stock-market-prediction_Linear_Regression.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":[{"sourceId":7137446,"sourceType":"datasetVersion","datasetId":4118797}],"dockerImageVersionId":30664,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Importing Libraries","metadata":{}},{"cell_type":"code","source":"#!pip install numpy","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:41.221256Z","iopub.execute_input":"2024-03-12T06:58:41.221608Z","iopub.status.idle":"2024-03-12T06:58:41.227409Z","shell.execute_reply.started":"2024-03-12T06:58:41.221579Z","shell.execute_reply":"2024-03-12T06:58:41.225730Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport sklearn","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:41.229263Z","iopub.execute_input":"2024-03-12T06:58:41.230121Z","iopub.status.idle":"2024-03-12T06:58:42.070346Z","shell.execute_reply.started":"2024-03-12T06:58:41.230082Z","shell.execute_reply":"2024-03-12T06:58:42.069450Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"markdown","source":"# Data cleaning","metadata":{}},{"cell_type":"code","source":"google = pd.read_csv('/kaggle/input/google-stock-price/GOOG.csv')\ngoogle.head()","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.071494Z","iopub.execute_input":"2024-03-12T06:58:42.071885Z","iopub.status.idle":"2024-03-12T06:58:42.115620Z","shell.execute_reply.started":"2024-03-12T06:58:42.071861Z","shell.execute_reply":"2024-03-12T06:58:42.114884Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"         Date      Open      High       Low     Close  Adj Close     Volume\n0  2004-08-19  2.490664  2.591785  2.390042  2.499133   2.499133  897427216\n1  2004-08-20  2.515820  2.716817  2.503118  2.697639   2.697639  458857488\n2  2004-08-23  2.758411  2.826406  2.716070  2.724787   2.724787  366857939\n3  2004-08-24  2.770615  2.779581  2.579581  2.611960   2.611960  306396159\n4  2004-08-25  2.614201  2.689918  2.587302  2.640104   2.640104  184645512","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>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>2004-08-19</td>\n      <td>2.490664</td>\n      <td>2.591785</td>\n      <td>2.390042</td>\n      <td>2.499133</td>\n      <td>2.499133</td>\n      <td>897427216</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2004-08-20</td>\n      <td>2.515820</td>\n      <td>2.716817</td>\n      <td>2.503118</td>\n      <td>2.697639</td>\n      <td>2.697639</td>\n      <td>458857488</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2004-08-23</td>\n      <td>2.758411</td>\n      <td>2.826406</td>\n      <td>2.716070</td>\n      <td>2.724787</td>\n      <td>2.724787</td>\n      <td>366857939</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2004-08-24</td>\n      <td>2.770615</td>\n      <td>2.779581</td>\n      <td>2.579581</td>\n      <td>2.611960</td>\n      <td>2.611960</td>\n      <td>306396159</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2004-08-25</td>\n      <td>2.614201</td>\n      <td>2.689918</td>\n      <td>2.587302</td>\n      <td>2.640104</td>\n      <td>2.640104</td>\n      <td>184645512</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"google.info()","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.117697Z","iopub.execute_input":"2024-03-12T06:58:42.117965Z","iopub.status.idle":"2024-03-12T06:58:42.143004Z","shell.execute_reply.started":"2024-03-12T06:58:42.117942Z","shell.execute_reply":"2024-03-12T06:58:42.142063Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 4858 entries, 0 to 4857\nData columns (total 7 columns):\n #   Column     Non-Null Count  Dtype  \n---  ------     --------------  -----  \n 0   Date       4858 non-null   object \n 1   Open       4858 non-null   float64\n 2   High       4858 non-null   float64\n 3   Low        4858 non-null   float64\n 4   Close      4858 non-null   float64\n 5   Adj Close  4858 non-null   float64\n 6   Volume     4858 non-null   int64  \ndtypes: float64(5), int64(1), object(1)\nmemory usage: 265.8+ KB\n","output_type":"stream"}]},{"cell_type":"code","source":"google['Date'] = pd.to_datetime(google['Date'])","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.144325Z","iopub.execute_input":"2024-03-12T06:58:42.144779Z","iopub.status.idle":"2024-03-12T06:58:42.154763Z","shell.execute_reply.started":"2024-03-12T06:58:42.144743Z","shell.execute_reply":"2024-03-12T06:58:42.153828Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"","metadata":{}},{"cell_type":"code","source":"print(f'Datefrome contains stock prices between {google.Date.min()} {google.Date.max()}')\nprint(f'Total days = {(google.Date.max()-google.Date.min()).days} days')","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.156174Z","iopub.execute_input":"2024-03-12T06:58:42.156797Z","iopub.status.idle":"2024-03-12T06:58:42.166058Z","shell.execute_reply.started":"2024-03-12T06:58:42.156761Z","shell.execute_reply":"2024-03-12T06:58:42.164847Z"},"trusted":true},"execution_count":7,"outputs":[{"name":"stdout","text":"Datefrome contains stock prices between 2004-08-19 00:00:00 2023-12-05 00:00:00\nTotal days = 7047 days\n","output_type":"stream"}]},{"cell_type":"code","source":"google.describe()","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.167211Z","iopub.execute_input":"2024-03-12T06:58:42.167573Z","iopub.status.idle":"2024-03-12T06:58:42.199304Z","shell.execute_reply.started":"2024-03-12T06:58:42.167547Z","shell.execute_reply":"2024-03-12T06:58:42.198446Z"},"trusted":true},"execution_count":8,"outputs":[{"execution_count":8,"output_type":"execute_result","data":{"text/plain":"                                Date         Open         High          Low  \\\ncount                           4858  4858.000000  4858.000000  4858.000000   \nmean   2014-04-12 02:30:17.044051200    41.477174    41.917059    41.055491   \nmin              2004-08-19 00:00:00     2.470490     2.534002     2.390042   \n25%              2009-06-16 06:00:00    12.846597    12.954195    12.712414   \n50%              2014-04-12 12:00:00    26.499958    26.728268    26.289323   \n75%              2019-02-07 18:00:00    57.367250    58.028500    56.962251   \nmax              2023-12-05 00:00:00   151.863495   152.100006   149.887497   \nstd                              NaN    38.590695    39.031758    38.193016   \n\n             Close    Adj Close        Volume  \ncount  4858.000000  4858.000000  4.858000e+03  \nmean     41.494404    41.494404  1.189152e+08  \nmin       2.490913     2.490913  1.584340e+05  \n25%      12.834642    12.834642  2.854912e+07  \n50%      26.537501    26.537501  6.168836e+07  \n75%      57.611249    57.611249  1.467329e+08  \nmax     150.709000   150.709000  1.650833e+09  \nstd      38.618107    38.618107  1.512424e+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>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>count</th>\n      <td>4858</td>\n      <td>4858.000000</td>\n      <td>4858.000000</td>\n      <td>4858.000000</td>\n      <td>4858.000000</td>\n      <td>4858.000000</td>\n      <td>4.858000e+03</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2014-04-12 02:30:17.044051200</td>\n      <td>41.477174</td>\n      <td>41.917059</td>\n      <td>41.055491</td>\n      <td>41.494404</td>\n      <td>41.494404</td>\n      <td>1.189152e+08</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>2004-08-19 00:00:00</td>\n      <td>2.470490</td>\n      <td>2.534002</td>\n      <td>2.390042</td>\n      <td>2.490913</td>\n      <td>2.490913</td>\n      <td>1.584340e+05</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2009-06-16 06:00:00</td>\n      <td>12.846597</td>\n      <td>12.954195</td>\n      <td>12.712414</td>\n      <td>12.834642</td>\n      <td>12.834642</td>\n      <td>2.854912e+07</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2014-04-12 12:00:00</td>\n      <td>26.499958</td>\n      <td>26.728268</td>\n      <td>26.289323</td>\n      <td>26.537501</td>\n      <td>26.537501</td>\n      <td>6.168836e+07</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2019-02-07 18:00:00</td>\n      <td>57.367250</td>\n      <td>58.028500</td>\n      <td>56.962251</td>\n      <td>57.611249</td>\n      <td>57.611249</td>\n      <td>1.467329e+08</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2023-12-05 00:00:00</td>\n      <td>151.863495</td>\n      <td>152.100006</td>\n      <td>149.887497</td>\n      <td>150.709000</td>\n      <td>150.709000</td>\n      <td>1.650833e+09</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>NaN</td>\n      <td>38.590695</td>\n      <td>39.031758</td>\n      <td>38.193016</td>\n      <td>38.618107</td>\n      <td>38.618107</td>\n      <td>1.512424e+08</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"code","source":"plt.plot(google['Date'],google['Close'])\nplt.xlabel('Dates')\nplt.ylabel('Price')\nplt.title('Stock Prices of Google')\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.200613Z","iopub.execute_input":"2024-03-12T06:58:42.200876Z","iopub.status.idle":"2024-03-12T06:58:42.521825Z","shell.execute_reply.started":"2024-03-12T06:58:42.200853Z","shell.execute_reply":"2024-03-12T06:58:42.521131Z"},"trusted":true},"execution_count":9,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import StandardScaler\n\nfrom sklearn.metrics import mean_squared_error as mse\nfrom sklearn.metrics import r2_score","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.523098Z","iopub.execute_input":"2024-03-12T06:58:42.523590Z","iopub.status.idle":"2024-03-12T06:58:42.623713Z","shell.execute_reply.started":"2024-03-12T06:58:42.523564Z","shell.execute_reply":"2024-03-12T06:58:42.622641Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"code","source":"x = np.array(google.index).reshape(-1,1)\ny = google['Close']\nx_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.3, random_state=101)","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.626319Z","iopub.execute_input":"2024-03-12T06:58:42.626633Z","iopub.status.idle":"2024-03-12T06:58:42.633682Z","shell.execute_reply.started":"2024-03-12T06:58:42.626606Z","shell.execute_reply":"2024-03-12T06:58:42.632929Z"},"trusted":true},"execution_count":11,"outputs":[]},{"cell_type":"code","source":"scaler = StandardScaler().fit(x_train)","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.634857Z","iopub.execute_input":"2024-03-12T06:58:42.635383Z","iopub.status.idle":"2024-03-12T06:58:42.642891Z","shell.execute_reply.started":"2024-03-12T06:58:42.635351Z","shell.execute_reply":"2024-03-12T06:58:42.641934Z"},"trusted":true},"execution_count":12,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.644266Z","iopub.execute_input":"2024-03-12T06:58:42.644576Z","iopub.status.idle":"2024-03-12T06:58:42.727380Z","shell.execute_reply.started":"2024-03-12T06:58:42.644550Z","shell.execute_reply":"2024-03-12T06:58:42.726546Z"},"trusted":true},"execution_count":13,"outputs":[]},{"cell_type":"code","source":"lm = LinearRegression()\nlm.fit(x_train,y_train)","metadata":{"execution":{"iopub.status.busy":"2024-03-12T06:58:42.728506Z","iopub.execute_input":"2024-03-12T06:58:42.728900Z","iopub.status.idle":"2024-03-12T06:58:42.753614Z","shell.execute_reply.started":"2024-03-12T06:58:42.728866Z","shell.execute_reply":"2024-03-12T06:58:42.752484Z"},"trusted":true},"execution_count":14,"outputs":[{"execution_count":14,"output_type":"execute_result","data":{"text/plain":"LinearRegression()","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"},"metadata":{}}]},{"cell_type":"code","source":"# Assuming x_train is a 2D array or DataFrame and Y_train is a 1D array or Series\nplt.scatter(x_train[:, 0], y_train, label='Actual', marker='o')  # Plot actual values\nplt.plot(x_train[:, 0], lm.predict(x_train).T, label='Predicted')  # Plot predicted values\n\nplt.xlabel('Day')\nplt.ylabel('Value')\nplt.title('Actual vs. Predicted Values (Train)')\nplt.legend()\nplt.grid(True)\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-03-12T07:00:37.413706Z","iopub.execute_input":"2024-03-12T07:00:37.414435Z","iopub.status.idle":"2024-03-12T07:00:37.763881Z","shell.execute_reply.started":"2024-03-12T07:00:37.414399Z","shell.execute_reply":"2024-03-12T07:00:37.762773Z"},"trusted":true},"execution_count":22,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"#calculating scores for model evaluation\nscores = 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))}'''\nprint(scores)","metadata":{"execution":{"iopub.status.busy":"2024-03-12T07:07:32.025562Z","iopub.execute_input":"2024-03-12T07:07:32.026679Z","iopub.status.idle":"2024-03-12T07:07:32.038420Z","shell.execute_reply.started":"2024-03-12T07:07:32.026631Z","shell.execute_reply":"2024-03-12T07:07:32.037352Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"\nMetric           Train                Test        \nr2_score  0.7769716098371854\t0.7675221972985163\nMSE       338.4133736863397\t331.4197278971651\n","output_type":"stream"}]}]}