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Graduation project proposal report of undergraduate film box office data analysis and visualization system based on Python
1. Research background and significance
As an important part of the global entertainment industry, the film industry generates a large amount of box office data every year. Analysis and visualization of these data can help film producers, distributors and theaters better understand market trends and audience preferences, and provide decision support for film planning, production and marketing. As a powerful, easy-to-learn and easy-to-use programming language, Python is widely used in the fields of data processing and analysis. Therefore, building a movie box office data analysis and visualization system based on Python has important practical significance and application value.
2. Research status at home and abroad
At present, domestic and foreign research on movie box office data analysis mainly focuses on box office prediction, influencing factor analysis, movie evaluation, etc. In terms of box office prediction, researchers use historical box office data, film characteristics, market environment and other factors to build prediction models to achieve accurate predictions of future box office. In terms of the analysis of influencing factors, the key factors affecting the box office are revealed through correlation analysis between box office data and factors such as film type, cast, and release time. In terms of movie evaluation, by analyzing audience ratings, comments and other data, we can understand the audience's satisfaction and word-of-mouth communication with the movie.
However, most current research remains at the data analysis level and lacks intuitive data visualization tools. Therefore, this research aims to build a movie box office data analysis and visualization system based on Python, combining data analysis results with visualization technology to provide users with a more intuitive and convenient data analysis experience.
3. Research ideas and methods
This study will adopt the following research ideas and methods:
- Data collection: Collect historical box office data, film characteristics data, market environment data, etc. from public movie box office data sources.
- Data preprocessing: Clean, integrate and format the collected raw data to provide usable data sets for subsequent analysis.
- Data analysis: Use Python's data analysis library and machine learning algorithms to perform statistical analysis, correlation analysis, and predictive analysis on data sets.
- Data visualization: Use Python's visualization library to display data analysis results graphically, including line charts, bar charts, scatter plots, heat maps, etc.
- System design and implementation: Design and implement a movie box office data analysis and visualization system based on Python's Web development framework, including front-end and back-end development, interface design, interactive function implementation, etc.
4. Research content and innovation points
The research content of this study includes the collection and preprocessing of movie box office data, the research and application of data analysis methods, the implementation of data visualization technology, and the design and development of the movie box office data analysis visualization system. The innovation points are mainly reflected in the following aspects:
- Build a complete movie box office data analysis and visualization system based on Python, providing a full-process solution from data collection to result display.
- Introduce advanced machine learning algorithms to achieve accurate predictions of movie box office and in-depth exploration of influencing factors.
- Design a variety of data visualization views to help users understand movie box office data from different dimensions and discover the patterns and trends hidden behind the data.
5. Backend functional requirement analysis and front-end functional requirement analysis
Backend functional requirement analysis includes data collection and cleaning modules, data storage and management modules, data analysis and prediction modules, etc. Front-end functional requirements analysis includes user login and permission management modules, data visualization display modules, interactive operation modules, etc. These modules will work together to provide users with integrated movie box office data analysis and visualization services.
6. Research ideas and feasibility of research methods
This study uses Python as a development tool, using its powerful data processing capabilities and rich visualization libraries to ensure the smooth progress of the research. At the same time, team members have solid programming foundation and data analysis capabilities, and can cope with challenges and problems that may arise in research. Therefore, this research idea and research method are feasible.
7. Research progress arrangement
The research progress will be arranged according to requirements analysis, system design, coding implementation, testing and optimization, thesis writing and defense, etc. to ensure the project is completed on time.
8. Thesis (design) writing outline
The paper writing outline will include abstract, introduction, research background and significance, domestic and foreign research status, research ideas and methods, system design and implementation, experimental results and analysis, conclusions and prospects, etc., to present the complete research process and results.
9. Main References
(Here are the main documents and related materials referenced in this study, including but not limited to academic journal articles, conference papers, and monographs. etc.)