building a website for stock price prediction

stock_price_prediction_app.py

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+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split, GridSearchCV, cross_val_score
+from sklearn.linear_model import LinearRegression
+from sklearn.svm import SVR
+from sklearn.ensemble import RandomForestRegressor, AdaBoostRegressor, GradientBoostingRegressor
+from sklearn.tree import DecisionTreeRegressor
+from sklearn.neighbors import KNeighborsRegressor
+from sklearn.metrics import mean_squared_error, mean_absolute_error, mean_absolute_percentage_error
+from sklearn.preprocessing import MinMaxScaler
+import xgboost as xgb
+import matplotlib.pyplot as plt
+import streamlit as st
+from sklearn.impute import SimpleImputer
+
+# Load and preprocess the dataset
+@st.cache_data
+def load_data():
+    df = pd.read_csv('SBIN.csv')
+    df.drop(['Date', 'Adj Close'], axis=1, inplace=True)  # Drop irrelevant columns
+
+    # Handle missing values
+    imputer = SimpleImputer(strategy='mean')
+    df = pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
+
+    # Calculate VWAP
+    df['VWAP'] = (df['Close'] * df['Volume']).cumsum() / df['Volume'].cumsum()
+
+    return df
+
+# Train the models
+def train_models(df):
+    # Select features and target variable
+    X = df[['Open', 'High', 'Low', 'Volume', 'VWAP']]
+    y = df['Close']
+
+    # Split the data into training and testing sets
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Scale the features
+    scaler = MinMaxScaler()
+    X_train_scaled = scaler.fit_transform(X_train)
+    X_test_scaled = scaler.transform(X_test)
+
+    models = {
+        'Linear Regression': LinearRegression(),
+        'SVR': SVR(),
+        'Random Forest': RandomForestRegressor(),
+        'Gradient Boosting': GradientBoostingRegressor(),
+        'XGBoost': xgb.XGBRegressor(),
+        'AdaBoost': AdaBoostRegressor(),
+        'Decision Tree': DecisionTreeRegressor(),
+        'KNeighbors': KNeighborsRegressor()
+    }
+
+    trained_models = {}
+
+    for name, model in models.items():
+        model.fit(X_train_scaled, y_train)
+        trained_models[name] = model
+
+    return trained_models, scaler, X_test_scaled, y_test
+
+# Load data and train models
+df = load_data()
+trained_models, scaler, X_test_scaled, y_test = train_models(df)
+
+# Streamlit app layout
+st.title("Stock Price Prediction App")
+
+# User input for stock features
+open_price = st.number_input("Open Price:", min_value=0.0, format="%.2f")
+high_price = st.number_input("High Price:", min_value=0.0, format="%.2f")
+low_price = st.number_input("Low Price:", min_value=0.0, format="%.2f")
+volume = st.number_input("Volume:", min_value=0, format="%d")
+
+# Calculate VWAP for user input
+vwap = (open_price + high_price + low_price) / 3  # Simplified VWAP calculation
+
+# Dropdown for model selection
+model_selection = st.selectbox("Select Model for Prediction:", options=list(trained_models.keys()))
+
+# Button to predict
+if st.button("Predict Closing Price"):
+    input_data = np.array([[open_price, high_price, low_price, volume, vwap]])
+    input_scaled = scaler.transform(input_data)
+
+    # Make prediction using the selected model
+    model = trained_models[model_selection]
+    prediction = model.predict(input_scaled)
+
+    # Display the prediction
+    st.success(f"The predicted closing price is: ${prediction[0]:.2f}")
+
+# Optional: Visualize KNN Hyperparameter Tuning
+if st.checkbox("Show KNN Hyperparameter Tuning Plot"):
+    k_values = range(1, 31)  # Example range for k
+    mse_values = []
+
+    X = df[['Open', 'High', 'Low', 'Volume', 'VWAP']]
+    y = df['Close']
+
+    for k in k_values:
+        knn_model = KNeighborsRegressor(n_neighbors=k)
+        mse = -cross_val_score(knn_model, scaler.transform(X), y, cv=5, scoring='neg_mean_squared_error').mean()
+        mse_values.append(mse)
+
+    plt.figure(figsize=(10, 6))
+    plt.plot(k_values, mse_values, marker='o', linestyle='-')
+    plt.title('KNN Hyperparameter Tuning: MSE vs. Number of Neighbors')
+    plt.xlabel('Number of Neighbors (k)')
+    plt.ylabel('Mean Squared Error (MSE)')
+    plt.xticks(k_values)  # Show all k values on x-axis
+    plt.grid()
+
+    # Show the plot in Streamlit
+    st.pyplot(plt)