📊 IMDb Movie Data Analysis

🌟 Overview

This project analyzes IMDb movie data to extract valuable insights using Python. It leverages powerful libraries like Pandas, Matplotlib, Seaborn, and others to explore trends, correlations, and patterns in the dataset.

Key Features

• Data preprocessing and cleaning for missing or inconsistent entries.
• Exploratory Data Analysis (EDA) with descriptive statistics and visualizations.
• Insights into genres, revenue, ratings, and release trends.
• Advanced visualizations showcasing trends and relationships.

---

📂 Dataset

• Source: IMDb Movie Data
• Size: ~1,048,575 movies
• Attributes:
  • Title: Name of the movie
  • Genre: Movie genre(s)
  • Director: Movie director(s)
  • Vote_average: IMDb user rating
  • Vote_count: Number of IMDb votes
  • Release Date: Date of release
  • Runtime: Movie runtime (minutes)
  • Id : Id in the dataset
  • Status : Released or to be released
  • Revenue: Money generated
  • Adult: Is it for adults
  • Budget: Amount spent on production
  • Imdb_id: Movie ID
  • Original_language: Language spoken in the movie '

---

🔍 Insights Gained

Key Questions Answered:

1. Which genres are most popular?
2. What are the highest-rated movies?
3. How do IMDb ratings vary over the years?
4. Are runtime and ratings correlated?
5. How do the runtime vary over the year?
6. What are the Top 10 most voted movies?
7. Does number of votes correlate to the IMDB rating?

---

📈 Visualizations

Here are some sample visualizations generated during the analysis:

1.Top 10 Most Voted Movies

2. Trend In Movie Runtimes

3. Trend In IMDB Rating

---

📋 Summary & Conclusion

Insights:

After analyzing the 1 million+ records in the dataset, we arrived at the following conclusions:

1. The dataset contains no data related to the director/directors of the movies that have been released.
2. The dataset includes many outliers, such as release dates extending to the year 2099.
3. There are over 13,000+ combined genres with a rating of 10.
4. The runtime of movies has increased over the past decades at a constant rate.
5. There is no correlation between runtime and IMDb ratings.
6. The vote count and IMDb ratings show a weak positive correlation.
7. The maximum revenue generated by a movie in the dataset was $4,999,999,999.