Update app.py

app.py

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+import streamlit as st
+import pandas as pd
+import seaborn as sns
+import numpy as np
+import pickle
+import matplotlib.pyplot as plt
+from src.data_preparation import preprocess_data
+from src.clustering import perform_clustering, plot_clusters
+from src.feature_selection import select_features_pca, select_features_rfe, select_features_rf
+import os
+from sklearn.preprocessing import StandardScaler
+
+def load_data(dataset_choice):
+    if dataset_choice == "Insurance":
+        data = pd.read_sas('a2z_insurance.sas7bdat',encoding='latin1') 
+    elif dataset_choice == "Retail":
+        data = pd.read_csv('retaildata.csv', encoding='latin1') 
+    elif dataset_choice == "Banking":
+        data = pd.read_csv('bankingdata.csv', encoding='latin1')
+    return data
+    
+    return data
+# Function to summarize cluster characteristics
+def summarize_cluster_characteristics(clustered_data, labels, cluster_number):
+    cluster_data = clustered_data[labels == cluster_number]
+    summary = cluster_data.mean().to_dict()
+    return summary
+
+# Function to display Business Understanding section
+def display_business_understanding():
+    st.subheader("Business Objective")
+    st.write("""
+    ###### Customer segmentation is a fundamental task in marketing and customer relationship management. With the advancements in data analytics and machine learning, it is now possible to group customers into distinct segments with a high degree of precision, allowing businesses to tailor their marketing strategies and offerings to each segment's unique needs and preferences.
+
+    ###### Through this customer segmentation, businesses can achieve:
+    - **Personalization**: Tailoring marketing strategies to meet the unique needs of each segment.
+    - **Optimization**: Efficient allocation of marketing resources.
+    - **Insight**: Gaining a deeper understanding of the customer base.
+    - **Engagement**: Enhancing customer engagement and satisfaction.
+
+    ###### => Problem/Requirement: Utilize machine learning and data analysis techniques in Python to perform customer segmentation.
+    
+    """)
+    st.image("Customer-Segmentation.png", caption="Customer Segmentation", use_column_width=True)
+
+# Function to display Dataset section
+def display_dataset_selection():
+    dataset_choice = st.selectbox("Select Dataset", ("Insurance", "Retail", "Banking"))
+    data = load_data(dataset_choice)
+    st.write(f"Dataset: {dataset_choice}")
+    st.write("Number of rows:", data.shape[0])
+    st.write("Number of columns:", data.shape[1])
+    st.write("First five rows of the data:")
+    st.write(data.head())
+    return data
+# Function to display Modeling & Evaluation section
+def display_modeling_evaluation():
+    dataset_choice = st.selectbox("Select Dataset", ("Insurance", "Retail", "Banking"))
+    data = load_data(dataset_choice)
+    data = preprocess_data(data)
+
+    # Sidebar for feature selection and clustering method
+    st.sidebar.header("Feature Selection and Clustering Method")
+    feature_selection_method = st.sidebar.selectbox("Select feature selection method", ('PCA', 'RFE', 'Random Forest'))
+    n_clusters = st.sidebar.slider("Number of clusters", min_value=2, max_value=10, value=3)
+
+    if feature_selection_method == 'PCA':
+        n_components = st.sidebar.slider("Number of PCA components", min_value=2, max_value=10, value=5)
+    elif feature_selection_method in ['RFE', 'Random Forest']:
+        n_features_to_select = st.sidebar.slider("Number of features to select", min_value=2, max_value=10, value=5)
+    
+    # Perform clustering on button click
+    if st.sidebar.button("Cluster"):
+        if feature_selection_method == 'PCA':
+            selected_data, selected_features = select_features_pca(data, n_components)
+        elif feature_selection_method == 'RFE':
+            selected_data, selected_features = select_features_rfe(data, n_features_to_select)
+        elif feature_selection_method == 'Random Forest':
+            selected_data, selected_features = select_features_rf(data, n_features_to_select)
+
+        st.write(f"Selected Features: {selected_features}")
+        clustered_data, score, df_value_scaled, labels, model = perform_clustering(selected_data, n_clusters)
+        st.write(f"Number of Clusters: {n_clusters}")
+        st.write(f"Silhouette Score: {score}")
+        st.write("Clustered Data")
+        st.write(clustered_data)
+        st.write("Cluster Visualization")
+        plot_clusters(df_value_scaled, labels)
+
+        # Store selected features and model in session state
+        st.session_state.selected_features = selected_features
+        st.session_state.model = model
+        st.session_state.clustered_data = clustered_data
+        st.session_state.labels = labels
+        st.session_state.df_value_scaled = df_value_scaled
+
+    # Predict new data based on selected features
+    if 'selected_features' in st.session_state and 'model' in st.session_state:
+        st.write("### Predict Cluster")
+
+        # Use st.form to handle input fields
+        with st.form(key='prediction_form'):
+            user_input = {}
+            for feature in st.session_state.selected_features:
+                user_input[feature] = st.number_input(f'Enter {feature}', value=0.0)
+
+            submit_button = st.form_submit_button(label='Predict')
+
+            if submit_button:
+                user_df = pd.DataFrame(user_input, index=[0])
+
+                scaler = StandardScaler()
+                user_df_scaled = scaler.fit_transform(user_df)
+
+                cluster = st.session_state.model.predict(user_df_scaled)
+                st.write(f'The predicted cluster for the input data is: {cluster[0]}')
+
+                # Get the clustered data and labels from session state
+                clustered_data = st.session_state.clustered_data
+                labels = st.session_state.labels
+                df_value_scaled = st.session_state.df_value_scaled
+
+                # Summarize cluster characteristics
+                summary = summarize_cluster_characteristics(clustered_data, labels, cluster[0])
+
+                # Generate and display the inference
+                inference = f"Based on the input features, the customer belongs to Cluster {cluster[0]}, which is characterized by the following average values:\n"
+                for feature, value in summary.items():
+                    inference += f"- {feature}: {value:.2f}\n"
+                st.write(inference)
+
+                plot_clusters(df_value_scaled, labels, new_data_point=user_df_scaled)
+
+
+# Main app structure
+def main():
+    st.title("Customer Segmentation Demo")
+    st.header("Customer Segmentation")
+
+    # Sidebar menu options
+    menu = ["Business Understanding", "Dataset", "Modeling & Prediction"]
+    choice = st.sidebar.selectbox('Menu', menu)
+
+    if choice == 'Business Understanding':
+        display_business_understanding()
+
+    elif choice == 'Dataset':
+        display_dataset_selection()
+
+    elif choice == 'Modeling & Prediction':
+        display_modeling_evaluation()
+
+if __name__ == "__main__":
+    main()