building a website for stock price prediction. #125
Labels
enhancement
New feature or request
gssoc-ext
GSSoC'24 Extended Version
hacktoberfest
Hacktober Collaboration
hacktoberfest-accepted
Hacktoberfest 2024
level3
45 Points 🥉
Is this a unique feature?
Is your feature request related to a problem/unavailable functionality? Please describe.
I am looking to create a user-friendly interface for an existing stock price prediction model. Currently, the model is not easily accessible to users, making it difficult for them to leverage the predictions effectively.
Proposed Solution
I propose developing a Streamlit web app that showcases the existing stock price prediction model. The app will allow users to input relevant stock features, view predicted prices, and visualize historical stock data. This will enhance user experience and make the model more accessible. Streamlit's interactive widgets and layout options will be utilized to create an intuitive interface for users.
Screenshots
No response
Do you want to work on this issue?
Yes
If "yes" to above, please explain how you would technically implement this (issue will not be assigned if this is skipped)
Set Up Environment:
Install libraries such as Streamlit, pandas, NumPy, and any machine learning libraries used in the stock price prediction model.
Build the Streamlit App Interface:
Create a user-friendly interface with input fields for relevant stock features (e.g., age, volume, historical prices) and a submit button for predictions.
Implement Prediction Logic:
Upon submission, retrieve input data and pass it to the stock price prediction model to generate predictions.
Display the predicted stock price on the app interface.
Data Visualization:
Use Streamlit's plotting capabilities (e.g., st.line_chart) to visualize historical stock prices and predicted trends.
Deploy the App:
Deploy the app using Streamlit Sharing or another cloud service to make it accessible to users.
Provide clear instructions on usage and interpretation of predictions.
Testing and Feedback:
Test the app with various scenarios to ensure reliability and accuracy.
Gather user feedback for future improvements.
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