add confusion_matrix
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@@ -1,11 +1,11 @@
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import streamlit as st
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from sklearn.linear_model import LogisticRegression
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from sklearn.model_selection import train_test_split
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from sklearn.metrics import accuracy_score
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from sklearn.metrics import accuracy_score,confusion_matrix
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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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import seaborn as sns
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st.header("Prediction: Classification")
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@@ -63,24 +63,20 @@ if "data" in st.session_state:
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st.write("Prediction:", prediction[0])
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if len(data_name) == 1:
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fig = plt.figure()
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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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print([x[0] for x in X.values.tolist()])
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cm = confusion_matrix(y, y_pred)
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sns.heatmap(cm, annot=True, fmt="d")
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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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plt.xlabel('Predicted')
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plt.ylabel('True')
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X = dataframe_sorted[data_name[0]]
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y = dataframe_sorted[target_name]
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st.pyplot(fig)
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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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@@ -2,7 +2,6 @@ import streamlit as st
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from sklearn.linear_model import LinearRegression
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import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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st.header("Prediction: Regression")
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@@ -31,6 +30,7 @@ if "data" in st.session_state:
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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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if len(data_name) == 1:
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X = dataframe_sorted[data_name[0]]
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y = dataframe_sorted[target_name]
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