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assignmentwine.py

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+import streamlit as st
+import pandas as pd
+import matplotlib.pyplot as plt
+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
+
+# Title
+st.title("🍷 Wine Quality Analysis App")
+
+# Load dataset
+@st.cache_data
+def load_data():
+    url = "https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv"
+    return pd.read_csv(url, sep=";")
+
+df = load_data()
+st.subheader("Preview of Dataset")
+st.write(df.head())
+
+# PCA
+features = df.drop("quality", axis=1)
+scaler = StandardScaler()
+scaled_features = scaler.fit_transform(features)
+
+pca = PCA()
+pca_result = pca.fit_transform(scaled_features)
+explained_variance = np.cumsum(pca.explained_variance_ratio_)
+
+st.subheader("PCA Explained Variance")
+fig, ax = plt.subplots()
+ax.plot(range(1, len(explained_variance)+1), explained_variance, marker="o")
+ax.set_xlabel("Number of Principal Components")
+ax.set_ylabel("Cumulative Explained Variance")
+st.pyplot(fig)
+
+# Clustering
+pca_features = PCA(n_components=0.85).fit_transform(scaled_features)
+
+inertia, silhouette = [], []
+K = range(2, 11)
+for k in K:
+    km = KMeans(n_clusters=k, random_state=42, n_init=10)
+    labels = km.fit_predict(pca_features)
+    inertia.append(km.inertia_)
+    silhouette.append(silhouette_score(pca_features, labels))
+
+st.subheader("Elbow & Silhouette Method")
+fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))
+ax1.plot(K, inertia, marker="o")
+ax1.set_title("Elbow Method")
+ax1.set_xlabel("Clusters")
+ax1.set_ylabel("Inertia")
+
+ax2.plot(K, silhouette, marker="o", color="orange")
+ax2.set_title("Silhouette Score")
+ax2.set_xlabel("Clusters")
+ax2.set_ylabel("Score")
+st.pyplot(fig)
+
+# Apply clustering with 3 clusters
+kmeans = KMeans(n_clusters=3, random_state=42, n_init=10)
+df["Cluster"] = kmeans.fit_predict(pca_features)
+
+st.subheader("Cluster Profiles")
+st.write(df.groupby("Cluster").mean())
+
+# Business Insights
+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"
+}
+st.subheader("Business Insights")
+for cluster, desc in cluster_insights.items():
+    st.write(f"**Cluster {cluster}:** {desc}")
+
+# ----------------------
+# Interactive Section
+# ----------------------
+st.subheader("🍷 Explore Wines Interactively")
+
+# Slider for alcohol content
+alcohol_val = st.slider(
+    "Select minimum alcohol content",
+    float(df['alcohol'].min()),
+    float(df['alcohol'].max()),
+    float(df['alcohol'].min())
+)
+filtered_df = df[df['alcohol'] >= alcohol_val]
+st.write(f"Wines with alcohol ≥ {alcohol_val}")
+st.dataframe(filtered_df)
+
+# Slider for pH
+ph_val = st.slider(
+    "Select maximum pH",
+    float(df['pH'].min()),
+    float(df['pH'].max()),
+    float(df['pH'].max())
+)
+ph_filtered = filtered_df[filtered_df['pH'] <= ph_val]
+st.write(f"Wines with alcohol ≥ {alcohol_val} and pH ≤ {ph_val}")
+st.dataframe(ph_filtered)
+
+# Dropdown for cluster selection
+cluster_select = st.selectbox("Select Cluster to View", options=sorted(df['Cluster'].unique()))
+cluster_filtered = df[df['Cluster'] == cluster_select]
+st.write(f"Wines in Cluster {cluster_select}")
+st.dataframe(cluster_filtered)