Design and implementation of visual analysis of e-commerce sales data and product recommendation system based on python

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Visual analysis of e-commerce sales data and design and implementation of product recommendation system based on Python

1. Research background and significance

With the continuous development of Internet technology, e-commerce has become an important engine of global economic development. E-commerce platforms generate a large amount of sales data every day, which hides valuable information such as market trends, consumer preferences and marketing strategies. However, manually analyzing this data is a tedious and time-consuming task, so it is of great significance to develop a system that can automatically capture, clean, analyze and visualize e-commerce sales data.

2. Research status at home and abroad

At home and abroad, many scholars and companies have conducted research and practice on visual analysis of e-commerce sales data and product recommendation systems. Abroad, large e-commerce platforms such as Amazon and eBay have developed their own data analysis tools to provide merchants with visual analysis services of sales data. Domestically, e-commerce platforms such as Taobao and JD.com have also launched similar services. However, these tools usually only provide basic chart display and simple statistical analysis functions, and cannot meet merchants' needs for in-depth data mining and personalization. Therefore, this research aims to develop a more powerful and flexible e-commerce sales data visual analysis and product recommendation system.

3. Research ideas and methods

This study uses Python as the main programming language, combined with crawler technology, data processing technology, machine learning algorithms and Web development technology, to design and implement a visual analysis and product recommendation system for e-commerce sales data. The specific ideas and methods are as follows:

1. Data capture: Use Python's crawler library Scrapy to capture sales data on the e-commerce platform, including product information, sales records, and user reviews. In order to improve crawling efficiency, distributed crawler technology is used.
2. Data cleaning and processing: Use the Pandas library to clean and process the captured raw data, including removing duplicate values, filling in missing values, processing outliers, etc. At the same time, natural language processing (NLP) technology is used to perform sentiment analysis on user reviews and extract useful information.
3. Data visualization analysis: Use Python visualization libraries such as Matplotlib and Seaborn to draw charts to display the trends and distribution of sales data, such as changes in sales over time, sales distribution in different price ranges, etc. At the same time, combined with interactive visualization technology, users can query and analyze data more conveniently.
4. Product recommendation system design: Use machine learning algorithms such as collaborative filtering and deep learning technology to model and analyze sales data to achieve personalized product recommendation. Specifically, it includes strategies such as user behavior-based recommendations, content-based recommendations, and hybrid recommendations. At the same time, consider using knowledge graph technology to enhance the accuracy and diversity of recommendations.
5. System implementation and testing: Integrate and implement the above modules to form a complete e-commerce sales data visual analysis and product recommendation system. Use automated testing tools such as Selenium for functional testing and performance testing to ensure system stability and reliability.
6. Security and privacy protection: During the system design and implementation process, the security and privacy protection of user data are fully considered. Encryption technology, access control and other measures are used to protect the security and privacy of user data.

4. Research content and innovation points

The main contents of this study include the following aspects:

1. Data capture and cleaning: Capture and clean the sales data on the e-commerce platform to ensure the accuracy and completeness of the data.
2. Data visualization analysis: Display the trends and distribution of sales data through charts to help merchants better understand market conditions and consumer preferences.
3. Product recommendation system design: Based on machine learning algorithms and knowledge graph technology, the personalized recommendation function of products can be realized to improve consumers’ shopping experience and merchants’ sales.
4. System implementation and testing: Integrate and implement the above modules to form a complete e-commerce sales data visual analysis and product recommendation system, and conduct functional testing and performance testing.

The innovation points are mainly reflected in the following aspects:

1. Integration of multiple technologies: This study integrates crawler technology, data processing technology, machine learning algorithms and Web development technology to form a new e-commerce sales data analysis and recommendation solution.
2. Personalized recommendation strategy: This study uses user behavior-based recommendation, content-based recommendation and hybrid recommendation strategies to achieve the personalized recommendation function of products, improving the accuracy and diversity of recommendations.
3. Interactive visual analysis: This study combines interactive visualization technology to display and analyze sales data, allowing users to query and analyze data more conveniently, improving the efficiency and accuracy of data analysis.
4. Security and privacy protection: This study fully considered the security and privacy protection of user data during the system design and implementation process, and adopted encryption technology, access control and other measures to protect the security and privacy of user data and improve the system's security and privacy. Security and trustworthiness.

5. Backend functional requirement analysis and front-end functional requirement analysis

Backend functional requirements analysis mainly includes the following aspects:

1. Data capture and cleaning module: realizes the function of automatically capturing and cleaning sales data on the e-commerce platform to ensure the accuracy and completeness of the data.
2. Data storage and management module: The function of storing and managing captured data includes database design and management interface development.
3. Data visualization analysis module: The function of realizing chart display and analysis of sales data includes chart type selection, data query and filtering and other functions.
4. Product recommendation algorithm module: implements personalized recommendation functions for products, including algorithm selection, model training and prediction.
5. System management and monitoring module: The functions to monitor and manage the running status of the system include user management, log management, exception handling and other functions.

Front-end functional requirements analysis mainly includes the following aspects:

1. User login and rights management functions: Implement user registration, login and rights management functions to ensure system security and access control.
2. Data visualization display function: The function of displaying chart data obtained from background analysis on the front end includes chart type selection, data query and filtering functions.
3. Product recommendation display function: The function of displaying the products recommended by the backend on the front end includes functions such as recommendation list display and product details viewing.
4. Interactive operation function: The function of realizing interactive operation on the front end includes data query, filtering, sorting and other operations, as well as interactive operation of charts.
5. Responsive layout and compatibility optimization function: Implement compatibility optimization function for different screen sizes and different browsers to ensure system stability and user experience consistency.

6. Research progress arrangement

This research plan is divided into the following stages:

1. The first stage (1-2 months): Conduct demand research and technical pre-research to clarify the system’s functions and implementation plans. At the same time, complete the writing of the proposal report.
2. The second stage (2-4 months): Complete the development of various backend functional modules, including data capture and cleaning module, data storage and management module, data visualization analysis module and product recommendation algorithm module, etc. Unit testing and functional testing are performed at the same time to ensure the stability and reliability of each module.
3. The third stage (4-6 months): Complete the development of various front-end functional modules, including user login and permission management functions, data visualization display functions, product recommendation display functions and interactive operation functions, etc. At the same time, integration testing and performance testing are performed to ensure the overall stability and reliability of the system. At the same time, the writing of the mid-term report was completed.
4. The fourth stage (6-8 months): Carry out system debugging and optimization work, including optimization of front-end and back-end interactions, database query optimization, etc. At the same time, we conduct user research and demand feedback collection to improve and perfect the system. Finally, complete the writing of the graduation thesis and prepare for the defense.

7. Paper (design) writing outline

1. introduction

1.1 Research background and significance
1.2 Research status at home and abroad
1.3 Research objectives, research content and innovation points

1. Relevant theoretical and technical foundations

2.1 Basics of Python programming language
2.2 Principle and implementation of crawler technology
2.3 Data cleaning and processing technology
2.4 Data visualization analysis technology
2.5 Principle and implementation of product recommendation algorithm

1. System requirements analysis and design

3.1 E-commerce platform sales data analysis and recommendation demand survey
3.2 Overall system architecture design
3.3 Backend function module design and implementation plan 3.5 Database design and implementation plan
3.4 Front-end functional module design and implementation plan

1. System implementation and testing

4.1 Implementation and testing of various back-end functional modules
4.2 Implementation and testing of various front-end functional modules
4.3 System integration testing and performance testing
4.4 System security and privacy protection solution implementation and testing

1. System application and effect evaluation

5.1 Application examples of the system in e-commerce platform sales data analysis
5.2 Application examples and effect evaluation of the system in product recommendation
5.3 Users Feedback collection and analysis, and discussion of system improvement directions

1. Conclusion and Outlook

6.1 Summary of research work and review of results
6.2 Outlook and suggestions for future work
6.3 Acknowledgments and references

8. Main references

[Main references are listed here, including relevant books, journal papers, conference papers, online resources, etc., and the format meets the school’s requirements]

9. Appendix

[Appendices are listed here, including system source code, test reports, user manuals, etc., which can be added if necessary]

At this point, this graduation project proposal report has been fully presented. Through this research, we hope to develop a powerful visual analysis and product recommendation system for e-commerce sales data to help merchants better understand market conditions and consumer preferences, and improve sales and user satisfaction. At the same time, we also hope that this study can provide useful reference and reference for research and practice in related fields.

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research background and meaning

In recent years, with the rapid development of the e-commerce market, more and more people have begun to choose to shop online. For e-commerce platforms, how to increase sales, strengthen user stickiness and improve user satisfaction is an important issue. Therefore, visual analysis of e-commerce sales data and the design and implementation of product recommendation systems have become hot spots in the current e-commerce research field.

This research aims to use Python language to conduct visual analysis of e-commerce sales data, and then design and implement an e-commerce product recommendation system based on Python. Through visual analysis, you can discover the rules of user purchasing behavior and improve sales and user satisfaction. The product recommendation system based on Python can improve the user's shopping experience, promote the formation of user purchasing behavior, and strengthen user stickiness.

Research status at home and abroad

Domestic and foreign scholars have also done a lot of meaningful research on visual analysis of e-commerce sales data and product recommendation systems. For example, American scholars Koren et al. (2009) proposed a recommendation algorithm based on matrix decomposition and applied it to the Netflix movie recommendation system; Li Hui et al. (2011) studied the impact of user behavior characteristics on product sales. Based on this A product recommendation system based on collaborative filtering was developed; Chinese researchers Hu Jianping and others (2013) used data mining tools to analyze sales data on Tmall and JD.com websites, and obtained the characteristics and rules of the use of sales data, and Corresponding strategies and methods are proposed.

However, there are still some problems in domestic and foreign research. For example, previous research mainly focused on the application and analysis of a single algorithm and ignored the combined use of multiple algorithms. At the same time, the research on data visualization analysis was not in-depth enough.

Research ideas and methods

This research will conduct visual analysis of e-commerce sales data based on Python language, and design and implement an e-commerce product recommendation system based on Python. Specifically, the research methods are as follows:

1. Data acquisition and preprocessing: This study will use the Python programming language to acquire and preprocess data.

2. Data visualization analysis: By using related visualization tools such as Matplotlib, Seaborn and Plotly in Python, we can conduct visual analysis of e-commerce sales data, including the rules of user purchasing behavior, product sales, user needs, etc.

3. Research on product recommendation algorithms: This research will combine a variety of recommendation algorithms, including collaborative filtering algorithms, content-based recommendation algorithms and matrix decomposition-based recommendation algorithms, to recommend products to users from different perspectives.

4. Design and implementation of product recommendation system: Based on the above research, a Python-based e-commerce product recommendation system is designed and implemented, including user login, shopping cart function, product recommendation, etc.

Research internal customers and innovation points

The innovations of this study mainly include the following aspects:

1. Integrating multiple recommendation algorithms: Previous research mainly focused on the application and analysis of a single algorithm. This study will integrate multiple recommendation algorithms to recommend products from different angles to improve the accuracy and user satisfaction of the recommendation system.

2. Data visualization analysis: This study will conduct visual analysis of e-commerce sales data, discover patterns from the perspective of user behavior, and improve sales and user satisfaction.

3. Recommendation system design based on Python: This study will use the Python programming language to design and implement a recommendation system so that the system has high efficiency, powerful data processing capabilities and a good user experience.

Backend functional requirement analysis and front-end functional requirement analysis

Backend functional requirements analysis:

1. Data acquisition and preprocessing functions: obtain data from the database, and perform data cleaning and processing.

2. Product recommendation algorithm implementation: Use Python to implement multiple recommendation algorithms.

3. Recommended result display function: Based on the algorithm results, the recommended results are displayed to the front end.

Front-end functional requirements analysis:

1. User registration and login functions: Allow users to register and log in to the system.

2. Product classification and search function: allows users to search for products based on product classification and keywords.

3. Product details display function: Display product details, including product attributes, price, inventory, etc.

4. User shopping cart function: allows users to add items to the shopping cart and perform checkout operations.

5. Product recommendation function: recommend similar or related products to users.

Research ideas, research methods, feasibility

This research mainly uses the Python programming language to conduct visual analysis of e-commerce sales data, and designs and implements a Python-based product recommendation system. Among them, the data visualization analysis mainly uses related visualization tools such as Matplotlib, Seaborn and Plotly in Python. The product recommendation algorithm is based on a variety of algorithms. On this basis, an e-commerce product recommendation system based on Python is developed.

Because Python has high efficiency, powerful data processing capabilities and good user experience, it is feasible to develop an e-commerce product recommendation system based on Python.

Research schedule

The schedule of this study is as follows:

1. The first stage (1-2 weeks): Obtain e-commerce sales data and perform data preprocessing.

2. The second stage (2-4 weeks): Use Python to visually analyze the data and derive the patterns of sales data.

3. The third stage (4-6 weeks): Research product recommendation algorithms and implement various algorithms in Python.

4. The fourth stage (6-8 weeks): Based on the above research results, design and implement the product recommendation system.

5. The fifth phase (8-10 weeks): System testing and performance optimization.

6. The sixth stage (10-12 weeks): thesis (design) writing.