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You can use datasets from sources like IMDb or The Movie Database (TMDb). For this project, you’ll need information such as movie titles, release dates, genres, ratings, box office earnings, and more.

Project Goals:

  1. Load and clean the movie dataset.
  2. Explore movie genres and their popularity.
  3. Analyze the relationship between ratings and box office earnings.
  4. Identify trends in movie releases over the years.
  5. Create visualizations to convey insights effectively.

Python Code:

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Load the movie dataset
data = pd.read_csv('movie_dataset.csv')  # Replace with your dataset file

# Data Cleaning
# Handle missing values, data type conversion, etc.

# Explore movie genres and popularity
genre_counts = data['genres'].str.split('|', expand=True).stack().value_counts()

plt.figure(figsize=(10, 6))
plt.title('Popularity of Movie Genres')
plt.ylabel('Number of Movies')

# Analyze ratings vs. box office earnings
sns.scatterplot(x='rating', y='box_office', data=data)
plt.title('Ratings vs. Box Office Earnings')
plt.ylabel('Box Office Earnings')

# Trends in movie releases over the years
data['release_year'] = pd.to_datetime(data['release_date']).dt.year

yearly_movie_counts = data['release_year'].value_counts().sort_index()

plt.figure(figsize=(12, 6))
plt.plot(yearly_movie_counts.index, yearly_movie_counts.values, marker='o')
plt.title('Number of Movies Released Over the Years')
plt.ylabel('Number of Movies')

Keep in mind that the ‘movie_dataset.csv’ file should contain your movie data with relevant columns like ‘genres’, ‘rating’, ‘box_office’, ‘release_date’, etc. You might also consider additional analyses like sentiment analysis based on movie reviews, analyzing actors/directors’ impact on box office, or exploring correlations between budget and earnings. As always, adapt the code and the analyses to match your dataset and project goals.

1 Comment

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