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@ -22,10 +22,6 @@ if "data" in st.session_state:
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model = LinearRegression()
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model = LinearRegression()
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model.fit(X, y)
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model.fit(X, y)
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y_pred = [model.predict(pd.DataFrame([pred_value[0]], columns=data_name)) for pred_value in X.values.tolist()]
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r2 = r2_score(y, y_pred)
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st.write('R-squared score:', r2)
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st.subheader("Enter values for prediction")
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st.subheader("Enter values for prediction")
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pred_values = [st.number_input(f"Value for {feature}", value=0.0) for feature in data_name]
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pred_values = [st.number_input(f"Value for {feature}", value=0.0) for feature in data_name]
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prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
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prediction = model.predict(pd.DataFrame([pred_values], columns=data_name))
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@ -35,8 +31,11 @@ if "data" in st.session_state:
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fig = plt.figure()
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fig = plt.figure()
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dataframe_sorted = pd.concat([X, y], axis=1).sort_values(by=data_name)
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dataframe_sorted = pd.concat([X, y], axis=1).sort_values(by=data_name)
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if len(data_name) == 1:
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if len(data_name) == 1:
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y_pred = [model.predict(pd.DataFrame([pred_value[0]], columns=data_name)) for pred_value in X.values.tolist()]
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r2 = r2_score(y, y_pred)
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st.write('R-squared score:', r2)
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X = dataframe_sorted[data_name[0]]
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X = dataframe_sorted[data_name[0]]
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y = dataframe_sorted[target_name]
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y = dataframe_sorted[target_name]
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