Stock Price Prediction with Random Forest & Live Yahoo Data.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"pip install yfinance","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-04-30T22:50:59.310255Z","iopub.execute_input":"2024-04-30T22:50:59.310705Z","iopub.status.idle":"2024-04-30T22:51:42.698448Z","shell.execute_reply.started":"2024-04-30T22:50:59.310672Z","shell.execute_reply":"2024-04-30T22:51:42.696925Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Collecting yfinance\n  Downloading yfinance-0.2.38-py2.py3-none-any.whl.metadata (11 kB)\nRequirement already satisfied: pandas>=1.3.0 in /opt/conda/lib/python3.10/site-packages (from yfinance) (2.2.2)\nRequirement already satisfied: numpy>=1.16.5 in /opt/conda/lib/python3.10/site-packages (from yfinance) (1.26.4)\nRequirement already satisfied: requests>=2.31 in /opt/conda/lib/python3.10/site-packages (from yfinance) (2.31.0)\nCollecting multitasking>=0.0.7 (from yfinance)\n  Downloading multitasking-0.0.11-py3-none-any.whl.metadata (5.5 kB)\nRequirement already satisfied: lxml>=4.9.1 in /opt/conda/lib/python3.10/site-packages (from yfinance) (5.2.1)\nRequirement already satisfied: appdirs>=1.4.4 in /opt/conda/lib/python3.10/site-packages (from yfinance) (1.4.4)\nRequirement already satisfied: pytz>=2022.5 in /opt/conda/lib/python3.10/site-packages (from yfinance) (2023.3.post1)\nRequirement already satisfied: frozendict>=2.3.4 in /opt/conda/lib/python3.10/site-packages (from yfinance) (2.4.2)\nCollecting peewee>=3.16.2 (from yfinance)\n  Downloading peewee-3.17.3.tar.gz (3.0 MB)\n\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.0/3.0 MB\u001b[0m \u001b[31m39.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25h  Installing build dependencies ... \u001b[?25ldone\n\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: beautifulsoup4>=4.11.1 in /opt/conda/lib/python3.10/site-packages (from yfinance) (4.12.2)\nRequirement already satisfied: html5lib>=1.1 in /opt/conda/lib/python3.10/site-packages (from yfinance) (1.1)\nRequirement already satisfied: soupsieve>1.2 in /opt/conda/lib/python3.10/site-packages (from beautifulsoup4>=4.11.1->yfinance) (2.5)\nRequirement already satisfied: six>=1.9 in /opt/conda/lib/python3.10/site-packages (from html5lib>=1.1->yfinance) (1.16.0)\nRequirement already satisfied: webencodings in /opt/conda/lib/python3.10/site-packages (from html5lib>=1.1->yfinance) (0.5.1)\nRequirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.3.0->yfinance) (2.9.0.post0)\nRequirement already satisfied: tzdata>=2022.7 in /opt/conda/lib/python3.10/site-packages (from pandas>=1.3.0->yfinance) (2023.4)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests>=2.31->yfinance) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.31->yfinance) (3.6)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.31->yfinance) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.31->yfinance) (2024.2.2)\nDownloading yfinance-0.2.38-py2.py3-none-any.whl (72 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m73.0/73.0 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading multitasking-0.0.11-py3-none-any.whl (8.5 kB)\nBuilding wheels for collected packages: peewee\n  Building wheel for peewee (pyproject.toml) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for peewee: filename=peewee-3.17.3-cp310-cp310-linux_x86_64.whl size=293152 sha256=01255dc17fed677b18ae0235a0e2736864b21f71218a6b351f1627ab5eb4fec6\n  Stored in directory: /root/.cache/pip/wheels/0d/32/b7/cad9f818b37cf97df4c87a8308da86a433af81651d98f8d8aa\nSuccessfully built peewee\nInstalling collected packages: peewee, multitasking, yfinance\nSuccessfully installed multitasking-0.0.11 peewee-3.17.3 yfinance-0.2.38\nNote: you may need to restart the kernel to use updated packages.\n","output_type":"stream"}]},{"cell_type":"code","source":"import yfinance as yf\nimport pandas as pd\nimport os","metadata":{"execution":{"iopub.status.busy":"2024-04-30T22:51:47.775590Z","iopub.execute_input":"2024-04-30T22:51:47.776176Z","iopub.status.idle":"2024-04-30T22:51:48.634811Z","shell.execute_reply.started":"2024-04-30T22:51:47.776129Z","shell.execute_reply":"2024-04-30T22:51:48.633811Z"},"trusted":true},"execution_count":4,"outputs":[]},{"cell_type":"code","source":"#Define the ticker symbol and period for which we want to fetch the stock prices\nticker = \"DLF.NS\"\nstart_date = \"2021-01-04\"\nend_date = \"2024-05-01\"\n\n#Fetch the stock prices for the specified period\ndata = yf.download(ticker, start=start_date, end=end_date)\n\n#Print the fetched stock prices\nprint(data)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T22:54:34.446896Z","iopub.execute_input":"2024-04-30T22:54:34.447380Z","iopub.status.idle":"2024-04-30T22:54:34.588983Z","shell.execute_reply.started":"2024-04-30T22:54:34.447347Z","shell.execute_reply":"2024-04-30T22:54:34.587978Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stderr","text":"[*********************100%%**********************]  1 of 1 completed","output_type":"stream"},{"name":"stdout","text":"                  Open        High         Low       Close   Adj Close  \\\nDate                                                                     \n2021-01-04  239.850006  245.000000  233.199997  244.300003  238.937897   \n2021-01-05  241.949997  246.949997  239.500000  241.750000  236.443878   \n2021-01-06  241.699997  244.399994  234.649994  241.050003  235.759232   \n2021-01-07  244.899994  249.300003  242.600006  245.399994  240.013748   \n2021-01-08  247.899994  252.449997  245.899994  248.300003  242.850128   \n...                ...         ...         ...         ...         ...   \n2024-04-24  889.799988  898.200012  885.500000  893.500000  893.500000   \n2024-04-25  893.500000  897.000000  882.750000  893.799988  893.799988   \n2024-04-26  896.000000  911.599976  893.750000  908.000000  908.000000   \n2024-04-29  913.000000  915.000000  876.849976  886.099976  886.099976   \n2024-04-30  888.799988  907.500000  881.900024  891.849976  891.849976   \n\n              Volume  \nDate                  \n2021-01-04  17172770  \n2021-01-05  17130907  \n2021-01-06  12995777  \n2021-01-07  16159966  \n2021-01-08  15045597  \n...              ...  \n2024-04-24   4102872  \n2024-04-25   2376516  \n2024-04-26   4053816  \n2024-04-29   3109393  \n2024-04-30   4507370  \n\n[820 rows x 6 columns]\n","output_type":"stream"},{"name":"stderr","text":"\n","output_type":"stream"}]},{"cell_type":"code","source":"df = pd.DataFrame(data)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T22:55:40.800810Z","iopub.execute_input":"2024-04-30T22:55:40.801311Z","iopub.status.idle":"2024-04-30T22:55:40.808063Z","shell.execute_reply.started":"2024-04-30T22:55:40.801269Z","shell.execute_reply":"2024-04-30T22:55:40.806761Z"},"trusted":true},"execution_count":9,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T22:56:20.726178Z","iopub.execute_input":"2024-04-30T22:56:20.726720Z","iopub.status.idle":"2024-04-30T22:56:20.748066Z","shell.execute_reply.started":"2024-04-30T22:56:20.726680Z","shell.execute_reply":"2024-04-30T22:56:20.746460Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"                  Open        High         Low       Close   Adj Close  \\\nDate                                                                     \n2021-01-04  239.850006  245.000000  233.199997  244.300003  238.937897   \n2021-01-05  241.949997  246.949997  239.500000  241.750000  236.443878   \n2021-01-06  241.699997  244.399994  234.649994  241.050003  235.759232   \n2021-01-07  244.899994  249.300003  242.600006  245.399994  240.013748   \n2021-01-08  247.899994  252.449997  245.899994  248.300003  242.850128   \n\n              Volume  \nDate                  \n2021-01-04  17172770  \n2021-01-05  17130907  \n2021-01-06  12995777  \n2021-01-07  16159966  \n2021-01-08  15045597  ","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>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    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-01-04</th>\n      <td>239.850006</td>\n      <td>245.000000</td>\n      <td>233.199997</td>\n      <td>244.300003</td>\n      <td>238.937897</td>\n      <td>17172770</td>\n    </tr>\n    <tr>\n      <th>2021-01-05</th>\n      <td>241.949997</td>\n      <td>246.949997</td>\n      <td>239.500000</td>\n      <td>241.750000</td>\n      <td>236.443878</td>\n      <td>17130907</td>\n    </tr>\n    <tr>\n      <th>2021-01-06</th>\n      <td>241.699997</td>\n      <td>244.399994</td>\n      <td>234.649994</td>\n      <td>241.050003</td>\n      <td>235.759232</td>\n      <td>12995777</td>\n    </tr>\n    <tr>\n      <th>2021-01-07</th>\n      <td>244.899994</td>\n      <td>249.300003</td>\n      <td>242.600006</td>\n      <td>245.399994</td>\n      <td>240.013748</td>\n      <td>16159966</td>\n    </tr>\n    <tr>\n      <th>2021-01-08</th>\n      <td>247.899994</td>\n      <td>252.449997</td>\n      <td>245.899994</td>\n      <td>248.300003</td>\n      <td>242.850128</td>\n      <td>15045597</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T22:56:53.551141Z","iopub.execute_input":"2024-04-30T22:56:53.551742Z","iopub.status.idle":"2024-04-30T22:56:53.575661Z","shell.execute_reply.started":"2024-04-30T22:56:53.551695Z","shell.execute_reply":"2024-04-30T22:56:53.574590Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nDatetimeIndex: 820 entries, 2021-01-04 to 2024-04-30\nData columns (total 6 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Open       820 non-null    float64\n 1   High       820 non-null    float64\n 2   Low        820 non-null    float64\n 3   Close      820 non-null    float64\n 4   Adj Close  820 non-null    float64\n 5   Volume     820 non-null    int64  \ndtypes: float64(5), int64(1)\nmemory usage: 44.8 KB\n","output_type":"stream"}]},{"cell_type":"code","source":"df['date']=pd.to_datetime(df.index)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T22:57:46.818018Z","iopub.execute_input":"2024-04-30T22:57:46.818497Z","iopub.status.idle":"2024-04-30T22:57:46.830877Z","shell.execute_reply.started":"2024-04-30T22:57:46.818464Z","shell.execute_reply":"2024-04-30T22:57:46.829037Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"df.head()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T22:58:04.318666Z","iopub.execute_input":"2024-04-30T22:58:04.319192Z","iopub.status.idle":"2024-04-30T22:58:04.342752Z","shell.execute_reply.started":"2024-04-30T22:58:04.319150Z","shell.execute_reply":"2024-04-30T22:58:04.340629Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"                  Open        High         Low       Close   Adj Close  \\\nDate                                                                     \n2021-01-04  239.850006  245.000000  233.199997  244.300003  238.937897   \n2021-01-05  241.949997  246.949997  239.500000  241.750000  236.443878   \n2021-01-06  241.699997  244.399994  234.649994  241.050003  235.759232   \n2021-01-07  244.899994  249.300003  242.600006  245.399994  240.013748   \n2021-01-08  247.899994  252.449997  245.899994  248.300003  242.850128   \n\n              Volume       date  \nDate                             \n2021-01-04  17172770 2021-01-04  \n2021-01-05  17130907 2021-01-05  \n2021-01-06  12995777 2021-01-06  \n2021-01-07  16159966 2021-01-07  \n2021-01-08  15045597 2021-01-08  ","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>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>date</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2021-01-04</th>\n      <td>239.850006</td>\n      <td>245.000000</td>\n      <td>233.199997</td>\n      <td>244.300003</td>\n      <td>238.937897</td>\n      <td>17172770</td>\n      <td>2021-01-04</td>\n    </tr>\n    <tr>\n      <th>2021-01-05</th>\n      <td>241.949997</td>\n      <td>246.949997</td>\n      <td>239.500000</td>\n      <td>241.750000</td>\n      <td>236.443878</td>\n      <td>17130907</td>\n      <td>2021-01-05</td>\n    </tr>\n    <tr>\n      <th>2021-01-06</th>\n      <td>241.699997</td>\n      <td>244.399994</td>\n      <td>234.649994</td>\n      <td>241.050003</td>\n      <td>235.759232</td>\n      <td>12995777</td>\n      <td>2021-01-06</td>\n    </tr>\n    <tr>\n      <th>2021-01-07</th>\n      <td>244.899994</td>\n      <td>249.300003</td>\n      <td>242.600006</td>\n      <td>245.399994</td>\n      <td>240.013748</td>\n      <td>16159966</td>\n      <td>2021-01-07</td>\n    </tr>\n    <tr>\n      <th>2021-01-08</th>\n      <td>247.899994</td>\n      <td>252.449997</td>\n      <td>245.899994</td>\n      <td>248.300003</td>\n      <td>242.850128</td>\n      <td>15045597</td>\n      <td>2021-01-08</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"#Using plotly for advance data representation\nimport plotly.graph_objects as go\n\n# Create the candlestick chart\nfig = go.Figure(data=[go.Candlestick(x=df['date'],\n                                    open=df['Open'],\n                                    high=df['High'],\n                                    low=df['Low'],\n                                    close=df['Close'])])\n\n# Customize the chart layout\nfig.update_layout(title='Stock Price Chart DLF.NS', yaxis_title='Price (₹)', xaxis_rangeslider_visible=False)\n\n# Display the chart\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T23:14:22.495891Z","iopub.execute_input":"2024-04-30T23:14:22.496395Z","iopub.status.idle":"2024-04-30T23:14:22.539338Z","shell.execute_reply.started":"2024-04-30T23:14:22.496361Z","shell.execute_reply":"2024-04-30T23:14:22.537827Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/html":"<div>                            <div id=\"34d56592-c62d-43ac-bc1a-15b9a995c73d\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if 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"},"metadata":{}}]},{"cell_type":"code","source":"df.reset_index(drop=True, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T23:18:39.205296Z","iopub.execute_input":"2024-04-30T23:18:39.205826Z","iopub.status.idle":"2024-04-30T23:18:39.212896Z","shell.execute_reply.started":"2024-04-30T23:18:39.205785Z","shell.execute_reply":"2024-04-30T23:18:39.211317Z"},"trusted":true},"execution_count":20,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\n\n#Split the dataset into training and testing sets\nX = df[['Open', 'Close', 'High', 'Low', 'Adj Close']] #Input Features\ny = df['Close'] #Target Variable\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=79)\n\n#Build the Random Forest Regression Model\nrf = RandomForestRegressor(n_estimators=200, random_state=79)\n\n#Train the model\nrf.fit(X_train, y_train)\n\n#Make predictions on the testing set\ny_pred = rf.predict(X_test)\n\n#Evaluate the model using mean squared error\nmse = mean_squared_error(y_test, y_pred)\nprint('Mean Squared Error:', mse)","metadata":{"execution":{"iopub.status.busy":"2024-04-30T23:57:59.908317Z","iopub.execute_input":"2024-04-30T23:57:59.910121Z","iopub.status.idle":"2024-04-30T23:58:00.762611Z","shell.execute_reply.started":"2024-04-30T23:57:59.910059Z","shell.execute_reply":"2024-04-30T23:58:00.761420Z"},"trusted":true},"execution_count":60,"outputs":[{"name":"stdout","text":"Mean Squared Error: 0.9714702139355751\n","output_type":"stream"}]},{"cell_type":"code","source":"df.tail()","metadata":{"execution":{"iopub.status.busy":"2024-04-30T23:58:07.587143Z","iopub.execute_input":"2024-04-30T23:58:07.587597Z","iopub.status.idle":"2024-04-30T23:58:07.604995Z","shell.execute_reply.started":"2024-04-30T23:58:07.587565Z","shell.execute_reply":"2024-04-30T23:58:07.603422Z"},"trusted":true},"execution_count":61,"outputs":[{"execution_count":61,"output_type":"execute_result","data":{"text/plain":"           Open        High         Low       Close   Adj Close\n815  889.799988  898.200012  885.500000  893.500000  893.500000\n816  893.500000  897.000000  882.750000  893.799988  893.799988\n817  896.000000  911.599976  893.750000  908.000000  908.000000\n818  913.000000  915.000000  876.849976  886.099976  886.099976\n819  888.799988  907.500000  881.900024  891.849976  891.849976","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>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>815</th>\n      <td>889.799988</td>\n      <td>898.200012</td>\n      <td>885.500000</td>\n      <td>893.500000</td>\n      <td>893.500000</td>\n    </tr>\n    <tr>\n      <th>816</th>\n      <td>893.500000</td>\n      <td>897.000000</td>\n      <td>882.750000</td>\n      <td>893.799988</td>\n      <td>893.799988</td>\n    </tr>\n    <tr>\n      <th>817</th>\n      <td>896.000000</td>\n      <td>911.599976</td>\n      <td>893.750000</td>\n      <td>908.000000</td>\n      <td>908.000000</td>\n    </tr>\n    <tr>\n      <th>818</th>\n      <td>913.000000</td>\n      <td>915.000000</td>\n      <td>876.849976</td>\n      <td>886.099976</td>\n      <td>886.099976</td>\n    </tr>\n    <tr>\n      <th>819</th>\n      <td>888.799988</td>\n      <td>907.500000</td>\n      <td>881.900024</td>\n      <td>891.849976</td>\n      <td>891.849976</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"import numpy as np\n\n#Input values to predict the stock price\nnew_data = np.array([[888.799988, 907.500000, 881.900024, 891.849976, 891.849976]])\n\n#Make predictions using the trained model\npredicted_price = rf.predict(new_data)\n\n#Print the predicted stock price\nprint('Predicted Stock Price:', predicted_price[0])","metadata":{"execution":{"iopub.status.busy":"2024-05-01T00:03:34.379571Z","iopub.execute_input":"2024-05-01T00:03:34.380009Z","iopub.status.idle":"2024-05-01T00:03:34.405809Z","shell.execute_reply.started":"2024-05-01T00:03:34.379976Z","shell.execute_reply":"2024-05-01T00:03:34.404009Z"},"trusted":true},"execution_count":62,"outputs":[{"name":"stdout","text":"Predicted Stock Price: 898.443244934082\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/base.py:439: UserWarning:\n\nX does not have valid feature names, but RandomForestRegressor was fitted with feature names\n\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}