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.preprocessing import LabelEncoder
+from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
+from sklearn.linear_model import LogisticRegression, LinearRegression
+from sklearn.svm import SVC, SVR
+from sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor
+from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
+from sklearn.naive_bayes import GaussianNB
+from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, mean_squared_error, mean_absolute_error, r2_score
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from io import BytesIO
+
+# File uploader
+st.title("Model Training with Metrics and Correlation Heatmap")
+uploaded_file = st.file_uploader("Choose a CSV file", type=["csv"])
+
+if uploaded_file is not None:
+    df = pd.read_csv(uploaded_file)
+    
+    # Show the dataset
+    st.write("Dataset:")
+    st.dataframe(df)
+
+    # Convert categorical (str) data to numerical
+    st.write("Converting Categorical Columns to Numerical Values:")
+    label_encoder = LabelEncoder()
+    
+    for col in df.columns:
+        if df[col].dtype == 'object' or len(df[col].unique()) <= 10:
+            st.write(f"Encoding Column: **{col}**")
+            df[col] = label_encoder.fit_transform(df[col])
+    
+    # Display the dataset after conversion
+    st.write("Dataset After Conversion:")
+    st.dataframe(df)
+    
+    # Handle Null Values (Missing Data)
+    st.write("Handling Missing (Null) Values:")
+    fill_method = st.selectbox("Choose how to handle missing values", ["Drop rows", "Fill with mean/median"])
+    if fill_method == "Drop rows":
+        df = df.dropna()
+    elif fill_method == "Fill with mean/median":
+        for col in df.columns:
+            if df[col].dtype in ['float64', 'int64']:
+                df[col].fillna(df[col].mean(), inplace=True)
+            else:
+                df[col].fillna(df[col].mode()[0], inplace=True)
+    
+    # Handle Outliers using IQR method
+    st.write("Handling Outliers:")
+    def remove_outliers_iqr(dataframe):
+        Q1 = dataframe.quantile(0.25)
+        Q3 = dataframe.quantile(0.75)
+        IQR = Q3 - Q1
+        return dataframe[~((dataframe < (Q1 - 1.5 * IQR)) | (dataframe > (Q3 + 1.5 * IQR))).any(axis=1)]
+    
+    df = remove_outliers_iqr(df)
+    
+    # Cap Extreme Values
+    st.write("Handling Extreme Values (Capping):")
+    def cap_extreme_values(dataframe):
+        for col in dataframe.select_dtypes(include=[np.number]).columns:
+            lower_limit = dataframe[col].quantile(0.05)
+            upper_limit = dataframe[col].quantile(0.95)
+            dataframe[col] = np.clip(dataframe[col], lower_limit, upper_limit)
+        return dataframe
+    
+    df = cap_extreme_values(df)
+    
+    # Show cleaned dataset
+    st.write("Cleaned Dataset:")
+    st.dataframe(df)
+
+    # Add clean data download option
+    st.subheader("Download Cleaned Dataset")
+    st.download_button(
+        label="Download Cleaned Dataset (CSV)",
+        data=df.to_csv(index=False),
+        file_name="cleaned_dataset.csv",
+        mime="text/csv"
+    )
+
+    # Correlation Heatmap
+    st.subheader("Correlation Heatmap")
+    corr = df.corr()
+    plt.figure(figsize=(10, 8))
+    sns.heatmap(corr, annot=True, cmap="coolwarm", fmt=".2f", cbar=True)
+    st.pyplot(plt)
+    
+    # Save heatmap as PNG
+    buf = BytesIO()
+    plt.savefig(buf, format="png")
+    buf.seek(0)
+    st.download_button(
+        label="Download Correlation Heatmap as PNG",
+        data=buf,
+        file_name="correlation_heatmap.png",
+        mime="image/png"
+    )
+    
+    # Highlight highly correlated pairs
+    st.subheader("Highly Correlated Features")
+    high_corr = corr.abs().unstack().sort_values(ascending=False).drop_duplicates()
+    high_corr = high_corr[high_corr.index.get_level_values(0) != high_corr.index.get_level_values(1)]
+    st.write(high_corr_df)
+
+    target = st.selectbox("Select Target Variable", df.columns)
+    features = [col for col in df.columns if col != target]
+    X = df[features]
+    y = df[target]
+    
+    if y.dtype == 'object' or len(y.unique()) <= 10:  # Categorical target (classification)
+        st.subheader("Classification Model Training")
+        classifiers = {
+            'Logistic Regression': LogisticRegression(max_iter=5000, solver='saga', penalty='l1'),
+            'Decision Tree': DecisionTreeClassifier(),
+            'Random Forest': RandomForestClassifier(),
+            'Support Vector Machine (SVM)': SVC(),
+            'K-Nearest Neighbors (k-NN)': KNeighborsClassifier(),
+            'Naive Bayes': GaussianNB()
+        }
+
+        metrics = []
+        train_size = st.slider("Select Training Size", min_value=0.1, max_value=0.9, value=0.8)
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=42)
+
+        for name, classifier in classifiers.items():
+            classifier.fit(X_train, y_train)
+            y_pred = classifier.predict(X_test)
+            metrics.append({
+                'Model': name,
+                'Accuracy': round(accuracy_score(y_test, y_pred), 2),
+                'Precision': round(precision_score(y_test, y_pred, zero_division=1, average='macro'), 2),
+                'Recall': round(recall_score(y_test, y_pred, zero_division=1, average='macro'), 2),
+                'F1-Score': round(f1_score(y_test, y_pred, zero_division=1, average='macro'), 2)
+            })
+
+        metrics_df = pd.DataFrame(metrics)
+        st.subheader("Classification Model Performance Metrics")
+        st.dataframe(metrics_df)
+
+        # Save metrics as PNG (table form)
+        fig, ax = plt.subplots(figsize=(8, 4))
+        ax.axis('tight')
+        ax.axis('off')
+        table = plt.table(cellText=metrics_df.values, colLabels=metrics_df.columns, cellLoc='center', loc='center')
+        table.auto_set_font_size(False)
+        table.set_fontsize(10)
+        table.auto_set_column_width(col=list(range(len(metrics_df.columns))))
+        buf = BytesIO()
+        fig.savefig(buf, format="png")
+        buf.seek(0)
+        st.download_button(
+            label="Download Classification Metrics Table as PNG",
+            data=buf,
+            file_name="classification_metrics_table.png",
+            mime="image/png"
+        )
+        
+        # Visualization (Bar Graphs for Classification)
+        st.subheader("Classification Model Performance Metrics Graph")
+        metrics_df.set_index('Model', inplace=True)
+        metrics_df.plot(kind='bar', figsize=(10, 6), colormap='viridis', rot=45)
+        plt.title("Classification Models - Performance Metrics")
+        plt.ylabel("Scores")
+        plt.xlabel("Models")
+        st.pyplot(plt)
+
+    else:  # Continuous target (regression)
+        st.subheader("Regression Model Training")
+        regressors = {
+            'Linear Regression': LinearRegression(),
+            'Decision Tree Regressor': DecisionTreeRegressor(),
+            'Random Forest Regressor': RandomForestRegressor(),
+            'Support Vector Regressor (SVR)': SVR(),
+            'K-Nearest Neighbors Regressor (k-NN)': KNeighborsRegressor()
+        }
+
+        regression_metrics = []
+        train_size = st.slider("Select Training Size", min_value=0.1, max_value=0.9, value=0.8)
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=1-train_size, random_state=42)
+
+        for name, regressor in regressors.items():
+            regressor.fit(X_train, y_train)
+            y_pred = regressor.predict(X_test)
+            regression_metrics.append({
+                'Model': name,
+                'Mean Squared Error (MSE)': round(mean_squared_error(y_test, y_pred), 2),
+                'Mean Absolute Error (MAE)': round(mean_absolute_error(y_test, y_pred), 2),
+                'R² Score': round(r2_score(y_test, y_pred), 2)
+            })
+
+        regression_metrics_df = pd.DataFrame(regression_metrics)
+        st.subheader("Regression Model Performance Metrics")
+        st.dataframe(regression_metrics_df)
+
+        # Save metrics as PNG (table form)
+        fig, ax = plt.subplots(figsize=(8, 4))
+        ax.axis('tight')
+        ax.axis('off')
+        table = plt.table(cellText=regression_metrics_df.values, colLabels=regression_metrics_df.columns, cellLoc='center', loc='center')
+        table.auto_set_font_size(False)
+        table.set_fontsize(10)
+        table.auto_set_column_width(col=list(range(len(regression_metrics_df.columns))))
+        buf = BytesIO()
+        fig.savefig(buf, format="png")
+        buf.seek(0)
+        st.download_button(
+            label="Download Regression Metrics Table as PNG",
+            data=buf,
+            file_name="regression_metrics_table.png",
+            mime="image/png"
+        )
+        
+        # Visualization (Bar Graphs for Regression)
+        st.subheader("Regression Model Performance Metrics Graph")
+        regression_metrics_df.set_index('Model', inplace=True)
+        regression_metrics_df.plot(kind='bar', figsize=(10, 6), colormap='coolwarm', rot=45)
+        plt.title("Regression Models - Performance Metrics")
+        plt.ylabel("Scores")
+        plt.xlabel("Models")
+        st.pyplot(plt)