Update app.py

app.py

@@ -393,6 +393,100 @@ df.drop(columns={col_to_delete}, inplace=True)
         col1, col2, col3 = st.columns([1, 0.7, 1])
         if col2.button("Show DataFrame", use_container_width=True):
             st.dataframe(df, use_container_width=True)
+
+        #start point
+
+        # Histograms for Numerical Features
+        hist = st.checkbox("Show Histograms", value=False)
+        new_line()
+        if hist:
+            numeric_cols = df.select_dtypes(include=np.number).columns.tolist()
+            col_for_hist = st.selectbox("Select Column for Histogram", options=numeric_cols)
+            num_bins = st.slider("Select Number of Bins", min_value=10, max_value=100, value=30)
+            fig, ax = plt.subplots()
+            df[col_for_hist].hist(bins=num_bins, ax=ax, color='skyblue')
+            ax.set_title(f'Histogram of {col_for_hist}')
+            st.pyplot(fig)
+            new_line()
+        
+        # Box Plots for Numerical Features
+        boxplot = st.checkbox("Show Box Plots", value=False)
+        new_line()
+        if boxplot:
+            numeric_cols = df.select_dtypes(include=np.number).columns.tolist()
+            col_for_box = st.selectbox("Select Column for Box Plot", options=numeric_cols)
+            fig, ax = plt.subplots()
+            df.boxplot(column=[col_for_box], ax=ax)
+            ax.set_title(f'Box Plot of {col_for_box}')
+            st.pyplot(fig)
+            new_line()
+        
+        # Scatter Plots for Numerical Features
+        scatter = st.checkbox("Show Scatter Plots", value=False)
+        new_line()
+        if scatter:
+            numeric_cols = df.select_dtypes(include=np.number).columns.tolist()
+            x_col = st.selectbox("Select X-axis Column", options=numeric_cols, index=0)
+            y_col = st.selectbox("Select Y-axis Column", options=numeric_cols, index=1 if len(numeric_cols) > 1 else 0)
+            fig, ax = plt.subplots()
+            df.plot(kind='scatter', x=x_col, y=y_col, ax=ax, color='red')
+            ax.set_title(f'Scatter Plot between {x_col} and {y_col}')
+            st.pyplot(fig)
+            new_line()
+        
+        # Pair Plots for Numerical Features
+        pairplot = st.checkbox("Show Pair Plots", value=False)
+        new_line()
+        if pairplot:
+            sns.pairplot(df.select_dtypes(include=np.number))
+            st.pyplot()
+        
+        # Count Plots for Categorical Data
+        countplot = st.checkbox("Show Count Plots", value=False)
+        new_line()
+        if countplot:
+            categorical_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
+            col_for_count = st.selectbox("Select Column for Count Plot", options=categorical_cols)
+            fig, ax = plt.subplots()
+            sns.countplot(x=df[col_for_count], data=df, ax=ax)
+            ax.set_title(f'Count Plot of {col_for_count}')
+            st.pyplot(fig)
+            new_line()
+        
+        # Pie Charts for Categorical Data
+        pie_chart = st.checkbox("Show Pie Charts", value=False)
+        new_line()
+        if pie_chart:
+            categorical_cols = df.select_dtypes(include=['object', 'category']).columns.tolist()
+            col_for_pie = st.selectbox("Select Column for Pie Chart", options=categorical_cols)
+            pie_data = df[col_for_pie].value_counts()
+            fig, ax = plt.subplots()
+            ax.pie(pie_data, labels=pie_data.index, autopct='%1.1f%%', startangle=90)
+            ax.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.
+            ax.set_title(f'Pie Chart of {col_for_pie}')
+            st.pyplot(fig)
+            new_line()
+        
+        # Feature Importance (Only if a model has been trained)
+        if 'trained_model' in st.session_state and st.session_state.trained_model is not None:
+            feature_importance = st.checkbox("Show Feature Importance", value=False)
+            new_line()
+            if feature_importance:
+                model = st.session_state.trained_model
+                importances = pd.Series(model.feature_importances_, index=X_train.columns)
+                fig, ax = plt.subplots()
+                importances.sort_values().plot(kind='barh', ax=ax)
+                ax.set_title('Feature Importance')
+                st.pyplot(fig)
+                new_line()
+        
+        # Interactive Data Tables
+        interactive_table = st.checkbox("Show Interactive Data Table", value=False)
+        new_line()
+        if interactive_table:
+            st.dataframe(df)
+            new_line()
+
         
 
     # Missing Values