diff --git a/Youtube music b/Youtube music
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+# Which ten songs have the highest calculated engagement rate (Likes to Views ratio), indicating the highest viewer appreciation?"
+# 1. Import necessary libraries
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+# Set visualization style and parameters
+sns.set_style('dark')
+plt.rcParams['figure.figsize'] = (12, 7)
+
+# --- 2. Data Loading ---
+# NOTE: Replace 'youtube_hits_2025.csv' with your actual file path.
+try:
+    file_path = 'youtube_hits_2025.csv'
+    df = pd.read_csv(file_path)
+    print("YouTube Music Hits data successfully loaded!")
+except FileNotFoundError:
+    print(f"Error: File '{file_path}' not found. Creating a dummy DataFrame for demonstration.")
+    # Create a minimal dummy DataFrame for structural demonstration
+    data = {
+        'Title': ['Song A', 'Song B', 'Song C', 'Song D', 'Song E'],
+        'Artist': ['Artist 1', 'Artist 2', 'Artist 1', 'Artist 3', 'Artist 4'],
+        'Genre': ['Pop', 'Rock', 'Pop', 'Hip Hop', 'Pop'],
+        'Views': [1500000000, 500000000, 1800000000, 900000000, 1200000000],
+        'Likes': [15000000, 5000000, 18000000, 9000000, 12000000],
+        'Comments': [500000, 100000, 600000, 300000, 400000],
+        'Release_Date': ['2024-11-20', '2025-01-15', '2024-12-01', '2025-02-10', '2025-03-05']
+    }
+    df = pd.DataFrame(data)
+
+# Initial Data Exploration
+print("\n--- Initial Data Info ---")
+print(df.head())
+print(df.info())
+
+
+# --- 3. Data Cleaning and Feature Engineering ---
+
+# 3.1. Convert 'Release_Date' to datetime and extract time features
+df['Release_Date'] = pd.to_datetime(df['Release_Date'])
+df['Release_Month'] = df['Release_Date'].dt.month_name()
+
+# 3.2. Calculate Engagement Rate (Likes to Views Ratio)
+# A key metric for content quality, excluding spam views
+df['Engagement_Rate'] = (df['Likes'] / df['Views']) * 100
+df.sort_values(by='Engagement_Rate', ascending=False, inplace=True)
+
+
+# --- 4. Exploratory Data Analysis (EDA) & Insights ---
+
+# 4.1. Top 10 Most Viewed Songs
+top_views = df.sort_values(by='Views', ascending=False).head(10)
+print("\n--- 4.1 Top 10 Most Viewed Songs ---")
+print(top_views[['Title', 'Artist', 'Views']])
+
+# 4.2. Top 10 Songs by Engagement Rate
+top_engagement = df.sort_values(by='Engagement_Rate', ascending=False).head(10)
+print("\n--- 4.2 Top 10 Songs by Engagement Rate (%) ---")
+print(top_engagement[['Title', 'Artist', 'Engagement_Rate']])
+
+
+# 4.3. Genre Popularity and Performance (Total Views)
+genre_views = df.groupby('Genre')['Views'].sum().sort_values(ascending=False)
+print("\n--- 4.3 Total Views by Genre ---")
+print(genre_views)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(x=genre_views.index, y=genre_views.values, palette='plasma')
+plt.title('Total Views by Music Genre ')
+plt.ylabel('Total Views (in Billions)')
+plt.xlabel('Genre')
+plt.xticks(rotation=45, ha='right')
+plt.tight_layout()
+plt.show()
+
+
+# 4.4. Artist Dominance (Number of Hits)
+artist_hits = df['Artist'].value_counts().head(10)
+print("\n--- 4.4 Top 10 Artists by Number of Hits ---")
+print(artist_hits)
+
+plt.figure(figsize=(10, 6))
+sns.barplot(y=artist_hits.index, x=artist_hits.values, palette='viridis')
+plt.title('Top 10 Artists by Count of Songs in the Top 100')
+plt.xlabel('Number of Hits')
+plt.ylabel('Artist')
+plt.show()
+
+# 4.5. Monthly Release Trend
+monthly_releases = df['Release_Month'].value_counts()
+
+plt.figure(figsize=(10, 6))
+sns.lineplot(x=monthly_releases.index, y=monthly_releases.values, marker='o', linewidth=3, color='red')
+plt.title('Distribution of Top 100 Releases by Month')
+plt.ylabel('Number of Songs Released')
+plt.xlabel('Month')
+plt.show()