Facebook_Stock_Prediction_using_SVR.ipynb

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  "metadata": {
    "colab": {
      "name": "Facebook Stock Prediction using SVR.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyNDqwJyfoXGlZRXVFVSFECs",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/AgniRejasa/python-for-finance/blob/master/Facebook_Stock_Prediction_using_SVR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JwXM6-Z-Gwax",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Description: This program predicts the price of FB stock for a specific day"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tff531KbHOnt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#import the libraries\n",
        "from sklearn.svm import SVR\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YPziBy0VICEV",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "ec353e38-35d6-441a-f0fd-cbb454ff1156"
      },
      "source": [
        "#Load the data\n",
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-3ac6973f-8005-4148-8427-89a4768f2ec1\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-3ac6973f-8005-4148-8427-89a4768f2ec1\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving FB_stock.csv to FB_stock.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kJcel6UUK81O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 731
        },
        "outputId": "31520121-3909-4113-97fb-759147a23c01"
      },
      "source": [
        "#Store and show the data\n",
        "df = pd.read_csv(\"FB_stock.csv\")\n",
        "#df['Date'] = pd.to_datetime(df['Date'], format=\"%Y-%m-%d\")\n",
        "df = df.iloc[1:]\n",
        "df"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2019-05-01</td>\n",
              "      <td>196.179993</td>\n",
              "      <td>193.009995</td>\n",
              "      <td>194.779999</td>\n",
              "      <td>193.029999</td>\n",
              "      <td>15996600</td>\n",
              "      <td>193.029999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019-05-02</td>\n",
              "      <td>194.000000</td>\n",
              "      <td>189.750000</td>\n",
              "      <td>193.000000</td>\n",
              "      <td>192.529999</td>\n",
              "      <td>13209500</td>\n",
              "      <td>192.529999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2019-05-03</td>\n",
              "      <td>196.160004</td>\n",
              "      <td>193.710007</td>\n",
              "      <td>194.380005</td>\n",
              "      <td>195.470001</td>\n",
              "      <td>14575400</td>\n",
              "      <td>195.470001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2019-05-06</td>\n",
              "      <td>194.279999</td>\n",
              "      <td>190.550003</td>\n",
              "      <td>191.240005</td>\n",
              "      <td>193.880005</td>\n",
              "      <td>13994900</td>\n",
              "      <td>193.880005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2019-05-07</td>\n",
              "      <td>192.899994</td>\n",
              "      <td>187.850006</td>\n",
              "      <td>192.539993</td>\n",
              "      <td>189.770004</td>\n",
              "      <td>16253000</td>\n",
              "      <td>189.770004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2019-05-08</td>\n",
              "      <td>190.720001</td>\n",
              "      <td>188.550003</td>\n",
              "      <td>189.389999</td>\n",
              "      <td>189.539993</td>\n",
              "      <td>12505700</td>\n",
              "      <td>189.539993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2019-05-09</td>\n",
              "      <td>189.770004</td>\n",
              "      <td>186.259995</td>\n",
              "      <td>187.199997</td>\n",
              "      <td>188.649994</td>\n",
              "      <td>12967000</td>\n",
              "      <td>188.649994</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2019-05-10</td>\n",
              "      <td>190.000000</td>\n",
              "      <td>184.589996</td>\n",
              "      <td>188.250000</td>\n",
              "      <td>188.339996</td>\n",
              "      <td>12578500</td>\n",
              "      <td>188.339996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2019-05-13</td>\n",
              "      <td>185.429993</td>\n",
              "      <td>180.839996</td>\n",
              "      <td>183.500000</td>\n",
              "      <td>181.539993</td>\n",
              "      <td>16833300</td>\n",
              "      <td>181.539993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2019-05-14</td>\n",
              "      <td>183.490005</td>\n",
              "      <td>178.100006</td>\n",
              "      <td>182.520004</td>\n",
              "      <td>180.729996</td>\n",
              "      <td>17628100</td>\n",
              "      <td>180.729996</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2019-05-15</td>\n",
              "      <td>187.279999</td>\n",
              "      <td>180.020004</td>\n",
              "      <td>180.419998</td>\n",
              "      <td>186.270004</td>\n",
              "      <td>16746900</td>\n",
              "      <td>186.270004</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>2019-05-16</td>\n",
              "      <td>188.580002</td>\n",
              "      <td>185.050003</td>\n",
              "      <td>185.050003</td>\n",
              "      <td>186.990005</td>\n",
              "      <td>12953100</td>\n",
              "      <td>186.990005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>2019-05-17</td>\n",
              "      <td>187.580002</td>\n",
              "      <td>184.279999</td>\n",
              "      <td>184.839996</td>\n",
              "      <td>185.300003</td>\n",
              "      <td>10485400</td>\n",
              "      <td>185.300003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>2019-05-20</td>\n",
              "      <td>184.229996</td>\n",
              "      <td>181.369995</td>\n",
              "      <td>181.880005</td>\n",
              "      <td>182.720001</td>\n",
              "      <td>10352000</td>\n",
              "      <td>182.720001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>2019-05-21</td>\n",
              "      <td>185.699997</td>\n",
              "      <td>183.889999</td>\n",
              "      <td>184.570007</td>\n",
              "      <td>184.820007</td>\n",
              "      <td>7502800</td>\n",
              "      <td>184.820007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>2019-05-22</td>\n",
              "      <td>186.740005</td>\n",
              "      <td>183.610001</td>\n",
              "      <td>184.729996</td>\n",
              "      <td>185.320007</td>\n",
              "      <td>9213800</td>\n",
              "      <td>185.320007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>2019-05-23</td>\n",
              "      <td>183.899994</td>\n",
              "      <td>179.669998</td>\n",
              "      <td>182.419998</td>\n",
              "      <td>180.869995</td>\n",
              "      <td>12768800</td>\n",
              "      <td>180.869995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>2019-05-24</td>\n",
              "      <td>183.630005</td>\n",
              "      <td>180.830002</td>\n",
              "      <td>182.330002</td>\n",
              "      <td>181.059998</td>\n",
              "      <td>8807700</td>\n",
              "      <td>181.059998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>2019-05-28</td>\n",
              "      <td>184.710007</td>\n",
              "      <td>181.449997</td>\n",
              "      <td>181.539993</td>\n",
              "      <td>184.309998</td>\n",
              "      <td>14843300</td>\n",
              "      <td>184.309998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>2019-05-29</td>\n",
              "      <td>184.559998</td>\n",
              "      <td>181.350006</td>\n",
              "      <td>183.500000</td>\n",
              "      <td>182.190002</td>\n",
              "      <td>12797700</td>\n",
              "      <td>182.190002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>2019-05-30</td>\n",
              "      <td>183.479996</td>\n",
              "      <td>180.889999</td>\n",
              "      <td>183.080002</td>\n",
              "      <td>183.009995</td>\n",
              "      <td>8581500</td>\n",
              "      <td>183.009995</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2019-05-31</td>\n",
              "      <td>180.539993</td>\n",
              "      <td>177.160004</td>\n",
              "      <td>180.279999</td>\n",
              "      <td>177.470001</td>\n",
              "      <td>15226500</td>\n",
              "      <td>177.470001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date        High         Low  ...       Close    Volume   Adj Close\n",
              "1   2019-05-01  196.179993  193.009995  ...  193.029999  15996600  193.029999\n",
              "2   2019-05-02  194.000000  189.750000  ...  192.529999  13209500  192.529999\n",
              "3   2019-05-03  196.160004  193.710007  ...  195.470001  14575400  195.470001\n",
              "4   2019-05-06  194.279999  190.550003  ...  193.880005  13994900  193.880005\n",
              "5   2019-05-07  192.899994  187.850006  ...  189.770004  16253000  189.770004\n",
              "6   2019-05-08  190.720001  188.550003  ...  189.539993  12505700  189.539993\n",
              "7   2019-05-09  189.770004  186.259995  ...  188.649994  12967000  188.649994\n",
              "8   2019-05-10  190.000000  184.589996  ...  188.339996  12578500  188.339996\n",
              "9   2019-05-13  185.429993  180.839996  ...  181.539993  16833300  181.539993\n",
              "10  2019-05-14  183.490005  178.100006  ...  180.729996  17628100  180.729996\n",
              "11  2019-05-15  187.279999  180.020004  ...  186.270004  16746900  186.270004\n",
              "12  2019-05-16  188.580002  185.050003  ...  186.990005  12953100  186.990005\n",
              "13  2019-05-17  187.580002  184.279999  ...  185.300003  10485400  185.300003\n",
              "14  2019-05-20  184.229996  181.369995  ...  182.720001  10352000  182.720001\n",
              "15  2019-05-21  185.699997  183.889999  ...  184.820007   7502800  184.820007\n",
              "16  2019-05-22  186.740005  183.610001  ...  185.320007   9213800  185.320007\n",
              "17  2019-05-23  183.899994  179.669998  ...  180.869995  12768800  180.869995\n",
              "18  2019-05-24  183.630005  180.830002  ...  181.059998   8807700  181.059998\n",
              "19  2019-05-28  184.710007  181.449997  ...  184.309998  14843300  184.309998\n",
              "20  2019-05-29  184.559998  181.350006  ...  182.190002  12797700  182.190002\n",
              "21  2019-05-30  183.479996  180.889999  ...  183.009995   8581500  183.009995\n",
              "22  2019-05-31  180.539993  177.160004  ...  177.470001  15226500  177.470001\n",
              "\n",
              "[22 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JTysdZGwNc7Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "25ec8654-8049-46c0-f220-bcd9c7e3700d"
      },
      "source": [
        "#Get the number of rows and columns\n",
        "df.shape"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(22, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mfc9z1r3Nn-X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 80
        },
        "outputId": "8cb133a5-7d0f-4fcc-aa05-75b138edd8aa"
      },
      "source": [
        "#Get and print the last row of data\n",
        "actual_price = df.tail(1)\n",
        "actual_price"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Date</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Adj Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>2019-05-31</td>\n",
              "      <td>180.539993</td>\n",
              "      <td>177.160004</td>\n",
              "      <td>180.279999</td>\n",
              "      <td>177.470001</td>\n",
              "      <td>15226500</td>\n",
              "      <td>177.470001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          Date        High         Low  ...       Close    Volume   Adj Close\n",
              "22  2019-05-31  180.539993  177.160004  ...  177.470001  15226500  177.470001\n",
              "\n",
              "[1 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gpLGTweSNzD1",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "outputId": "aad6346f-9743-4543-ceef-96367c4f68f8"
      },
      "source": [
        "#prepare the data for training the SVR models\n",
        "#Get all of the data except for the last row\n",
        "df = df.head(len(df)-1)\n",
        "#Print the new data set\n",
        "print(df)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "          Date        High         Low  ...       Close    Volume   Adj Close\n",
            "1   2019-05-01  196.179993  193.009995  ...  193.029999  15996600  193.029999\n",
            "2   2019-05-02  194.000000  189.750000  ...  192.529999  13209500  192.529999\n",
            "3   2019-05-03  196.160004  193.710007  ...  195.470001  14575400  195.470001\n",
            "4   2019-05-06  194.279999  190.550003  ...  193.880005  13994900  193.880005\n",
            "5   2019-05-07  192.899994  187.850006  ...  189.770004  16253000  189.770004\n",
            "6   2019-05-08  190.720001  188.550003  ...  189.539993  12505700  189.539993\n",
            "7   2019-05-09  189.770004  186.259995  ...  188.649994  12967000  188.649994\n",
            "8   2019-05-10  190.000000  184.589996  ...  188.339996  12578500  188.339996\n",
            "9   2019-05-13  185.429993  180.839996  ...  181.539993  16833300  181.539993\n",
            "10  2019-05-14  183.490005  178.100006  ...  180.729996  17628100  180.729996\n",
            "11  2019-05-15  187.279999  180.020004  ...  186.270004  16746900  186.270004\n",
            "12  2019-05-16  188.580002  185.050003  ...  186.990005  12953100  186.990005\n",
            "13  2019-05-17  187.580002  184.279999  ...  185.300003  10485400  185.300003\n",
            "14  2019-05-20  184.229996  181.369995  ...  182.720001  10352000  182.720001\n",
            "15  2019-05-21  185.699997  183.889999  ...  184.820007   7502800  184.820007\n",
            "16  2019-05-22  186.740005  183.610001  ...  185.320007   9213800  185.320007\n",
            "17  2019-05-23  183.899994  179.669998  ...  180.869995  12768800  180.869995\n",
            "18  2019-05-24  183.630005  180.830002  ...  181.059998   8807700  181.059998\n",
            "19  2019-05-28  184.710007  181.449997  ...  184.309998  14843300  184.309998\n",
            "20  2019-05-29  184.559998  181.350006  ...  182.190002  12797700  182.190002\n",
            "21  2019-05-30  183.479996  180.889999  ...  183.009995   8581500  183.009995\n",
            "\n",
            "[21 rows x 7 columns]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QUhheKAzOFyM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create empty lists ot store the independent and dependent data\n",
        "days = list()\n",
        "adj_close_prices = list()"
      ],
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3RwNRWrpORwq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "7fe88a93-b14a-4dd2-f40a-a3093426f02e"
      },
      "source": [
        "#Get the data and adjusted close prices\n",
        "df_days = df.loc[:, 'Date']\n",
        "df_adj_close = df.loc[:, 'Adj Close']\n",
        "df_days"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     2019-05-01\n",
              "2     2019-05-02\n",
              "3     2019-05-03\n",
              "4     2019-05-06\n",
              "5     2019-05-07\n",
              "6     2019-05-08\n",
              "7     2019-05-09\n",
              "8     2019-05-10\n",
              "9     2019-05-13\n",
              "10    2019-05-14\n",
              "11    2019-05-15\n",
              "12    2019-05-16\n",
              "13    2019-05-17\n",
              "14    2019-05-20\n",
              "15    2019-05-21\n",
              "16    2019-05-22\n",
              "17    2019-05-23\n",
              "18    2019-05-24\n",
              "19    2019-05-28\n",
              "20    2019-05-29\n",
              "21    2019-05-30\n",
              "Name: Date, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vl_TT5xcQiy2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Create the independent data set\n",
        "for day in df_days:\n",
        "  days.append([int(day.split('-')[2])])\n",
        "  #days.append([int(day.day)])\n",
        "\n",
        "#Create the dependent data set\n",
        "for adj_close_price in df_adj_close:\n",
        "  adj_close_prices.append(float(adj_close_price))"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1GmHxF9uR3NA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "d7218f2f-1092-4b70-ec95-86e0bed1a7cc"
      },
      "source": [
        "#Print the days and the adj close prices\n",
        "print(days)\n",
        "print(adj_close_prices)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[1], [2], [3], [6], [7], [8], [9], [10], [13], [14], [15], [16], [17], [20], [21], [22], [23], [24], [28], [29], [30]]\n",
            "[193.02999877929688, 192.52999877929688, 195.47000122070312, 193.8800048828125, 189.7700042724609, 189.5399932861328, 188.6499938964844, 188.33999633789062, 181.5399932861328, 180.72999572753903, 186.2700042724609, 186.9900054931641, 185.3000030517578, 182.72000122070312, 184.82000732421875, 185.32000732421875, 180.8699951171875, 181.05999755859372, 184.30999755859372, 182.19000244140625, 183.00999450683597]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgPAkQRPWXDb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "664e2148-0c0a-4a86-ccf4-3a7a5cd1ca89"
      },
      "source": [
        "#Create the 3 Support Vector Regression Models\n",
        "\n",
        "#Create and train a SVr model using a linear kernel\n",
        "lin_svr = SVR(kernel='linear', C=1000.0)\n",
        "lin_svr.fit(days, adj_close_prices)\n",
        "\n",
        "#Create and train a SVr model using a polynomial kernel\n",
        "poly_svr = SVR(kernel='poly', C=1000.0, degree = 2)\n",
        "poly_svr.fit(days, adj_close_prices)\n",
        "\n",
        "#Create and train a SVr model using a rbf kernel\n",
        "rbf_svr = SVR(kernel='rbf', C=1000.0, gamma = 0.15)\n",
        "rbf_svr.fit(days, adj_close_prices)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1000.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1, gamma=0.15,\n",
              "    kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Xejznq4VXCvS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "outputId": "9f0cfbd8-1f46-4400-bf7e-28c951bf2387"
      },
      "source": [
        "#plot the models on a grapd to see which has the best fit to the original data\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.scatter(days, adj_close_prices, color = 'red', label = 'Data')\n",
        "plt.plot(days, rbf_svr.predict(days), color = 'green', label = 'RBF Model')\n",
        "plt.plot(days, poly_svr.predict(days), color = 'orange', label = 'Poly Model')\n",
        "plt.plot(days, lin_svr.predict(days), color = 'blue', label = 'Linear Model')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_hvlJeW7Ylpv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68
        },
        "outputId": "9726f154-a25d-455f-fc77-bf4943510354"
      },
      "source": [
        "#Show the predicted price for the given day\n",
        "day = [[31]]\n",
        "\n",
        "print('The RBF SVR predicted: ', rbf_svr.predict(day))\n",
        "print('The Linear SVR predicted: ', lin_svr.predict(day))\n",
        "print('The Polynomial SVR predicted: ', poly_svr.predict(day))"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The RBF SVR predicted:  [185.04603397]\n",
            "The Linear SVR predicted:  [181.04143808]\n",
            "The Polynomial SVR predicted:  [180.39533557]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "if-fwDOLY91l",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "cd40e815-8da8-4261-da02-cb0be829b628"
      },
      "source": [
        "#print the actual price of the stock on day 31\n",
        "print('The actual price:', actual_price['Adj Close'][22])"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "The actual price: 177.47000122070312\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "znYd10dHZ7ea",
        "colab_type": "text"
      },
      "source": [
        "# New Section"
      ]
    }
  ]
}