Created a Web Page for predicting the stock price and also implemente…

…d concept of ensemble learning

main.py

@@ -0,0 +1,68 @@
+from flask import Flask, render_template, request
+import numpy as np
+import pickle
+import os
+import pandas as pd
+from sklearn.model_selection import train_test_split
+import xgboost as xgb
+
+app = Flask(__name__)
+
+models_dir = "Stock-Price-Prediction/prediction.pkl"
+data_file = "Stock-Price-Prediction/Updated_SBIN.csv"
+
+df = pd.read_csv(data_file)
+print(df.columns)
+
+# Load the model
+try:
+    with open(models_dir, "rb") as stock_file:
+        stock_model = pickle.load(stock_file)
+except FileNotFoundError:
+    stock_model = None
+
+def save_data(inputs, close_prediction):
+    new_data = pd.DataFrame([inputs + [close_prediction]], columns=['Open', 'High', 'Low', 'Volume', 'Close'])
+    with open(data_file, mode='a', newline='') as file:
+        new_data.to_csv(file, header=False, index=False)
+
+def retrain_model():
+    X = df[['Open', 'High', 'Low', 'Volume']]
+    y = df['Close']
+
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    stock_model_xgb = xgb.XGBRegressor(objective='reg:squarederror', random_state=42)
+    stock_model_xgb.fit(X_train, y_train)
+
+    with open(models_dir, "wb") as stock_file_xgb:
+        pickle.dump(stock_model_xgb, stock_file_xgb)
+
+@app.route('/')
+def home():
+    return render_template('home.html')
+
+@app.route('/predict_close', methods=['GET', 'POST'])
+def predict_close():
+    if request.method == 'POST':
+        try:
+            inputs = [
+                float(request.form.get('Open')),
+                float(request.form.get('High')),
+                float(request.form.get('Low')),
+                float(request.form.get('Volume'))
+            ]
+
+            close_prediction = stock_model.predict(np.array([inputs]))[0] if stock_model else None
+
+            save_data(inputs, close_prediction)
+            retrain_model()
+
+            return str(round(close_prediction, 2)) if close_prediction is not None else "Error: Stock model not loaded."
+        except Exception as e:
+            return f"An error occurred: {e}"
+    # If it's a GET request, render the stock.html page
+    return render_template('stock.html')
+
+if __name__ == "__main__":
+    app.run(debug=True)

requirements.txt

@@ -1,5 +1,7 @@
+Flask==2.3.2
 pandas==2.1.1
 numpy==1.26.0
 scikit-learn==1.3.0
 statsmodels==0.14.0
-pickle-mixin==1.0.2
+pickle-mixin==1.0.2
+xgboost

static/css/home.css

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+body {
+    font-family: 'Arial', sans-serif;
+    background-color: #f0f8ff; /* Light blue background */
+    margin: 0;
+    padding: 20px;
+}
+
+.container {
+    max-width: 600px;
+    margin: auto;
+    background: white;
+    padding: 30px;
+    border-radius: 10px;
+    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
+    text-align: center;
+}
+
+h1 {
+    color: #003366; /* Dark blue for heading */
+    font-size: 2.5em;
+}
+
+p {
+    color: #555; /* Dark gray for paragraph */
+    font-size: 1.2em;
+}
+
+nav {
+    margin-top: 20px;
+}
+
+ul {
+    list-style-type: none;
+    padding: 0;
+}
+
+li {
+    display: inline;
+    margin: 0 15px;
+}
+
+a {
+    text-decoration: none;
+    color: #0066cc; /* Blue link color */
+    font-weight: bold;
+}
+
+a:hover {
+    color: #004999; /* Darker blue on hover */
+}

static/css/stock.css

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+body {
+    font-family: 'Verdana', sans-serif;
+    background-color: #f5f5f5; /* Light gray background */
+    margin: 0;
+    padding: 20px;
+}
+
+.container {
+    max-width: 600px;
+    margin: auto;
+    background: white;
+    padding: 30px;
+    border-radius: 10px;
+    box-shadow: 0 4px 15px rgba(0, 0, 0, 0.1);
+}
+
+h1 {
+    color: #ff4500; /* Orange-red for heading */
+    font-size: 2.5em;
+    text-align: center;
+}
+
+label {
+    margin-top: 10px;
+    display: block;
+    font-weight: bold;
+    color: #333; /* Dark gray for labels */
+}
+
+input[type="text"] {
+    width: 100%;
+    padding: 10px;
+    margin: 5px 0 15px 0;
+    border: 1px solid #ccc;
+    border-radius: 4px;
+}
+
+button {
+    background-color: #28a745; /* Green button */
+    color: white;
+    padding: 10px;
+    border: none;
+    border-radius: 4px;
+    cursor: pointer;
+    width: 100%; /* Full-width button */
+}
+
+button:hover {
+    background-color: #218838; /* Darker green on hover */
+}
+
+.result {
+    margin-top: 20px;
+    padding: 10px;
+    color: white;
+    background-color: green; /* Green background for success */
+    border-radius: 4px;
+    display: none; /* Initially hidden */
+}

templates/home.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>Stock Price Predictor</title>
+    <link rel="stylesheet" href="static/css/home.css"> <!-- Link to your CSS -->
+</head>
+<body>
+    <div class="container">
+        <h1>Welcome to Our Stock Price Predictor</h1>
+        <p>Use this tool to predict stock prices based on historical data.</p>
+        <nav>
+            <ul>
+                <li><a href="{{ url_for('predict_close') }}">Stock Price Prediction</a></li>
+            </ul>
+        </nav>
+    </div>
+</body>
+</html>

templates/stock.html

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+<!doctype html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1">
+    <link rel="stylesheet" href="/static/css/stock.css"> <!-- Link to your CSS -->
+    <title>Stock Price Prediction</title>
+</head>
+<body>
+    <div class="container">
+        <h1>Stock Price Prediction</h1>
+        <form id="stockForm" method="post" onsubmit="send_data(event)">
+            <label for="Open">Open</label>
+            <input type="number" step="0.01" id="Open" name="Open" required>
+
+            <label for="High">High</label>
+            <input type="number" step="0.01" id="High" name="High" required>
+
+            <label for="Low">Low</label>
+            <input type="number" step="0.01" id="Low" name="Low" required>
+
+            <label for="Volume">Volume</label>
+            <input type="number" step="1" id="Volume" name="Volume" required>
+
+            <button type="submit" class="btn">Predict Stock Price</button>
+        </form>
+        <div id="prediction" class="prediction-result"></div>
+    </div>
+
+    <script>
+        function send_data(event) {
+            event.preventDefault();  // Prevent the default form submission
+            var form = document.getElementById('stockForm');
+            var formData = new FormData(form);
+            var xhr = new XMLHttpRequest();
+            xhr.open('POST', '/predict_close', true);  // Ensure this matches your Flask route
+            document.getElementById("prediction").innerHTML = "Predicting Stock Price...";
+            xhr.onreadystatechange = function () {
+                if (xhr.readyState == XMLHttpRequest.DONE) {
+                    document.getElementById("prediction").innerHTML = "Predicted Stock Price: " + xhr.responseText;
+                }
+            }
+            xhr.send(formData);
+        }
+    </script>
+</body>
+</html>