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requirements.txt

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+streamlit
+pandas
+seaborn
+matplotlib
+

wine_analysis_app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+import matplotlib.pyplot as plt
+import plotly.express as px
+import plotly.graph_objects as go
+
+# Set page configuration
+st.set_page_config(
+    page_title="Wine Quality Analysis",
+    page_icon="🍷",
+    layout="wide"
+)
+
+# Title and description
+st.title("🍷 Wine Quality Analysis")
+st.markdown("""
+This app analyzes the Wine Quality dataset using unsupervised learning techniques.
+Explore the dataset, visualize PCA components, and see clustering results.
+""")
+
+# Load the dataset
+@st.cache_data
+def load_data():
+    wine_url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv'
+    wine_data = pd.read_csv(wine_url, sep=';')
+    return wine_data
+
+wine_data = load_data()
+
+# Sidebar for navigation
+st.sidebar.title("Navigation")
+options = st.sidebar.radio("Select a section:", 
+                          ["Dataset Overview", "PCA Analysis", "Clustering Analysis", "Cluster Insights"])
+
+# Dataset Overview Section
+if options == "Dataset Overview":
+    st.header("Dataset Overview")
+    
+    st.subheader("First few rows of the dataset")
+    st.dataframe(wine_data.head())
+    
+    st.subheader("Dataset Information")
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.write("**Shape:**", wine_data.shape)
+        st.write("**Columns:**", list(wine_data.columns))
+    
+    with col2:
+        st.write("**Missing values:**")
+        missing_values = wine_data.isnull().sum()
+        st.write(missing_values)
+    
+    st.subheader("Feature Distributions")
+    selected_feature = st.selectbox("Select a feature to visualize:", wine_data.columns[:-1])
+    
+    fig = px.histogram(wine_data, x=selected_feature, title=f"Distribution of {selected_feature}")
+    st.plotly_chart(fig)
+    
+    st.subheader("Quality Distribution")
+    quality_counts = wine_data['quality'].value_counts().sort_index()
+    fig = px.bar(x=quality_counts.index, y=quality_counts.values, 
+                 labels={'x': 'Quality Score', 'y': 'Count'},
+                 title="Distribution of Wine Quality Scores")
+    st.plotly_chart(fig)
+
+# PCA Analysis Section
+elif options == "PCA Analysis":
+    st.header("Principal Component Analysis (PCA)")
+    
+    # Prepare the data
+    features = wine_data.drop('quality', axis=1)
+    scaler = StandardScaler()
+    scaled_features = scaler.fit_transform(features)
+    
+    # Perform PCA
+    pca = PCA()
+    pca_result = pca.fit_transform(scaled_features)
+    
+    # Explained variance
+    explained_variance = np.cumsum(pca.explained_variance_ratio_)
+    
+    # Plot explained variance
+    fig = go.Figure()
+    fig.add_trace(go.Scatter(x=list(range(1, len(explained_variance)+1)), 
+                            y=explained_variance,
+                            mode='lines+markers',
+                            name='Cumulative Explained Variance'))
+    fig.add_trace(go.Scatter(x=list(range(1, len(explained_variance)+1)), 
+                            y=[0.80]*len(explained_variance),
+                            mode='lines',
+                            name='80% Variance Threshold',
+                            line=dict(dash='dash')))
+    fig.update_layout(title='PCA Explained Variance',
+                      xaxis_title='Number of Principal Components',
+                      yaxis_title='Cumulative Explained Variance')
+    st.plotly_chart(fig)
+    
+    # Choose optimal components
+    optimal_components = np.argmax(explained_variance >= 0.80) + 1
+    st.write(f"**Optimal number of principal components:** {optimal_components} (explains ~80% of variance)")
+    
+    # PCA component interpretation
+    pca_components = pd.DataFrame(pca.components_, columns=features.columns)
+    main_components = pca_components.iloc[:optimal_components]
+    
+    st.subheader("Main Principal Components Interpretation")
+    
+    for i, row in main_components.iterrows():
+        st.write(f"**PC{i+1}** represents major influence from:")
+        sorted_features = row.abs().sort_values(ascending=False)
+        top_features = list(sorted_features.items())[:3]
+        
+        for feature, value in top_features:
+            st.write(f"  - {feature} (weight {value:.2f})")
+    
+    # Visualize PCA results
+    st.subheader("PCA Visualization")
+    
+    # Select components to visualize
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        x_component = st.selectbox("X-axis component", 
+                                  [f"PC{i+1}" for i in range(optimal_components)], 
+                                  index=0)
+    with col2:
+        y_component = st.selectbox("Y-axis component", 
+                                  [f"PC{i+1}" for i in range(optimal_components)], 
+                                  index=1)
+    
+    x_idx = int(x_component[2:]) - 1
+    y_idx = int(y_component[2:]) - 1
+    
+    # Create scatter plot
+    fig = px.scatter(x=pca_result[:, x_idx], y=pca_result[:, y_idx],
+                     color=wine_data['quality'],
+                     labels={'x': x_component, 'y': y_component, 'color': 'Quality'},
+                     title=f"{y_component} vs {x_component} Colored by Quality")
+    st.plotly_chart(fig)
+
+# Clustering Analysis Section
+elif options == "Clustering Analysis":
+    st.header("Clustering Analysis")
+    
+    # Prepare the data
+    features = wine_data.drop('quality', axis=1)
+    scaler = StandardScaler()
+    scaled_features = scaler.fit_transform(features)
+    
+    # Perform PCA for dimensionality reduction
+    pca = PCA(n_components=0.85)
+    pca_features = pca.fit_transform(scaled_features)
+    
+    # Determine optimal number of clusters
+    inertia = []
+    silhouette = []
+    k_range = range(2, 11)
+    
+    for k in k_range:
+        kmeans = KMeans(n_clusters=k, random_state=42)
+        labels = kmeans.fit_predict(pca_features)
+        inertia.append(kmeans.inertia_)
+        
+        if k > 1:  # Silhouette score requires at least 2 clusters
+            silhouette.append(silhouette_score(pca_features, labels))
+        else:
+            silhouette.append(0)
+    
+    # Plot elbow and silhouette methods
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=list(k_range), y=inertia, mode='lines+markers'))
+        fig.update_layout(title='Elbow Method',
+                          xaxis_title='Number of Clusters',
+                          yaxis_title='Inertia')
+        st.plotly_chart(fig)
+    
+    with col2:
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(x=list(k_range)[1:], y=silhouette[1:], mode='lines+markers'))
+        fig.update_layout(title='Silhouette Method',
+                          xaxis_title='Number of Clusters',
+                          yaxis_title='Silhouette Score')
+        st.plotly_chart(fig)
+    
+    # Let user select number of clusters
+    k_optimal = st.slider("Select number of clusters:", min_value=2, max_value=10, value=3)
+    
+    # Apply K-Means with selected clusters
+    kmeans = KMeans(n_clusters=k_optimal, random_state=42)
+    cluster_labels = kmeans.fit_predict(pca_features)
+    
+    # Add cluster labels to the dataframe
+    wine_data_clustered = wine_data.copy()
+    wine_data_clustered['Cluster'] = cluster_labels
+    
+    # Visualize clusters
+    st.subheader("Cluster Visualization")
+    
+    # Create scatter plot of clusters
+    fig = px.scatter(x=pca_features[:, 0], y=pca_features[:, 1],
+                     color=cluster_labels,
+                     labels={'x': 'PC1', 'y': 'PC2', 'color': 'Cluster'},
+                     title="Clusters Visualized in PCA Space")
+    st.plotly_chart(fig)
+    
+    # Show cluster profiles
+    st.subheader("Cluster Profiles")
+    cluster_profiles = wine_data_clustered.groupby('Cluster').mean()
+    st.dataframe(cluster_profiles)
+
+# Cluster Insights Section
+elif options == "Cluster Insights":
+    st.header("Cluster Business Insights")
+    
+    # Prepare the data (same as in clustering section)
+    features = wine_data.drop('quality', axis=1)
+    scaler = StandardScaler()
+    scaled_features = scaler.fit_transform(features)
+    
+    pca = PCA(n_components=0.85)
+    pca_features = pca.fit_transform(scaled_features)
+    
+    # Use 3 clusters as in the original analysis
+    kmeans = KMeans(n_clusters=3, random_state=42)
+    cluster_labels = kmeans.fit_predict(pca_features)
+    
+    wine_data_clustered = wine_data.copy()
+    wine_data_clustered['Cluster'] = cluster_labels
+    
+    # Define cluster insights (based on the original analysis)
+    cluster_insights = {
+        0: "Premium Taste Wines: High alcohol, balanced acidity, high quality",
+        1: "Sweet & Mild Wines: High sugar, low acidity, moderate quality",
+        2: "Sharp & Preservative-heavy Wines: High acidity, high sulfates, lower quality"
+    }
+    
+    # Display insights
+    for cluster, desc in cluster_insights.items():
+        st.subheader(f"Cluster {cluster}")
+        st.write(desc)
+        
+        # Show statistics for this cluster
+        cluster_data = wine_data_clustered[wine_data_clustered['Cluster'] == cluster]
+        st.write(f"Number of wines in this cluster: {len(cluster_data)}")
+        st.write(f"Average quality: {cluster_data['quality'].mean():.2f}")
+        
+        # Show key characteristics
+        key_features = ['alcohol', 'residual sugar', 'volatile acidity', 'citric acid', 'sulphates']
+        cluster_means = cluster_data[key_features].mean()
+        
+        fig = go.Figure()
+        fig.add_trace(go.Bar(x=key_features, y=cluster_means.values,
+                            name=f"Cluster {cluster}"))
+        fig.update_layout(title=f"Key Features for Cluster {cluster}",
+                          yaxis_title="Average Value")
+        st.plotly_chart(fig)
+        
+        st.write("---")
+