Upload W3-assignment-streamlit.py

W3-assignment-streamlit.py

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+# Assume our stakeholder is a brand advertiser who wants to understand short-video audience segments. 
+# The clustering analysis helps identify which audience groups are best suited for different types of
+# campaigns (broad awareness vs. engagement-driven vs. knowledge-sharing).
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+from sklearn.impute import SimpleImputer
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+
+st.set_page_config(page_title="Short-Video Audience Segments", layout="wide")
+
+# Sidebar — Controls
+st.sidebar.title("Controls")
+
+# Set CSV path here
+CSV_PATH = "data/youtube_shorts_tiktok_trends_2025.csv"
+df_raw = pd.read_csv(CSV_PATH, low_memory=False)
+
+# Choose number of clusters (K)
+k = st.sidebar.slider("Number of clusters (K)", min_value=2, max_value=8, value=4, step=1)
+
+# Metric to compare across clusters
+core_metrics = ["views","likes","comments","shares","saves","engagement_rate"]
+metric = st.sidebar.selectbox("Metric to compare ", core_metrics, index=5)
+
+# Optional filters for display (do not refit model)
+platform_filter_on = st.sidebar.checkbox("Filter by platform ", value=False)
+region_filter_on   = st.sidebar.checkbox("Filter by region ", value=False)
+
+# Data loading & preprocessing
+@st.cache_data(show_spinner=True)
+def load_data(path):
+    df = pd.read_csv(path, low_memory=False)
+    return df
+
+@st.cache_data(show_spinner=True)
+def build_features(df_raw: pd.DataFrame):
+    # Select useful columns for engagement & context
+    use_cols = [
+        "platform","region","category","sound_type",
+        "week_of_year","duration_sec",
+        "views","likes","comments","shares","saves",
+        "engagement_rate","engagement_share_rate"
+    ]
+    df = df_raw[use_cols].copy()
+    # Basic cleaning: keep valid duration & views
+    df = df[(df["duration_sec"] > 0) & (df["views"] > 0)]
+    # Construct per-view rates to offset scale bias(构造每次观看的比率，缓解规模差异)
+    for col in ["likes","comments","shares","saves"]:
+        df[f"{col}_rate"] = (df[col] / df["views"].clip(lower=1)).astype(float)
+
+    # Log-transform heavy-tailed count features
+    for col in ["views","likes","comments","shares","saves","duration_sec"]:
+        df[f"log_{col}"] = np.log1p(df[col])
+    # Week-of-year cyclic encoding
+    df["week_sin"] = np.sin(2*np.pi*df["week_of_year"]/52.0)
+    df["week_cos"] = np.cos(2*np.pi*df["week_of_year"]/52.0)
+
+    # Numeric + Categorical features
+    num_feats = [
+        "log_views","log_likes","log_comments","log_shares","log_saves",
+        "log_duration_sec",
+        "likes_rate","comments_rate","shares_rate","saves_rate",
+        "engagement_rate","engagement_share_rate",
+        "week_sin","week_cos"
+    ]
+    cat_feats = ["platform","region","category","sound_type"]
+
+    # One-hot for categoricals (drop_first to avoid perfect collinearity)
+    df_model = pd.get_dummies(df[num_feats + cat_feats], drop_first=True).astype(float)
+    feature_names = df_model.columns.tolist()
+
+    # Impute & Scale
+    imp = SimpleImputer(strategy="median")
+    X_num = imp.fit_transform(df_model.values)
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X_num)
+
+    return df, df_model, feature_names, X_scaled
+
+@st.cache_resource(show_spinner=True)
+def fit_pca(X_scaled: np.ndarray, var_threshold: float = 0.80):
+    pca = PCA(n_components=None, random_state=42)
+    X_pca = pca.fit_transform(X_scaled)
+    exp = pca.explained_variance_ratio_
+    cum = np.cumsum(exp)
+    k_opt = int(np.argmax(cum >= var_threshold)) + 1
+    return pca, X_pca, exp, cum, k_opt
+
+@st.cache_resource(show_spinner=True)
+def fit_kmeans(X_embed: np.ndarray, n_clusters: int):
+    km = KMeans(n_clusters=n_clusters, random_state=42, n_init=20)
+    labels = km.fit_predict(X_embed)
+    try:
+        sil = silhouette_score(X_embed, labels)
+    except Exception:
+        sil = np.nan
+    return km, labels, sil
+
+# Load & Prep
+df_raw = load_data(csv_path)
+df_clean, df_model, feature_names, X_scaled = build_features(df_raw)
+pca, X_pca, exp, cum, k_opt = fit_pca(X_scaled, var_threshold=0.80)
+
+# Use first k_opt PCs for clustering
+X_k = X_pca[:, :k_opt]
+
+# Fit KMeans with selected k
+km, labels, sil = fit_kmeans(X_k, n_clusters=k)
+df_show = df_clean.copy()
+df_show["cluster"] = labels
+
+# Optional display filters (platform/region)
+if platform_filter_on:
+    platforms = ["(All)"] + sorted(df_show["platform"].dropna().unique().tolist())
+    chosen_platform = st.sidebar.selectbox("Platform", platforms, index=0)
+    if chosen_platform != "(All)":
+        df_show = df_show[df_show["platform"] == chosen_platform]
+
+if region_filter_on:
+    regions = ["(All)"] + sorted(df_show["region"].dropna().unique().tolist())
+    chosen_region = st.sidebar.selectbox("Region", regions, index=0)
+    if chosen_region != "(All)":
+        df_show = df_show[df_show["region"] == chosen_region]
+
+# Header — Business framing
+st.title("Short-Video Audience Segments (TikTok & YouTube Shorts)")
+st.caption("We identify audience segments via PCA + KMeans to support content and ad strategy.")
+
+colA, colB, colC = st.columns(3)
+with colA:
+    st.metric("Rows used", f"{len(df_show):,}")
+with colB:
+    st.metric("K (clusters)", k)
+with colC:
+    st.metric("Silhouette", f"{sil:.3f}" if not np.isnan(sil) else "—")
+
+st.markdown("---")
+
+# Cluster profile table
+cluster_profile = (
+    df_show.groupby("cluster")[core_metrics]
+    .mean()
+    .sort_index()
+)
+
+st.subheader("Cluster Profiles — Mean Metrics")
+st.dataframe(cluster_profile.style.format("{:,.2f}"))
+
+# Chart: Compare chosen metric across clusters
+st.subheader(f"Compare clusters by: **{metric}**")
+
+fig, ax = plt.subplots(figsize=(7,4))
+cluster_profile[metric].plot(kind="bar", ax=ax)
+ax.set_xlabel("Cluster")
+ax.set_ylabel(metric)
+ax.set_title(f"{metric} by Cluster")
+ax.grid(axis="y", linestyle="--", alpha=0.3)
+st.pyplot(fig, use_container_width=True)
+
+# Dynamic insights
+def insight_text(cp: pd.DataFrame):
+    # Find clusters with max metrics
+    tops = {m: int(cp[m].idxmax()) for m in cp.columns}
+    lines_en = [
+        f"- Highest **engagement_rate**: Cluster {tops['engagement_rate']} (best for community/interaction).",
+        f"- Highest **views**: Cluster {tops['views']} (best for broad awareness).",
+        f"- Highest **saves**: Cluster {tops['saves']} (good for knowledge/utility content).",
+    ]
+    return "\n".join(lines_en) 
+
+st.markdown("### Dynamic Insights")
+st.markdown(insight_text(cluster_profile))
+
+# Optional: Diagnostics
+with st.expander("Model Diagnostics"):
+    st.write(f"Using first **{k_opt} PCs** to reach ≥80% cumulative explained variance.")
+    # small curve
+    fig2, ax2 = plt.subplots(figsize=(5,3))
+    ax2.plot(range(1, len(cum)+1), cum, marker="o")
+    ax2.axhline(0.80, color="r", linestyle="--")
+    ax2.set_xlabel("PCs")
+    ax2.set_ylabel("Cumulative explained variance")
+    ax2.set_title("PCA Explained Variance")
+    ax2.grid(axis="y", linestyle="--", alpha=0.3)
+    st.pyplot(fig2, use_container_width=False)
+
+st.markdown("---")
+st.caption("Note: Clustering is fitted on full data (then filtered for display).")