diff --git a/Contributing.md b/Contributing.md index c245b91..2697890 100644 --- a/Contributing.md +++ b/Contributing.md @@ -87,6 +87,39 @@ If you're introducing new algorithms or modifying existing ones, please update t ## πŸ§‘β€πŸ€β€πŸ§‘ 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. +## πŸ”„ 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') + + ## πŸŽ‰ 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. diff --git a/Stock_Price_Prediction(Updated).ipynb b/Stock_Price_Prediction(Updated).ipynb index c108568..540eace 100644 --- a/Stock_Price_Prediction(Updated).ipynb +++ b/Stock_Price_Prediction(Updated).ipynb @@ -38,15 +38,8 @@ "outputs": [ { "data": { - "application/vnd.google.colaboratory.intrinsic+json": { - "summary": "{\n \"name\": \"df\",\n \"rows\": 7074,\n \"fields\": [\n {\n \"column\": \"Date\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 7074,\n \"samples\": [\n \"11-08-2016\",\n \"30-10-2007\",\n \"17-01-2017\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Open\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 154.7732294451065,\n \"min\": 13.478195,\n \"max\": 703.650024,\n \"num_unique_values\": 4758,\n \"samples\": [\n 174.399994,\n 31.0324,\n 187.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"High\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 156.34507839355788,\n \"min\": 13.935802,\n \"max\": 728.349976,\n \"num_unique_values\": 5403,\n \"samples\": [\n 473.0,\n 495.450012,\n 78.321663\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Low\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 152.98051601861624,\n \"min\": 13.214009,\n \"max\": 694.200012,\n \"num_unique_values\": 5488,\n \"samples\": [\n 60.2957,\n 22.677523,\n 16.983376\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 154.63054905628158,\n \"min\": 13.346102,\n \"max\": 725.25,\n \"num_unique_values\": 5975,\n \"samples\": [\n 633.599976,\n 241.100006,\n 107.834999\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Adj Close\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 152.90324918554683,\n \"min\": 9.53141,\n \"max\": 725.25,\n \"num_unique_values\": 6575,\n \"samples\": [\n 12.345289,\n 223.836212,\n 16.758821\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Volume\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 34627439.399630256,\n \"min\": 0.0,\n \"max\": 446948261.0,\n \"num_unique_values\": 6948,\n \"samples\": [\n 29959130.0,\n 1648453.0,\n 14077470.0\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", - "type": "dataframe", - "variable_name": "df" - }, "text/html": [ - "\n", - "
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When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + "c:\\Users\\balbi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:87: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n" ] } @@ -4933,7 +4454,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 40, @@ -4961,10 +4482,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 2ms/step\n", - "RMSE: 2.767324426981657\n", - "MAE: 1.7200117039677931\n", - "MAPE: 0.010909365212209457\n", + "\u001b[1m45/45\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 3ms/step\n", + "RMSE: 2.8762951713017757\n", + "MAE: 1.8845151984048805\n", + "MAPE: 0.015311824533529984\n", "\n" ] } @@ -4988,7 +4509,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 42, "metadata": { "id": "keiZDN4w7UH0" }, @@ -5006,7 +4527,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 43, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -5019,7 +4540,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python3.10/dist-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", + "c:\\Users\\balbi\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\keras\\src\\layers\\rnn\\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n", " super().__init__(**kwargs)\n" ] } @@ -5032,7 +4553,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 44, "metadata": { "id": "3UJtO3wC7WWe" }, @@ -5044,7 +4565,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" @@ -5052,18 +4573,7 @@ "id": "ld9dofMD7YNO", "outputId": "2b3021f3-d88f-431c-a059-a9b6e065a29b" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "model10.fit(X_train_reshaped, y_train[n_steps-1:], epochs=100, batch_size=32, verbose=0)" ] @@ -5215,6 +4725,97 @@ "plt.tight_layout()\n", "plt.show()\n" ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "import pickle\n", + "with open('prediction.pkl','wb') as file:\n", + " pickle.dump(model5, file) " + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "New CSV file created with the desired columns!\n" + ] + } + ], + "source": [ + "file_path = \"SBIN.csv\" \n", + "df = pd.read_csv(file_path)\n", + "\n", + "df = df.drop(columns=['Date', 'Adj Close'])\n", + "\n", + "new_file_path = \"Updated_SBIN.csv\"\n", + "df.to_csv(new_file_path, index=False)\n", + "\n", + "print(\"New CSV file created with the desired columns!\")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "file_path = \"Updated_SBIN.csv\" # Replace with your actual CSV file path\n", + "df = pd.read_csv(file_path)\n", + "\n", + "# Rearrange the columns so that 'Volume' comes before 'Close'\n", + "df = df[['Open', 'High', 'Low', 'Volume', 'Close']]\n", + "\n", + "# Save the updated dataframe to a new CSV file\n", + "new_file_path = \"Updated_SBIN.csv\" # Replace with your desired output CSV file path\n", + "df.to_csv(new_file_path, index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original dataset shape: (7074, 5)\n", + "Cleaned dataset shape: (7065, 5)\n", + "Cleaned dataset saved to Updated_SBIN.csv\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "# Load the original dataset\n", + "data_file = \"Updated_SBIN.csv\" # Path to your original CSV file\n", + "df = pd.read_csv(data_file)\n", + "\n", + "# Display the original dataset shape\n", + "print(\"Original dataset shape:\", df.shape)\n", + "\n", + "# Remove rows with any null values\n", + "df_cleaned = df.dropna()\n", + "\n", + "# Display the cleaned dataset shape\n", + "print(\"Cleaned dataset shape:\", df_cleaned.shape)\n", + "\n", + "# Save the cleaned dataset to a new CSV file\n", + "cleaned_data_file = \"Updated_SBIN.csv\" # Path for the cleaned CSV file\n", + "df_cleaned.to_csv(cleaned_data_file, index=False)\n", + "\n", + "print(f\"Cleaned dataset saved to {cleaned_data_file}\")\n" + ] } ], "metadata": { @@ -5226,7 +4827,16 @@ "name": "python3" }, "language_info": { - "name": "python" + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.6" } }, "nbformat": 4, diff --git a/Updated_SBIN.csv b/Updated_SBIN.csv new file mode 100644 index 0000000..82042ad --- /dev/null +++ b/Updated_SBIN.csv @@ -0,0 +1,7066 @@ +Open,High,Low,Volume,Close +18.691147,18.978922,18.540184,43733533.0,18.82324 +18.894005,18.964767,17.738192,56167280.0,18.224106 +18.327892,18.568489,17.643839,68296318.0,17.738192 +17.502312,17.832542,17.223972,86073880.0,17.676863 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+++ b/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) diff --git a/prediction.pkl b/prediction.pkl new file mode 100644 index 0000000..43f2a00 Binary files /dev/null and b/prediction.pkl differ diff --git a/requirements.txt b/requirements.txt index 0edd2aa..d0176d5 100644 --- a/requirements.txt +++ b/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 diff --git a/static/css/home.css b/static/css/home.css new file mode 100644 index 0000000..d8f0a59 --- /dev/null +++ b/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 */ +} diff --git a/static/css/stock.css b/static/css/stock.css new file mode 100644 index 0000000..7d22f7a --- /dev/null +++ b/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 */ +} diff --git a/templates/home.html b/templates/home.html new file mode 100644 index 0000000..858ccb0 --- /dev/null +++ b/templates/home.html @@ -0,0 +1,20 @@ + + + + + + Stock Price Predictor + + + +
+

Welcome to Our Stock Price Predictor

+

Use this tool to predict stock prices based on historical data.

+ +
+ + diff --git a/templates/stock.html b/templates/stock.html new file mode 100644 index 0000000..0a0acb3 --- /dev/null +++ b/templates/stock.html @@ -0,0 +1,47 @@ + + + + + + + Stock Price Prediction + + +
+

Stock Price Prediction

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