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import numpy as np |
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import pandas as pd |
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import yfinance as yf |
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from keras.models import load_model |
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from sklearn.preprocessing import MinMaxScaler |
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model = load_model("stock_dl_model.h5") |
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def predict(stock_symbol="POWERGRID.NS"): |
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start = pd.to_datetime("2000-01-01") |
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end = pd.to_datetime("2024-10-01") |
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df = yf.download(stock_symbol, start=start, end=end) |
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data_training = pd.DataFrame(df['Close'][0:int(len(df)*0.70)]) |
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data_testing = pd.DataFrame(df['Close'][int(len(df)*0.70): int(len(df))]) |
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scaler = MinMaxScaler(feature_range=(0, 1)) |
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data_training_array = scaler.fit_transform(data_training) |
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past_100_days = data_training.tail(100) |
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final_df = past_100_days.append(data_testing, ignore_index=True) |
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input_data = scaler.fit_transform(final_df) |
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x_test, y_test = [], [] |
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for i in range(100, input_data.shape[0]): |
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x_test.append(input_data[i-100:i]) |
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y_test.append(input_data[i, 0]) |
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x_test, y_test = np.array(x_test), np.array(y_test) |
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y_predicted = model.predict(x_test) |
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scaler = scaler.scale_ |
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scale_factor = 1 / scaler[0] |
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y_predicted = y_predicted * scale_factor |
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y_test = y_test * scale_factor |
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return { |
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"prediction": y_predicted.tolist() |
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} |
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def __call__(self, inputs): |
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stock_symbol = inputs.get("inputs", "POWERGRID.NS") |
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return predict(stock_symbol) |
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