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b6bdba6
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Parent(s):
fc8ccc7
Update app.py
Browse files
app.py
CHANGED
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@@ -202,11 +202,10 @@ def main():
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"random_forest.csv",
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"text/csv",
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key='download_csv')
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-
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st.text("Model report : \n " + classification_report(y, pred))
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# Accuracy score
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if data_encoded.shape[1] == 15:
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rf_score = accuracy_score(pred,y)
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st.write(":green[score d'exactitude]")
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st.write(f"{round(rf_score*100,2)}% d'exactitude")
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@@ -231,9 +230,10 @@ def main():
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"discriminant.csv",
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"text/csv",
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key='download_csv')
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st.text("Model report : \n " + classification_report(y, pred))
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if data_encoded.shape[1] == 15:
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# Accuracy score
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lda_score = accuracy_score(pred,y)
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st.subheader(":green[score d'exactitude]")
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@@ -269,8 +269,9 @@ def main():
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"xgboost.csv",
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"text/csv",
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key='download_csv')
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st.text("Model report : \n " + classification_report(y, pred))
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if data_encoded.shape[1] == 15:
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# Accuracy score
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xg_score = accuracy_score(pred,y)
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st.subheader(":green[score d'exactitude]")
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@@ -296,8 +297,9 @@ def main():
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"neural_network.csv",
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"text/csv",
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key='download_csv')
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st.text("Model report : \n " + classification_report(y, pred))
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if data_encoded.shape[1] == 15:
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# Accuracy score
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ann_score = accuracy_score(pred,y)
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st.subheader(":green[score d'exactitude]")
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"random_forest.csv",
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"text/csv",
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key='download_csv')
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# Accuracy score
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if data_encoded.shape[1] == 15:
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st.text("Model report : \n " + classification_report(y, pred))
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rf_score = accuracy_score(pred,y)
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st.write(":green[score d'exactitude]")
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st.write(f"{round(rf_score*100,2)}% d'exactitude")
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"discriminant.csv",
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"text/csv",
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key='download_csv')
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#st.text("Model report : \n " + classification_report(y, pred))
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if data_encoded.shape[1] == 15:
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st.text("Model report : \n " + classification_report(y, pred))
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# Accuracy score
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lda_score = accuracy_score(pred,y)
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st.subheader(":green[score d'exactitude]")
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"xgboost.csv",
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"text/csv",
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key='download_csv')
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#st.text("Model report : \n " + classification_report(y, pred))
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if data_encoded.shape[1] == 15:
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st.text("Model report : \n " + classification_report(y, pred))
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# Accuracy score
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xg_score = accuracy_score(pred,y)
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st.subheader(":green[score d'exactitude]")
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"neural_network.csv",
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"text/csv",
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key='download_csv')
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#st.text("Model report : \n " + classification_report(y, pred))
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if data_encoded.shape[1] == 15:
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+
st.text("Model report : \n " + classification_report(y, pred))
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# Accuracy score
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ann_score = accuracy_score(pred,y)
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st.subheader(":green[score d'exactitude]")
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