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| import streamlit as st | |
| import pickle | |
| import numpy as np | |
| from sklearn.preprocessing import StandardScaler | |
| # loading the saved model | |
| filename = "" | |
| scaler = StandardScaler() | |
| loaded_model = "" | |
| # price prediction function | |
| def stock_price_prediction(input_data, loaded_model): | |
| input_arr = np.asarray(input_data, dtype=float).reshape(1, -1) | |
| print(input_arr) | |
| scaled_input = scaler.fit_transform(input_arr) | |
| print(scaled_input) | |
| # Predict the stock price | |
| prediction = loaded_model.predict(scaled_input) | |
| return f'The predicted stock price is: {prediction[0]}' | |
| def main(): | |
| st.title("Stock Price Prediction Models") | |
| # allowing user to select a model | |
| st.sidebar.header("**Select a model to use for the prediction**") | |
| with st.sidebar: | |
| selected_model = st.radio( | |
| "", | |
| ["**Random Forest**", "**Decision Tree**", "**CatBoost**", "**Gradient Boost**", "**XGBoost**"], | |
| captions = ["Accuracy: 98.5%", "Accuracy: 98%", "Accuracy: 97.5%", "Accuracy: 97.4%", "Accuracy: 89%"]) | |
| if selected_model == "**Random Forest**": | |
| filename = "Random_Forest_trained_model.pkl" | |
| loaded_model = pickle.load(open(filename, 'rb')) | |
| elif selected_model == "**Decision Tree**": | |
| filename = "Decision_Tree_Regression_trained_model.pkl" | |
| loaded_model = pickle.load(open(filename, 'rb')) | |
| elif selected_model == "**CatBoost**": | |
| filename = "CatBoostRegressor_trained_model.pkl" | |
| loaded_model = pickle.load(open(filename, 'rb')) | |
| elif selected_model == "**Gradient Boost**": | |
| filename = "Gradient_Boosting_Regression_trained_model.pkl" | |
| loaded_model = pickle.load(open(filename, 'rb')) | |
| elif selected_model == "**XGBoost**": | |
| filename = "XGBoost_trained_model.pkl" | |
| loaded_model = pickle.load(open(filename, 'rb')) | |
| # getting the input data from the user | |
| st.subheader("**Input required values**",) | |
| daily_ret_pct = st.text_input('Daily Return as %') | |
| daily_var = st.text_input('Daily Variation') | |
| macd = st.text_input('MACD') | |
| rsi = st.text_input('RSI') | |
| ema = st.text_input('EMA') | |
| # code for Prediction | |
| prediction = '' | |
| # creating a button for Prediction | |
| if st.button('Predict Stock Price'): | |
| input_data = [daily_ret_pct, daily_var, macd, rsi, ema] | |
| if all(input_data): | |
| prediction = stock_price_prediction(input_data, loaded_model) | |
| else: | |
| prediction = 'Please fill in all fields' | |
| st.success(prediction) | |
| if __name__ == '__main__': | |
| main() |