This project is a data analysis and visualization of top-rated movies. Using Python, it explores trends in popularity, ratings, release years, and voting patterns to better understand what makes a movie successful.

• Data Cleaning – handled missing values and formatted release dates.
• Exploratory Data Analysis (EDA):
  • Popularity vs. Average Ratings (scatter plot)
  • Distribution of Movies by Release Year
  • Ratings trends across different years
  • Box plots for numeric features (popularity, vote counts, ratings, etc.)
  • Vote Count vs. Average Rating relationship
  • Top 10 Most Popular Movies (bar chart)
  • Movies Released Per Year (line chart)
  • Top 10 Movies by Highest Vote Count
• Interactive Visualizations – built with Plotly for dynamic exploration.

• Python
• Pandas – data manipulation
• Matplotlib & Seaborn – static visualization
• Plotly Express – interactive charts

• Movies with higher popularity do not always have the best ratings.
• The number of movies released per year has shown an increasing trend.
• Some movies stand out with very high vote counts, indicating wide audience engagement.

1. Clone this repository:

   git clone https://github.com/your-username/top-rated-movie-analysis.git
   cd top-rated-movie-analysis
   Install dependencies:

pip install -r requirements.txt Open Jupyter Notebook and run: jupyter notebook "Top Rated Movie Analysis (Minor Project -2).ipynb" Project Structure Top Rated Movie Analysis/ │── Top Rated Movie Analysis (Minor Project -2).ipynb # Main notebook │── movie 3.csv # Dataset (local) │── requirements.txt # Dependencies │── README.md # Project documentation