8BZgVKYRdd63gS8A69Ljh4HTswjELXFm1e9y2hlL1vPhdE46qPkk4P3AocCPgaPTpM6sy0TE8yVLD+Ztsdy1JEno74CfAB/J++9J6baBkr5EUrc8qouIWyV9GSAidkjKZO0+J3Fm1e9sku7HQ8n3WLKlwHJgPnApSQI6qLCmX07X7rPqs0HSGNJ/cCTNJEmK8uTSiLhM0hsi4p6sg+kmG4HxWQfRSa2S+vHKz1gjkMnEE09sMMuJorFkZ5C0yuVqLFnJHp3Bq1sVc7lHp1UfSa8DrgAmkuwIMIlkvNKDmQZWgcJWgb1ly0CAkolZfUgma6yJiHdkF1XnSHov8C7gcJKxce8FPh0R17f7wO6IxUmcWX6kew++m6Ql65KI+O+MQzKrOunvyVvTw8ci4i9ZxlOpdEHvF4E3kCyG/So5XOAbSXN4JYnbDjwN3FS8u0aeSDoGOIXkn9FfRMTdWcTh7lSzKuexZGa7Jula4GtpwlYDfI1k4Plekj6ds394TgFOIGnpKd19JnckHQccDxxGksjdQ7K22jZJ/fO4F2xE/B74vaT+wMis4nBLnFmVk7SMV8aS3UnJZAaPJTMDSY9FxMHp1/8MvC0iTpQ0jiRhOCLTADtB0syIuDPrOHaHpNOAb5LM5rwvLZ4O/DNJl+TXIuKkbKLrHEk/BD4AbCXpst8L+FJEXNbTsbglzqz6bSP5kPg48K+UjCUjn/vAmnW1lqKvjwF+ChARSyXlrrUi7RJ+Q9F2dY8Al0fE2uyi6pRLgJMi4q9FZQ9Luhv4E/ncSvA1EbEuTVDvAD5GkqA6iTOzV4uICVnHYJYHkvYmWZpjJjCn6NTATALqJEmHkIyFu5Nk71SA1wOPS/q7koSo2g0sF29EPCppOUmLVt4UNrs/FrglIjan2wr2OCdxZmbWG3yZZNHVrcDvC8MMJL0eeC7DuDrjMpK14W4sLkxbfv6DZIxsXvSX1K90AoOkASRDulozimt3PCbp18BBwCclDcoqEI+JMzOzXkHSWGAs8JdI/7ilrXN9IyI3iZykhRExuY1zT0bEpJ6OqbMkfR3YG/hgRKxLy4YD3wFWRMTHMgyvU9Kk7SSSn7NF6Tpxh0XErT0ei5M4MzOz6iHp6Ygou1WYpGfytKZiOnvzOyTbUj2VFh8A3AB8OCLytptGVXESZ2ZmVkUk/Ri4JyK+VVJ+EXBMRJyWSWC7QdJ4kiVGBCyIiGczDqlikv43ImZJWs2rVwkQyYLlo3s8JidxZmZm1SNdFuV2ku3CipflGA6cGBFLsolszyapISJWSNq33PksElMncWZmZlVGUl+S3VkOJ2np+Qvww7zucGDdw0mcmZlZFZL0+oi4b9dXWk+QdD8li60Xi4jX9WA4gJM4MzOzqiTpYaAVuBy4PiJa2n+EdSdJx7Z3PiJ+11OxFDiJMzMzq1KS3gh8mGQB42uB70TEM5kGZVXDSZyZmVmVk3QE8HOSNdd+DXwiIp7INKg9jKQbaL879V09GA7gHRvMzMyqVtoSdwFwNPDf6e14koSu7ILA1m1+mXUApdwSZ2ZmVoUkLQA2AP8F3BAR24vO3RoRb84sOKsKTuLMzMyqkKRpEfFA1nFYQtI/R8S3JH2NMt2qEXFxT8fk7lQzM7MqFBEPSKoDXgMMLCq/K7uo9miF2cEby5zLpEXMSZyZmVkVknQGcBkwAlhGsufoX4ApWca1B6uR9GFgddaBFDiJMzMzq06XAFOB2yLiSEknALnbN7UXuRx4EFhAsotGMbfEmZmZ2cu2R8SqdAsuIuI3kr6adVB7sLOB9wGHAt8HrouINVkG1CfLFzczM7M2bZEk4ElJF0o6BRiadVB7qoiYHxHHAWcAo4A/SPqxpMOzisktcWZmZtXpM8Aw4N+A7wB1JLs3WIYiYpGkbwDPA5cCtwOPZBGLkzgzM7MqI+ko4IPAwWnRo8CnIuL+7KLas6WtoicB7yfpUv0xcHRELMosJq8TZ2ZmVj0kTQduAa4A/kgyiL6Q1L0lIv6YYXh7LEnLgOXAfOBOSiYzRMRjPR6TkzgzM7PqIemnwNUR8dOS8n8A3h8Rp2YS2B5O0mJeSdyCV89QjYjYr8djchJnZmZWPSQtjIiy+6K2d872PJ6damZmVl02d/Kc7WE8scHMzKy69Jd0EDsvKAvQv6eDserl7lQzM7MqUjL2qlQmY6+sOjmJMzMzM8shj4kzMzMzyyEncWZmZmY55CTOzHJP0mxJ0cbt77r4tWZKmivJn59mlinPTjWz3uR0YGlJWVevoj4TmAN8AdjRxc9tZtZhTuLMrDd5OCKeyjqISqV7MvaLiK1Zx2Jm+eHuADPr9SQNlvRVSYskbU3vP13cJSppoKRvSHpU0kZJz0v6haQDi66ZS9IKB7Ct0GWbnpuZHs8see1CV++EorLFkq6VdLakJ4CtwNvSc6+V9HNJayS9JOkeSW8sec6jJP1GUnN6zTOSvt2V3zMzq35uiTOz3qRGUvHnWmF/w9uAg4HPAwuA1wOfBUYC/5peOwCoJekmXZGe+zBwr6SDIuJ54L+BccA5wDFA627EehxwBNAErAIWS5oC3A38GTiPZHX+DwK/lTQjIh6UNDStz5+A2cAGYAIwYzdiMbMcchJnZr3JEyXH9wBXkiRcx0bEXWn5/yY9mMyR9NWIWBUR64BzCw+UVEOSLK0E3g18IyKWSiqMuftjRGzfjVhHAFPT5LDwmv8LPAccX+halXQb8ChJ0nkqcGD62Isj4pGi55u/G7GYWQ65O9XMepO3A0cV3c4B3gw8C/xBUt/CDbgd6EfSKgeApHdJ+qOktcB2YBMwFHhNN8R6X0kCNwg4FrgB2FEUp4DfAm9KL30SWAtcKeksSft0Q2xmlgNO4sysN3k0Ih4ouv0NGA3sC2wruf0pfUw9gKRTgB8BjwPvAY4mSQRXAwO7IdYVJccjgRqSFrfSWD8CjJDUJ20xPA5YDnwbeC4dx/fObojRzKqYu1PNrLdrBhYB72rj/OL0/h+BpyJiduGEpH4kyVVHtKT3pRuU17dxfemeh2tJliyZB1xd9gERO9L7h4F3pi1104BPAT+W9NqIeLSD8ZpZzjmJM7Pe7lbgncDGiCgdM1dsMEkXarF/ImkdK7YlvR9EMqmg4Nn0/lCSrtqCt3UkyIjYJOlu4LXAQ4WEbReP2Q7cJ+mzwN8DB5GMnzOzPYCTODPr7X4AvJ9kMsPXgb+QtJbtT5L4nBoRm0mSvVMlfQP4JUkL14UkLWTFCosH/6ukXwOtadftCkm/Az4l6QWSGadnAftVEOvHgLuA2yRdRdLluhcwBaiJiE9KOhk4H7iZpIVxCPBRkoTy3gpey8xyzkmcmfVqEbFN0knAJ0mSn4kkExaeBn5FskYbwPeAfYCzgQ8A9wOnAD8tecpfkoxF+zDwOZKJB0rPnQV8B/hPku7V/yFZsuR7HYz1IUlHkaxF959AHcmYvIeAK9LLngReIhk710CSvN0PnBARpbtVmFkvpojSYRlmZmZmVu08O9XMzMwsh5zEmZmZmeWQkzgzMzOzHHISZ2ZmZpZDTuLMzMzMcshJnJmZmVkOOYkzMzMzyyEncWZmZmY59P8BlrgzSICCiG0AAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Plot the feature importances in a bar plot\n",
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(data=feature_importance_df_1, x='Feature', y='Importance', color='#17becf',  alpha=0.7, edgecolor='black')\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Importance')\n",
        "plt.title('Feature Importances')\n",
        "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
        "\n",
        "sns.despine()\n",
        "plt.gca().patch.set_facecolor('none')\n",
        "plt.gca().set_frame_on(True)\n",
        "\n",
        "\n",
        "plt.grid(axis='x')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BwaQTAZ4tpcf"
      },
      "source": [
        "### Support Vector Regression\n",
        "\n",
        "A model used for regression tasks specifically through finding a function that approximates the relationship between the input features and the target variable, while controlling the margin of error."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "ApZhorYAtvGZ",
        "outputId": "135de6af-841e-4947-8645-da7ffe88cfb9"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR(epsilon=0.2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR(epsilon=0.2)</pre></div></div></div></div></div>"
            ],
            "text/plain": [
              "SVR(epsilon=0.2)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from sklearn.svm import SVR\n",
        "\n",
        "regr = SVR(C=1.0, epsilon=0.2)\n",
        "\n",
        "regr.fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GMQSPdC-t-3_",
        "outputId": "22b0a6fe-40e4-4958-9f5b-bf958682e8d8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "SVR R^2: -0.04471690218943092\n",
            "SVR MAE: 0.3235623886053753\n",
            "SVR MSE: 0.22387157557060747\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "r2_svr = regr.score(X_test, y_test)\n",
        "print(\"SVR R^2:\", r2_svr)\n",
        "\n",
        "predicted = regr.predict(X_test)\n",
        "\n",
        "MAE_svr = mean_absolute_error(y_test, predicted)\n",
        "print(\"SVR MAE:\", MAE_svr)\n",
        "mean_squared_score_svr = mean_squared_error(y_test, predicted)\n",
        "print(\"SVR MSE:\", mean_squared_score_svr)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### SVR - Actual vs. Predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "Z6TwD17eDjoa",
        "outputId": "62cd846f-cc9b-40c4-dd47-4c6e5c2f0e42"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "sns.scatterplot(x=y_test, y=predicted, color='#1f77b4', alpha=0.5, edgecolor='none')\n",
        "sns.lineplot(x=np.linspace(min(y_test), max(y_test)), y=np.linspace(min(y_test), max(y_test)), color='red', linestyle='dashed')\n",
        "\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.title('SVR - Actual vs. Predicted')\n",
        "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
        "\n",
        "sns.despine()\n",
        "plt.gca().patch.set_facecolor('none')\n",
        "plt.gca().set_frame_on(True)\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fHls2Q0npU95"
      },
      "source": [
        "### MLP Regressor\n",
        "\n",
        "The multi-layered perceptron regression model is an artificial neural network used for regression tasks. It is used in approximating complex non-linear relationships and mapping feature values to corresponding output values.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ygE6Fvpl_R7O"
      },
      "outputs": [],
      "source": [
        "from sklearn.neural_network import MLPRegressor\n",
        "\n",
        "smlp = MLPRegressor(hidden_layer_sizes=1000, activation='relu', solver='adam', batch_size=32, learning_rate='invscaling', max_iter=10000, alpha=0.0001).fit(X_train, y_train)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tz0yBuddpUXv",
        "outputId": "9d272c1b-3831-45bd-a10b-71266c69f9a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "MLP Regressor R^2: -0.06420248395389594\n",
            "MLP Regressor MAE: 0.33416011477004043\n",
            "MLP Regressor MSE: 0.22804712579036424\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "R2_mlp = smlp.score(X_test, y_test)\n",
        "print(\"MLP Regressor R^2:\", R2_mlp)\n",
        "\n",
        "predicted = smlp.predict(X_test)\n",
        "\n",
        "MAE_mlp = mean_absolute_error(y_test, predicted)\n",
        "print(\"MLP Regressor MAE:\", MAE_mlp)\n",
        "mean_squared_score_mlp = mean_squared_error(y_test, predicted)\n",
        "print(\"MLP Regressor MSE:\", mean_squared_score_mlp)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### MLP Regressor - Actual vs. Predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "So474VaFFL3y",
        "outputId": "c02ebec9-3f92-4468-d8d1-581570a85ff5"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "sns.scatterplot(x=y_test, y=predicted, color='#1f77b4', alpha=0.5, edgecolor='none')\n",
        "sns.lineplot(x=np.linspace(min(y_test), max(y_test)), y=np.linspace(min(y_test), max(y_test)), color='red', linestyle='dashed')\n",
        "\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.title('MLP Regressor - Actual vs. Predicted')\n",
        "\n",
        "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
        "sns.despine()\n",
        "plt.gca().patch.set_facecolor('none')\n",
        "plt.gca().set_frame_on(True)\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kHK65AUgrglD"
      },
      "source": [
        "### Linear Regression\n",
        "\n",
        "This is a simple model that attempts to minimize the sum of squared errors. The final model predicts along a linear slope line that goes through the data points. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "6yTFLWhTrj0s"
      },
      "outputs": [],
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "clf_1 = LinearRegression().fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lPhZoj_FsdJB",
        "outputId": "dd1afe0e-9e69-45da-c5fc-0cae94eed755"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Linear Regression R^2: -0.0007016649741236058\n",
            "Linear Regression MAE: 0.3404332764766683\n",
            "Linear Regression MSE 0.2144395844887611\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "val_acc_ln = clf_1.score(X_test, y_test)\n",
        "\n",
        "print(\"Linear Regression R^2:\", val_acc_ln)\n",
        "\n",
        "\n",
        "predicted = clf_1.predict(X_test)\n",
        "MAE_ln = mean_absolute_error(y_test, predicted)\n",
        "print(\"Linear Regression MAE:\", MAE_ln)\n",
        "mean_squared_score_ln = mean_squared_error(y_test, predicted)\n",
        "print(\"Linear Regression MSE\", mean_squared_score_ln)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Linear Regression - Actual vs. Predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "k4l5e5lRFWFm",
        "outputId": "0867e54c-c7d2-4047-a5b3-0f132cdb0d95"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "# Scatter plot for the actual vs. predicted values\n",
        "sns.scatterplot(x=y_test, y=predicted, color='#1f77b4', alpha=0.5, edgecolor='none')\n",
        "sns.lineplot(x=np.linspace(min(y_test), max(y_test)), y=np.linspace(min(y_test), max(y_test)), color='red', linestyle='dashed')\n",
        "\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.title('Linear Regression - Actual vs. Predicted')\n",
        "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
        "\n",
        "sns.despine()\n",
        "plt.gca().patch.set_facecolor('none')\n",
        "plt.gca().set_frame_on(True)\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Feature Importance In Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 911
        },
        "id": "FRPeSFa4INXY",
        "outputId": "46153f3a-9755-4073-ae6a-672092f09088"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Relative Influence of Features:\n",
            "        Feature  Importance\n",
            "4         Month    0.024669\n",
            "2           Z\\r    0.007264\n",
            "9        Second    0.003136\n",
            "3           Day    0.000082\n",
            "10  Millisecond    0.000000\n",
            "0             X   -0.001522\n",
            "1             Y   -0.001735\n",
            "8        Minute   -0.004456\n",
            "5     DayOfWeek   -0.004879\n",
            "6       Quarter   -0.006226\n",
            "7          Hour   -0.014279\n"
          ]
        }
      ],
      "source": [
        "# Get the coefficients (feature importances) from the trained model\n",
        "coefficients = clf_1.coef_\n",
        "\n",
        "# Create a DataFrame to store the feature importances\n",
        "feature_importance_df_2 = pd.DataFrame({'Feature': X_test_df.columns, 'Importance': coefficients})\n",
        "\n",
        "# Sort the features by importance in descending order\n",
        "feature_importance_df_2 = feature_importance_df_2.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "print(\"Relative Influence of Features:\")\n",
        "print(feature_importance_df_2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ") missing from current font.l\\Temp\\ipykernel_19916\\816986420.py:10: UserWarning: Glyph 13 (\n",
            "  plt.tight_layout()\n",
            ") missing from current font.l\\Programs\\Python\\Python310\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 13 (\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "sns.barplot(data=feature_importance_df_2, x='Feature', y='Importance', color='#17becf', alpha=0.7, edgecolor='black')\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Importance')\n",
        "plt.title('Feature Importances in Linear Regression Model')\n",
        "plt.tight_layout()\n",
        "\n",
        "\n",
        "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
        "sns.despine()\n",
        "\n",
        "plt.gca().patch.set_facecolor('none')\n",
        "plt.gca().set_frame_on(True)\n",
        "\n",
        "plt.grid(axis='x')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xbz1kcA5rk1R"
      },
      "source": [
        "### Random Forest\n",
        "\n",
        "This is a supervised machine learning model that uses the mean result from decision trees in order to predict an output."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "uhnE0E22rncV"
      },
      "outputs": [],
      "source": [
        "clf_2 = RandomForestRegressor(max_depth=2, random_state=42).fit(X_train, y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g-edWgV8rtUG",
        "outputId": "16afc010-246c-47b0-f12b-67873c5e8d4e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Random Forest R^2: -0.0014886206464004292\n",
            "Random Forest MAE: 0.3405884869516936\n",
            "Random Forest MSE 0.2146082206100755\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "val_acc_rf = clf_2.score(X_test, y_test)\n",
        "\n",
        "print(\"Random Forest R^2:\", val_acc_rf)\n",
        "\n",
        "\n",
        "predicted = clf_2.predict(X_test)\n",
        "MAE_rf = mean_absolute_error(y_test, predicted)\n",
        "print(\"Random Forest MAE:\", MAE_rf)\n",
        "mean_squared_score_rf = mean_squared_error(y_test, predicted)\n",
        "print(\"Random Forest MSE\", mean_squared_score_rf)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Random Forest - Actual vs. Predicted"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "w09e3mj5FmJH",
        "outputId": "6d11cffb-01d6-416f-ea65-d439060844a4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "sns.scatterplot(x=y_test, y=predicted, color='#1f77b4', alpha=0.5, edgecolor='none')\n",
        "sns.lineplot(x=np.linspace(min(y_test), max(y_test)), y=np.linspace(min(y_test), max(y_test)), color='red', linestyle='dashed')\n",
        "\n",
        "plt.xlabel('Actual')\n",
        "plt.ylabel('Predicted')\n",
        "plt.title('Random Forest - Actual vs. Predicted')\n",
        "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
        "\n",
        "sns.despine()\n",
        "plt.gca().patch.set_facecolor('none')\n",
        "plt.gca().set_frame_on(True)\n",
        "\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "#### Feature Importance In Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 911
        },
        "id": "Oe9WUP7vJoFr",
        "outputId": "06dfae64-5263-4ef8-bd90-9de2e1214ebb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Relative Influence of Features:\n",
            "        Feature  Importance\n",
            "1             Y    0.421510\n",
            "8        Minute    0.129558\n",
            "2           Z\\r    0.127120\n",
            "0             X    0.123066\n",
            "9        Second    0.070646\n",
            "5     DayOfWeek    0.066045\n",
            "3           Day    0.034490\n",
            "4         Month    0.013680\n",
            "7          Hour    0.010584\n",
            "6       Quarter    0.003302\n",
            "10  Millisecond    0.000000\n"
          ]
        }
      ],
      "source": [
        "# Get the feature importances from the trained model\n",
        "feature_importance = clf_2.feature_importances_\n",
        "feature_names = X_test_df.columns\n",
        "\n",
        "# Create a DataFrame to store the feature importances\n",
        "feature_importance_df_3 = pd.DataFrame({'Feature': feature_names, 'Importance': feature_importance})\n",
        "\n",
        "# Sort the features by importance in descending order\n",
        "feature_importance_df_3 = feature_importance_df_3.sort_values(by='Importance', ascending=False)\n",
        "\n",
        "print(\"Relative Influence of Features:\")\n",
        "print(feature_importance_df_3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ") missing from current font.l\\Temp\\ipykernel_19916\\139572493.py:10: UserWarning: Glyph 13 (\n",
            "  plt.tight_layout()\n",
            ") missing from current font.l\\Programs\\Python\\Python310\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 13 (\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "plt.figure(figsize=(10, 6))\n",
        "\n",
        "sns.barplot(data=feature_importance_df_3, x='Feature', y='Importance', color='#17becf', alpha=0.7, edgecolor='black')\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Importance')\n",
        "plt.title('Feature Importances in RandomForestRegressor Model')\n",
        "plt.tight_layout()\n",
        "plt.grid(color='gray', linestyle='--', linewidth=0.5)\n",
        "\n",
        "sns.despine()\n",
        "plt.gca().patch.set_facecolor('none')\n",
        "plt.gca().set_frame_on(True)\n",
        "\n",
        "plt.grid(axis='x')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Se4G-JOmH77c",
        "outputId": "6bd57451-3139-4c85-f128-dbee7f9ef2fe"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "               Model       MAE       MSE       R^2\n",
            "0            XGBoost  0.352403  0.230952 -0.077759\n",
            "1                SVR  0.323562  0.223872 -0.044717\n",
            "2      MLP Regressor  0.334160  0.228047 -0.064202\n",
            "3  Linear Regression  0.340433  0.214440 -0.000702\n",
            "4      Random Forest  0.340588  0.214608 -0.001489\n"
          ]
        }
      ],
      "source": [
        "model_names = ['XGBoost', 'SVR', 'MLP Regressor', 'Linear Regression', 'Random Forest']\n",
        "mae_scores = {'XGBoost': MAE_xgb, 'SVR': MAE_svr, 'MLP Regressor': MAE_mlp,\n",
        "              'Linear Regression': MAE_ln, 'Random Forest': MAE_rf}\n",
        "mse_scores = {'XGBoost': mean_squared_score_xgb, 'SVR': mean_squared_score_svr, 'MLP Regressor': mean_squared_score_mlp,\n",
        "              'Linear Regression': mean_squared_score_ln, 'Random Forest': mean_squared_score_rf}\n",
        "r2_scores = {'XGBoost': val_score_xgb, 'SVR': r2_svr, 'MLP Regressor': R2_mlp,\n",
        "             'Linear Regression': val_acc_ln, 'Random Forest': val_acc_rf}\n",
        "\n",
        "results_df = pd.DataFrame({'Model': model_names, 'MAE': [mae_scores[model] for model in model_names],\n",
        "                           'MSE': [mse_scores[model] for model in model_names], 'R^2': [r2_scores[model] for model in model_names]})\n",
        "print(results_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Plotting Error Values"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Create a DataFrame from the scores dictionaries\n",
        "results_df = pd.DataFrame({'Model': model_names, 'MAE': [mae_scores[model] for model in model_names],\n",
        "                           'MSE': [mse_scores[model] for model in model_names], 'R^2': [r2_scores[model] for model in model_names]})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "_OhjS_nLLb4c",
        "outputId": "fb803642-8be5-4d42-b4ff-6be870e4e1cb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "\n",
        "# Plot MAE scores\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(data=results_df, x='Model', y='MAE', color='#2ca02c',alpha=0.7, edgecolor='black')\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('Mean Absolute Error (MAE)')\n",
        "plt.title('Mean Absolute Error (MAE) for Different Models')\n",
        "\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "sns.despine()\n",
        "\n",
        "plt.grid(axis='y')\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "\n",
        "# Plot MSE scores\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(data=results_df, x='Model', y='MSE', color='#9467bd',alpha=0.7, edgecolor='black')\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('Mean Squared Error (MSE)')\n",
        "plt.title('Mean Squared Error (MSE) for Different Models')\n",
        "\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "sns.despine()\n",
        "\n",
        "plt.grid(axis='y')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "\n",
        "# Plot R^2 scores\n",
        "plt.figure(figsize=(10, 6))\n",
        "sns.barplot(data=results_df, x='Model', y='R^2', color='#ff7f0e',alpha=0.7, edgecolor='black')\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('R^2 Score')\n",
        "plt.title('R^2 Score for Different Models')\n",
        "\n",
        "plt.xticks(rotation=45, ha='right')\n",
        "plt.tight_layout()\n",
        "sns.despine()\n",
        "\n",
        "plt.grid(axis='y')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lz_zmb1xK7Ei",
        "outputId": "594d638b-e4f4-48bb-ee1f-fac97ac3318c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "        Feature  Importance_XGBoost  Importance_LinearRegression  \\\n",
            "0           Z\\r            0.126057                     0.007264   \n",
            "1             Y            0.123776                    -0.001735   \n",
            "2          Hour            0.112574                    -0.014279   \n",
            "3        Minute            0.111738                    -0.004456   \n",
            "4         Month            0.110232                     0.024669   \n",
            "5             X            0.108772                    -0.001522   \n",
            "6           Day            0.107846                     0.000082   \n",
            "7        Second            0.103845                     0.003136   \n",
            "8     DayOfWeek            0.095160                    -0.004879   \n",
            "9       Quarter            0.000000                    -0.006226   \n",
            "10  Millisecond            0.000000                     0.000000   \n",
            "\n",
            "    Importance_RandomForest  \n",
            "0                  0.127120  \n",
            "1                  0.421510  \n",
            "2                  0.010584  \n",
            "3                  0.129558  \n",
            "4                  0.013680  \n",
            "5                  0.123066  \n",
            "6                  0.034490  \n",
            "7                  0.070646  \n",
            "8                  0.066045  \n",
            "9                  0.003302  \n",
            "10                 0.000000  \n"
          ]
        }
      ],
      "source": [
        "feature_importance_df_1.rename(columns={'Importance': 'Importance_XGBoost'}, inplace=True)\n",
        "feature_importance_df_2.rename(columns={'Importance': 'Importance_LinearRegression'}, inplace=True)\n",
        "feature_importance_df_3.rename(columns={'Importance': 'Importance_RandomForest'}, inplace=True)\n",
        "\n",
        "merged_df = pd.merge(feature_importance_df_1, feature_importance_df_2, on='Feature', how='outer')\n",
        "merged_df = pd.merge(merged_df, feature_importance_df_3, on='Feature', how='outer')\n",
        "\n",
        "print(merged_df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Comparison of Models"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 678
        },
        "id": "UUyc5a7PLM2m",
        "outputId": "6abe5666-81a3-4e4a-fe4e-686c61457ff5"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            ") missing from current font.l\\Temp\\ipykernel_19916\\1467680380.py:18: UserWarning: Glyph 13 (\n",
            "  plt.tight_layout()\n",
            ") missing from current font.l\\Programs\\Python\\Python310\\lib\\site-packages\\IPython\\core\\pylabtools.py:151: UserWarning: Glyph 13 (\n",
            "  fig.canvas.print_figure(bytes_io, **kw)\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# Set a custom color palette for the plot\n",
        "custom_palette = sns.color_palette(\"muted\")  # Using Seaborn's muted color palette\n",
        "sns.set_context(\"notebook\", rc={\"font.size\": 14, \"axes.labelsize\": 16, \"axes.titlesize\": 18})\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "\n",
        "# Bar plot for feature importances using Seaborn\n",
        "sns.barplot(data=merged_df, x='Feature', y='Importance_XGBoost', color=custom_palette[0], alpha=0.7, label='XGBoost', edgecolor='black')\n",
        "sns.barplot(data=merged_df, x='Feature', y='Importance_LinearRegression', color=custom_palette[1], alpha=0.7, label='Linear Regression', edgecolor='black')\n",
        "sns.barplot(data=merged_df, x='Feature', y='Importance_RandomForest', color=custom_palette[2], alpha=0.7, label='Random Forest', edgecolor='black')\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "plt.xlabel('Features')\n",
        "plt.ylabel('Importance')\n",
        "plt.title('Feature Importances for XGBoost, Linear Regression, and RandomForest')\n",
        "\n",
        "plt.legend()\n",
        "plt.tight_layout()\n",
        "plt.grid(axis='y',color='gray', linestyle='--', linewidth=0.5)\n",
        "\n",
        "sns.despine()\n",
        "plt.gca().set_frame_on(True)\n",
        "plt.gca().patch.set_facecolor('none')\n",
        "\n",
        "plt.show()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Results\n",
        "\n",
        "Earthquakes as a whole are a fairly random and unpredictable phenomena. We found that deterministic models were not suitable for predicting earthquake magnitudes as seen by our low MAE, MSE, and R^2 values. Though relative influence was an interesting factor to consider, the earthquake data does not fit these models. We hope to find success in the near future through exploring generative models as suitable models for earthquake prediction."
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}