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california-housing-linear-simple
donaldsebleung/california-housing-linear-simple
california-housing-linear-simple is a machine learning model from donaldsebleung. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for mindspore. The card lists the license as apache-2.0.
A simple linear regression model to predict house prices in California
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Updated Apr 7, 2026
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At a glance
- Library
- mindspore
- License
- apache-2.0
- Access
- Public
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026
- SHA
- 747e6e30
Model card
From the Hugging Face model README
Predicting California house prices with MindSpore
A simple linear regression model to predict house prices in California
Overview
A simple linear regression model to predict house prices in California.
This model was trained on the California Housing Dataset using MindSpore 2.8.0 and exported in MindIR format as california-housing-linear-simple.mindir.
Dependencies
- MindSpore 2.8.0
- scikit-learn 1.8.0
The dependencies are specified in the included requirements.txt.
pip install -r requirements.txt
Usage
Load the model weights and initialize the mindspore.nn.GraphCell.
import mindspore
import mindspore.nn as nn
graph = mindspore.load('california-housing-linear-simple.mindir')
model = nn.GraphCell(graph)
Model inputs
A mindspore.Tensor of shape (n, 8) where n is the number of input samples.
Each input feature has dtype=mindspore.dtype.float16.
Model outputs
A mindspore.Tensor of shape (n, 1) where n is the number of output predictions.
Each output prediction has dtype=mindspore.dtype.float16.
End-to-end example
See the included notebook 01-load-infer.ipynb for details.
- Library
- mindspore
- License
- apache-2.0
- Languages
- en
- Created
- Apr 7, 2026
- Updated
- Apr 7, 2026