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app.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.impute import SimpleImputer
+from imblearn.over_sampling import SMOTE
+from imblearn.under_sampling import RandomUnderSampler
+import numpy as np
+from sklearn.pipeline import Pipeline
+from imblearn.pipeline import make_pipeline
+import imblearn
+import openpyxl
+
+st.title('Huayra app')
+
+# 读取 Excel 文件
+df = pd.read_csv(r'D:/vscodeim/ML0517/CBT.csv')
+df1 = df.drop('UserID',axis=1)
+
+st.write("# <center><span style='font-size:24px'>Here is the DataFrame after cleaning</span></center>", unsafe_allow_html=True)
+st.dataframe(df1)
+
+st.image('D:/vscodeim/ML0517/xgboost log error.png')
+st.image('D:/vscodeim/ML0517/classfication auc.png')
+
+st.write("Higher AUC values indicate that the model performs better in distinguishing between positive and negative samples.")
+st.write("We get an AUC of **1** for the training set and **0.9985630707839712** for the test set. This indicates that the model performs very well.")
+st.image('D:/vscodeim/ML0517/precision recall curve.png')
+st.image('D:/vscodeim/ML0517/receiver operating character example.png')
+
+st.write("The P-R Curve (Precision-Recall Curve) is a curve used to evaluate the performance of a binary classification model, which shows the precision of the model under different recall rates.") 
+
+st.write("Ideally, the P-R curve should be as close to the upper right as possible, which means that the model maintains a high precision at high recall.")
+st.image('D:/vscodeim/ML0517/predicted values.png')