Create app.py

app.py

@@ -0,0 +1,118 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.cluster import KMeans, DBSCAN, AgglomerativeClustering
+from sklearn.metrics import silhouette_score
+from sklearn.preprocessing import StandardScaler
+from statsmodels.tsa.arima.model import ARIMA
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Streamlit app title
+st.title('Clustering and Time Series Analysis')
+
+# Step 1: Upload CSV file
+uploaded_file = st.file_uploader("Upload a CSV file", type=["csv"])
+
+if uploaded_file is not None:
+    data = pd.read_csv(uploaded_file)
+    st.write("Dataset Preview:", data.head())
+
+    # Step 2: Data Preprocessing
+    # Selecting only numerical columns for clustering
+    numerical_cols = data.select_dtypes(include=[np.number]).columns.tolist()
+    st.write("Numerical columns for clustering:", numerical_cols)
+
+    # Option to scale data or not
+    scale_data = st.checkbox("Scale Data", value=True)
+    if scale_data:
+        scaler = StandardScaler()
+        data_scaled = scaler.fit_transform(data[numerical_cols])
+    else:
+        data_scaled = data[numerical_cols].values
+
+    # Step 3: Clustering Algorithm Selection
+    clustering_method = st.selectbox("Choose a clustering method", ["K-Means", "Hierarchical Clustering", "DBSCAN"])
+
+    if clustering_method == "K-Means":
+        k_range = st.slider("Select number of clusters for K-Means", min_value=2, max_value=7, value=3)
+        kmeans = KMeans(n_clusters=k_range, random_state=42)
+        cluster_labels = kmeans.fit_predict(data_scaled)
+        silhouette_avg = silhouette_score(data_scaled, cluster_labels)
+        st.write(f"K-Means Silhouette Score for {k_range} clusters: {silhouette_avg}")
+
+    elif clustering_method == "Hierarchical Clustering":
+        k_range = st.slider("Select number of clusters for Hierarchical Clustering", min_value=2, max_value=7, value=3)
+        hierarchical = AgglomerativeClustering(n_clusters=k_range)
+        cluster_labels = hierarchical.fit_predict(data_scaled)
+        silhouette_avg = silhouette_score(data_scaled, cluster_labels)
+        st.write(f"Hierarchical Clustering Silhouette Score for {k_range} clusters: {silhouette_avg}")
+
+    elif clustering_method == "DBSCAN":
+        eps_value = st.slider("Select eps value for DBSCAN", min_value=0.1, max_value=2.0, value=0.5)
+        min_samples_value = st.slider("Select minimum samples for DBSCAN", min_value=1, max_value=10, value=5)
+        dbscan = DBSCAN(eps=eps_value, min_samples=min_samples_value)
+        cluster_labels = dbscan.fit_predict(data_scaled)
+        
+        # Check if DBSCAN found valid clusters
+        if len(set(cluster_labels)) > 1:
+            silhouette_avg = silhouette_score(data_scaled, cluster_labels)
+            st.write(f"DBSCAN Silhouette Score: {silhouette_avg}")
+        else:
+            st.write("DBSCAN did not form valid clusters. Try adjusting eps or min_samples.")
+
+    # Step 4: Visualize the clusters if valid
+    if len(set(cluster_labels)) > 1:
+        st.write("Cluster Labels:", np.unique(cluster_labels))
+        sns.scatterplot(x=data_scaled[:, 0], y=data_scaled[:, 1], hue=cluster_labels, palette='Set1')
+        st.pyplot(plt)
+
+    # Step 5: ARIMA Time Series Analysis
+    # Checking if there are any time-related columns
+    time_series_col = None
+    for col in data.columns:
+        if pd.api.types.is_datetime64_any_dtype(data[col]):
+            time_series_col = col
+            break
+
+    if time_series_col:
+        st.write("Time Series Analysis (ARIMA) on column:", time_series_col)
+        time_series_data = data[time_series_col].dropna()
+        
+        # ARIMA model order
+        p = st.number_input("ARIMA p value", min_value=0, max_value=5, value=1)
+        d = st.number_input("ARIMA d value", min_value=0, max_value=2, value=1)
+        q = st.number_input("ARIMA q value", min_value=0, max_value=5, value=1)
+        
+        arima_model = ARIMA(time_series_data, order=(p, d, q))
+        arima_result = arima_model.fit()
+        
+        # Display ARIMA result summary
+        st.write(arima_result.summary())
+        
+        # Plotting the original and forecast
+        fig, ax = plt.subplots()
+        arima_result.plot_predict(dynamic=False, ax=ax)
+        st.pyplot(fig)
+
+    # Step 6: Create Silhouette Score Table for K-Means and Hierarchical Clustering
+    st.write("### Silhouette Score Table for 2-7 Clusters")
+    silhouette_scores = {'Number of Clusters': [], 'K-Means Silhouette Score': [], 'Hierarchical Silhouette Score': []}
+
+    for n_clusters in range(2, 8):
+        # K-Means
+        kmeans = KMeans(n_clusters=n_clusters, random_state=42)
+        kmeans_labels = kmeans.fit_predict(data_scaled)
+        kmeans_silhouette = silhouette_score(data_scaled, kmeans_labels)
+
+        # Hierarchical
+        hierarchical = AgglomerativeClustering(n_clusters=n_clusters)
+        hierarchical_labels = hierarchical.fit_predict(data_scaled)
+        hierarchical_silhouette = silhouette_score(data_scaled, hierarchical_labels)
+
+        silhouette_scores['Number of Clusters'].append(n_clusters)
+        silhouette_scores['K-Means Silhouette Score'].append(kmeans_silhouette)
+        silhouette_scores['Hierarchical Silhouette Score'].append(hierarchical_silhouette)
+
+    silhouette_df = pd.DataFrame(silhouette_scores)
+    st.write(silhouette_df)