Create app.py

app.py

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+# streamlit_app.py
+
+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import VotingClassifier, StackingClassifier
+from sklearn.linear_model import LogisticRegression
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
+from sklearn.svm import SVC
+from sklearn.metrics import accuracy_score, confusion_matrix, roc_curve, auc, classification_report
+
+# Title and description
+st.title("Classification Model Comparison: Stacking and Voting Classifiers")
+st.write("""
+### Predict target goals using different ensemble techniques
+This application compares the performance of Stacking and Voting classifiers on the provided dataset.
+""")
+
+# File upload
+uploaded_file = st.file_uploader("Upload your CSV file", type=["csv"])
+
+if uploaded_file is not None:
+    df = pd.read_csv(uploaded_file)
+
+    st.write("### Raw Data")
+    st.write(df)
+
+    # Correlation Matrix
+    corrMatrix = df.corr()
+
+    # Plot heatmap
+    st.write("### Correlation Heatmap")
+    plt.figure(figsize=(25, 10))
+    color_palette = sns.color_palette("viridis", as_cmap=True)
+    ax = sns.heatmap(corrMatrix, vmin=-1, vmax=1, center=0, cmap=color_palette, annot=True, fmt=".2f", linewidths=0.5, square=True, cbar_kws={"shrink": 0.75})
+    plt.title('Correlation Heatmap', fontsize=20, pad=20)
+    plt.xticks(rotation=45, ha='right')
+    plt.yticks(rotation=0)
+    st.pyplot(plt)
+
+    # Replace target variable
+    df['Target_goal'] = df['Target_goal'].replace({1: 0, 2: 1})
+
+    # Define features and target variable
+    X = df.drop(columns=['Target_goal'])
+    y = df['Target_goal']
+
+    # Split the data
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+
+    # Standardize the data
+    scaler = StandardScaler()
+    X_train = scaler.fit_transform(X_train)
+    X_test = scaler.transform(X_test)
+
+    # Define base models for stacking and voting
+    estimators = [
+        ('lr', LogisticRegression()),
+        ('dt', DecisionTreeClassifier()),
+        ('rf', RandomForestClassifier()),
+        ('gb', GradientBoostingClassifier()),
+        ('svc', SVC(probability=True))
+    ]
+
+    # Stacking Classifier
+    stacking_clf = StackingClassifier(estimators=estimators, final_estimator=LogisticRegression())
+    stacking_clf.fit(X_train, y_train)
+    y_pred_stack = stacking_clf.predict(X_test)
+    y_pred_stack_proba = stacking_clf.predict_proba(X_test)[:, 1]
+
+    # Voting Classifier
+    voting_clf = VotingClassifier(estimators=estimators, voting='soft')
+    voting_clf.fit(X_train, y_train)
+    y_pred_vote = voting_clf.predict(X_test)
+    y_pred_vote_proba = voting_clf.predict_proba(X_test)[:, 1]
+
+    # Evaluation
+    st.write("### Accuracy Scores")
+    accuracy_stack = accuracy_score(y_test, y_pred_stack)
+    accuracy_vote = accuracy_score(y_test, y_pred_vote)
+    st.write(f'Stacking Classifier Accuracy: {accuracy_stack:.2f}')
+    st.write(f'Voting Classifier Accuracy: {accuracy_vote:.2f}')
+
+    # Classification Reports
+    st.write("### Classification Reports")
+    st.write("#### Stacking Classifier")
+    st.text(classification_report(y_test, y_pred_stack))
+
+    st.write("#### Voting Classifier")
+    st.text(classification_report(y_test, y_pred_vote))
+
+    # Confusion Matrix
+    st.write("### Confusion Matrix for Stacking Classifier")
+    conf_matrix_stack = confusion_matrix(y_test, y_pred_stack)
+    plt.figure(figsize=(6, 5))
+    sns.heatmap(conf_matrix_stack, annot=True, fmt='d', cmap='Blues')
+    plt.title('Stacking Classifier Confusion Matrix')
+    plt.xlabel('Predicted')
+    plt.ylabel('Actual')
+    st.pyplot(plt)
+
+    st.write("### Confusion Matrix for Voting Classifier")
+    conf_matrix_vote = confusion_matrix(y_test, y_pred_vote)
+    plt.figure(figsize=(6, 5))
+    sns.heatmap(conf_matrix_vote, annot=True, fmt='d', cmap='Blues')
+    plt.title('Voting Classifier Confusion Matrix')
+    plt.xlabel('Predicted')
+    plt.ylabel('Actual')
+    st.pyplot(plt)
+
+    # ROC Curve and AUC
+    fpr_stack, tpr_stack, _ = roc_curve(y_test, y_pred_stack_proba)
+    roc_auc_stack = auc(fpr_stack, tpr_stack)
+
+    fpr_vote, tpr_vote, _ = roc_curve(y_test, y_pred_vote_proba)
+    roc_auc_vote = auc(fpr_vote, tpr_vote)
+
+    plt.figure(figsize=(10, 6))
+    plt.plot(fpr_stack, tpr_stack, color='blue', lw=2, label='Stacking Classifier (AUC = %0.2f)' % roc_auc_stack)
+    plt.plot(fpr_vote, tpr_vote, color='red', lw=2, label='Voting Classifier (AUC = %0.2f)' % roc_auc_vote)
+    plt.plot([0, 1], [0, 1], color='gray', lw=1, linestyle='--')
+    plt.xlim([0.0, 1.0])
+    plt.ylim([0.0, 1.05])
+    plt.xlabel('False Positive Rate')
+    plt.ylabel('True Positive Rate')
+    plt.title('ROC Curve')
+    plt.legend(loc="lower right")
+    st.pyplot(plt)