Commit
•
9583856
1
Parent(s):
845e256
Update app.py
Browse files
app.py
CHANGED
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@@ -34,20 +34,19 @@ demo = gr.Interface(
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gr.Slider(minimum=350, maximum=3000, randomize=True, step = 1,label="Living Area ***(Sq.mtr)***"),
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gr.Slider(minimum=1, maximum=4, randomize=True,step = 1, label="Number of Bedrooms"),
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gr.Slider(minimum=1, maximum=3, randomize=True,step = 1, label="Number of Bathrooms"),
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gr.Slider(minimum=1,maximum=3,randomize=True,step=
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],
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"number",
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examples=[
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[1000,
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[2000,1200,2,3,1],
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[4000,1900,2,3,2],
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[28000,3000,5,3,3],
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],
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title="Indian House Price Prediction Model",
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description="""
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***Overview:***
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This model predicts house prices in India using Linear Regression. Trained on key features like lot area, living area, bedrooms, bathrooms, and floors, it estimates house prices based on historical data.
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***🚀Features:***
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@@ -58,19 +57,19 @@ This model predicts house prices in India using Linear Regression. Trained on ke
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- Floors
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***⚒How it works:***
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Linear Regression analyzes the relationship between input features and house prices, establishing a linear equation for predictions.
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***📝Usage:***
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1. Input specific values for lot area, living area, bedrooms, bathrooms, and floors.
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2. Get an estimated house price based on the learned linear relationship.
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***📳Application:***
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Useful for individuals in the Indian real estate market to gauge approximate house prices. Ideal for property buyers, sellers, and real estate professionals for informed decision-making.
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***📊Disclaimer:***
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This model provides estimations. Actual house prices may vary due to unconsidered factors. Consult real estate experts for precise valuations.
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***🤗
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- Mean Absolute Error (MAE): 0.11
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- The Output of the Model depends on the DataSet
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- For demo and learning purposes only.
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gr.Slider(minimum=350, maximum=3000, randomize=True, step = 1,label="Living Area ***(Sq.mtr)***"),
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gr.Slider(minimum=1, maximum=4, randomize=True,step = 1, label="Number of Bedrooms"),
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gr.Slider(minimum=1, maximum=3, randomize=True,step = 1, label="Number of Bathrooms"),
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gr.Slider(minimum=1,maximum=3,randomize=True,step=1,label="Number of stories/Floors")
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],
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"number",
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examples=[
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[1000, 400, 1, 1, 1],
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[2000,1200,2,3,1],
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[4000,1900,2,3,2],
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[28000,3000,5,3,3],
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],
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title="Indian House Price Prediction Model",
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description="""
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***Overview:***
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This model predicts house prices in India using Linear Regression. Trained on key features like lot area, living area, bedrooms, bathrooms, and floors, it estimates house prices based on historical data.
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***🚀Features:***
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- Floors
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***⚒How it works:***
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+
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Linear Regression analyzes the relationship between input features and house prices, establishing a linear equation for predictions.
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***📝Usage:***
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+
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1. Input specific values for lot area, living area, bedrooms, bathrooms, and floors.
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2. Get an estimated house price based on the learned linear relationship.
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***📊Disclaimer:***
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This model provides estimations. Actual house prices may vary due to unconsidered factors. Consult real estate experts for precise valuations.
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***🤗Model Info:***
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- Mean Absolute Error (MAE): 0.11
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- The Output of the Model depends on the DataSet
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- For demo and learning purposes only.
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