63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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from sklearn.preprocessing import PolynomialFeatures
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.model_selection import train_test_split
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# data = pd.read_csv("static/obligacjePL.csv")
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#
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# months = data['Data']
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# prices = data['Otwarcie']
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#
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# months = np.array(months).reshape(-1, 1)
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#
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# model = LinearRegression()
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# model.fit(months, prices)
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#
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# future_months = np.array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]).reshape(-1, 1)
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# predictions = model.predict(future_months)
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# plt.plot(months, prices, label='actual')
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# plt.plot(future_months, predictions, label='prediction')
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# plt.legend()
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# plt.xlabel('Months')
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# plt.ylabel('Prices')
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# plt.title('Stock Prices Prediction')
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# plt.grid()
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# plt.show()
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data = pd.read_csv("static/AAPL2.csv")
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months = data['Date']
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prices = data['Open']
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months = np.array(months).reshape(-1, 1)
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poly_feat = PolynomialFeatures(degree=2)
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months_poly = poly_feat.fit_transform(months)
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model = LinearRegression()
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model.fit(months_poly, prices)
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future_months = np.array([12, 13, 14, 15, 16, 17, 18]).reshape(-1, 1)
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future_months_poly = poly_feat.fit_transform(future_months)
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predictions = model.predict(future_months_poly)
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plt.plot(months, prices, color='cyan')
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plt.plot(future_months, predictions, color='pink')
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plt.title('Polynomial Regression')
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plt.xlabel('Months')
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plt.ylabel('Prices')
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plt.xticks(range(1, 18))
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plt.show()
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X_train, X_test, y_train, y_test = train_test_split(months_poly, prices, test_size=0.2, random_state=0)
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# Fit the model to the training data
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model = LinearRegression()
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model.fit(X_train, y_train)
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# Make predictions on the test data
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y_pred = model.predict(X_test)
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# Calculate the MSE and R-Squared
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mse = mean_squared_error(y_test, y_pred)
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r2 = r2_score(y_test, y_pred)
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# Print the results
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print("Mean Squared Error:", mse)
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print("R-Squared:", r2)
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