Movie-Data-Analysis-Dashboard
This project demonstrates a comprehensive analysis of movie data. ##
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Data Manipulation: Pandas for data cleaning, transformation, and analysis
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Data Visualization: Matplotlib and Seaborn for creating insightful plots and charts
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Database Integration: SQLAlchemy for connecting to and querying a PostgreSQL database
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API Integration: Requests library for fetching movie data from the OMDB API
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Statistical Analysis: Correlation analysis and descriptive statistics
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Natural Language Processing (NLP): Basic text analysis for movie titles
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Version Control: Git for code management and collaboration
About the Project
This project analyzes movie data from 2021 to 2023, focusing on various aspects such as ratings, view counts, and title word frequencies. The analysis includes:
- Data collection from the OMDB API
- Data cleaning and preprocessing
- Exploratory Data Analysis (EDA) with visualizations
- Statistical analysis of movie ratings and view counts
- Trend analysis across years
- Word frequency analysis of movie titles
Key Visualizations
- Distribution of movie ratings
- Scatter plot of view count vs rating
- Top 10 most viewed movies
- Average rating trends by year
- Year-over-year comparison of average rating and view count
- Box plots of ratings and view counts across years
- Word frequency analysis in movie titles
The project demonstrates the ability to handle real-world data, generate meaningful insights, and present findings through clear and informative visualizations.
How to Use
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Clone the repository:
git clone https://github.com/Atharvack/movie-data-analysis-dashboard.git -
Run the Jupyter notebooks to see the analysis and generate visualizations.
Note: You’ll need to:
Set up your own PostgreSQL database Obtain an OMDB API key to fully replicate the data collection process