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{
"name": "28_Stock_Price_Prediction_LSTM_YahooFinance_ML",
"query": "Could you help me build a stock price prediction system using an LSTM model and the Yahoo Finance dataset? Please clean the data, including handling missing values and outliers, and use a time window to convert the time series data to a supervised learning problem. The LSTM model should be implemented in `src/model.py`, and the dataset loading, cleaning, and conversion should be implemented in `src/data_loader.py`. Save the prediction results to `results/predictions.txt` and generate and save interactive charts of the prediction results in `results/figures/prediction_interactive.html` using Plotly. Create a Jupyter Notebook with model architecture visualization, training process, and prediction results and save it as a PDF report at `results/report.pdf`.",
"tags": [
"Financial Analysis",
"Supervised Learning",
"Time Series Forecasting"
],
"requirements": [
{
"requirement_id": 0,
"prerequisites": [],
"criteria": "The \"LSTM\" model is implemented in `src/model.py`.",
"category": "Machine Learning Method",
"satisfied": null
},
{
"requirement_id": 1,
"prerequisites": [],
"criteria": "The \"Yahoo Finance\" dataset is loaded in `src/data_loader.py`.",
"category": "Dataset or Environment",
"satisfied": null
},
{
"requirement_id": 2,
"prerequisites": [
1
],
"criteria": "Data cleaning, including handling missing values and outliers, is performed in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 3,
"prerequisites": [
0,
2
],
"criteria": "A time window is used to convert the time series data to a supervised learning problem. Please save the implementation in `src/data_loader.py`.",
"category": "Data preprocessing and postprocessing",
"satisfied": null
},
{
"requirement_id": 4,
"prerequisites": [
2,
3
],
"criteria": "Prediction results are saved in `results/predictions.txt`.",
"category": "Other",
"satisfied": null
},
{
"requirement_id": 5,
"prerequisites": [
0,
1,
2
],
"criteria": "Interactive charts of prediction results are generated using \"Plotly\" and saved in `results/figures/prediction_interactive.html`.",
"category": "Visualization",
"satisfied": null
},
{
"requirement_id": 6,
"prerequisites": [
0,
1,
2,
3,
4
],
"criteria": "A Jupyter Notebook containing the model architecture visualization, training process, and prediction results are created and saved as PDF report as `results/report.pdf`.",
"category": "Other",
"satisfied": null
}
],
"preferences": [],
"is_kaggle_api_needed": false,
"is_training_needed": true,
"is_web_navigation_needed": false
} |