Create StockPredictor.py

StockPredictor.py

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+!pip install neuralprophet
+
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from neuralprophet import NeuralProphet
+
+import warnings
+warnings.filterwarnings('ignore')
+
+import os
+for dirname, _, filesnames in os.walk('yourstockdata.csv')
+    for filenames in filesnames:
+        print(os.path.join(dirname, filename))
+
+df = pd.read_csv('youstockdata.csv')
+
+df.head()
+
+df.info()
+
+df['Date'] = pd.to_datetime(df['Date'])
+
+df.dtypes
+
+df = df[['Date', 'Close']]
+
+df.head()
+
+df.columns = ['ds', 'y']
+
+df.head()
+
+plt.plot(df['ds'], df['y'], label='actual', c='g')
+plt.title('Stock Data')
+plt.xlabel('Date')
+plt.ylabel('Stock Price')
+plt.show()
+
+model = NeuralProphet(
+    batch_size=16
+)
+
+model.fit(df)
+
+future = model.make_future_dataframe(df, periods=365)
+
+forecast = model.predict(future)
+forecast
+
+actual_prediction = model.predict(df)
+
+plt.plot(df['ds'], df['y'], label='actual', c='g')
+plt.plot(actual_prediction['ds'], actual_prediction['yhat1'], label='prediction_actual', c='r')
+plt.plot(forecast['ds'], forecast['yhat1'], label='future_prediction', c='b')
+plt.xlabel('Date')
+plt.ylabel('Stock Price')
+plt.legend()
+
+plt.show()
+
+model.plot_components(forecast)
+