Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.metrics import accuracy_score
+from sklearn.ensemble import RandomForestClassifier
+from sklearn import datasets
+cancer =  datasets.load_breast_cancer()
+a=pd.DataFrame(cancer.data,columns = cancer.feature_names)
+b=pd.DataFrame(cancer.target,columns=['target'])
+x_train,x_test,y_train,y_test=train_test_split(a,b,test_size=0.2)
+model=RandomForestClassifier()
+model.fit(x_train,y_train)
+st.sidebar.header("User Input Features")
+user_input = st.sidebar.slider("Select a feature value", float(a.min().min()), float(a.max().max()), float(a.mean().mean()))
+
+# Display the user input features
+st.sidebar.write("Selected feature value:", user_input)
+
+
+# Model Accuracy
+y_pred = model.predict(x_test)
+accuracy = accuracy_score(y_test, y_pred)
+st.write("Model Accuracy:", accuracy)