StockPricePredictorVer2.ipynb

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      "name": "StockPricePredictorVer2.ipynb",
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      "source": [
        "# Import the libraries\n",
        "from sklearn.svm import SVR\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "# !pip install yfinance\n",
        "import yfinance as yf\n",
        "from datetime import date\n",
        "from datetime import datetime\n",
        "from dateutil.relativedelta import relativedelta\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('fivethirtyeight')"
      ]
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      "cell_type": "code",
      "source": [
        "# Load the data\n",
        "# Insert Stock data\n",
        "start_date = datetime.now() - relativedelta(days=31) # Get date 31 days ago from today\n",
        "end_date = date.today()\n",
        "\n",
        "# Create empty dataframe\n",
        "stock_df = pd.DataFrame()\n",
        "\n",
        "# Download Stock Price Data\n",
        "stock = []\n",
        "# stock = yf.download(tickers = \"AAPL MSFT AMZN TSLA NVDA GOOG GOOGL FB ASML AVGO\", start=start_date, end=end_date)\n",
        "ticker = \"TSLA\"\n",
        "stock = yf.download(ticker, start=start_date, end=end_date)\n",
        "stock_df = stock_df.append(stock, sort=False)\n",
        "# Add stock symbol column\n",
        "stock_df['Symbol'] = ticker\n",
        "\n",
        "stock_df"
      ],
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          "text": [
            "\r[*********************100%***********************]  1 of 1 completed\n"
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            "text/plain": [
              "                   Open         High          Low        Close    Adj Close  \\\n",
              "Date                                                                          \n",
              "2022-03-07   856.299988   866.140015   804.570007   804.580017   804.580017   \n",
              "2022-03-08   795.530029   849.989990   782.169983   824.400024   824.400024   \n",
              "2022-03-09   839.479980   860.559998   832.010010   858.969971   858.969971   \n",
              "2022-03-10   851.450012   854.450012   810.359985   838.299988   838.299988   \n",
              "2022-03-11   840.200012   843.799988   793.770020   795.349976   795.349976   \n",
              "2022-03-14   780.609985   800.700012   756.039978   766.369995   766.369995   \n",
              "2022-03-15   775.270020   805.570007   756.570007   801.890015   801.890015   \n",
              "2022-03-16   809.000000   842.000000   802.260010   840.229980   840.229980   \n",
              "2022-03-17   830.989990   875.000000   825.719971   871.599976   871.599976   \n",
              "2022-03-18   874.489990   907.849976   867.390015   905.390015   905.390015   \n",
              "2022-03-21   914.979980   942.849976   907.090027   921.159973   921.159973   \n",
              "2022-03-22   930.000000   997.859985   921.750000   993.979980   993.979980   \n",
              "2022-03-23   979.940002  1040.699951   976.400024   999.109985   999.109985   \n",
              "2022-03-24  1009.729980  1024.489990   988.799988  1013.919983  1013.919983   \n",
              "2022-03-25  1008.000000  1021.799988   997.320007  1010.640015  1010.640015   \n",
              "2022-03-28  1065.099976  1097.880005  1053.599976  1091.839966  1091.839966   \n",
              "2022-03-29  1107.989990  1114.770020  1073.109985  1099.569946  1099.569946   \n",
              "2022-03-30  1091.170044  1113.949951  1084.000000  1093.989990  1093.989990   \n",
              "2022-03-31  1094.569946  1103.140015  1076.640015  1077.599976  1077.599976   \n",
              "2022-04-01  1081.150024  1094.750000  1066.640015  1084.589966  1084.589966   \n",
              "2022-04-04  1089.380005  1149.910034  1072.530029  1145.449951  1145.449951   \n",
              "2022-04-05  1136.300049  1152.869995  1087.300049  1091.260010  1091.260010   \n",
              "2022-04-06  1073.469971  1079.000000  1027.699951  1045.760010  1045.760010   \n",
              "\n",
              "              Volume Symbol  \n",
              "Date                         \n",
              "2022-03-07  24164700   TSLA  \n",
              "2022-03-08  26799700   TSLA  \n",
              "2022-03-09  19728000   TSLA  \n",
              "2022-03-10  19549500   TSLA  \n",
              "2022-03-11  22272800   TSLA  \n",
              "2022-03-14  23717400   TSLA  \n",
              "2022-03-15  22280400   TSLA  \n",
              "2022-03-16  28009600   TSLA  \n",
              "2022-03-17  22194300   TSLA  \n",
              "2022-03-18  33408500   TSLA  \n",
              "2022-03-21  27327200   TSLA  \n",
              "2022-03-22  35289500   TSLA  \n",
              "2022-03-23  40225400   TSLA  \n",
              "2022-03-24  22973600   TSLA  \n",
              "2022-03-25  20677200   TSLA  \n",
              "2022-03-28  34168700   TSLA  \n",
              "2022-03-29  24538300   TSLA  \n",
              "2022-03-30  19955000   TSLA  \n",
              "2022-03-31  16330900   TSLA  \n",
              "2022-04-01  18012900   TSLA  \n",
              "2022-04-04  27345300   TSLA  \n",
              "2022-04-05  26691700   TSLA  \n",
              "2022-04-06  29710800   TSLA  "
            ],
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              "    <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",
              "      <th>Symbol</th>\n",
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              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
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              "      <th></th>\n",
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              "    <tr>\n",
              "      <th>2022-03-07</th>\n",
              "      <td>856.299988</td>\n",
              "      <td>866.140015</td>\n",
              "      <td>804.570007</td>\n",
              "      <td>804.580017</td>\n",
              "      <td>804.580017</td>\n",
              "      <td>24164700</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-08</th>\n",
              "      <td>795.530029</td>\n",
              "      <td>849.989990</td>\n",
              "      <td>782.169983</td>\n",
              "      <td>824.400024</td>\n",
              "      <td>824.400024</td>\n",
              "      <td>26799700</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-09</th>\n",
              "      <td>839.479980</td>\n",
              "      <td>860.559998</td>\n",
              "      <td>832.010010</td>\n",
              "      <td>858.969971</td>\n",
              "      <td>858.969971</td>\n",
              "      <td>19728000</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-10</th>\n",
              "      <td>851.450012</td>\n",
              "      <td>854.450012</td>\n",
              "      <td>810.359985</td>\n",
              "      <td>838.299988</td>\n",
              "      <td>838.299988</td>\n",
              "      <td>19549500</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-11</th>\n",
              "      <td>840.200012</td>\n",
              "      <td>843.799988</td>\n",
              "      <td>793.770020</td>\n",
              "      <td>795.349976</td>\n",
              "      <td>795.349976</td>\n",
              "      <td>22272800</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-14</th>\n",
              "      <td>780.609985</td>\n",
              "      <td>800.700012</td>\n",
              "      <td>756.039978</td>\n",
              "      <td>766.369995</td>\n",
              "      <td>766.369995</td>\n",
              "      <td>23717400</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-15</th>\n",
              "      <td>775.270020</td>\n",
              "      <td>805.570007</td>\n",
              "      <td>756.570007</td>\n",
              "      <td>801.890015</td>\n",
              "      <td>801.890015</td>\n",
              "      <td>22280400</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-16</th>\n",
              "      <td>809.000000</td>\n",
              "      <td>842.000000</td>\n",
              "      <td>802.260010</td>\n",
              "      <td>840.229980</td>\n",
              "      <td>840.229980</td>\n",
              "      <td>28009600</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-17</th>\n",
              "      <td>830.989990</td>\n",
              "      <td>875.000000</td>\n",
              "      <td>825.719971</td>\n",
              "      <td>871.599976</td>\n",
              "      <td>871.599976</td>\n",
              "      <td>22194300</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-18</th>\n",
              "      <td>874.489990</td>\n",
              "      <td>907.849976</td>\n",
              "      <td>867.390015</td>\n",
              "      <td>905.390015</td>\n",
              "      <td>905.390015</td>\n",
              "      <td>33408500</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-21</th>\n",
              "      <td>914.979980</td>\n",
              "      <td>942.849976</td>\n",
              "      <td>907.090027</td>\n",
              "      <td>921.159973</td>\n",
              "      <td>921.159973</td>\n",
              "      <td>27327200</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-22</th>\n",
              "      <td>930.000000</td>\n",
              "      <td>997.859985</td>\n",
              "      <td>921.750000</td>\n",
              "      <td>993.979980</td>\n",
              "      <td>993.979980</td>\n",
              "      <td>35289500</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-23</th>\n",
              "      <td>979.940002</td>\n",
              "      <td>1040.699951</td>\n",
              "      <td>976.400024</td>\n",
              "      <td>999.109985</td>\n",
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              "      <td>40225400</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-24</th>\n",
              "      <td>1009.729980</td>\n",
              "      <td>1024.489990</td>\n",
              "      <td>988.799988</td>\n",
              "      <td>1013.919983</td>\n",
              "      <td>1013.919983</td>\n",
              "      <td>22973600</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-25</th>\n",
              "      <td>1008.000000</td>\n",
              "      <td>1021.799988</td>\n",
              "      <td>997.320007</td>\n",
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              "      <th>2022-03-28</th>\n",
              "      <td>1065.099976</td>\n",
              "      <td>1097.880005</td>\n",
              "      <td>1053.599976</td>\n",
              "      <td>1091.839966</td>\n",
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              "      <td>34168700</td>\n",
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              "      <th>2022-03-29</th>\n",
              "      <td>1107.989990</td>\n",
              "      <td>1114.770020</td>\n",
              "      <td>1073.109985</td>\n",
              "      <td>1099.569946</td>\n",
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              "      <td>24538300</td>\n",
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              "      <th>2022-03-30</th>\n",
              "      <td>1091.170044</td>\n",
              "      <td>1113.949951</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>1093.989990</td>\n",
              "      <td>1093.989990</td>\n",
              "      <td>19955000</td>\n",
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              "      <th>2022-03-31</th>\n",
              "      <td>1094.569946</td>\n",
              "      <td>1103.140015</td>\n",
              "      <td>1076.640015</td>\n",
              "      <td>1077.599976</td>\n",
              "      <td>1077.599976</td>\n",
              "      <td>16330900</td>\n",
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              "      <th>2022-04-01</th>\n",
              "      <td>1081.150024</td>\n",
              "      <td>1094.750000</td>\n",
              "      <td>1066.640015</td>\n",
              "      <td>1084.589966</td>\n",
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              "      <td>18012900</td>\n",
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              "      <th>2022-04-04</th>\n",
              "      <td>1089.380005</td>\n",
              "      <td>1149.910034</td>\n",
              "      <td>1072.530029</td>\n",
              "      <td>1145.449951</td>\n",
              "      <td>1145.449951</td>\n",
              "      <td>27345300</td>\n",
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              "      <th>2022-04-05</th>\n",
              "      <td>1136.300049</td>\n",
              "      <td>1152.869995</td>\n",
              "      <td>1087.300049</td>\n",
              "      <td>1091.260010</td>\n",
              "      <td>1091.260010</td>\n",
              "      <td>26691700</td>\n",
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              "      <th>2022-04-06</th>\n",
              "      <td>1073.469971</td>\n",
              "      <td>1079.000000</td>\n",
              "      <td>1027.699951</td>\n",
              "      <td>1045.760010</td>\n",
              "      <td>1045.760010</td>\n",
              "      <td>29710800</td>\n",
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              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Get only Adjusted Close price\n",
        "# stock_df = stock_df['Adj Close']\n",
        "# # Print new data\n",
        "# print(stock_df.head())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YKXjAWQ6TmFm",
        "outputId": "59d6ef26-386c-42bc-f0e4-4d28aa7f9d2a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date\n",
            "2017-04-07    60.507999\n",
            "2017-04-10    62.478001\n",
            "2017-04-11    61.742001\n",
            "2017-04-12    59.368000\n",
            "2017-04-13    60.799999\n",
            "Name: Adj Close, dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show and store last row of data\n",
        "actual_price = stock_df.tail(1)\n",
        "# Show the data\n",
        "actual_price"
      ],
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        "colab": {
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      "outputs": [
        {
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            "text/plain": [
              "                   Open    High          Low       Close   Adj Close  \\\n",
              "Date                                                                   \n",
              "2022-04-06  1073.469971  1079.0  1027.699951  1045.76001  1045.76001   \n",
              "\n",
              "              Volume Symbol  \n",
              "Date                         \n",
              "2022-04-06  29710800   TSLA  "
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    {
      "cell_type": "code",
      "source": [
        "# Get all of the data except the last row\n",
        "df = stock_df.head(len(stock_df)-1)\n",
        "# Show the data\n",
        "df"
      ],
      "metadata": {
        "colab": {
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          "height": 771
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        "id": "cw-TOJZiV0G7",
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            "text/plain": [
              "                   Open         High          Low        Close    Adj Close  \\\n",
              "Date                                                                          \n",
              "2022-03-07   856.299988   866.140015   804.570007   804.580017   804.580017   \n",
              "2022-03-08   795.530029   849.989990   782.169983   824.400024   824.400024   \n",
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              "2022-03-21   914.979980   942.849976   907.090027   921.159973   921.159973   \n",
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              "2022-03-31  1094.569946  1103.140015  1076.640015  1077.599976  1077.599976   \n",
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              "2022-04-05  1136.300049  1152.869995  1087.300049  1091.260010  1091.260010   \n",
              "\n",
              "              Volume Symbol  \n",
              "Date                         \n",
              "2022-03-07  24164700   TSLA  \n",
              "2022-03-08  26799700   TSLA  \n",
              "2022-03-09  19728000   TSLA  \n",
              "2022-03-10  19549500   TSLA  \n",
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              "2022-03-14  23717400   TSLA  \n",
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              "2022-03-18  33408500   TSLA  \n",
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              "      <td>824.400024</td>\n",
              "      <td>824.400024</td>\n",
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              "      <td>TSLA</td>\n",
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              "      <td>839.479980</td>\n",
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              "      <td>832.010010</td>\n",
              "      <td>858.969971</td>\n",
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              "      <td>19728000</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-10</th>\n",
              "      <td>851.450012</td>\n",
              "      <td>854.450012</td>\n",
              "      <td>810.359985</td>\n",
              "      <td>838.299988</td>\n",
              "      <td>838.299988</td>\n",
              "      <td>19549500</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-14</th>\n",
              "      <td>780.609985</td>\n",
              "      <td>800.700012</td>\n",
              "      <td>756.039978</td>\n",
              "      <td>766.369995</td>\n",
              "      <td>766.369995</td>\n",
              "      <td>23717400</td>\n",
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              "      <th>2022-03-15</th>\n",
              "      <td>775.270020</td>\n",
              "      <td>805.570007</td>\n",
              "      <td>756.570007</td>\n",
              "      <td>801.890015</td>\n",
              "      <td>801.890015</td>\n",
              "      <td>22280400</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-16</th>\n",
              "      <td>809.000000</td>\n",
              "      <td>842.000000</td>\n",
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              "      <td>840.229980</td>\n",
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              "      <td>22194300</td>\n",
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              "      <td>874.489990</td>\n",
              "      <td>907.849976</td>\n",
              "      <td>867.390015</td>\n",
              "      <td>905.390015</td>\n",
              "      <td>905.390015</td>\n",
              "      <td>33408500</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-21</th>\n",
              "      <td>914.979980</td>\n",
              "      <td>942.849976</td>\n",
              "      <td>907.090027</td>\n",
              "      <td>921.159973</td>\n",
              "      <td>921.159973</td>\n",
              "      <td>27327200</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-22</th>\n",
              "      <td>930.000000</td>\n",
              "      <td>997.859985</td>\n",
              "      <td>921.750000</td>\n",
              "      <td>993.979980</td>\n",
              "      <td>993.979980</td>\n",
              "      <td>35289500</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-23</th>\n",
              "      <td>979.940002</td>\n",
              "      <td>1040.699951</td>\n",
              "      <td>976.400024</td>\n",
              "      <td>999.109985</td>\n",
              "      <td>999.109985</td>\n",
              "      <td>40225400</td>\n",
              "      <td>TSLA</td>\n",
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              "      <th>2022-03-24</th>\n",
              "      <td>1009.729980</td>\n",
              "      <td>1024.489990</td>\n",
              "      <td>988.799988</td>\n",
              "      <td>1013.919983</td>\n",
              "      <td>1013.919983</td>\n",
              "      <td>22973600</td>\n",
              "      <td>TSLA</td>\n",
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              "    <tr>\n",
              "      <th>2022-03-25</th>\n",
              "      <td>1008.000000</td>\n",
              "      <td>1021.799988</td>\n",
              "      <td>997.320007</td>\n",
              "      <td>1010.640015</td>\n",
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              "      <td>20677200</td>\n",
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              "      <th>2022-03-28</th>\n",
              "      <td>1065.099976</td>\n",
              "      <td>1097.880005</td>\n",
              "      <td>1053.599976</td>\n",
              "      <td>1091.839966</td>\n",
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              "      <td>34168700</td>\n",
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              "      <th>2022-03-29</th>\n",
              "      <td>1107.989990</td>\n",
              "      <td>1114.770020</td>\n",
              "      <td>1073.109985</td>\n",
              "      <td>1099.569946</td>\n",
              "      <td>1099.569946</td>\n",
              "      <td>24538300</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-30</th>\n",
              "      <td>1091.170044</td>\n",
              "      <td>1113.949951</td>\n",
              "      <td>1084.000000</td>\n",
              "      <td>1093.989990</td>\n",
              "      <td>1093.989990</td>\n",
              "      <td>19955000</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-31</th>\n",
              "      <td>1094.569946</td>\n",
              "      <td>1103.140015</td>\n",
              "      <td>1076.640015</td>\n",
              "      <td>1077.599976</td>\n",
              "      <td>1077.599976</td>\n",
              "      <td>16330900</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-01</th>\n",
              "      <td>1081.150024</td>\n",
              "      <td>1094.750000</td>\n",
              "      <td>1066.640015</td>\n",
              "      <td>1084.589966</td>\n",
              "      <td>1084.589966</td>\n",
              "      <td>18012900</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-04</th>\n",
              "      <td>1089.380005</td>\n",
              "      <td>1149.910034</td>\n",
              "      <td>1072.530029</td>\n",
              "      <td>1145.449951</td>\n",
              "      <td>1145.449951</td>\n",
              "      <td>27345300</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-05</th>\n",
              "      <td>1136.300049</td>\n",
              "      <td>1152.869995</td>\n",
              "      <td>1087.300049</td>\n",
              "      <td>1091.260010</td>\n",
              "      <td>1091.260010</td>\n",
              "      <td>26691700</td>\n",
              "      <td>TSLA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create empty lists to store the independent data and dependent data\n",
        "tempdays = list() # tempdays required for adding unique day values into days list\n",
        "days = list()\n",
        "adj_close_prices = list()"
      ],
      "metadata": {
        "id": "5nvrewu8WEnq"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get only the date and the adjusted close prices\n",
        "df_days = df.index\n",
        "df_adj_close = df.loc[:, 'Adj Close']"
      ],
      "metadata": {
        "id": "8VY88PyJWYkl"
      },
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_days"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vqI0EsF3fKGJ",
        "outputId": "8f6e882e-87d1-4e01-a59a-bdab8797c88c"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "DatetimeIndex(['2022-03-07', '2022-03-08', '2022-03-09', '2022-03-10',\n",
              "               '2022-03-11', '2022-03-14', '2022-03-15', '2022-03-16',\n",
              "               '2022-03-17', '2022-03-18', '2022-03-21', '2022-03-22',\n",
              "               '2022-03-23', '2022-03-24', '2022-03-25', '2022-03-28',\n",
              "               '2022-03-29', '2022-03-30', '2022-03-31', '2022-04-01',\n",
              "               '2022-04-04', '2022-04-05'],\n",
              "              dtype='datetime64[ns]', name='Date', freq=None)"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_adj_close"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yNl9937afOkE",
        "outputId": "9f3f5d2d-bb85-498c-a74d-c3a89ede2f37"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date\n",
              "2022-03-07     804.580017\n",
              "2022-03-08     824.400024\n",
              "2022-03-09     858.969971\n",
              "2022-03-10     838.299988\n",
              "2022-03-11     795.349976\n",
              "2022-03-14     766.369995\n",
              "2022-03-15     801.890015\n",
              "2022-03-16     840.229980\n",
              "2022-03-17     871.599976\n",
              "2022-03-18     905.390015\n",
              "2022-03-21     921.159973\n",
              "2022-03-22     993.979980\n",
              "2022-03-23     999.109985\n",
              "2022-03-24    1013.919983\n",
              "2022-03-25    1010.640015\n",
              "2022-03-28    1091.839966\n",
              "2022-03-29    1099.569946\n",
              "2022-03-30    1093.989990\n",
              "2022-03-31    1077.599976\n",
              "2022-04-01    1084.589966\n",
              "2022-04-04    1145.449951\n",
              "2022-04-05    1091.260010\n",
              "Name: Adj Close, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the independent data set (dates)\n",
        "for day in df_days:\n",
        "  day = day.strftime(\"%d\") # Converting datetime/timestamp day to string\n",
        "  tempdays.append([int(day)]) # Appending int day to tempdays list\n",
        "\n",
        "# Appending day to days list\n",
        "for day in tempdays:\n",
        "  if day not in days: # Checks if day in tempdays is not in days list\n",
        "    days.append(day) # If day in tempdays is not present in days list, append to days list to generate list with day unique values\n",
        "\n",
        "# Create the dependent data set (adj close prices)\n",
        "for adj_close_price in df_adj_close:\n",
        "  adj_close_prices.append(float(adj_close_price))"
      ],
      "metadata": {
        "id": "ax_5a8DcfVTV"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Display days\n",
        "days"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJw-x2FqgzuC",
        "outputId": "6d302348-cecf-4a04-837d-a190de000805"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[7],\n",
              " [8],\n",
              " [9],\n",
              " [10],\n",
              " [11],\n",
              " [14],\n",
              " [15],\n",
              " [16],\n",
              " [17],\n",
              " [18],\n",
              " [21],\n",
              " [22],\n",
              " [23],\n",
              " [24],\n",
              " [25],\n",
              " [28],\n",
              " [29],\n",
              " [30],\n",
              " [31],\n",
              " [1],\n",
              " [4],\n",
              " [5]]"
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Display adj close prices\n",
        "adj_close_prices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehxtnJZLi5qn",
        "outputId": "e79e0e7c-6b7f-4596-e6ad-99d16f5ec9fa"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[804.5800170898438,\n",
              " 824.4000244140625,\n",
              " 858.969970703125,\n",
              " 838.2999877929688,\n",
              " 795.3499755859375,\n",
              " 766.3699951171875,\n",
              " 801.8900146484375,\n",
              " 840.22998046875,\n",
              " 871.5999755859375,\n",
              " 905.3900146484375,\n",
              " 921.1599731445312,\n",
              " 993.97998046875,\n",
              " 999.1099853515625,\n",
              " 1013.9199829101562,\n",
              " 1010.6400146484375,\n",
              " 1091.8399658203125,\n",
              " 1099.5699462890625,\n",
              " 1093.989990234375,\n",
              " 1077.5999755859375,\n",
              " 1084.5899658203125,\n",
              " 1145.449951171875,\n",
              " 1091.260009765625]"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating 3 models\n",
        "lin_svr = SVR(kernel='linear', C=1000.0)\n",
        "lin_svr.fit(tempdays, adj_close_prices)\n",
        "\n",
        "poly_svr = SVR(kernel='poly', C=1000.0, degree=2)\n",
        "poly_svr.fit(tempdays, adj_close_prices)\n",
        "\n",
        "rbf_svr = SVR(kernel='rbf', C=1000.0, gamma=0.85)\n",
        "rbf_svr.fit(tempdays, adj_close_prices)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4ZkQ0PjDqFLh",
        "outputId": "1514b671-0b2c-446d-ce7e-8ac3d9da1626"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SVR(C=1000.0, gamma=0.85)"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the models\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.scatter(tempdays, adj_close_prices, color='black', label='Data')\n",
        "plt.plot(tempdays, rbf_svr.predict(tempdays), color='green', label='RBF Model')\n",
        "plt.plot(tempdays, poly_svr.predict(tempdays), color='orange', label='Polynomial Model')\n",
        "plt.plot(tempdays, lin_svr.predict(tempdays), color='blue', label='Linear Model')\n",
        "plt.xlabel('Days')\n",
        "plt.ylabel('Adj Close Price ($)')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 537
        },
        "id": "OcNY_wv3rjn3",
        "outputId": "45f67159-7d6d-4e5e-ca40-a00faefdb544"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show predicted price for given day\n",
        "yesterday_date = date.today() - relativedelta(days=1)\n",
        "yesterday_date = yesterday_date.strftime(\"%d\")\n",
        "day = [[yesterday_date]]\n",
        "print(day)\n",
        "print('The RBF SVR predicted price:', rbf_svr.predict(day))\n",
        "print('The Linear SVR predicted price:', lin_svr.predict(day))\n",
        "print('The Polynomial SVR predicted price:', poly_svr.predict(day))\n",
        "print('The actual price:', actual_price['Adj Close'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_WUAjdBDuaPo",
        "outputId": "38234951-3900-475e-99af-0fe71eec6cfa"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[['06']]\n",
            "The RBF SVR predicted price: [934.79499909]\n",
            "The Linear SVR predicted price: [802.26524605]\n",
            "The Polynomial SVR predicted price: [815.21406883]\n",
            "The actual price: Date\n",
            "2022-04-06    1045.76001\n",
            "Name: Adj Close, dtype: float64\n"
          ]
        }
      ]
    }
  ]
}