matsammut commited on
Commit
b832f73
·
verified ·
1 Parent(s): eba369c

Update app.py

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Files changed (1) hide show
  1. app.py +11 -1
app.py CHANGED
@@ -17,7 +17,7 @@ def predict(age, workclass, education, marital_status, occupation, relationship,
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  "relationship", "race", "gender", "capital_gain", "capital_loss",
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  "hours_per_week", "native_country"]
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  df = pd.DataFrame(index=features, columns=columns)
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- fixed_features = cleaning_features(features)
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  # prediction = model.predict(features)
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  # prediction = 1
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  # return "Income >50K" if prediction == 1 else "Income <=50K"
@@ -63,6 +63,16 @@ def pca(data):
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  data = pd.concat([data, pca_df], axis=1)
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  return data
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  # Create the Gradio interface
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  interface = gr.Interface(
 
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  "relationship", "race", "gender", "capital_gain", "capital_loss",
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  "hours_per_week", "native_country"]
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  df = pd.DataFrame(index=features, columns=columns)
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+ fixed_features = cleaning_features(df)
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  # prediction = model.predict(features)
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  # prediction = 1
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  # return "Income >50K" if prediction == 1 else "Income <=50K"
 
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  data = pd.concat([data, pca_df], axis=1)
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  return data
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+ def hbdscan_tranform(df_transformed):
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+ df_transformed['capital-gain'] = np.log1p(df_transformed['capital-gain'])
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+ df_transformed['capital-loss'] = np.log1p(df_transformed['capital-loss'])
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+
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+ # Apply RobustScaler to all numerical features
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+ numerical_features = ['age', 'capital-gain', 'capital-loss', 'hours-per-week']
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+ scaler = RobustScaler()
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+ df_transformed[numerical_features] = scaler.fit_transform(df_transformed[numerical_features])
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+ return df_transformed
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+
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  # Create the Gradio interface
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  interface = gr.Interface(