How sanket124w/stock-prediction works
This is a small Flask stock-price prediction app. A user enters a closing price in the web form; the Flask route validates it, asks a startup-trained linear regression model for a next-close estimate, and renders the result with the latest available close. The model trains from the bundled CSV using each close as input and the following row’s close as target. The graph reflects the sampled app source; notebook contents and the document are not inspected, so their workflows are not inferred.
Main entry points: app.py Flask route · index.html Price form · long_stock_price_dataset.csv Stock dataset
Components 7
Web interface
- Price form
templates/index.html - Page styles
static/style.css
Prediction runtime
- Flask route
app.py - Linear model
app.py
Training data
- Stock dataset
long_stock_price_dataset.csv
Other components
- User
- Scikit-learn
Connections 8
- User → Price form: enters price
- Price form → Flask route: submits form
- Flask route → Stock dataset: reads at startup
- Flask route → Scikit-learn: fits model
- Flask route → Linear model: stores trained model
- Flask route → Linear model: requests prediction
- Linear model → Scikit-learn: predicts with
- Flask route → Price form: renders result