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README.md

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----
-title: USLAssignment
-emoji: 😻
-colorFrom: gray
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.46.1
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# spotify

requirements.txt

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+appdirs==1.4.4
+argon2-cffi==21.1.0
+asttokens==2.4.1
+attrs==23.2.0
+streamlit
+pandas
+numpy
+matplotlib
+seaborn
+altair
+scikit-learn

spotify_clusters_app.py

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+# --- Imports ---
+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import altair as alt
+from sklearn.preprocessing import StandardScaler
+from sklearn.decomposition import PCA
+from sklearn.cluster import KMeans
+
+# --- Page Config ---
+st.set_page_config(
+    page_title="Spotify Song Clustering — Business Insights",
+    page_icon="🎵",
+    layout="wide",
+)
+
+# --- Title / Sidebar ---
+st.title("🎵 Spotify Song Clustering — Business Insights Dashboard")
+st.sidebar.header("Filters 📊")
+
+# --- Data Loading & Caching (mantido) ---
+@st.cache_data
+def load_raw_data():
+    url = 'https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2020/2020-01-21/spotify_songs.csv'
+    return pd.read_csv(url)
+
+df = load_raw_data()
+
+# --- Business Problem Statement (mantido e no estilo do 1º app) ---
+st.markdown("""
+#### Business Problem
+Spotify aims to deliver smarter recommendations and more engaging playlists by understanding the *mood* and *context* of songs, not just their genre.  
+This dashboard uses **unsupervised learning** to reveal actionable clusters, helping Spotify personalize user experience, optimize curation, and unlock new business opportunities.
+""")
+
+with st.expander("📊 **Key Components of the Analysis**"):
+    st.markdown("""
+- **Audio features**: `danceability`, `energy`, `loudness`, `speechiness`, `acousticness`, `instrumentalness`, `liveness`, `valence`.
+- **Dimensionality reduction**: PCA for visualization (2D/3D) e melhor separação de grupos.
+- **Clustering**: KMeans para identificar segmentos semelhantes por “mood/context”.
+- **Business view**: Perfis de clusters com recomendações acionáveis.
+""")
+
+# --- Cleaning & Scaling (mantido) ---
+@st.cache_data
+def clean_and_scale(songs):
+    drop_cols = [
+        "track_id", "track_name", "track_artist", "track_album_id",
+        "track_album_name", "track_album_release_date",
+        "playlist_name", "playlist_id", "tempo", "duration_ms"
+    ]
+    songs_clean = songs.drop(columns=drop_cols, errors="ignore")
+    features = [
+        "danceability", "energy", "loudness", "speechiness", "acousticness",
+        "instrumentalness", "liveness", "valence"
+    ]
+    X = songs_clean[features].dropna()
+    scaler = StandardScaler()
+    X_scaled = scaler.fit_transform(X)
+    return songs, songs_clean, X, X_scaled, features
+
+songs = load_raw_data()
+songs, songs_clean, X, X_scaled, features = clean_and_scale(songs)
+
+# --- Sidebar Filters (no estilo do 1º app; seguros quanto a colunas) ---
+# Playlist genre filter
+if "playlist_genre" in songs.columns:
+    all_genres = sorted(list(pd.Series(songs["playlist_genre"].dropna().unique()).astype(str)))
+else:
+    all_genres = []
+selected_genres = st.sidebar.multiselect("Select Playlist Genre 🎧", all_genres, default=all_genres if all_genres else None)
+
+# Popularity range filter
+if "track_popularity" in songs.columns:
+    pop_min, pop_max = int(songs["track_popularity"].min()), int(songs["track_popularity"].max())
+    pop_range = st.sidebar.slider("Track Popularity Range ⭐", pop_min, pop_max, (pop_min, pop_max))
+else:
+    pop_range = None
+
+# Apply filters to an auxiliary DataFrame (apenas para visuais que usam 'songs')
+songs_filtered = songs.copy()
+if selected_genres:
+    songs_filtered = songs_filtered[songs_filtered["playlist_genre"].astype(str).isin(selected_genres)]
+if pop_range:
+    songs_filtered = songs_filtered[(songs_filtered["track_popularity"] >= pop_range[0]) &
+                                    (songs_filtered["track_popularity"] <= pop_range[1])]
+
+# --- Controls for PCA & KMeans (mantidos) ---
+st.sidebar.header("🔎 Explore Clusters")
+n_components = st.sidebar.slider("PCA Components (for visualization)", 2, len(features), 3)
+k_clusters = st.sidebar.slider("Number of clusters (KMeans)", 2, 15, 10)
+
+@st.cache_data
+def run_pca(X_scaled, n_components):
+    pca = PCA(n_components=n_components)
+    return pca.fit_transform(X_scaled)
+
+@st.cache_data
+def run_kmeans(X_pca, k_clusters):
+    kmeans = KMeans(n_clusters=k_clusters, random_state=42, n_init=10)
+    return kmeans.fit_predict(X_pca)
+
+X_pca = run_pca(X_scaled, n_components)
+clusters = run_kmeans(X_pca, k_clusters)
+
+songs_clustered = songs_clean.loc[X.index].copy()
+songs_clustered["cluster"] = clusters
+
+# --- Visualization Selector (como no 1º app) ---
+st.header("Analysis 📊")
+visualization_option = st.selectbox(
+    "Select Visualization 🎨",
+    [
+        "2D PCA Scatter (by cluster)",
+        "3D PCA Scatter (by cluster)",
+        "Cluster Profiles — Average Audio Features (heatmap)",
+        "Feature Distributions by Cluster (boxplots)",
+        "Correlation Heatmap of Audio Features",
+        "Are clusters separable by popularity? (Altair scatter)"
+    ],
+)
+
+# --- Visualizations ---
+if visualization_option == "2D PCA Scatter (by cluster)":
+    fig, ax = plt.subplots(figsize=(8, 5))
+    sns.scatterplot(x=X_pca[:, 0], y=X_pca[:, 1], hue=clusters, palette="tab10", s=12, ax=ax, legend=False)
+    ax.set_xlabel("PC1")
+    ax.set_ylabel("PC2")
+    ax.set_title("2D PCA — Songs clustered")
+    st.pyplot(fig)
+
+elif visualization_option == "3D PCA Scatter (by cluster)":
+    if n_components >= 3:
+        from mpl_toolkits.mplot3d import Axes3D  # noqa: F401
+        fig = plt.figure(figsize=(8, 6))
+        ax = fig.add_subplot(111, projection='3d')
+        sc = ax.scatter(X_pca[:, 0], X_pca[:, 1], X_pca[:, 2], c=clusters, cmap='tab10', s=8)
+        ax.set_xlabel("PC1")
+        ax.set_ylabel("PC2")
+        ax.set_zlabel("PC3")
+        ax.set_title("3D PCA — Songs clustered")
+        st.pyplot(fig)
+    else:
+        st.info("Select at least 3 PCA components for 3D visualization.")
+
+elif visualization_option == "Cluster Profiles — Average Audio Features (heatmap)":
+    st.subheader("Cluster Profiles — Average Audio Features")
+    cluster_profile = songs_clustered.groupby("cluster")[features].mean().round(2)
+    fig, ax = plt.subplots(figsize=(10, 5))
+    sns.heatmap(cluster_profile, annot=True, cmap="viridis", ax=ax)
+    ax.set_title("Average Feature Values per Cluster")
+    st.pyplot(fig)
+    st.dataframe(cluster_profile)
+
+elif visualization_option == "Feature Distributions by Cluster (boxplots)":
+    st.subheader("Feature Distributions by Cluster")
+    selected_feature = st.selectbox("Select feature", features, index=0)
+    fig, ax = plt.subplots(figsize=(10, 5))
+    sns.boxplot(data=songs_clustered, x="cluster", y=selected_feature, ax=ax)
+    ax.set_title(f"Distribution of {selected_feature} by cluster")
+    st.pyplot(fig)
+
+elif visualization_option == "Correlation Heatmap of Audio Features":
+    st.subheader("Correlation Heatmap")
+    corr = pd.DataFrame(X, columns=features).corr()
+    fig, ax = plt.subplots(figsize=(8, 6))
+    sns.heatmap(corr, annot=True, cmap="coolwarm", center=0, ax=ax)
+    ax.set_title("Correlation among audio features")
+    st.pyplot(fig)
+
+elif visualization_option == "Are clusters separable by popularity? (Altair scatter)":
+    # Só usa se houver track_popularity; mapeia com as amostras do cluster
+    if "track_popularity" in songs.columns:
+        # alinhar índices: precisamos trazer popularity de 'songs' para 'songs_clustered'
+        pop_series = songs.loc[songs_clustered.index, "track_popularity"] if "track_popularity" in songs.columns else pd.Series(index=songs_clustered.index, dtype=float)
+        viz_df = songs_clustered.copy()
+        viz_df["track_popularity"] = pop_series
+        viz_df = viz_df.dropna(subset=["track_popularity"])
+        chart = alt.Chart(viz_df.reset_index(drop=True)).mark_point(filled=True).encode(
+            alt.X('track_popularity:Q', title='Track popularity'),
+            alt.Y('valence:Q', title='Valence'),
+            alt.Color('cluster:N'),
+            alt.OpacityValue(0.7),
+            tooltip=['cluster:N'] + [c for c in ["valence", "energy", "danceability"] if c in viz_df.columns]
+        ).properties(height=450)
+        st.altair_chart(chart, use_container_width=True)
+    else:
+        st.warning("`track_popularity` not available in this dataset.")
+
+# --- Cluster Insights (mantidos) ---
+st.sidebar.markdown("---")
+selected_cluster = st.sidebar.selectbox("Select cluster for details", sorted(songs_clustered["cluster"].unique()))
+
+cluster_business = {
+    0: "Acoustic / Chill 🌿: Calm, relaxing. Use in study/wellness playlists.",
+    1: "Classic Rock 🎸: Guitar-driven, nostalgic. Promote with live events.",
+    2: "EDM / Dance 🎧: High-energy. Add to workout/party playlists.",
+    3: "Electropop / Dance Pop 🔥: Catchy, upbeat. Viral playlists, social media.",
+    4: "Hard Rock / Metal 🤘: Loud, intense. Niche playlists, festival tie-ins.",
+    5: "Indie / Alternative 🌌: Creative, experimental. Discovery playlists.",
+    6: "Latin / Reggaeton 🌴: Rhythmic, upbeat. Geo-targeted playlists.",
+    7: "Pop Mainstream 🎶: Balanced, mass-market. Chart-topping hits.",
+    8: "R&B / Soul 💜: Smooth, emotional. Romance/mood playlists.",
+    9: "Rap / Trap 🎤: Speech-heavy, beat-driven. Youth/urban playlists."
+}
+
+cluster_actions = {
+    0: "Focus/study playlists, wellness app partnerships.",
+    1: "Live concert tie-ins, nostalgic campaigns.",
+    2: "Workout/party playlists, fitness brand collaborations.",
+    3: "Viral playlist promotion, social media campaigns.",
+    4: "Niche playlist curation, festival partnerships.",
+    5: "Discovery playlists, support for emerging artists.",
+    6: "Geo-targeted playlists, dance event promotions.",
+    7: "Algorithmic playlist anchors, sponsored content.",
+    8: "Romance/mood playlists, lifestyle brand partnerships.",
+    9: "Youth/urban playlists, influencer collaborations."
+}
+
+st.markdown(f"### Cluster {selected_cluster} — Business Insights")
+st.markdown(f"**Business Description:** {cluster_business.get(selected_cluster, 'Segmented by mood/context.')}")
+st.markdown(f"**Actionable Recommendation:** {cluster_actions.get(selected_cluster, 'Curate and promote according to cluster characteristics.')}")
+st.markdown("**Sample Songs in this Cluster:**")
+sample_cols = [c for c in ["track_name", "track_artist", "playlist_genre"] if c in songs.columns]
+st.write(songs.loc[songs_clustered[songs_clustered['cluster'] == selected_cluster].index, sample_cols].head(10))
+
+# --- Dataset Overview (no estilo do 1º app) ---
+st.header("Dataset Overview")
+st.dataframe(songs.describe(include='all').transpose())
+
+# --- Insights Expander (no estilo do 1º app) ---
+with st.expander("Interpreting the visualizations"):
+    st.markdown("""
+1. **PCA scatter** — clusters tendem a ocupar regiões distintas, sugerindo *moods* diferentes (e.g., alto `energy` + baixo `acousticness` próximos).
+2. **Heatmap de perfis** — médias por cluster evidenciam contrastes claros (e.g., `valence`/`danceability` altos para pop/dance).
+3. **Boxplots por cluster** — mostram dispersão e outliers por feature, úteis para ajustar K ou features.
+4. **Correlação** — `energy` e `loudness` costumam correlacionar; atenção ao leakage de escala.
+5. **Popularidade vs. valence** — clusters com maior `valence`/`danceability` podem apresentar popularidade maior (insight para marketing).
+""")
+
+# --- Rationale & Strategic Insights (mantido) ---
+st.header("Rationale & Strategic Insights")
+st.markdown("""
+### Why This Approach?
+- **Business Need:** Genre-based recommendations miss nuances of mood/context. Clustering por audio features revela segmentos acionáveis.
+- **Data Cleaning:** Remoção de colunas não-audio e linhas incompletas.
+- **Feature Selection:** Foco nos 8 atributos mais ligados a “mood/context”.
+- **Scaling:** `StandardScaler` equilibra contribuições.
+- **PCA:** Visualização e separação mais clara.
+- **KMeans:** Grupos interpretáveis para ação.
+
+### Strategic Insights for Stakeholders
+- **Personalization:** Recomendações contextuais aumentam relevância e satisfação.
+- **Playlist Curation:** Atribuição automática de novas faixas a clusters específicos.
+- **Marketing & Engagement:** Campanhas e playlists temáticas por cluster.
+- **Artist Discovery:** Tendências emergentes por segmento.
+- **Partnerships & Revenue:** Parcerias alinhadas ao *mood* (wellness, fitness, etc.).
+- **Continuous Improvement:** Medir performance por cluster → iterar K, features e regras.
+""")
+
+# --- Footer ---
+st.markdown("---")
+st.markdown("© 2025 Spotify Clustering Assignment — Powered by Streamlit")