Merge pull request #151 from balbirs22/BS

Created a Web Page for predicting the stock price and also implemented concept of ensemble learning closes #132

Contributing.md

@@ -87,6 +87,39 @@ If you're introducing new algorithms or modifying existing ones, please update t
 ## 🧑‍🤝‍🧑 Community
 Join the discussion in the [Issues](https://github.com/your-repo/issues) section! Share your ideas, ask questions, and collaborate on exciting features with fellow contributors.
 
+## 🔄 Recent Updates
+
+### Changes in Flask Route for Stock Prediction:
+- The route `/predict_close` has been updated to handle both `GET` and `POST` requests.
+  - **GET** request: Renders the `stock.html` form when the user navigates to the prediction page.
+  - **POST** request: Processes the stock price prediction based on user input (Open, High, Low, and Volume).
+
+Make sure to follow the updated code structure when adding new routes for similar tasks. The current structure is as follows:
+
+```python
+@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}"
+    # For GET request, render the stock.html page
+    return render_template('stock.html')
+
+
 ## 🎉 Thank You!
 Every contribution counts! Whether you’re fixing a bug, improving documentation, or building a new feature, we appreciate your efforts to make this project better.
 

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

@@ -1,54 +1,59 @@
-absl-py==2.1.0
-astunparse==1.6.3
-certifi==2024.8.30
-charset-normalizer==3.4.0
-contourpy==1.3.0
-cycler==0.12.1
-flatbuffers==24.3.25
-fonttools==4.54.1
-gast==0.6.0
-google-pasta==0.2.0
-grpcio==1.66.2
-h5py==3.12.1
-idna==3.10
-joblib==1.4.2
-keras==3.6.0
-kiwisolver==1.4.7
-libclang==18.1.1
-Markdown==3.7
-markdown-it-py==3.0.0
-MarkupSafe==3.0.1
-matplotlib==3.9.2
-mdurl==0.1.2
-ml-dtypes==0.4.1
-namex==0.0.8
-numpy==1.26.4  
-opt_einsum==3.4.0
-optree==0.13.0
-packaging==24.1
-pandas==2.2.3  
-pillow==10.4.0
-protobuf==4.25.5
-Pygments==2.18.0
-pyparsing==3.1.4
-python-dateutil==2.9.0.post0
-pytz==2024.2
-requests==2.32.3
-rich==13.9.2
-scikit-learn==1.5.2  
-scipy==1.14.1
-seaborn==0.13.2
-setuptools==75.1.0
-six==1.16.0
-statsmodels==0.14.0  
-tensorboard==2.17.1
-tensorboard-data-server==0.7.2
-tensorflow==2.17.0
-termcolor==2.5.0
-threadpoolctl==3.5.0
-typing_extensions==4.12.2
-tzdata==2024.2
-urllib3==2.2.3
-Werkzeug==3.0.4
-wheel==0.44.0
-wrapt==1.16.0
+
+Flask==2.3.2
+statsmodels==0.14.0
+pickle-mixin==1.0.2
+xgboost
+absl-py==2.1.0
+astunparse==1.6.3
+certifi==2024.8.30
+charset-normalizer==3.4.0
+contourpy==1.3.0
+cycler==0.12.1
+flatbuffers==24.3.25
+fonttools==4.54.1
+gast==0.6.0
+google-pasta==0.2.0
+grpcio==1.66.2
+h5py==3.12.1
+idna==3.10
+joblib==1.4.2
+keras==3.6.0
+kiwisolver==1.4.7
+libclang==18.1.1
+Markdown==3.7
+markdown-it-py==3.0.0
+MarkupSafe==3.0.1
+matplotlib==3.9.2
+mdurl==0.1.2
+ml-dtypes==0.4.1
+namex==0.0.8
+numpy==1.26.4  
+opt_einsum==3.4.0
+optree==0.13.0
+packaging==24.1
+pandas==2.2.3  
+pillow==10.4.0
+protobuf==4.25.5
+Pygments==2.18.0
+pyparsing==3.1.4
+python-dateutil==2.9.0.post0
+pytz==2024.2
+requests==2.32.3
+rich==13.9.2
+scikit-learn==1.5.2  
+scipy==1.14.1
+seaborn==0.13.2
+setuptools==75.1.0
+six==1.16.0
+statsmodels==0.14.0  
+tensorboard==2.17.1
+tensorboard-data-server==0.7.2
+tensorflow==2.17.0
+termcolor==2.5.0
+threadpoolctl==3.5.0
+typing_extensions==4.12.2
+tzdata==2024.2
+urllib3==2.2.3
+Werkzeug==3.0.4
+wheel==0.44.0
+wrapt==1.16.0

static/css/home.css

@@ -0,0 +1,50 @@
+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

@@ -0,0 +1,59 @@
+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

@@ -0,0 +1,20 @@
+<!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>