Update projects/conda.md
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Great! Let's update the project to use conda for creating the Python environment and pip for package management. Here's the updated project structure and instructions:
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Project Structure:
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```
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stock_prediction/
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├── data/
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│ └── stock_data.db
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├── scripts/
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│ ├── fetch_stock_data.py
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│ └── verify_data.py
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├── app/
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│ └── stock_prediction_app.py
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├── environment.yml
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└── requirements.txt
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```
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1. `stock_prediction/data/stock_data.db`:
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- This is the SQLite database file that will store the historical stock data.
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2. `stock_prediction/scripts/fetch_stock_data.py`:
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- The content of this file remains the same as in the previous response.
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3. `stock_prediction/scripts/verify_data.py`:
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- The content of this file remains the same as in the previous response.
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4. `stock_prediction/app/stock_prediction_app.py`:
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- The content of this file remains the same as in the previous response.
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5. `stock_prediction/environment.yml`:
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```yaml
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name: stock_prediction
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channels:
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- defaults
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dependencies:
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- python=3.9
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- pip
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- pip:
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- -r requirements.txt
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```
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6. `stock_prediction/requirements.txt`:
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```
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dash
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pandas
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numpy
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yfinance
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scikit-learn
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tensorflow
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keras
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plotly
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```
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To run the project:
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1. Create a conda environment using the `environment.yml` file:
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```
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conda env create -f environment.yml
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```
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2. Activate the conda environment:
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```
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conda activate stock_prediction
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```
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3. Fetch historical stock data:
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```
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python scripts/fetch_stock_data.py --symbol ^GSPC --start_date 2000-01-01 --end_date 2023-05-31
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```
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You can change the stock symbol and date range as needed.
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4. Verify the stored data (optional):
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```
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python scripts/verify_data.py
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```
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5. Run the Dash application:
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```
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python app/stock_prediction_app.py
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```
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6. Open a web browser and go to `http://localhost:8050` to access the application.
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7. Select a stock symbol from the dropdown menu to see the actual and predicted stock prices on the graph.
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The `environment.yml` file defines the conda environment named `stock_prediction` and specifies the Python version as 3.9. It also includes the `pip` package manager and instructs conda to install the packages listed in the `requirements.txt` file using pip.
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The `requirements.txt` file remains the same and lists the required Python packages for the project.
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By using conda for environment management and pip for package management, you can ensure a consistent and reproducible environment for your project.
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Remember to activate the conda environment before running the scripts or the Dash application.
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---
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Project Structure:
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```
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stock_prediction/
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├── data/
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│ └── stock_data.db
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├── scripts/
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│ ├── fetch_stock_data.py
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│ └── verify_data.py
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├── app/
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│ └── stock_prediction_app.py
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└── requirements.txt
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```
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1. `stock_prediction/data/stock_data.db`:
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- This is the SQLite database file that will store the historical stock data.
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2. `stock_prediction/scripts/fetch_stock_data.py`:
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```python
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import argparse
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import yfinance as yf
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import pandas as pd
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import sqlite3
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parser = argparse.ArgumentParser(description="Fetch historical stock data from Yahoo Finance.")
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parser.add_argument("--symbol", type=str, default="^GSPC", help="Stock symbol (default: ^GSPC)")
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parser.add_argument("--start_date", type=str, default="2000-01-01", help="Start date (default: 2000-01-01)")
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parser.add_argument("--end_date", type=str, default="2023-05-31", help="End date (default: 2023-05-31)")
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args = parser.parse_args()
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symbol = args.symbol
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start_date = args.start_date
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end_date = args.end_date
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conn = sqlite3.connect("../data/stock_data.db")
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data = yf.download(symbol, start=start_date, end=end_date)
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table_name = f"{symbol}_prices"
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data.to_sql(name=table_name, con=conn, if_exists="replace")
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print(f"Data for {symbol} stored in the database.")
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conn.close()
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```
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3. `stock_prediction/scripts/verify_data.py`:
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```python
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import sqlite3
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import pandas as pd
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conn = sqlite3.connect("../data/stock_data.db")
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tables = pd.read_sql_query("SELECT name FROM sqlite_master WHERE type='table'", conn)
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print("Tables in the database:")
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print(tables)
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table_name = "^GSPC_prices"
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data = pd.read_sql_query(f"SELECT * FROM {table_name}", conn)
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print(f"\nData from the {table_name} table:")
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print(data.head())
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conn.close()
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```
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4. `stock_prediction/app/stock_prediction_app.py`:
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```python
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import dash
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import dash_core_components as dcc
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import dash_html_components as html
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from dash.dependencies import Input, Output
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import pandas as pd
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import sqlite3
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import numpy as np
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from sklearn.preprocessing import MinMaxScaler
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import LSTM, Dense
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import plotly.graph_objects as go
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app = dash.Dash(__name__)
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app.layout = html.Div([
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html.H1("Stock Price Prediction"),
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html.Div([
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html.Label("Select Stock Symbol"),
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dcc.Dropdown(
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id="stock-dropdown",
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options=[{"label": "S&P 500", "value": "^GSPC"},
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{"label": "Dow Jones", "value": "^DJI"},
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{"label": "Nasdaq", "value": "^IXIC"}],
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value="^GSPC"
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)
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]),
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html.Div([
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dcc.Graph(id="stock-graph")
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])
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])
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@app.callback(Output("stock-graph", "figure"),
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[Input("stock-dropdown", "value")])
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def update_graph(stock_symbol):
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conn = sqlite3.connect("../data/stock_data.db")
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data = pd.read_sql_query(f"SELECT Date, Close FROM {stock_symbol}_prices", conn)
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conn.close()
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scaler = MinMaxScaler(feature_range=(0, 1))
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scaled_data = scaler.fit_transform(data["Close"].values.reshape(-1, 1))
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lookback = 60
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X, y = [], []
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for i in range(lookback, len(scaled_data)):
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X.append(scaled_data[i - lookback:i, 0])
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y.append(scaled_data[i, 0])
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X, y = np.array(X), np.array(y)
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X = np.reshape(X, (X.shape[0], X.shape[1], 1))
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model = Sequential()
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model.add(LSTM(units=50, return_sequences=True, input_shape=(X.shape[1], 1)))
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model.add(LSTM(units=50))
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model.add(Dense(1))
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model.compile(loss="mean_squared_error", optimizer="adam")
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model.fit(X, y, epochs=10, batch_size=32)
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last_data = scaled_data[-lookback:]
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X_test = []
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for i in range(lookback, len(last_data)):
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X_test.append(last_data[i - lookback:i, 0])
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X_test = np.array(X_test)
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X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
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predicted_prices = model.predict(X_test)
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predicted_prices = scaler.inverse_transform(predicted_prices)
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fig = go.Figure()
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fig.add_trace(go.Scatter(x=data["Date"], y=data["Close"], name="Actual Price"))
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fig.add_trace(go.Scatter(x=data["Date"][-len(predicted_prices):], y=predicted_prices.flatten(), name="Predicted Price"))
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fig.update_layout(title=f"{stock_symbol} Stock Price Prediction", xaxis_title="Date", yaxis_title="Price")
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return fig
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if __name__ == "__main__":
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app.run_server(debug=True)
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```
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5. `stock_prediction/requirements.txt`:
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```
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dash
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pandas
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numpy
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yfinance
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scikit-learn
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tensorflow
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keras
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plotly
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```
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Certainly! Let's combine Dash, deep learning, and price prediction capabilities into a working project. We'll create a web application using Dash that allows users to select a stock symbol, trains an LSTM model on the historical data, and displays the predicted stock prices.
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Here's a step-by-step guide to create the project:
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