Tesla_Stock_Price_Prediction.ipynb

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    "colab": {
      "name": "Tesla Stock Price Prediction.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPLtm9pUFBYCPPHPGOrLNDy",
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        "<a href=\"https://colab.research.google.com/github/cybercat37794/Tesla-Stock-Price-Prediction/blob/main/Tesla_Stock_Price_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
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        "!pip install chart_studio"
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: chart_studio in /usr/local/lib/python3.7/dist-packages (1.1.0)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from chart_studio) (1.15.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.7/dist-packages (from chart_studio) (2.23.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from chart_studio) (1.3.3)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from chart_studio) (4.4.1)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (2021.10.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests->chart_studio) (1.24.3)\n"
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              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
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              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
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      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import chart_studio.plotly as py\n",
        "import plotly.graph_objs as go\n",
        "from plotly.offline import plot\n",
        "\n",
        "#for offline plotting\n",
        "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\n",
        "init_notebook_mode(connected=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tesla = pd.read_csv('https://raw.githubusercontent.com/cybercat37794/Tesla-Stock-Price-Prediction/main/tesla.csv')\n",
        "tesla.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
        },
        "id": "QqQITeMrTwGB",
        "outputId": "9911162a-e0da-400f-ad99-b20508514063"
      },
      "execution_count": 26,
      "outputs": [
        {
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          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>29-06-2010</td>\n",
              "      <td>19.000000</td>\n",
              "      <td>25.00</td>\n",
              "      <td>17.540001</td>\n",
              "      <td>23.889999</td>\n",
              "      <td>23.889999</td>\n",
              "      <td>18766300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30-06-2010</td>\n",
              "      <td>25.790001</td>\n",
              "      <td>30.42</td>\n",
              "      <td>23.299999</td>\n",
              "      <td>23.830000</td>\n",
              "      <td>23.830000</td>\n",
              "      <td>17187100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01-07-2010</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>25.92</td>\n",
              "      <td>20.270000</td>\n",
              "      <td>21.959999</td>\n",
              "      <td>21.959999</td>\n",
              "      <td>8218800</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>02-07-2010</td>\n",
              "      <td>23.000000</td>\n",
              "      <td>23.10</td>\n",
              "      <td>18.709999</td>\n",
              "      <td>19.200001</td>\n",
              "      <td>19.200001</td>\n",
              "      <td>5139800</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>06-07-2010</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>20.00</td>\n",
              "      <td>15.830000</td>\n",
              "      <td>16.110001</td>\n",
              "      <td>16.110001</td>\n",
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            "text/plain": [
              "         Date       Open   High        Low      Close  Adj Close    Volume\n",
              "0  29-06-2010  19.000000  25.00  17.540001  23.889999  23.889999  18766300\n",
              "1  30-06-2010  25.790001  30.42  23.299999  23.830000  23.830000  17187100\n",
              "2  01-07-2010  25.000000  25.92  20.270000  21.959999  21.959999   8218800\n",
              "3  02-07-2010  23.000000  23.10  18.709999  19.200001  19.200001   5139800\n",
              "4  06-07-2010  20.000000  20.00  15.830000  16.110001  16.110001   6866900"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tesla.info()"
      ],
      "metadata": {
        "colab": {
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        },
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        "outputId": "b4e6c553-c74e-46be-ea88-01d8479e69ad"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2193 entries, 0 to 2192\n",
            "Data columns (total 7 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   Date       2193 non-null   object \n",
            " 1   Open       2193 non-null   float64\n",
            " 2   High       2193 non-null   float64\n",
            " 3   Low        2193 non-null   float64\n",
            " 4   Close      2193 non-null   float64\n",
            " 5   Adj Close  2193 non-null   float64\n",
            " 6   Volume     2193 non-null   int64  \n",
            "dtypes: float64(5), int64(1), object(1)\n",
            "memory usage: 120.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tesla['Date'] = pd.to_datetime(tesla['Date'])"
      ],
      "metadata": {
        "id": "ftEAApIcVM0o"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'Dateframe contains stock prices between {tesla.Date.min()} and {tesla.Date.max()}')\n",
        "print(f'Total days = {(tesla.Date.max() - tesla.Date.min()).days} days')"
      ],
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        "colab": {
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      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dateframe contains stock prices between 2010-01-07 00:00:00 and 2019-12-03 00:00:00\n",
            "Total days = 3617 days\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tesla.describe()"
      ],
      "metadata": {
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      "execution_count": 30,
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          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2193.000000</td>\n",
              "      <td>2193.000000</td>\n",
              "      <td>2193.000000</td>\n",
              "      <td>2193.000000</td>\n",
              "      <td>2193.000000</td>\n",
              "      <td>2.193000e+03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>175.652882</td>\n",
              "      <td>178.710262</td>\n",
              "      <td>172.412075</td>\n",
              "      <td>175.648555</td>\n",
              "      <td>175.648555</td>\n",
              "      <td>5.077449e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>115.580903</td>\n",
              "      <td>117.370092</td>\n",
              "      <td>113.654794</td>\n",
              "      <td>115.580771</td>\n",
              "      <td>115.580771</td>\n",
              "      <td>4.545398e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>16.139999</td>\n",
              "      <td>16.629999</td>\n",
              "      <td>14.980000</td>\n",
              "      <td>15.800000</td>\n",
              "      <td>15.800000</td>\n",
              "      <td>1.185000e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>33.110001</td>\n",
              "      <td>33.910000</td>\n",
              "      <td>32.459999</td>\n",
              "      <td>33.160000</td>\n",
              "      <td>33.160000</td>\n",
              "      <td>1.577800e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>204.990005</td>\n",
              "      <td>208.160004</td>\n",
              "      <td>201.669998</td>\n",
              "      <td>204.990005</td>\n",
              "      <td>204.990005</td>\n",
              "      <td>4.171700e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>262.000000</td>\n",
              "      <td>265.329987</td>\n",
              "      <td>256.209991</td>\n",
              "      <td>261.739990</td>\n",
              "      <td>261.739990</td>\n",
              "      <td>6.885600e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>386.690002</td>\n",
              "      <td>389.609985</td>\n",
              "      <td>379.350006</td>\n",
              "      <td>385.000000</td>\n",
              "      <td>385.000000</td>\n",
              "      <td>3.716390e+07</td>\n",
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              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2c02b4f7-e702-4174-94b5-4a6d1c38ec3b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-2c02b4f7-e702-4174-94b5-4a6d1c38ec3b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "              Open         High  ...    Adj Close        Volume\n",
              "count  2193.000000  2193.000000  ...  2193.000000  2.193000e+03\n",
              "mean    175.652882   178.710262  ...   175.648555  5.077449e+06\n",
              "std     115.580903   117.370092  ...   115.580771  4.545398e+06\n",
              "min      16.139999    16.629999  ...    15.800000  1.185000e+05\n",
              "25%      33.110001    33.910000  ...    33.160000  1.577800e+06\n",
              "50%     204.990005   208.160004  ...   204.990005  4.171700e+06\n",
              "75%     262.000000   265.329987  ...   261.739990  6.885600e+06\n",
              "max     386.690002   389.609985  ...   385.000000  3.716390e+07\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tesla[['Open', 'High', 'Low', 'Close', 'Adj Close']].plot(kind = 'box')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "KqSp0JV6W5Xa",
        "outputId": "3f6e438d-4327-49ae-b834-c4aff95de200"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f77c69a33d0>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# setting the layout for our plot\n",
        "layout = go.Layout(\n",
        "    title='Stock Price of Tesla',\n",
        "    xaxis=dict(\n",
        "        title='Date',\n",
        "        titlefont=dict(\n",
        "            family='Courier New, monospace',\n",
        "            size=18,\n",
        "            color='#7f7f7f'\n",
        "        )\n",
        "    ),\n",
        "    yaxis=dict(\n",
        "        title='Price',\n",
        "        titlefont=dict(\n",
        "            family='Courier New, monospace',\n",
        "            size=18,\n",
        "            color='#7f7f7f'\n",
        "        )\n",
        "    )\n",
        ")\n",
        "tesla_data = [{'x':tesla['Date'], 'y':tesla['Close']}]\n",
        "plot = go.Figure(data=tesla_data, layout=layout)"
      ],
      "metadata": {
        "id": "pSgC8a-sZoqq"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iplot(plot)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "_fuQW7KlZjOj",
        "outputId": "f4edb683-c8c4-4d9c-a039-d335d64e022d"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "        \n",
              "        \n",
              "            <div id=\"8e9be89e-0a90-4350-a821-e70de39fe7ec\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                require([\"plotly\"], function(Plotly) {\n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"8e9be89e-0a90-4350-a821-e70de39fe7ec\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        '8e9be89e-0a90-4350-a821-e70de39fe7ec',\n",
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# building the regression model\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# for preprocessing\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# for model evaluation\n",
        "from sklearn.metrics import mean_squared_error as mse\n",
        "from sklearn.metrics import r2_score"
      ],
      "metadata": {
        "id": "SjJLhGUTpRrg"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# split the data into train and test sets\n",
        "X = np.array(tesla.index).reshape(-1, 1)\n",
        "Y = tesla['Close']\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.3, random_state=101)\n"
      ],
      "metadata": {
        "id": "BfohzQsms_Ex"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# feature scaling\n",
        "scaler = StandardScaler().fit(X_train)\n"
      ],
      "metadata": {
        "id": "EUBVQ2yit6wT"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n"
      ],
      "metadata": {
        "id": "6IaYy3PCuMYp"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# creating a linear model\n",
        "lm = LinearRegression()\n",
        "lm.fit(X_train, Y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vP9DhILEuSqA",
        "outputId": "11c64c4f-9ca6-407d-c730-564f98c397df"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot actual and predicted values for train dataset\n",
        "trace0 = go.Scatter(\n",
        "    x = X_train.T[0],\n",
        "    y = Y_train,\n",
        "    mode = 'markers',\n",
        "    name = 'Actual'\n",
        ")\n",
        "trace1 = go.Scatter(\n",
        "    x = X_train.T[0],\n",
        "    y = lm.predict(X_train).T,\n",
        "    mode = 'lines',\n",
        "    name = 'Predicted'\n",
        ")\n",
        "tesla_data = [trace0, trace1]\n",
        "layout.xaxis.title.text = 'Day'\n",
        "plot2 = go.Figure(data = tesla_data, layout = layout)"
      ],
      "metadata": {
        "id": "vpni3Yc7ufah"
      },
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "iplot(plot2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "wtrvs7osvXfR",
        "outputId": "7316a131-0c99-4a92-fca8-1bdfd976922d"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "        \n",
              "        \n",
              "            <div id=\"a045b557-6259-453c-adbe-c784401a08bc\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>\n",
              "            <script type=\"text/javascript\">\n",
              "                require([\"plotly\"], function(Plotly) {\n",
              "                    window.PLOTLYENV=window.PLOTLYENV || {};\n",
              "                    \n",
              "                if (document.getElementById(\"a045b557-6259-453c-adbe-c784401a08bc\")) {\n",
              "                    Plotly.newPlot(\n",
              "                        'a045b557-6259-453c-adbe-c784401a08bc',\n",
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                "title": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# calculate scores for model evaluation\n",
        "scores = f'''\n",
        "{'Metric'.ljust(10)}{'Train'.center(20)}{'Test'.center(20)}\n",
        "{'r2_score'.ljust(10)}{r2_score(Y_train, lm.predict(X_train))}\\t{r2_score(Y_test, lm.predict(X_test))}\n",
        "{'MSE'.ljust(10)}{mse(Y_train, lm.predict(X_train))}\\t{mse(Y_test, lm.predict(X_test))}\n",
        "'''\n",
        "print(scores)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YRDsVXhkvaZp",
        "outputId": "fec35661-6c3f-4cb6-be59-25cfaec7ca45"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Metric           Train                Test        \n",
            "r2_score  0.8658871776828707\t0.8610649253244574\n",
            "MSE       1821.3833862936174\t1780.987539418845\n",
            "\n"
          ]
        }
      ]
    }
  ]
}