Local Chapter Berlin, Germany Local Chapter
Coordinated byGermany ,
Status: Completed
Project Duration: 17 Apr 2023 - 20 Jun 2023
Open Source resources available from this project
Project background.
Germany’s inflation rate showed no signs of easing at the start of the year, as energy and food price pressures remained high due to the war in Ukraine. Source: Reuters Feb 2023
Other sources show that the cost of food in Germany increased 19.20 percent in January of 2023 over the same month in the previous year. Source: Federal Statistical Office
Therefore a recommended system for grocery shopping in Berlin can be useful for several reasons. Firstly, the availability and cost of groceries can vary across different stores, which can make it difficult for buyers to find the best deals. Secondly, some buyers may prefer to shop at certain stores due to their quality or convenience, but may not be aware of other stores that offer similar benefits. Thirdly, some buyers may be new to an area and not know where to find the best stores for their needs.
By implementing a recommended system, buyers can receive personalised recommendations for the best stores based on their location, preferences, and needs. This can save buyers time and money by directing them to the stores that offer the best deals and quality products. Additionally, it can help buyers discover new stores that they may not have been aware of, expanding their options for grocery shopping. Overall, a recommended system for grocery shopping can improve the shopping experience for buyers and help them make more informed decisions.
We can combine the previous with the trending high prices of grocery items by incorporating price data into the system. This could involve analysing historical price data for various grocery items across different stores and locations, and using this data to make recommendations based not only on proximity and availability, but also on cost-effectiveness.
For example, the system could factor in the current and historical prices of various grocery items at different stores, along with the user’s location and preferences, to recommend the most cost-effective stores for the user to shop at. This would provide an additional layer of value to the system beyond simply recommending stores based on proximity and availability.
Additionally, the system could also monitor and analyze trends in grocery prices over time, and provide users with alerts and recommendations when certain items are trending higher or lower in price. This would allow users to plan their shopping more strategically, and potentially save money by buying certain items at the most opportune times. Overall, by combining a recommended system for grocery shopping with data on trending high prices, we can provide a more comprehensive and valuable solution for users.
The problem.
Help customers make informed decisions about where to shop for their groceries. With so many options available, it can be overwhelming to know which store has the best deals, selection, and quality for the customer’s specific needs and preferences. The recommended system can help to alleviate this problem by providing personalised recommendations based on factors such as location, product availability, price, and customer reviews. By doing so, customers can save time and money while also ensuring they are satisfied with their grocery shopping experience.
Project goals.
Project plan.
Week 1
Week 1: Project Planning and Data Collection
Gather data on grocery stores in Berlin, including pricing, product availability, and quality
Week 2
Week 2: Data Preprocessing and Cleaning
Clean and preprocess the collected data
Create a database of grocery stores and productsWeek 3
Week 3: Data Analysis and Pattern Identification Analyze the collected data to identify trends and patterns in pricing, product availability, and quality Explore machine learning algorithms suitable for building a recommendation engine
Week 4
Week 4: Recommendation Engine Development Develop and test a prototype recommendation engine using the selected machine learning algorithms Incorporate user feedback to improve the recommendation engine
Week 5
Week 5: User Interface Design Design a user interface that allows users to input their preferences and receive personalized recommendations for grocery stores in their area Create a mockup of the user interface
Week 6
Week 6: User Interface Development Implement the user interface design and integrate it with the recommendation engine Test the user interface with a small group of users
Week 7
Week 7: Evaluation and Refinement Evaluate the accuracy and effectiveness of the recommendation engine using metrics such as precision, recall, and F1 score Refine the recommendation engine based on the evaluation results
Week 8
Week 8: Final Testing and Deployment Conduct final testing of the recommendation engine and user interface Deploy the recommendation engine and user interface to a web server or cloud platform
Learning outcomes.
1- Understanding the data collection and preprocessing techniques required to build a recommendation system. 2- Familiarity with different machine learning algorithms for recommendation systems, such as collaborative filtering, content-based filtering, and hybrid approaches. 3- Experience with data visualisation techniques to explore and analyse the data. 4- Knowledge of how to evaluate the performance of a recommendation system using metrics such as precision, recall, F1 score, and accuracy. 5- Understanding of how to deploy a recommendation system in a production environment, such as a web application or mobile app.
