Delete main.py

main.py

@@ -1,126 +0,0 @@
-# -*- coding: utf-8 -*-
-"""
-Created on Sun Nov  6 18:29:53 2022
-
-@author: culli
-"""
-
-import pandas as pd
-import numpy as np
-import pandas_datareader.data as web
-import datetime as dt
-from datetime import timedelta
-from sklearn.preprocessing import MinMaxScaler
-import tensorflow as tf
-from tensorflow import lite
-from tensorflow.keras.models import Sequential
-from tensorflow.keras.layers import Dense, Dropout, LSTM
-import matplotlib.pyplot as plt
-
-# Load Training Data
-company = "BTC-USD"
-start = dt.datetime(2015,1,1)
-end = dt.datetime(2022,1,1)
-data = web.DataReader(company, "yahoo", start, end)
-
-
-# Prepare Data
-scaler = MinMaxScaler(feature_range=(0,1))
-scaled_data = scaler.fit_transform(data['Close'].values.reshape(-1,1))
-
-prediction_days = 60
-
-x_train = []
-y_train = []
-
-for x in range(prediction_days, len(scaled_data)):
-    x_train.append(scaled_data[x-prediction_days:x, 0])
-    y_train.append(scaled_data[x, 0])
-
-
-x_train, y_train = np.array(x_train), np.array(y_train)
-x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))
-
-
-# Build the model
-model = Sequential()
-
-model.add(LSTM(units=50, return_sequences=True, input_shape = (x_train.shape[1], 1)))
-model.add(Dropout(0.2))
-model.add(LSTM(units=50, return_sequences=True))
-model.add(Dropout(0.2))
-model.add(LSTM(units=50))
-model.add(Dropout(0.2))
-model.add(Dense(units=1))
-
-
-model.compile(optimizer="adam", loss="mean_squared_error")
-model.fit(x_train, y_train, epochs=25, batch_size=64)
-
-# Load Data
-test_start = dt.datetime(2022,1,1)
-test_end = dt.datetime.now()
-
-test_data = web.DataReader(company, "yahoo", test_start, test_end)
-actual_prices = test_data["Close"].values
-
-total_dataset = pd.concat((data["Close"], test_data["Close"]), axis = 0)
-
-
-
-model_inputs = total_dataset[len(total_dataset) - len(test_data) - prediction_days:].values # Important
-model_inputs = model_inputs.reshape(-1, 1)
-model_inputs = scaler.transform(model_inputs)
-
-# Loop for making predictions
-for x in range(prediction_days):
-    
-    
-    # Make Predictions on Test Data
-    x_test = []
-    
-    for x in range(prediction_days, len(model_inputs)+1):
-        x_test.append(model_inputs[x-prediction_days:x, 0])
-    
-    x_test = np.array(x_test)
-    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))
-    
-    predicted_prices = model.predict(x_test)
-    predicted_prices = scaler.inverse_transform(predicted_prices)
-    
-    
-    # Predict Next Day
-    real_data = [model_inputs[len(model_inputs) + 1 - prediction_days:len(model_inputs+1), 0]]
-    real_data = np.array(real_data)
-    real_data = np.reshape(real_data, (real_data.shape[0], real_data.shape[1], 1))
-    
-    # Formatting the prediction and printing it
-    prediction = model.predict(real_data)
-    prediction = scaler.inverse_transform(prediction)
-    print(f"Prediction: {prediction}")
-    
-    # Adding the prediction back into the model for the next prediction
-    model_prediction_input = prediction
-    model_prediction_input = model_prediction_input.reshape(-1, 1)
-    model_prediction_input = scaler.transform(model_prediction_input)
-    model_inputs = np.concatenate((model_inputs, model_prediction_input))
-
-
-plt.plot(actual_prices, color="black", label=f"Actual {company} Price")
-plt.plot(predicted_prices, color="green", label=f"Predicted {company} Price")
-plt.title(f"{company} Share Price")
-plt.xlabel("Days")
-plt.ylabel(f"{company} Share Price")
-plt.legend()
-plt.show()
-
-
-tf.keras.models.save_model(model,"model.pbtxt")
-converter = lite.TFLiteConverter.from_keras_model(model = model)
-converter.optimizations = [tf.lite.Optimize.DEFAULT]
-converter.experimental_new_converter=True
-converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
-tf.lite.OpsSet.SELECT_TF_OPS]
-model_tflite = converter.convert()
-open("BTCPrediction.tflite", "wb").write(model_tflite)
-