Upload 2 files

app_clustering.py

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+import streamlit as st
+from streamlit_option_menu import option_menu
+import pandas as pd
+from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
+from sklearn.preprocessing import StandardScaler
+from sklearn.metrics import silhouette_score, davies_bouldin_score, calinski_harabasz_score
+import plotly.express as px
+
+st.set_page_config(layout="wide")
+
+st.title("Student Behavior Clustering 📊✨")
+st.write("This app performs clustering on student behavior data to identify patterns and segments of students.")
+
+# Two option menus: App, About
+tabs = ["App", "About"]
+app_mode = option_menu(None, options=tabs, icons=["📊", "❓"], default_index=0, orientation="horizontal")
+
+# --- Sidebar for Settings and File Upload ---
+st.sidebar.header("Data and Clustering Settings")
+
+# File Upload
+uploaded_file = st.sidebar.file_uploader(
+    "Choose a CSV file or use default:", type=["csv"]
+)
+
+# Use a default dataset if no file is uploaded
+if uploaded_file is None:
+    df = pd.read_csv("clustering_data.csv")
+else:
+    df = pd.read_csv(uploaded_file)
+
+# --- Data Preprocessing (Example: Handling Missing Values) ---
+# Replace this with your specific data cleaning needs
+df.fillna(df.mean(), inplace=True)
+
+# --- Feature Engineering (Example) ---
+df['engagement_score'] = (
+        df['attendance_rate'] * 0.5 +
+        df['test_average'] * 0.5
+)
+
+# Select features for clustering
+features = df[['attendance_rate', 'test_average', 'engagement_score']]
+
+# Standard Scaling
+scaler = StandardScaler()
+scaled_features = scaler.fit_transform(features)
+
+# Sidebar for Algorithm Selection and Parameter Tuning
+st.sidebar.header("Clustering Settings")
+algorithm = st.sidebar.selectbox(
+    "Select Algorithm:",
+    ("KMeans", "DBSCAN", "Hierarchical")
+)
+
+# Default values for parameters
+n_clusters_kmeans = 3
+eps = 0.5
+min_samples = 5
+n_clusters_hierarchical = 3
+linkage = 'ward'
+
+# Parameter tuning section
+with st.sidebar.expander("Algorithm Parameters"):
+    if algorithm == "KMeans":
+        n_clusters_kmeans = st.slider(
+            "Number of Clusters (K)", 2, 10, 3,
+            help="Number of clusters to form for KMeans."
+        )
+    elif algorithm == "DBSCAN":
+        eps = st.slider(
+            "Epsilon (eps)", 0.1, 2.0, 0.5, 0.1,
+            help="Maximum distance between two samples for one to be considered as in the neighborhood of the other for DBSCAN."
+        )
+        min_samples = st.slider(
+            "Min Samples", 2, 10, 5,
+            help="The number of samples in a neighborhood for a point to be considered as a core point for DBSCAN."
+        )
+    else:  # Hierarchical
+        n_clusters_hierarchical = st.slider(
+            "Number of Clusters", 2, 10, 3,
+            help="Number of clusters to find for hierarchical clustering."
+        )
+        linkage = st.selectbox(
+            "Linkage", ['ward', 'complete', 'average', 'single'],
+            help="Which linkage criterion to use for hierarchical clustering."
+        )
+
+
+# Function to perform clustering
+def cluster_data(algo_name, **kwargs):
+    try:
+        if algo_name == "KMeans":
+            model = KMeans(n_clusters=kwargs.get('n_clusters', 3), random_state=42)
+        elif algo_name == "DBSCAN":
+            model = DBSCAN(eps=kwargs.get('eps', 0.5), min_samples=kwargs.get('min_samples', 5))
+        else:  # Hierarchical
+            model = AgglomerativeClustering(
+                n_clusters=kwargs.get('n_clusters', 3),
+                linkage=kwargs.get('linkage', 'ward')
+            )
+
+        clusters = model.fit_predict(scaled_features)
+        return clusters
+
+    except Exception as e:
+        st.error(f"An error occurred during clustering: {e}")
+        return None
+
+
+# Perform clustering
+clusters = cluster_data(
+    algorithm,
+    n_clusters=n_clusters_kmeans if algorithm == "KMeans" else n_clusters_hierarchical,
+    eps=eps if algorithm == "DBSCAN" else 0.5,
+    min_samples=min_samples if algorithm == "DBSCAN" else 5,
+    linkage=linkage if algorithm == "Hierarchical" else "ward",
+)
+
+# THE APP CONTENT
+if app_mode == "About":
+    st.write(
+        """
+        ## About
+        This app performs clustering on student behavior data to identify patterns and segments of students.
+
+        ### Data
+        The dataset contains student information such as attendance rate, test average, and engagement score.
+
+        ### Clustering Algorithms
+        - **KMeans:** Partitions data into K clusters based on feature similarity.
+        - **DBSCAN:** Density-based clustering to identify outliers and clusters of varying shapes.
+        - **Hierarchical:** Builds a tree of clusters to identify subgroups.
+
+        ### Evaluation Metrics
+        - **Silhouette Score:** Measures how similar an object is to its cluster compared to other clusters.
+        - **Davies-Bouldin Index:** Computes the average similarity between each cluster and its most similar one.
+        - **Calinski-Harabasz Index:** Ratio of the sum of between-clusters dispersion and within-cluster dispersion.
+
+        ### Cluster Profiling
+        - Parallel coordinates plot to visualize and compare clusters across multiple features.
+
+        ### Interpretation of Clusters
+        - Provides insights into each cluster based on the average values of features.
+        """
+    )
+    st.write(
+        """
+        ## How to Use
+        1. **Upload Data:** Upload your own CSV file or use the default dataset.
+        2. **Select Algorithm:** Choose between KMeans, DBSCAN, and Hierarchical clustering.
+        3. **Set Parameters:** Adjust the clustering parameters in the sidebar.
+        4. **Interpret Results:** Explore the clustered data, evaluation metrics, and cluster profiles.
+        """
+    )
+    st.write(
+        """
+        ## Contact
+        If you have any questions or feedback, feel free to connect with me on:
+        - [LinkedIn](https://www.linkedin.com/in/abdellatif-laghjaj)
+        - [GitHub](https://www.github.com/abdellatif-laghjaj)
+        """
+    )
+
+elif app_mode == "App":
+    if clusters is not None:
+        df['cluster'] = clusters
+
+        # --- Display Clustered Data ---
+        st.subheader(f"Clustered Data using {algorithm}:")
+        st.dataframe(df)
+
+        # --- Evaluation Metrics ---
+        if len(set(clusters)) > 1:
+            silhouette_avg = silhouette_score(scaled_features, clusters)
+            db_index = davies_bouldin_score(scaled_features, clusters)
+            ch_index = calinski_harabasz_score(scaled_features, clusters)
+
+            st.subheader("Clustering Evaluation Metrics")
+            st.markdown(f"**Silhouette Score:** {silhouette_avg:.2f}", unsafe_allow_html=True)
+            st.markdown(f"**Davies-Bouldin Index:** {db_index:.2f}", unsafe_allow_html=True)
+            st.markdown(f"**Calinski-Harabasz Index:** {ch_index:.2f}", unsafe_allow_html=True)
+        else:
+            st.warning("Evaluation metrics are not applicable. Only one cluster found.")
+
+        # --- Interactive 3D Scatter Plot with Plotly ---
+        st.subheader("Interactive 3D Cluster Visualization")
+        fig = px.scatter_3d(
+            df,
+            x='attendance_rate',
+            y='test_average',
+            z='engagement_score',
+            color='cluster',
+            title=f"Student Clusters ({algorithm})",
+            labels={'attendance_rate': 'Attendance Rate',
+                    'test_average': 'Test Average',
+                    'engagement_score': 'Engagement Score'}
+        )
+        st.plotly_chart(fig)
+
+        # --- Cluster Profiling (Example using Plotly) ---
+        st.subheader("Cluster Profile Visualization")
+        st.write("The parallel coordinates plot is a way to visualize and compare clusters across multiple features.")
+        profile_features = ['attendance_rate', 'test_average', 'engagement_score']
+        cluster_means = df.groupby('cluster')[profile_features].mean().reset_index()
+
+        fig_profile = px.parallel_coordinates(
+            cluster_means,
+            color='cluster',
+            dimensions=profile_features,
+            title="Parallel Coordinates Plot for Cluster Profiles"
+        )
+        st.plotly_chart(fig_profile)
+
+        # --- Dynamic Interpretation of Clusters ---
+        st.subheader("Interpretation of Clusters")
+        for cluster_num in cluster_means['cluster']:
+            cluster_data = cluster_means[cluster_means['cluster'] == cluster_num]
+            st.write(f"**Cluster {cluster_num}:**")
+            for feature in profile_features:
+                st.write(f"- **{feature.replace('_', ' ').title()}:** {cluster_data[feature].values[0]:.2f}")
+
+            highest_feature = cluster_data[profile_features].idxmax(axis=1).values[0]
+            lowest_feature = cluster_data[profile_features].idxmin(axis=1).values[0]
+
+            st.write(f"This cluster has the highest average {highest_feature.replace('_', ' ')} "
+                     f"and the lowest average {lowest_feature.replace('_', ' ')}.")
+            st.write("---")
+
+        # Additional insights based on cluster characteristics can be added here.
+    else:
+        st.warning("Please configure the clustering settings and run the algorithm first.")

clustering_data.csv

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+student_id,attendance_rate,participation_score,assignment_completion,test_average,extracurricular_activities
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