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import pandas as pd | ||
from sklearn.model_selection import train_test_split | ||
from sklearn.ensemble import RandomForestClassifier | ||
from sklearn.preprocessing import StandardScaler | ||
import joblib | ||
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class LoanApprovalModel: | ||
def __init__(self): | ||
self.model = RandomForestClassifier() | ||
self.scaler = StandardScaler() | ||
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def load_data(self, filepath): | ||
data = pd.read_csv(filepath) | ||
return data | ||
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def preprocess_data(self, data): | ||
# Perform preprocessing steps here | ||
X = data.drop('target', axis=1) | ||
y = data['target'] | ||
return train_test_split(X, y, test_size=0.2, random_state=42) | ||
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def train(self, X_train, y_train): | ||
self.scaler.fit(X_train) | ||
X_train_scaled = self.scaler.transform(X_train) | ||
self.model.fit(X_train_scaled, y_train) | ||
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def save_model(self, model_path, scaler_path): | ||
joblib.dump(self.model, model_path) | ||
joblib.dump(self.scaler, scaler_path) | ||
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if __name__ == "__main__": | ||
loan_model = LoanApprovalModel() | ||
data = loan_model.load_data('data/loan_data.csv') # Example path | ||
X_train, X_test, y_train, y_test = loan_model.preprocess_data(data) | ||
loan_model.train(X_train, y_train) | ||
loan_model.save_model('saved_models/model.pkl', 'saved_models/scaler.pkl') |
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models/BitcoinPricePredictor/notebooks/Bitcoin Price Prediction LSTM-checkpoint.ipynb
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models/BitcoinPricePredictor/notebooks/Bitcoin Price Prediction LSTM.ipynb
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import joblib | ||
import pandas as pd | ||
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class LoanApprovalPredictor: | ||
def __init__(self, model_path, scaler_path): | ||
self.model = joblib.load(model_path) | ||
self.scaler = joblib.load(scaler_path) | ||
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def predict(self, input_data): | ||
input_data_scaled = self.scaler.transform(input_data) | ||
return self.model.predict(input_data_scaled) | ||
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if __name__ == "__main__": | ||
predictor = LoanApprovalPredictor('saved_models/model.pkl', 'saved_models/scaler.pkl') | ||
# Example input data, replace with actual data | ||
input_data = pd.DataFrame([[...]], columns=[...]) # Replace with actual column names | ||
predictions = predictor.predict(input_data) | ||
print(predictions) |
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models/BitcoinPricePredictor/saved_model/bitcoin_price_prediction_lstm.py
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# -*- coding: utf-8 -*- | ||
"""Bitcoin Price Prediction LSTM.ipynb | ||
Automatically generated by Colab. | ||
Original file is located at | ||
https://colab.research.google.com/drive/1kinADIkfmyvxsBWbJpmNRlJSqLCH1MgE | ||
# Implementation of LSTM on Bitcoin Dataset | ||
""" | ||
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import pandas as pd | ||
import numpy as np | ||
import matplotlib.pyplot as plt | ||
from sklearn.preprocessing import MinMaxScaler | ||
from keras.models import Sequential | ||
from keras.layers import Dense, LSTM, Dropout | ||
from sklearn.metrics import mean_squared_error, mean_absolute_error, median_absolute_error,r2_score | ||
import warnings | ||
import seaborn as sns | ||
import tensorflow as tf | ||
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warnings.filterwarnings("ignore") | ||
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# Load the data | ||
BTC = pd.read_csv("BTC-USD.csv") | ||
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print(BTC.columns) | ||
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# Convert the 'Date' column to datetime and set it as the index | ||
BTC['Date'] = pd.to_datetime(BTC['Date']) | ||
BTC.set_index('Date', inplace=True) | ||
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BTC.head() | ||
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#Checking null value | ||
print(BTC.isnull().sum()) | ||
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# Plot the closing prices | ||
plt.figure(figsize=(10, 6)) | ||
plt.plot(BTC['Close'], label='BTC Close') | ||
plt.title('BTC Closing Prices') | ||
plt.xlabel('Date') | ||
plt.ylabel('Closing Price') | ||
plt.legend() | ||
plt.show() | ||
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# Time Series Scatter Plot | ||
plt.figure(figsize=(10, 6)) | ||
sns.scatterplot(x=BTC.index, y=nifty_50_df['Close']) | ||
plt.title('Time Series Scatter Plot of BTC Closing Prices') | ||
plt.xlabel('Date') | ||
plt.ylabel('Closing Price') | ||
plt.show() | ||
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# Prepare the data for modeling | ||
data = BTC[['Close']].values | ||
scaler = MinMaxScaler(feature_range=(0, 1)) | ||
scaled_data = scaler.fit_transform(data) | ||
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# Split the data into training and testing sets | ||
train_size = int(len(scaled_data) * 0.7) | ||
train_data, test_data = scaled_data[:train_size], scaled_data[train_size:] | ||
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# Create a function to create datasets for training and testing | ||
def create_dataset(dataset, time_step): | ||
X, Y = [], [] | ||
for i in range(len(dataset) - time_step - 1): | ||
a = dataset[i:(i + time_step), 0] | ||
X.append(a) | ||
Y.append(dataset[i + time_step, 0]) | ||
return np.array(X), np.array(Y) | ||
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# Create the training and testing datasets | ||
time_step = 50 | ||
X_train, y_train = create_dataset(train_data, time_step) | ||
X_test, y_test = create_dataset(test_data, time_step) | ||
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# Reshape the data for GRU layers | ||
X_train = X_train.reshape(X_train.shape[0], X_train.shape[1], 1) | ||
X_test = X_test.reshape(X_test.shape[0], X_test.shape[1], 1) | ||
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# Create the GRU model | ||
model = Sequential() | ||
model.add(LSTM(200, return_sequences=True, input_shape=(time_step, 1))) | ||
model.add(Dropout(0.4)) | ||
model.add(LSTM(160, return_sequences=False)) | ||
model.add(Dense(50)) | ||
model.add(Dense(1)) | ||
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# Compile the model | ||
model.compile(optimizer='adam', loss='mean_squared_error', metrics=[tf.keras.metrics.MeanAbsolutePercentageError()]) | ||
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# Train the model | ||
history = model.fit(X_train, y_train, batch_size=32, epochs=50, validation_data=(X_test, y_test)) | ||
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# Make predictions | ||
train_predict = model.predict(X_train) | ||
test_predict = model.predict(X_test) | ||
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# Inverse transform the predictions | ||
train_predict = scaler.inverse_transform(train_predict) | ||
test_predict = scaler.inverse_transform(test_predict) | ||
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# Inverse transform the original values | ||
original_y_train = scaler.inverse_transform([y_train]) | ||
original_y_test = scaler.inverse_transform([y_test]) | ||
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# Create plots for the predicted values | ||
train_predict_plot = np.empty_like(scaled_data) | ||
train_predict_plot[:, :] = np.nan | ||
train_predict_plot[time_step:len(train_predict) + time_step, :] = train_predict | ||
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test_predict_plot = np.empty_like(scaled_data) | ||
test_predict_plot[:, :] = np.nan | ||
test_predict_plot[len(train_predict) + (time_step * 2) + 1:len(scaled_data) - 1, :] = test_predict | ||
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plt.figure(figsize=(10, 6)) | ||
plt.plot(scaler.inverse_transform(scaled_data), label='Actual') | ||
plt.plot(train_predict_plot, label='Train Predict') | ||
plt.plot(test_predict_plot, label='Test Predict') | ||
plt.title('Actual vs Predicted Values') | ||
plt.xlabel('Date') | ||
plt.ylabel('Closing Price') | ||
plt.legend() | ||
plt.show() | ||
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# Plot the model loss and MAPE over epochs | ||
plt.figure(figsize=(12, 6)) | ||
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# Plot Loss | ||
plt.subplot(1, 2, 1) | ||
plt.plot(history.history['loss'], label='Training Loss') | ||
plt.plot(history.history['val_loss'], label='Validation Loss') | ||
plt.title('Model Loss (MSE) Over Epochs') | ||
plt.xlabel('Epochs') | ||
plt.ylabel('Loss (MSE)') | ||
plt.legend() | ||
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# Plot MAPE | ||
plt.subplot(1, 2, 2) | ||
plt.plot(history.history['mean_absolute_percentage_error'], label='Training MAPE') | ||
plt.plot(history.history['val_mean_absolute_percentage_error'], label='Validation MAPE') | ||
plt.title('Model Accuracy (MAPE) Over Epochs') | ||
plt.xlabel('Epochs') | ||
plt.ylabel('MAPE (%)') | ||
plt.legend() | ||
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plt.tight_layout() | ||
plt.show() | ||
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# Plot true vs predicted residuals | ||
plt.figure(figsize=(12, 6)) | ||
plt.plot(scaler.inverse_transform(scaled_data), label="True") | ||
plt.plot(test_predict_plot, label="Test Predicted") | ||
plt.title("True vs Predicted BTC Close Prices") | ||
plt.legend() | ||
plt.show() | ||
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# Calculate R² score, rmse, mae, mse, mate, smate for training and testing sets | ||
train_r2 = r2_score(original_y_train[0], train_predict[:, 0]) | ||
test_r2 = r2_score(original_y_test[0], test_predict[:, 0]) | ||
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train_rmse = np.sqrt(mean_squared_error(original_y_train[0], train_predict[:, 0])) | ||
test_rmse = np.sqrt(mean_squared_error(original_y_test[0], test_predict[:, 0])) | ||
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train_mae = mean_absolute_error(original_y_train[0], train_predict[:, 0]) | ||
test_mae = mean_absolute_error(original_y_test[0], test_predict[:, 0]) | ||
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train_mse = mean_squared_error(original_y_train[0], train_predict[:, 0]) | ||
test_mse = mean_squared_error(original_y_test[0], test_predict[:, 0]) | ||
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train_mate = median_absolute_error(original_y_train[0], train_predict[:, 0]) | ||
test_mate = median_absolute_error(original_y_test[0], test_predict[:, 0]) | ||
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train_smate = np.sqrt(mean_squared_error(original_y_train[0], train_predict[:, 0])) / np.mean(original_y_train) | ||
test_smate = np.sqrt(mean_squared_error(original_y_test[0], test_predict[:, 0])) / np.mean(original_y_test) | ||
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print(f'Train R²: {train_r2}') | ||
print(f'Test R²: {test_r2}') | ||
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print("Training RMSE: ", train_rmse) | ||
print("Testing RMSE: ", test_rmse) | ||
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print("Training MAE: ", train_mae) | ||
print("Testing MAE: ", test_mae) | ||
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print("Training MSE: ", train_mse) | ||
print("Testing MSE: ", test_mse) | ||
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print("Training MATE: ", train_mate) | ||
print("Testing MATE: ", test_mate) | ||
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print("Training SMATE: ", train_smate) | ||
print("Testing SMATE: ", test_smate) | ||
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from tabulate import tabulate | ||
import numpy as np | ||
# Create a table | ||
table = [ | ||
["Metric", "Training", "Testing"], | ||
["R² Score", f"{train_r2:.4f}", f"{test_r2:.4f}"], | ||
["RMSE", f"{train_rmse:.4f}", f"{test_rmse:.4f}"], | ||
["MSE", f"{train_mse:.4f}", f"{test_mse:.4f}"], | ||
["MAE", f"{train_mae:.4f}", f"{test_mae:.4f}"], | ||
["MATE", f"{train_mate:.4f}", f"{test_mate:.4f}"], | ||
["SMATE", f"{train_smate:.4f}", f"{test_smate:.4f}"] | ||
] | ||
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print(tabulate(table, headers="firstrow", tablefmt="grid")) | ||
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