PGA_Succ.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "svbhXQ-vuA5C"
      },
      "source": [
        "# PGA Success Predictor"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rVclfhECt8to"
      },
      "source": [
        "## Imports and Installations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "zdNa65Z3wHs0"
      },
      "outputs": [],
      "source": [
        "%load_ext autoreload\n",
        "%autoreload 2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6h5HmA5vtbzh",
        "outputId": "e57093a6-d686-41d2-b8e3-96f6e1d850cf"
      },
      "outputs": [],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "%cd 'drive/MyDrive/Cal Poly SLO/Graduate Program/CSC 570 (2)/Team Project'\n",
        "%ls"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "D_rIi16Ivpum"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import seaborn as sns\n",
        "from pylab import rcParams\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "SHvPiUjQ6wCV"
      },
      "outputs": [],
      "source": [
        "from sklearn.svm import SVR\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.linear_model import Ridge\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.neural_network import MLPRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "FHFzaJ2uuQ5g"
      },
      "outputs": [],
      "source": [
        "from data.data_handler import import_dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OipP0zsR0lxx"
      },
      "source": [
        "## Data Processing\n",
        "Player Name is used as an index (as it should be used as an identifier and not a feature) and all values are scaled with Min-Max in case data is not a normal distribution"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "zsjGv8Nlu_ob",
        "outputId": "999a69e5-371d-464a-e904-0b86130f5021"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rounds</th>\n",
              "      <th>Fairway Percentage</th>\n",
              "      <th>Avg Distance</th>\n",
              "      <th>gir</th>\n",
              "      <th>Average Putts</th>\n",
              "      <th>Average Scrambling</th>\n",
              "      <th>Average Score</th>\n",
              "      <th>Points</th>\n",
              "      <th>Wins</th>\n",
              "      <th>Average SG Putts</th>\n",
              "      <th>Average SG Total</th>\n",
              "      <th>SG:OTT</th>\n",
              "      <th>SG:APR</th>\n",
              "      <th>SG:ARG</th>\n",
              "      <th>Top 10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sean O'Hair</th>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.498819</td>\n",
              "      <td>0.577861</td>\n",
              "      <td>0.633133</td>\n",
              "      <td>0.444126</td>\n",
              "      <td>0.533570</td>\n",
              "      <td>0.368818</td>\n",
              "      <td>0.168987</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.607294</td>\n",
              "      <td>0.646661</td>\n",
              "      <td>0.614928</td>\n",
              "      <td>0.559602</td>\n",
              "      <td>0.521384</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Charlie Wi</th>\n",
              "      <td>0.546667</td>\n",
              "      <td>0.550207</td>\n",
              "      <td>0.360225</td>\n",
              "      <td>0.307808</td>\n",
              "      <td>0.209169</td>\n",
              "      <td>0.597156</td>\n",
              "      <td>0.350579</td>\n",
              "      <td>0.215554</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.673321</td>\n",
              "      <td>0.621906</td>\n",
              "      <td>0.474703</td>\n",
              "      <td>0.451603</td>\n",
              "      <td>0.855346</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scott Stallings</th>\n",
              "      <td>0.426667</td>\n",
              "      <td>0.384525</td>\n",
              "      <td>0.637899</td>\n",
              "      <td>0.481481</td>\n",
              "      <td>0.535817</td>\n",
              "      <td>0.401264</td>\n",
              "      <td>0.550158</td>\n",
              "      <td>0.159626</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.449136</td>\n",
              "      <td>0.451647</td>\n",
              "      <td>0.578389</td>\n",
              "      <td>0.454093</td>\n",
              "      <td>0.406918</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Danny Lee</th>\n",
              "      <td>0.573333</td>\n",
              "      <td>0.579740</td>\n",
              "      <td>0.454034</td>\n",
              "      <td>0.576076</td>\n",
              "      <td>0.300860</td>\n",
              "      <td>0.663507</td>\n",
              "      <td>0.438092</td>\n",
              "      <td>0.114738</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.626104</td>\n",
              "      <td>0.581834</td>\n",
              "      <td>0.537789</td>\n",
              "      <td>0.474634</td>\n",
              "      <td>0.616981</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Martin Laird</th>\n",
              "      <td>0.506667</td>\n",
              "      <td>0.643828</td>\n",
              "      <td>0.557223</td>\n",
              "      <td>0.604104</td>\n",
              "      <td>0.404011</td>\n",
              "      <td>0.668641</td>\n",
              "      <td>0.307261</td>\n",
              "      <td>0.161546</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.663340</td>\n",
              "      <td>0.715939</td>\n",
              "      <td>0.609619</td>\n",
              "      <td>0.566138</td>\n",
              "      <td>0.712579</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ryan Blaum</th>\n",
              "      <td>0.480000</td>\n",
              "      <td>0.628766</td>\n",
              "      <td>0.424015</td>\n",
              "      <td>0.628629</td>\n",
              "      <td>0.464183</td>\n",
              "      <td>0.452212</td>\n",
              "      <td>0.402490</td>\n",
              "      <td>0.103217</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.575816</td>\n",
              "      <td>0.565628</td>\n",
              "      <td>0.539663</td>\n",
              "      <td>0.499844</td>\n",
              "      <td>0.588050</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jason Bohn</th>\n",
              "      <td>0.520000</td>\n",
              "      <td>0.704962</td>\n",
              "      <td>0.251407</td>\n",
              "      <td>0.713213</td>\n",
              "      <td>0.498567</td>\n",
              "      <td>0.532385</td>\n",
              "      <td>0.378464</td>\n",
              "      <td>0.221555</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.616123</td>\n",
              "      <td>0.648085</td>\n",
              "      <td>0.561836</td>\n",
              "      <td>0.623716</td>\n",
              "      <td>0.520126</td>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dustin Johnson</th>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.369462</td>\n",
              "      <td>0.962477</td>\n",
              "      <td>0.676176</td>\n",
              "      <td>0.275072</td>\n",
              "      <td>0.546603</td>\n",
              "      <td>0.155559</td>\n",
              "      <td>0.411666</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.615355</td>\n",
              "      <td>0.830632</td>\n",
              "      <td>0.836040</td>\n",
              "      <td>0.703081</td>\n",
              "      <td>0.451572</td>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>William McGirt</th>\n",
              "      <td>0.706667</td>\n",
              "      <td>0.657413</td>\n",
              "      <td>0.397749</td>\n",
              "      <td>0.691191</td>\n",
              "      <td>0.449857</td>\n",
              "      <td>0.661532</td>\n",
              "      <td>0.297264</td>\n",
              "      <td>0.325732</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.704415</td>\n",
              "      <td>0.742119</td>\n",
              "      <td>0.651780</td>\n",
              "      <td>0.602552</td>\n",
              "      <td>0.566667</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mathew Goggin</th>\n",
              "      <td>0.533333</td>\n",
              "      <td>0.656232</td>\n",
              "      <td>0.429644</td>\n",
              "      <td>0.810811</td>\n",
              "      <td>0.808023</td>\n",
              "      <td>0.402844</td>\n",
              "      <td>0.521571</td>\n",
              "      <td>0.063370</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.421497</td>\n",
              "      <td>0.482280</td>\n",
              "      <td>0.618676</td>\n",
              "      <td>0.467787</td>\n",
              "      <td>0.453459</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9650 rows × 15 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                   Rounds  Fairway Percentage  Avg Distance       gir  \\\n",
              "Sean O'Hair      0.520000            0.498819      0.577861  0.633133   \n",
              "Charlie Wi       0.546667            0.550207      0.360225  0.307808   \n",
              "Scott Stallings  0.426667            0.384525      0.637899  0.481481   \n",
              "Danny Lee        0.573333            0.579740      0.454034  0.576076   \n",
              "Martin Laird     0.506667            0.643828      0.557223  0.604104   \n",
              "...                   ...                 ...           ...       ...   \n",
              "Ryan Blaum       0.480000            0.628766      0.424015  0.628629   \n",
              "Jason Bohn       0.520000            0.704962      0.251407  0.713213   \n",
              "Dustin Johnson   0.400000            0.369462      0.962477  0.676176   \n",
              "William McGirt   0.706667            0.657413      0.397749  0.691191   \n",
              "Mathew Goggin    0.533333            0.656232      0.429644  0.810811   \n",
              "\n",
              "                 Average Putts  Average Scrambling  Average Score    Points  \\\n",
              "Sean O'Hair           0.444126            0.533570       0.368818  0.168987   \n",
              "Charlie Wi            0.209169            0.597156       0.350579  0.215554   \n",
              "Scott Stallings       0.535817            0.401264       0.550158  0.159626   \n",
              "Danny Lee             0.300860            0.663507       0.438092  0.114738   \n",
              "Martin Laird          0.404011            0.668641       0.307261  0.161546   \n",
              "...                        ...                 ...            ...       ...   \n",
              "Ryan Blaum            0.464183            0.452212       0.402490  0.103217   \n",
              "Jason Bohn            0.498567            0.532385       0.378464  0.221555   \n",
              "Dustin Johnson        0.275072            0.546603       0.155559  0.411666   \n",
              "William McGirt        0.449857            0.661532       0.297264  0.325732   \n",
              "Mathew Goggin         0.808023            0.402844       0.521571  0.063370   \n",
              "\n",
              "                 Wins  Average SG Putts  Average SG Total    SG:OTT    SG:APR  \\\n",
              "Sean O'Hair       0.0          0.607294          0.646661  0.614928  0.559602   \n",
              "Charlie Wi        0.0          0.673321          0.621906  0.474703  0.451603   \n",
              "Scott Stallings   0.2          0.449136          0.451647  0.578389  0.454093   \n",
              "Danny Lee         0.0          0.626104          0.581834  0.537789  0.474634   \n",
              "Martin Laird      0.0          0.663340          0.715939  0.609619  0.566138   \n",
              "...               ...               ...               ...       ...       ...   \n",
              "Ryan Blaum        0.0          0.575816          0.565628  0.539663  0.499844   \n",
              "Jason Bohn        0.2          0.616123          0.648085  0.561836  0.623716   \n",
              "Dustin Johnson    0.2          0.615355          0.830632  0.836040  0.703081   \n",
              "William McGirt    0.2          0.704415          0.742119  0.651780  0.602552   \n",
              "Mathew Goggin     0.0          0.421497          0.482280  0.618676  0.467787   \n",
              "\n",
              "                   SG:ARG  Top 10  \n",
              "Sean O'Hair      0.521384     1.0  \n",
              "Charlie Wi       0.855346     3.0  \n",
              "Scott Stallings  0.406918     1.0  \n",
              "Danny Lee        0.616981     4.0  \n",
              "Martin Laird     0.712579     2.0  \n",
              "...                   ...     ...  \n",
              "Ryan Blaum       0.588050     2.0  \n",
              "Jason Bohn       0.520126     3.0  \n",
              "Dustin Johnson   0.451572    12.0  \n",
              "William McGirt   0.566667     2.0  \n",
              "Mathew Goggin    0.453459     1.0  \n",
              "\n",
              "[9650 rows x 15 columns]"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X, y = import_dataset('data/pgaTourData.csv', split_train_test=False, numpy_format=False)\n",
        "names = X['Player Name']\n",
        "X.index = names\n",
        "y.index = names\n",
        "X = X.drop(['Player Name'], axis=1)\n",
        "features = X.columns\n",
        "scaler = MinMaxScaler()\n",
        "X = pd.DataFrame(scaler.fit_transform(X), index=names, columns=features)\n",
        "Xy = X.join(y)\n",
        "Xy.dropna()\n",
        "Xy.index.name = None\n",
        "Xy = Xy.sample(frac=1)\n",
        "Xy"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dHnhlsmf4cC0"
      },
      "source": [
        "## Feature Extraction\n",
        "Heatmap is used to see which features are most correlated with the prediction column (Top 10). In this case, we see that Average Score, Average SG Total, and Points are the most indicative of being good features for Top 10 results. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "XZkFPZxpvKcj"
      },
      "outputs": [],
      "source": [
        "def plot_correlation(data):\n",
        "    fig, ax = plt.subplots(figsize=(20, 15))\n",
        "    sns.heatmap(data.corr(), linewidths=.3, annot=True, fmt=\".2f\")\n",
        "    plt.show()\n",
        "    fig.savefig('corr.png')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 958
        },
        "id": "mggytYQxw2Om",
        "outputId": "05491631-e10c-4c14-c108-bd009e6e5a2f"
      },
      "outputs": [
        {
          "data": {
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",
            "text/plain": [
              "<Figure size 1440x1080 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_correlation(Xy)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_hRdIsOH5kth"
      },
      "source": [
        "## Feature Selection\n",
        "Selecting Average Score, Average SG Total, and Points are selected to be used as the features to train regression models due to correlation with Top 10."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "j0vkujQR4n9P",
        "outputId": "a8ed4cbc-9ec8-479d-8064-257b4e0dc04f"
      },
      "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>Average Score</th>\n",
              "      <th>Average SG Total</th>\n",
              "      <th>Points</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sean O'Hair</th>\n",
              "      <td>0.368818</td>\n",
              "      <td>0.646661</td>\n",
              "      <td>0.168987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Charlie Wi</th>\n",
              "      <td>0.350579</td>\n",
              "      <td>0.621906</td>\n",
              "      <td>0.215554</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scott Stallings</th>\n",
              "      <td>0.550158</td>\n",
              "      <td>0.451647</td>\n",
              "      <td>0.159626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Danny Lee</th>\n",
              "      <td>0.438092</td>\n",
              "      <td>0.581834</td>\n",
              "      <td>0.114738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Martin Laird</th>\n",
              "      <td>0.307261</td>\n",
              "      <td>0.715939</td>\n",
              "      <td>0.161546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ryan Blaum</th>\n",
              "      <td>0.402490</td>\n",
              "      <td>0.565628</td>\n",
              "      <td>0.103217</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jason Bohn</th>\n",
              "      <td>0.378464</td>\n",
              "      <td>0.648085</td>\n",
              "      <td>0.221555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dustin Johnson</th>\n",
              "      <td>0.155559</td>\n",
              "      <td>0.830632</td>\n",
              "      <td>0.411666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>William McGirt</th>\n",
              "      <td>0.297264</td>\n",
              "      <td>0.742119</td>\n",
              "      <td>0.325732</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mathew Goggin</th>\n",
              "      <td>0.521571</td>\n",
              "      <td>0.482280</td>\n",
              "      <td>0.063370</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9650 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Average Score  Average SG Total    Points\n",
              "Sean O'Hair           0.368818          0.646661  0.168987\n",
              "Charlie Wi            0.350579          0.621906  0.215554\n",
              "Scott Stallings       0.550158          0.451647  0.159626\n",
              "Danny Lee             0.438092          0.581834  0.114738\n",
              "Martin Laird          0.307261          0.715939  0.161546\n",
              "...                        ...               ...       ...\n",
              "Ryan Blaum            0.402490          0.565628  0.103217\n",
              "Jason Bohn            0.378464          0.648085  0.221555\n",
              "Dustin Johnson        0.155559          0.830632  0.411666\n",
              "William McGirt        0.297264          0.742119  0.325732\n",
              "Mathew Goggin         0.521571          0.482280  0.063370\n",
              "\n",
              "[9650 rows x 3 columns]"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "X = Xy[['Average Score', 'Average SG Total', 'Points']]\n",
        "X"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Wgg_pTqV6Ftw",
        "outputId": "dea8a815-fc59-4084-840e-7d5caeb404a4"
      },
      "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>Top 10</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Sean O'Hair</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Charlie Wi</th>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Scott Stallings</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Danny Lee</th>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Martin Laird</th>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ryan Blaum</th>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jason Bohn</th>\n",
              "      <td>3.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dustin Johnson</th>\n",
              "      <td>12.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>William McGirt</th>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mathew Goggin</th>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9650 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Top 10\n",
              "Sean O'Hair         1.0\n",
              "Charlie Wi          3.0\n",
              "Scott Stallings     1.0\n",
              "Danny Lee           4.0\n",
              "Martin Laird        2.0\n",
              "...                 ...\n",
              "Ryan Blaum          2.0\n",
              "Jason Bohn          3.0\n",
              "Dustin Johnson     12.0\n",
              "William McGirt      2.0\n",
              "Mathew Goggin       1.0\n",
              "\n",
              "[9650 rows x 1 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y = Xy[['Top 10']]\n",
        "y"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "pAgFLalf6o9Y"
      },
      "outputs": [],
      "source": [
        "Xtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.20, random_state=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nybq58iz6Rto"
      },
      "source": [
        "## Regression Model Performances"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "74n2Sp518EZe"
      },
      "outputs": [],
      "source": [
        "def plot_regression(ytest, ypred):\n",
        "    fig, ax = plt.subplots()\n",
        "    ax.scatter(ypred, ytest, edgecolors=(0, 0, 1))\n",
        "    ax.plot([ytest.min(), ytest.max()], [ytest.min(), ytest.max()], 'r--', lw=3)\n",
        "    ax.set_xlabel('Predicted')\n",
        "    ax.set_ylabel('Actual')\n",
        "    plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "JLfqnE5k8NLk"
      },
      "outputs": [],
      "source": [
        "def calculate_error(ytest, ypred):\n",
        "    mae = mean_absolute_error(ytest, ypred)\n",
        "    mse = mean_squared_error(ytest, ypred)\n",
        "    r2 = r2_score(ytest, ypred)\n",
        "    print(\"The model performance for testing set\")\n",
        "    print(\"--------------------------------------\")\n",
        "    print('MAE is {}'.format(mae))\n",
        "    print('MSE is {}'.format(mse))\n",
        "    print('R2 score is {}'.format(r2))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "if9fVOML6gia"
      },
      "source": [
        "### SVM "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "GxCOXI1g6NYP",
        "outputId": "430fee3f-9f18-4fb3-fd54-a69550788bf3"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\kamen\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The model performance for testing set\n",
            "--------------------------------------\n",
            "MAE is 1.3917279201622148\n",
            "MSE is 3.4185220170769197\n",
            "R2 score is 0.22879766121940037\n"
          ]
        },
        {
          "data": {
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xkAt/Txg4Q9VRV3Nm8wPTqT/zBVr/eDC1J6wpWL/lsUMZXRfusl2zOybOLY8digScvzrc5u6q7bMUhtJ+RUqqdvXVzvKaNTB+fElTMvqOcrFzGt2kJwycXubMRMSJR7cMcl0f21bVLbtmd9rGtlUR3TLIc+6u2j5LYSjtN6xZA0ce6Vy109bmvGbPLnVWRhlghb/M6QkDp5c50xd24sFh77uuDwxp65ZdszttA0PaCA5733Purto+S2EoLXs6OuD662HaNHjuuUz80EOduDHgscJf5vSEgXPWuX5aXIyXHz/GGbty0iZaHpmau37JFALxYLfsml5ttz/eUDB2POKn5vDXnLnzcuuO7bMUhtKy5vnnobERrr0WolEnFgzCd77jaBcaC/7qNwYibua2cnsNdDtnTxg4Z8+Ja7DKMV4GqzLGy9TYSFz9Fc56CUa1bsTu2TW92taPjKbHHjzUGT/Vd/ac+B7ZPkthKC07du5UveIKVZ9P1XkoovM6/HDVl14qdXZGiaCv7Zw9yUA+uWsYXeJTn4InnsgsV1XBd78Ll14K/gH6149hJ3cNY6/miisyn48/Hl58ES67zIq+4Ypd72YYewMnnOBcvXPwwXD++XbnrVEU2+M3jP5Ec7Pj13nggcJ1P/6x4863om90ghV+w+gPqMJddznPuf3tb2HOHHjvvVJnZfRTrPAbRrmzcSOcdpojUWtpcWL//jfcd19p8zL6LVb4DaNcSSTgttuc4/YPP5yJjxkDS5Y4j0c0jN2gJIVfROaJyBoReUlE7hKRilLk0RdkmyPr940xfJS7RbK7tstQVQxfKIZIAl/Y3aqZPebwUblGzK4aLPPzKmbSrBkSS+cSqHQMoBn7p+KviCKSIBCOUTUolb/iC0XTeTc1KVWDYkjAfX1nudbvm+k3fFQ/NnW+9ppzdc7FFzuPREwxZw689BKcdFLpcjP6PX1+Hb+I7Af8BZisqu0icjewRFV/49Wnv17Hn22OjO0Is/XZA6ibsbrAIgl0y3Z5zqwohGJUH7yRnWtHU3fS6gKr5nlfChTYLVuWTGHo0S8TGBzpksHSzXzZsriB6ilvMvTI13JMmgvviBP3x6nPsmxufuAwQhUJhp6UZeZ8ZCrxXX78lbGcbdHyyFR80QAJlIQk8AUTBeuD+F1toalcZ82OEpXccbc/3sCvfhbuP6bOeBx+8hP41rcca2aKSZNg4ULnebiG0UXKxs6ZLPz/AKYC24E/AD9V1Se8+vTXwp9tjnzn9mNcLZSVy5yfSXdslxtbIgw78cWiVk3pCLnaLVv/eDCjLnimSwZLL/Nlaoz08kPTicWV4Wcuz2n79i+PZdiJLxb03/zAYQVtU3F/paMZcOvnZQvN3y792tT57rtw4IGZB6T4/fCNb8B110FlZUlTM/ofZXMDl6q+DfwIeBPYBGxzK/oicpGILBORZc3Zj4brR2SbI70slBvW+7ttu4xtq+rUqullt4xuGVR0fK/83cZIL7cF0EiwoG0qz/z+bm1T8di2Ks9+XrbQVK5e/fqVqXPECLjxRufz1KmOZO3737eib/QofV74RWQf4HRgf2AUUC0iX8xvp6q3qWqjqjbW99OnAmWbI70slOMmxLttuwwMaevUqulltwwOe7/o+F75u42RXq6KIeFoQdtUnvn93dqm4oEhbZ79vGyhqVy9+pW1qXPjxsLYOec4Dz9//nmYPr3vczL2ejwLv4j8TER+6vXagzlPAF5X1WZVjQL3Ax/dg/HKlmxzZM3hr9GyZIqrRbK7tkvdFaRlyRTHbPnoFFerppvdsmXJFGoOf63LBku3vFoWN1A5aVOBSTMgQnOeZTPeHmTro3lmzkemogkKtkXLI1MJiKC7gsQjfvf1HrbQVK6BeLCg3/bHG8rT1LlzJ1x+OUyYkKtOBucGrC9+0bFqGkZv4GZuSx73P6fYy6tfZy/gcGANUAUIcCdwabE+/dnOmW2OrBsR1fqR7hbJ7toug5VRlWBUIa4ScrdqZo9ZPzLXiNlVg2V+XsVMmoNroulc/BWOATRj/0yoL9yhEFd/KKqV1an8EyrBjnTeixYltLI6qvjd13eWa92ITL/6kWVq6ly6VHXChIxB8+CDVSORUmdl7IVQTnZOEfk28DkgBqwAZqlqxKt9fz25axg5bN3qyNQWLsyNn3CCczduPz2kaZQvXid3O73UQUTqgW8Ck4H09faqetzuJqOq1wHX7W5/w+h3PPSQ89jDd97JxIYOdfw6555rfh2jT+nKyd0mYB3OydhvA28Az/diToax97B5M5x9Npx+em7RP/NMWLsWzjvPir7R53Sl8A9T1duBqKo+rarnAx/p5bwMo//z+OOOVO33v8/ERoyAe++F+++HkSNLl5sxoOnKXS3JB3eySUROBt4BRvdeSoaxlzBmDOzYkVk+5xzn0E5trXcfw+gDurLHv0BEhgBfB74BLATm9WpWhrE3MHkyXHMNjB0Ljz0Gv/mNFX2jLLBn7hpGT/DKK87jDs86Kzfe0QGRCAweXJq8jAHNbisbROTXInJH/qt30tz7aGpyLJEpe6YEYviCznLKVullvXQze4ovQag6is/nbfhMtRGfY8kUSSCBWPqzL5hrvpSAk1fNkO7bQ+fMTc4lSqg6Y+SsrMqYOn3hKJVV3qbM/O8ZHhTN2V7ic7ZPV7f3/pNi+Hya3AaJbtlIu00sBj/8oaNX+PKXYf363PWhkBV9o+zodI9fRLJ3YSqAM4F3VPWrvZlYNv11j7+pSbnw0gg1n1qZY8hMRH3UHreOwOAILY9OITR8Kx3v1FKXZbbc/ngDGvMx5OTlabOnm40z3/ApEzcUGjsfmUoiJgxueJMdK8e6mi/ju/z4Qokcu2Zn9tAPNwT58986cvJuWdxAvD2ArzKWM1bz4gakI8Cdv/bnmDI7M5i2LJlCIhJAoz5mf8XHLTd776u42kQfnUL15I3oa+M6tZF2m1WrnOfbLl+eiX3qU85hHcMoA3rMzikiPuCPe3Idf3fpr4Xfy2655bFDkUAibcls/sNh1J9RaKvc8tih7Hfxn9NmTy8bZ7bhs6ixM5BAY75uGTOL2UObH5hO/ZmFBtDN909n+Kfd4+PGSo4psysG0y2PHUq8LUQgAB07vTUGxWyitSes6TlLZyQCCxY48rRYLBNvaIA77oBp0/Z8DsPoAXrSzjkJGLvnKe39eNktY9uqciyZiV3utsrYtiogY/bsiuGzqLFzy6BuGzOL2UMTEXcDqHZ4x/NNmV0xmMa2VaEdAaJtxYt2MZtoj1k6//53p7AvWJAp+uEwfO978M9/WtE3+gVdOca/Q0S2p17AYpw7eY1O8LJbBoa05VgyfRXutsrAkDYgY/bsiuGzqLFz2PvdNmYWs4f6wu4GUAl5x/NNmV0xmAaGtCGhGMGqGMUoZhPdY0vn++/DZZfBkUfCunWZ+FFHOYd8rrrKpGpGv6HTwq+qg1W1Juv1QVW1pzx3gQXz/Wx/vKHAkBmP+NOWzJZHpxAe00JLntly++MNBOLBHLOnm40z3/Dpaux8ZCrxiJ/KSZs8zZeaoMCu2Zk99OPH+AvyblncgMYKTZ3NixvwqRSYMjszmLYsmUK8PYjGhFnn7oZN9FFnu3XFRlqUM86Am25ytGoAgwbBz38OTz8NBxyw++MaRilwM7dprk1zaVdivfnq73bO+pHRtD0Tf1Ql4CynbJVe1ks3sycS12BVh4p4Gz5TbRDHkglxxR9Nf5ZArvkSv5PX4Jru20Nnz0nORUKDVRkjZ0VlxtQpoQ6tqPQ2ZeZ/z1B1R872Qpzt09XtPX5iVEUSyW0Q75aN1JOlSzM2zRNPVH3jjT0bzzD6ALpr50w+AL0KeAo4FkehDFADPKqqB/Xy/0lp+uvJXWMv44or4NBD4UtfMr+O0S/YHTvnxcBlOE/JeoFM4d8O3NzTCRpGWfDuu3DppfC5zxXejHXDDaXJyTB6GM/Cr6o3ATeJyKWq+rM+zMkw+h5V53GHl10G770HzzwDH/+4KRaMvZKuXM6ZEJGhqQUR2UdE5vReSobRx2zYACed5EjU3nvPib37LvzhDyVNyzB6i64U/gtVdWtqQVXfAy7stYwMo69IJODmm+GQQxyFcorx4+GJJ5y7cg1jL6Qrhd8nkjmTJSJ+INR7KRlGH/Dyy/Cxj8EllzjX6INzwvZrX3Nka5/4RGnzM4xepCv3rz8O3C0ivwAU+ArwaK9mZRi9RTQKP/oRfPvbjnohxUEHwe23wxFHlC43w+gjurLH/01gKTAbmAusBir3ZFIRGSoi94rIv0RknYj0m9+2bJPk8FEx6vfNfK7ZJ+bYL5NWykClY6vMNlimjJyBcMqQmWqf6ReqjnLwoQkCFbnxtEkzabwUXyzZJjOu+HKtmCIJp58v5rynxvFlDJ1VQ6LUDMksByoLrZb5Bs2qIRmDZiBcaM/Mbz9kWO53kUAMfzjPwpm0iGbyTNpMA53bOb3soQW8955zdU6q6AcCjjN/xYouFf0uz2MYZUyne/yqmhCRfwATgM8BtcCe3rl7E/CYqn5GREI49wuUPdn2xzFnZOyRtSe9jG9whPcfbMA/WKk7eVXaDvmL2w9DAgnqT8s1VSbiQsWYLUTeqWXQlDdzjJpb/zqRf60ey6DD3iw0bS6ZwtCjXyYwOELz4gZCo1qpP30lW/86ke3PTXC1YoZHtabnGXrka+l4annrXyfy/uqxDD8ry7L56BRa6zcye944/vq3IIvuiVJ1/ApqDwjz3tMHUXfKSuqzcvrF7QEgwC03+3K2U+0BYd579gBCI7bii9RSf+pKx8L5zIE528m5QzeAvyJeEE9EAtx6m1Ng3eyc+T+X9o21zJ43DXCxcQ4fDj/5iXMid/p0Zy9/6tTd+vkXnccwyphiN3B9EDgb+DywBfg98A1VHbdHE4rUAKuACeo1eR7lcgNXMfvjqAue4e1fHltgvnSLpYyTiV1B6s9YXmDU7MzGmZovZfYc87Uneef2Y4htr3C1Yqbsn6l++eN4WTFT87c+NJ3a017o1KApHSE6dgZdjZvZBlKvMbwMoVseO5R4e5CAX1ztnF4/l8pljbz+x7dhXN4/WVW4+27nOv1A122dRefpCeunYfQwu2Pn/BdwPHCqqh6VvJZ/DyxXaSYAzcCvRWSFiCwUkWqXhC8SkWUisqy5ubkHpt1zitkfAVfzpZcNM7atKm3lzLdSdmbjzDd7pvp4WTGz53Ebp9g84dGtRNsCXTJopuyZbsbNbAOp1xhehtDYtio0EvS0c7r9XPYZ/jaXvzYPPvAB+Mc/cjuIODdodaPoe83TY9ZPw+hDihX+s4B/A0+JyK9E5Hgyd+/uCQHgMOBWVZ0G7ASuym+kqrepaqOqNtbX1/fAtHtOMfsj4Gq+9LJhBoa0pa2c+VbKzmyc+WbPVB8vK2b2PG7jFJsnsrGWYFVm3KL2z6Q90824mW0g9RrDyxAaGNKGhKOeds78n8sx61/giYWXcCk/h3gcLrgg90TubuL1898j66dhlADPwq+qD6jq54ADgT/jPGB9hIjcKiKf3IM5NwIbVfW55PK9OP8RlD2u9sclU9KmzXjET8sjU3PWx9uDrqbK+K4A4TEtNC9uKDBqVk7a5BrPn695cQPhMS3pPl5WzOx5suOp5dR8XlbLWefmGTQfdjGOtgfT9kw342Yqh3Qsbzu1LJmCJnCNx9uDaNzbzpmar+KVADc8fCP/c891jGn7d6bBuHGwY0ev/Pz32PppGKXAzdzm9cI5sXsx8Kfu9HMZ51nggOTn+cANxdqXk50z2yRZPzKqdSMynwcPjTr2y6SV0l/h2CqzDZYpI6c/lDJkptpn+gWrOnTyIXH1h3PjaZNm0niJRJNtMuMiuVZMiDv9JOq8p8aRjKGzsqZDB9dklv0VhVbLfINmZU3GoOkPFdoz89vX1OZ+F/xR9YXyLJxJi2gmz6TN1N+5nfPpr96jm/0jMgZNUB02THXRItXEHpo5Pb5Xj1g/DaMXobt2zt5ERBqAhTg3gq0HzlPnjmBXyuXkrlGGbNrk3IR1//258bPPdvz5w4eXJi/DKAN2x87Za6jqSqAgGcPoFkuWwMyZsHVrJjZqFNx6K5x2WsnSMoxyZ3eeuWsY5cH++0NbW2b5wgthzRor+obRCVb4jf7LQQfBtdfChAmwdCncdhsMHVrqrAyj7LHCb/QP1q2De+4pjF95JaxeDccd1/c5GUY/xQq/Ud5Eo/Dd70JDg6NZ+L//y10fDEJ1wf1/hmEUwQq/Ub688AJ86EOORK2jA9rbYe7cUmdlGP0eK/xdoKnJMUx62TR9oRihqhi+oMf6pEmzICYJZ8xsc2ZOX7cxkp+Tlk6RWE4eEsi8KqtiVA3K5O2YL2Mu86SWM2PljJtsG6qOst+YRMYEmpwn2+qZsmjOmZttIM0YQQvtobn51OwTo9K3gx/4ryDWeDisWpX+ObRM+BCPHPtDVztmxoCaZUcNO1ZQM2kaRi4luY6/u5TyOv6mJmXW7ChRiVF98EZ2rh1N9eSN7HxpTKFFssMPogSGtBHfUVVgyUx0+Bhx1vIcA2Zo+FYi79TmtG15dArVkzfy/uqxBUbNqkmbqD7w3eQdrX58Ic3tm7RZ4kugUT/+yhjVh7zFjhXj8IXi1M1YnZNTOGn3zDZ2tr06nER7haflMzyqlV0ba0GlYMyWxQ0csH+Ida/G8IVjOes2P5A0leaNG6zbxtAjXmfzA4dxVOwvLExczAf1tfTPoI1Krg1cx02+rxII+xlycmYbti2dxocbgvz5bx1UZ1lOYzvCbH32gJz525ZO49YbzaRpDBx2R9JmANfMjxPzR6mbsZr2V0dSd1Ly/eRVVIzbgviVinFbqJuxGn9FDH84Tqx1MPWnrsxZX3/qSsRHbp+TVhN5q66gbWqO+lNX0v7qyJwx2v61X3o+EV9h3xmr8VdG8YfjiN9RRLe/OhJ/hVOE83OKvFWXs9z+6kji26td80/lGnmrDn847jpm3akrWfdKHH9ltGCdvzLqOm7Hplrq9n2LW3Quz8SPzyn6fx97KMedfCc3DboEqhLE/NGc/lXHr+CpZ+LUJXOvO8mZc/tzEwvmrzp+BdfMN6+OYZhLthM2rPejWpVrqyxiqARA8bRP5seyrZXZ8ey58tunx/OwcebnkW3zdJs/f16v/FO5JnYFSUSCnmNqR4BYtDA3L1OpdgS47f4FfLRjdTq+PVTFf338fH439VNowkdsifsjG8KjW0lEAgU/F6+f0Ya7zatjGLbH3wnjJsTThs3OrJmBIW2OSdLDkinhaEEs21qZHc+eK799ejyPedJ5hDNWTi9LaGq87Hk7s3z6KqLpOVy/ZyjWLVOphGL89Miz07HHRx3FJ2bdwu8aTgSRnO8UGNJW0N8XjhX8XLx+RmbSNAwr/J2yYL6fQDxIy5IpaVtm5aRN7hbJXQHiET+B2h2ulkxNUGDAzLZW5psx3YyaVQe+nbFZaqKwb9JmGY/40bjQ8shUKidtIr4rQMuSKa7mznxjp79mZ1HLZ3hMC/GI33XMlsUNHPRBP/H2YMG6tKn0jVo0Rnrc0MhW/qwf53v+Kznb97/M2PoEG1oPKNi2ifYggXiwwI758WP8tOTZTFNWUDNpGoYLbua2cnuV2s65aJFjmPSyaUowqsHKqErAY33SpFkQI+6MmW3OzOnrNkbyc9LSCdGcPPBnXhWVUa2szuTtmC+jLvOkljNj5YybbBus6tBRo+MZE2hynmyrZ8qiOXtOtoE0YwQdyQZ9QE7Vs7g7PWf2HIOHRhVf4XcKVjomTC87ZsaAmmVHDTlWUDNpGgMVysnO2V3MzrkXoAp33AFf/zps2+ZYM9euhWHDSp2ZYey12FU9RulYvx5OOAFmzXKKPsDmzfDww6XNyzAGKFb4jd4jHocbb4RDDoE//SkT/8AH4KmnHAWDYRh9jl3OafQOa9Y4z7p97rlMzOeDyy+Hb38bqtwvzzQMo/exwm/0LB0d8P3vw4IFjmAtxaGHwu23O+4dwzBKih3qMXqWrVudRx6min4wCN/5DixbZkXfMMoEK/xGzzJ8uFP4AQ4/HFasgG99C0Kh0uZlGEaakhV+EfGLyAoRKetLO1JmzkKzpWOazMQT7pZMSWTMm6m4ZGycEnAxVqbMm4FYoZ3TF8OfN1/GkukyT74NNJwxa+aYMwPu1s5AZV4+eRbRD4ReLrR8fulznOX7PYEXnkYOmZw2c+abQbPH81dEOfjQBIGgd85m2DSMnqGUx/i/BqwDakqYQ1FSZs5IB/irYwVmy0RbAH91R9raWXdSlqVyyRTi7QF8FTFXe2bF2BaqPvguW544GF9FoqBN5YR3aV8/onDOiJ9EAvyDI9SdvIrYjjCtfzoIX0ALbaGRAIMPeyPH7pnoAKlIFFo1l0xh6NEvE22t5v3VYzvNJ7Q+xDce+j2zonfwsYo/8toZlTkWzgdDp1J38opM7g81MGhqxjS6+Q/T8AUTOTmvu/8wJJxguIupdOdLY2itf4vZ88YBZtg0jD2hJHv8IjIaOBlYWIr5u0rKzOlmmqw/dSUS1BxrZ74lU3x42jMjb9Wx/bmJnm3a/rWf+5x+Rfyk7aDbn5uIPxx3t4VWRgvsniJ+d6vmjNVsf25i2gpaLJ/j3vgnzzxxNhfHFuInwR2h8xiy36YcC2d+PvWn5ZpG/RWxgjb+qkJ7Z7YNtf3VkWbYNIweoFR7/D8BrgQGezUQkYuAiwDGjh3bN1nlkTJzgreFspitUyPu5s3YtipQHMNlJyZMtzGz8ylm3kxbOvNydjNndsXiObL2da5b/EvOWPt0zvrXh4+kIhohEnCO43tZOLNNo25tivXLfjfDpmHsGX2+xy8ipwCbVfWFYu1U9TZVbVTVxvr6+j7KLpeUmbOYVbKYrTNlx8yPB4a04auIOiZMjzZe1k4JR3PsmcXMm4EhbQV2Ty9zZup7uH6Xt/bhC8FF/PH2OTlFvzm8D58N/JZzpv+YbZWZ/8OLjV+sTbF+2e9m2DSMPaMUh3qOBE4TkTeA3wHHiciiEuTRKSkzp5tpsnlxAxqVHGtnviVTE3jaM8NjWqg5/DXPNlUHvu0+Z1zQOGk7aM3hrxGP+N1toe3BArunatzdqrlkCjWHv5a2gqbW7bMuzsL7rqcp+mWGRbamt809409ksv9F7q8+heaHpxVYOPPzaX4o1zQa3xUoaBNvC3qaSlOWUTNsGkYP4GZu66sXcCzwcGftSmnnTJk5C82WjlUzE4+7WzKJZ8ybqTgZGyf+pJ0z24SZMm/6o4V2TomqL2++jCXTZZ58G2goY9bMMWf6C62dp/CgbqVG1VGsqYJuYIyeyCPFLaJEC8eXQjNods6+cIdOPiSu/oB3zmbYNIzugYed0+7c7YSZM4WZM/M3U9C1beYPqPz2XnG3WNBjnducXuO6zZPq78t7zx87q93LB8DUCESSoblzGfu97/Ho4OxTM/njueWZH8vPpasxu+3EMHqCkv4mqeqfVfWUUuZgFOGAA+C662DSJHjmGfj5z2Gw5/l4wzD6CbYLZTi8+CLcdVdh/BvfgFWr4Oij+z4nwzB6BSv8A51IxNmrP+wwOP98ePXV3PXBIFRWliY3wzB6BSv8A5nnnoPp0x2JWiwGu3bB3LmlzsowjF7GCv9AZOdOx4t/xBGONz/FEUdkBGuGYey12FU9A42lS+HCC+H11zOx6mr43vdgzhzw2zXyhrG3Y4V/oLB1K1xxBSzM0yN94hNw220wfnwpsjIMowTYoZ4u0NSkVFZlNMyFiuZEWj1coFhO6Yfz9MoF/XP6eIzRBY1ybv9Mv6f2OTOn6L/HUM7lDnx/fhjZf7TT1uemcs5VJ2drkjO6ZRcddSq/HEV17rbzZymow4Oi+ILu32n4qBhz5jpaZp9fPfXMTU3aaRvDMGyPv1OampRzzosTV0VCCr6EoxPOVhon1cHbn98fXzieozR2VMg+hn96OeHRrWz968QC7XHz4gYGTXkzR58cHtVK/ekr05pjXyieq11+ZCpDj3mRwOCIZ/9dbw1L65dv0ZP4+O//DMB9gTOYf/Istk8Shm9c7uS4MwACvqqMRtot17Qmee1oQsO3EnmntlA7HXX2J2qPc/JLKaolAL5wrt46+3uk1NBu3+m2OxuonvImY854jfaNtcyeN41sPXNTkzJ7XgdVx69gzBmtrm0Mw3CwPf5OuGZ+nIQo4lf8lVFHgZyvNE6qg8WvBVrh+lNXIj7SMTftcf2pKwv0yZG36opqjutOXsX25ya69z9lBZE3h+Xol58ffzA3ffRsPlu1iLmf+SY7DiQ3x5Aigdz8XRXNJ2U01JG36ty10+E4/nA8nV9KUe2mt87+Hik1tNt3qstbztczXzM/TtXxK3LGNoWzYbhjhb8TNqz3ox0BNBIktq2qqDrYS8OcUikDngrnbGVxSoOcojPNcfbnkdub+e0/v8pnIg8U9Lvx6C9yT/sXPBXTKc10V3Itpo5Obafs/FLbr6vfw22b5C9vWJ85Eb1hvd917Ow2hmE4WOHvhHET4kgohoSjRRXNxRTLEo6ml70Uzvn6ZF9Fpk9nmuPIxlpCtdv54oolPHH7HI57fRk/46uMGPxml9XHEorl6J47y7WYOjq1nbLzS22/zr6H22ev5Ww987gJcdexTeFsGIVY4e+EBfP9+FTQuBBvDzoK5HylcVIdrHEp0Ao3L25AE6Rj+drjVJt8fXJ4TEtRzXHLI1OpOfw1dm0YxtAHq1na8QkWPHELgzvaAaijhU+2Ly3I1U2H3Ly4Ae0QNJabv1uuaU3yo1MIj2lx105H/MQj/nR+KUW1m946+3uk1NBu26Qlbzlfz7xgvp+2pbl6aFM4G4YHbsrOcnuVUsus6qiZKyozGuZCRXM8rR4uUCyn9MN5euWC/nlaY9cx8jTKfjr0Sv9/aRsVOerkdRygRwWeKuznlQ+O5hlxUznnqpNzVMxp3bKLjjqlec5RVOduO1+WgjpU3aEScN8m9SOjOnuOo2UWX8JTz7xoUaLTNoYxkMBDyyzOuvKmsbFRly1bVuo0yotVqxy3zvLlmZjfD1deCddeCxUVpcvNMIyyQEReUNXG/Lhdztnf2LULFiyAH/zA8eukaGiAO+6AadNKlpphGP0DO8bf39ixA37xi0zRD4cd3cI//2lF3zCMLmGFv79RXw8//anz+aijnEM+V13l6JMNwzC6gB3qKXdeeQU++MHc2Oc/74jVTj0VfPZ/t2EY3cOqRrny3ntwwQVw4IHw7LO560Tg9NOt6BuGsVtY5ShHHngAJk92TtaqwqxZzkldwzCMHqDPC7+IjBGRp0RknYisEZGv9XUO3aGpSQmFveya+VbMhLe9M8946Wbb3Fc2cY/vLPj0p+Hf/07ncPdrh7JPZVvhWCnzZdGYyzpXi2huTs7nrH45fVxsoHltU8tDhkVpalKampT6fd23Tcr6OXxUjPp9HbtmzZAYgUpnjFB1lDlzEyX8V2AYexelOMYfA76uqstFZDDwgog8qaprS5BLUZqalC+dG0fCMYbn2TT9g9uI76jKMVNufrABVNJGzGI2Tk3A8NOTsbf24aQHX+THvivYJ7o9Pf8m9uWS8E/465njqBn9DxJutsxHppLo8DP8zOW5hsxIAF9FBA0GC+yZ8bZggUU02wAa2xFm67MH5Fg03713Or5wnEFT3mTn2tHUnbQ65/ukbKJu3/e82Q0Q9ROTOP5qd7PpzrWjiYzfyM41owmN2cHOliHUn5EZ47Y7G4AQt9xsf6Qaxp7S579FqrpJVZcnP+8A1gH79XUeXeGa+XHwuRs3Y62DC+L+cDzHiFnMxukPx6kYt4XR77/L7/95Cb/edXFO0f91+EtMqXmev545rrgt8+RV+CujhYbMyiiJ9gpXe6abRTTbALr9uYkFFs1Un5SZM//7pGyibjnWnrySmGpRs2n6fcZqOjbVFuZ96koW/sa8O4bRE5T0qh4RGQ9MA55zWXcRcBHA2LFj+zaxJBvW+9EEnjbL/HhsW1V6fQovw2VsWxWffOXv3Pjwf1MdzRy/f2vICL75yUu5697/hA4YO3p1l8ZyjalH7i4W0Wxzpts8qT5eOaRsol7rNRIk1uFu80z1Sb27bdvw6FaibXYRmmH0BCX7u1lEBgH3AZep6vb89ap6m6o2qmpjfX193ydIxszpZbN0M1/mGyi9DJeBIW28WjeWQMLZi00g/LRiNp86/+f8yX88wWHvd2sst5hn7i4W0ey53OZJ9fHKIWUT9VrfmZ0z+90r72BVDMMw9pySFH4RCeIU/SZVvb8UOXSFBfP9kHA3bgZqdxTE4xG/Y7/MMlB62TjjET/rdkzhxx+dySs14zmmainfOf5CWjeNThsw4xF/57bMR6YSbw8WGjLbg/gqd7naM90sotkG0JrDXyuwaKb6pMyc+d8nZRN1y7H1kQYCIkXNpun3JVMIjWwtzHtxA7PONdOmYfQEfS5pExEB7gRaVfWyrvQppaStqUk57/w4MRTtCCChGBoVJAAa9SGheCbe4Qe/c/WJ+ECj/nR8WmAZk2Mv89vQ2WiHgN9p44sqgUCEiFZAPNNeggk0AcRBQpozR3rOYDzZxlck5tI+eYGMBLPHzeSkUT/444hP0WiyXyy7jz/ve4sTz2orAWe5pjbGLT91DtFc9vU4LS2F28YXjpOI+Kkf6XznLc1+Bg2K09ahxHcFCFbFmHWu307sGkY38ZK0laLwHwU8C7xIugTxn6q6xKtPv7Zz7toF3/kO/PCHjlZh1arCO3ENwzB6gbKxc6rqX4CB8fTrv/zFufv2lVec5XgcLrkEnniitHkZhjGgsb+de4MdO5wCf/TRmaIPcMwx8POfly4vwzAMTNLW8zz2GFx8Mbz5ZiY2eLBzqOeii8yvYxhGybHC31Ns2QKXXw7/8z+58RkzHH/+mDGlycswDCMPK/w9xac/Dc88k1keNgxuugm+8AXHpmkYhlEm2HGHnuK73818PvtsWLsWZs60om8YRtlhhb8TmpqUUEWWfTIcRSSO+KO5RspjP8J1XMtpvvvx3fe/yIh6Z72v0MJZaPnMW5c2bGYZNPP7Jk2gOf3F3biZHw+lxneziIZyzZ01+8QYMizzPX1B77zyjZ41+8Roaura5cJz5iYIhAvzGT6q62MYhtE17FBPEZqalC+fF4NgnOFnOqbI4f+K8N0lN3Nn8EssPfOAHDPn9Ymr8YVjDJ+xLMdcOWjKmww98rW0TXPoMS8Sba0uNG0umcLQo18mMDjC5gcbEB/e67Nsmum5HmogvF/Gkrlj+Xg0Jviq4wV2UNoC+Ad35JoyH5lKaN/3iLzjSNLSls4TVzM01ebhBhJRX4ENtHLCu7T/3745+bQsmcJ5FweAIDNnev/lM2dugl/cHsNXEWN4nrkzMn4jF146FggXHcMwjK5je/xFuGZ+HPUp9aetpGrMZs5b8SB/fvwLfDKxlJ9yGaPq1+eaOSujBfbJlNEy26a5/bmJ7qbNGavZ/tzE9HhF12fZNNNznZZryfRXRhE/rnZQCWqhKfPkVUTeqku3d7N01p2y0tUG2vav/QryqZuxGg1GHctpERb+xn3bpYydNZ9a2ekYhmF0HdvjL4Jj5/RzcOVKfvDbn9L49rr0uqEd2znqjRU8NPlYwN3MmVqObhnkulysbbYtszvrsy2ZgLeh08OAmdiVMWh2xwaa3S+/7YYCBV8u0bYAtHsYOZM5bLjbPD2G0VPYHn8RPrD/Lq72X8+SO7+aU/Rf8h3EKSfcli764G7mhIx9Mn/Zy2KZatvZWF7rsy2ZRQ2dHnFfRcbc2R0baHa//LbjJhTfWw9WxTo1d3Y2hmEYXccKvxfLl/Nc9AgWxK8jnHCKaYcvwI8OvoDpoX/w5N9nFpo524MF9smU0TLbpllz+Gvups0lU6g5/LX0eEXXZ9k003M9lGvJjLcH0TiudlCNSqEp85GphMe0pNu7WTpbHm5wtYFWHfh2QT4tS6Yg0aBjOS3CrHPdt13K2Ln98YZOxzAMoxuoatm/pk+frn1GW5vqN7+p6verOo86VwV9Thr1EFaphDoU4oo/qhLsUEioBKOKP6pINBmPJuMdiuQt+5OfQ0XWpcYjmpzPpW86h6z+2e1DznLBOKEODabGz+6fHFOCzvdItR88NKo1tZnvKQHvvPK3yeChUV20KNGlzT57Tlz9ocJ86kd2fQzDMHIBlqlLTe1zO+fu0Kd2zpYWmDwZmpud5cpKuP56uOwy8Ntep2EY/QcvO6cd6smnrg5+9jPn87HHwurV8PWvW9E3DGOvwa7qWbvW2cPP5rOfdcRqJ55oUjXDMPY6Bm5Va2mBL30JDj4417EDjmZhxgwr+oZh7JUMvMqmCnff7ezlL1rkxGbNgvb20uZlGIbRRwyswv/OO3DmmfC5z2VO3gJ8+MMQiZQuL8MwjD5kYBR+Vbj9dmcv/8EHM/HRo+Hhh509/6FDS5aeYRhGX1KSwi8iJ4rIyyLymohc1auTrV8PJ5zgHM7Zti0T/8pXYM0aOPnkot2bmtQxUmaZLCWQb8FMFJg4c02VKYNlIt0v24CZHiNvOWeM7DZ5Y6VMn4V2zkR6Xc0+Mer3jeHzK/tPijFnbiJj3fSYP2XYTPWr3zfG8FExxJcgVB3F51OGj0qu9ymh6ijiS7D/pFyjZlOTM2dqbrNtGkZp6fOrekTED9wMfALYCDwvIg+p6toen+z++50TuG1ZioGJE2HhQvjYxzrt3tSkfOmCKAQT+IMJ6maszhgrsyySzYsb8A9uI76jKtdqmddGEzD89JXs/NcI2pKStvDoVrb+dWKhqfORqSRiwvDTV+bYKqsnb2THinFIRSyn/eYHDkP8CepPK2z//uqx7Njp4/1IgtqTXqS1tZpf3unMN3R0K80PNqSNnNn5hke10vbuUN7f7qN6ylu8t34E1QdvxN82mtqTVudYOGtnONbQlken0Fq/kdnzxgEhAGbP66Dq+BWMOaOV9o21zJ43DQiZbdMwSkQp9vg/DLymqutVtQP4HXB6r8x06KGQSDiffT644gpYtapLRR8cO6e/Moo/HE+bI92MlfWnriTWOrio1bL+1JX4w3Eqxm2h7V/75RgzXU2dJ69Kt8+3VfqTRT97nb8ySv1pK13b15+6EvGBPxx3NYNmGzmz8428VUfdjNX4w3HHvjnDGa/upNUFFs6UNTQ1Z9XxK7hmfpxr5sepOn5FTvvUOsMwSkMpCv9+wFtZyxuTsRxE5CIRWSYiy5qzT8R2h0mT4Dvfcf4DeO4554HnVVWd90uyYb2f2LaqHBOml7Ey23bZmdUy32TZHQtmdMsgVzNnMZtneHQrGgkS21ZFdMuggvm8zJqpeGxbVfqzV67ZxtG0UXO9nw3r/a7tN6y3G+IMo1SUovC7/X1fcNBXVW9T1UZVbayvr9/92ebNg2XLoLHgruVOGTchXmDd9DJWZtsuO7Na5pssu2PBTFk389t3ZreUcJTAkDZXM6iXWTMVDwxpS3/uzCqab9QcNyHu2t5sm4ZROkpR+DcCY7KWRwPv9NpsgQCEQrvVdcF8xxoZj/jT5kg3Y2Xz4gYCtTuKWi2bFzcQj/jZtWEYVQe+nWPMdDV1PjI13T7fVhnfFShoH28P0vxQg2v71PmFeMTvagbNNnJm5xse00LLkinEI37HvrnEGa/l0SkFFs6UNTQ1Z9vSaSyY72fBfD9tS6fltE+tMwyjRLiZ23rzhXNCeT2wP87Zv1XAwcX69KmdM49FixKOkTLLZIk/34IZLzBx5poqUwbLeLpftgEzPUbecs4Y2W3yxkqZPgvtnPH0usFDo1o3IqriS+j4iVGdPSeesW56zJ8ybKb61Y2Iav3IqCJxDVZ1qEhC60cm10tCg1UdisR1/MRco+aiRc6cqbnNtmkYfQPlZOcUkRnATwA/cIeqfrdY+z61cxqGYewleNk5SyJpU9UlwJJSzG0YhjHQGRh37hqGYRhprPAbhmEMMKzwG4ZhDDCs8BuGYQww+sUzd0WkGdiwm93rgJYeTKe36U/59qdcoX/l259yhf6Vb3/KFfYs33GqWnAHbL8o/HuCiCxzu5ypXOlP+fanXKF/5dufcoX+lW9/yhV6J1871GMYhjHAsMJvGIYxwBgIhf+2UifQTfpTvv0pV+hf+fanXKF/5dufcoVeyHevP8ZvGIZh5DIQ9vgNwzCMLKzwG4ZhDDD26sLfpw913wNEZIyIPCUi60RkjYh8rdQ5dYaI+EVkhYg8XOpcOkNEhorIvSLyr+Q2PqLUORVDROYl/x28JCJ3iUhFqXNKISJ3iMhmEXkpK1YrIk+KyKvJ931KmWM2HvnekPy3sFpEHhCRoSVMMY1brlnrviEiKiJ1PTHXXlv4sx7qfhIwGfi8iEwubVaexICvq+pBwEeAuWWca4qvAetKnUQXuQl4TFUPBKZSxnmLyH7AV4FGVT0ER11+dmmzyuE3wIl5sauApao6CViaXC4XfkNhvk8Ch6jqFOAV4D/6OikPfkNhrojIGOATwJs9NdFeW/jpy4e67yGquklVlyc/78ApTAXPIS4XRGQ0cDKwsNS5dIaI1ADHALcDqGqHqm4taVKdEwAqRSQAVNGbT6jrJqr6DNCaFz4duDP5+U7gjL7MqRhu+arqE6oaSy7+A+cpgCXHY9sC3AhcicsjaneXvbnwd+mh7uWGiIwHpgHPlTiVYvwE5x9iosR5dIUJQDPw6+ShqYUiUl3qpLxQ1beBH+Hs3W0CtqnqE6XNqlNGqOomcHZigOElzqc7nA88WuokvBCR04C3VXVVT467Nxf+Lj3UvZwQkUHAfcBlqrq91Pm4ISKnAJtV9YVS59JFAsBhwK2qOg3YSXkdisgheXz8dJxHk44CqkXki6XNau9ERK7GOczaVOpc3BCRKuBq4NqeHntvLvx9+1D3PUREgjhFv0lV7y91PkU4EjhNRN7AOXx2nIgsKm1KRdkIbFTV1F9Q9+L8R1CunAC8rqrNqhoF7gc+WuKcOuNdERkJkHzfXOJ8OkVEzgFOAWZq+d7M9AGcHYBVyd+30cByEdl3Twfemwv/88AkEdlfREI4J8geKnFOroiI4ByDXqeqPy51PsVQ1f9Q1dGqOh5nm/5JVct2j1RV/w28JSIHJEPHA2tLmFJnvAl8RESqkv8ujqeMT0YneQg4J/n5HODBEubSKSJyIvBN4DRVbSt1Pl6o6ouqOlxVxyd/3zYChyX/Te8Re23hT568uQR4HOcX525VXVParDw5EvgSzt7zyuRrRqmT2ou4FGgSkdVAA/BfpU3Hm+RfJvcCy4EXcX5Hy0YxICJ3AX8HDhCRjSJyAfB94BMi8irO1SffL2WO2Xjk+3NgMPBk8nftFyVNMolHrr0zV/n+lWMYhmH0BnvtHr9hGIbhjhV+wzCMAYYVfsMwjAGGFX7DMIwBhhV+wzCMAYYVfmNAICLx5KV7L4nIPcm7Ind3rN+IyGeSnxcWE+qJyLEi0u0bsETkjZ4yMRpGPlb4jYFCu6o2JI2XHcBXslcmba7dRlVnqWqxG8KOpfzvvDUGGFb4jYHIs8DE5N74UyLyW+DF5DMGbhCR55Ou9ovBubNaRH4uImtF5BGyJGQi8mcRaUx+PlFElovIKhFZmhTufQWYl/xr42gRqReR+5JzPC8iRyb7DhORJ5IiuV/i7poyjB4hUOoEDKMvSaqOTwIeS4Y+jONmf11ELsKxYX5IRMLAX0XkCRxb6gHAocAIHOXDHXnj1gO/Ao5JjlWrqq3Ju0LfV9UfJdv9FrhRVf8iImNx7iw/CLgO+IuqfkdETgYu6tUNYQxorPAbA4VKEVmZ/Pwsjhvpo8A/VfX1ZPyTwJTU8XtgCDAJx+d/l6rGgXdE5E8u438EeCY1lqq6edXBkbBNdjQ8ANSIyODkHJ9O9n1ERN7bva9pGJ1jhd8YKLSrakN2IFl8d2aHgEtV9fG8djPoXOktXWgDzuHVI1S13SUX86cYfYId4zeMDI8Ds5OKbETkg8mHtjwDnJ08BzAS+LhL378DHxOR/ZN9a5PxHThCsBRP4MgDSbZrSH58BpiZjJ0ElM1za429Dyv8hpFhIc7x++XiPPD6lzh/FT8AvIpjy7wVeDq/o6o24xyXv19EVgG/T65aDJyZOrlL8nm6yZPHa8lcXfRt4BgRWY5zyKnHnq9qGPmYndMwDGOAYXv8hmEYAwwr/IZhGAMMK/yGYRgDDCv8hmEYAwwr/IZhGAMMK/yGYRgDDCv8hmEYA4z/Dxf6GPr2SQFVAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "svm_reg = SVR(kernel='rbf')\n",
        "svm_reg.fit(Xtrain,ytrain)\n",
        "ypred = svm_reg.predict(Xtest)\n",
        "calculate_error(ytest, ypred)\n",
        "plot_regression(ytest, ypred)\n",
        "\n",
        "\n",
        "# Save model to pickle file\n",
        "file = open('models/SVM.pkl', 'wb')\n",
        "pickle.dump(svm_reg, file)\n",
        "file.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9VYVlHcN9Ei0"
      },
      "source": [
        "### Random Forest"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 401
        },
        "id": "IJeRDIs39Enm",
        "outputId": "26a6c2da-91bb-4a5c-d3f4-3465bcbacfd7"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "C:\\Users\\kamen\\AppData\\Local\\Temp/ipykernel_14516/1152017608.py:2: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().\n",
            "  rf_reg.fit(Xtrain,ytrain)\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The model performance for testing set\n",
            "--------------------------------------\n",
            "MAE is 1.4168638027752878\n",
            "MSE is 3.3872093558537064\n",
            "R2 score is 0.2358616489451355\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "rf_reg = RandomForestRegressor(max_depth=3, random_state=0)\n",
        "rf_reg.fit(Xtrain,ytrain)\n",
        "ypred = rf_reg.predict(Xtest)\n",
        "calculate_error(ytest, ypred)\n",
        "plot_regression(ytest, ypred)\n",
        "\n",
        "\n",
        "# Save model to pickle file\n",
        "file = open('models/RandomForestRegressor', 'wb')\n",
        "pickle.dump(rf_reg, file)\n",
        "file.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wTWUNz_U9alF"
      },
      "source": [
        "### Linear Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "SYy2UCKZ9aoW",
        "outputId": "eb7cb8d1-6539-4b5d-ac56-f620998567b4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The model performance for testing set\n",
            "--------------------------------------\n",
            "MAE is 1.4183754652673728\n",
            "MSE is 3.3991403700803566\n",
            "R2 score is 0.23317006877404012\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "lin_reg = LinearRegression()\n",
        "lin_reg.fit(Xtrain,ytrain)\n",
        "ypred = lin_reg.predict(Xtest)\n",
        "calculate_error(ytest, ypred)\n",
        "plot_regression(ytest, ypred)\n",
        "\n",
        "# Save model to pickle file\n",
        "file = open('models/LinearRegressor.pkl', 'wb')\n",
        "pickle.dump(lin_reg, file)\n",
        "file.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zWx-p4kt-n1H"
      },
      "source": [
        "### Ridge Regression"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "BXaXTV5e7dqI",
        "outputId": "9d95bada-4afb-4001-b5c3-4800e622b3d9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The model performance for testing set\n",
            "--------------------------------------\n",
            "MAE is 1.4184796871660394\n",
            "MSE is 3.3992978133005396\n",
            "R2 score is 0.23313455033097508\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "rdg_reg = Ridge(alpha=0.5)\n",
        "rdg_reg.fit(Xtrain,ytrain)\n",
        "ypred = rdg_reg.predict(Xtest)\n",
        "calculate_error(ytest, ypred)\n",
        "plot_regression(ytest, ypred)\n",
        "\n",
        "\n",
        "# Save model to pickle file\n",
        "file = open('models/RidgeRegressor.pkl', 'wb')\n",
        "pickle.dump(rdg_reg, file)\n",
        "file.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8g6-b0hx-qDH"
      },
      "source": [
        "### Decision Tree"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "TMIFoB_o-qLV",
        "outputId": "ba5e221a-1bd7-4b9b-a986-2880e81642af"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "The model performance for testing set\n",
            "--------------------------------------\n",
            "MAE is 1.4570112673739615\n",
            "MSE is 3.5175885032577607\n",
            "R2 score is 0.2064487322214914\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "dt_reg = DecisionTreeRegressor(random_state=0)\n",
        "dt_reg.fit(Xtrain,ytrain)\n",
        "ypred = dt_reg.predict(Xtest)\n",
        "calculate_error(ytest, ypred)\n",
        "plot_regression(ytest, ypred)\n",
        "\n",
        "# Save model to pickle file\n",
        "file = open('models/DecisionTree.pkl', 'wb')\n",
        "pickle.dump(dt_reg, file)\n",
        "file.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8CVBhxoW-qRX"
      },
      "source": [
        "### Neural Network Regressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 421
        },
        "id": "mzWqGLx2-Ofe",
        "outputId": "dfc43adb-f306-45bc-f191-1f2965dbf2eb"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\kamen\\anaconda3\\lib\\site-packages\\sklearn\\neural_network\\_multilayer_perceptron.py:1599: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
            "  y = column_or_1d(y, warn=True)\n"
          ]
        }
      ],
      "source": [
        "mlp_reg = MLPRegressor(random_state=0, max_iter=500)\n",
        "mlp_reg.fit(Xtrain,ytrain)\n",
        "ypred = mlp_reg.predict(Xtest)\n",
        "calculate_error(ytest, ypred)\n",
        "plot_regression(ytest, ypred)\n",
        "\n",
        "# Save model to pickle file\n",
        "file = open('models/NN.pkl', 'wb')\n",
        "pickle.dump(mlp_reg, file)\n",
        "file.close()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cPIs5-9D-hMZ"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
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