update
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@@ -4,6 +4,8 @@ from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from sklearn.preprocessing import LabelEncoder
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import pandas as pd
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import matplotlib.pyplot as plt
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st.header("Prediction: Classification")
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@@ -60,5 +62,26 @@ if "data" in st.session_state:
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prediction = label_encoders[target_name].inverse_transform(prediction)
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st.write("Prediction:", prediction[0])
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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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X = dataframe_sorted[data_name[0]]
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y = dataframe_sorted[target_name]
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prediction_array_y = [
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model.predict(pd.DataFrame([[dataframe_sorted[data_name[0]].iloc[i]]], columns=data_name))[0]
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for i in range(dataframe_sorted.shape[0])
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]
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plt.scatter(dataframe_sorted[data_name[0]], dataframe_sorted[target_name], color='b')
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plt.scatter(dataframe_sorted[data_name[0]], prediction_array_y, color='r')
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st.pyplot(fig)
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else:
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st.error("File not loaded")
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@@ -41,8 +41,8 @@ if "data" in st.session_state:
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]
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plt.scatter(dataframe_sorted[data_name[0]], dataframe_sorted[target_name], color='b')
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plt.scatter(dataframe_sorted[data_name[0]], prediction_array_y, color='r')
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else:
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plt.plot(dataframe_sorted[data_name[0]], prediction_array_y, color='r')
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elif len(data_name) == 2:
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ax = fig.add_subplot(111, projection='3d')
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prediction_array_y = [
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@@ -51,10 +51,9 @@ if "data" in st.session_state:
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]
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ax.scatter(dataframe_sorted[data_name[0]], dataframe_sorted[data_name[1]], dataframe_sorted[target_name], color='b')
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ax.scatter(dataframe_sorted[data_name[0]], dataframe_sorted[data_name[1]], prediction_array_y, color='r')
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ax.plot(dataframe_sorted[data_name[0]], dataframe_sorted[data_name[1]], prediction_array_y, color='r')
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st.pyplot(fig)
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else:
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st.error("File not loaded")
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