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---
title: Sale Predictions
emoji: 🏢
colorFrom: red
colorTo: blue
sdk: streamlit
sdk_version: 1.21.0
app_file: app.py
pinned: false
license: mit
---
## Background:
Whether we wish to predict the trend in financial markets or electricity consumption, time is an important factor that must now be considered in our models. For example, it would be interesting to forecast at what hour during the day is there going to be a peak consumption in electricity, such as to adjust the price or the production of electricity.
## The Process
The procedure begins with exporting the essential items from the notebook, followed by correctly designing an interface, importing the necessary objects for modeling, and then writing the code to process inputs. The procedure can be summarized as follows:
- Import machine learning components into the app script.
- Create an interface,
- Create a function to handle inputs.
- Values are passed through the interface.
- Restore these values in the backend,
- Apply the required processing,
- To produce predictions, submit the processed values to the ML model.
- Process the acquired predictions and present them on the interface.
Created by: Felix Kiprotich
https://www.linkedin.com/in/felix-kiprotich-a2ba1a1a4/
Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
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