JKISeason4-7

<p><strong>Challenge 7: AI-Generated Football Scouting Report</strong></p><p><strong>Level:</strong> Medium<br><br><strong>Description:&nbsp;</strong>You are working as a data analyst for a top European football club that is looking to recruit new talent for the upcoming season. The club has provided you with the <em>Football Players Stats 2024-2025 dataset </em>and wants to use AI-powered scouting to find undervalued players, rising stars etc. To answer these questions, you have been provided with three sample prompts. Either use those prompts or be creative with your own prompts and come up with a report for the scout. <strong>Note:</strong> It is not mandatory to use Open AI LLM models. You can also use local LLMs.<br><br><em>Prompt ideas:</em><br>- <em>"Summarize the strengths and weaknesses of Player X using the provided data."</em><br><em>- "Who is the most well-rounded midfielder based on - available statistics?"</em><br><em>- "Which players have similar playing styles to Player X based on their stats?”</em><br><br><em>Beginner-friendly objective(s):</em> 1. Set up the initial data reading process by configuring the CSV Reader node to import the dataset. 2. Filter the dataset based on specific criteria using the Row Filter node.<br><br><em>Intermediate-friendly objective(s):</em> 3. Convert the filtered data into JSON format and manage flow variables for dynamic workflow control. 4. Create and configure prompts for the language model using Variable Expression nodes. 5. Integrate the language model interaction by setting up the LLM Prompter nodes to generate responses based on the prompts. 6. Compile the final report by configuring the Report PDF Writer node to output the results.<br><br><strong>Author</strong>: <strong>Sanket Joshi</strong><br><br><strong>Dataset:</strong> <strong>Football Players Dataset in KNIME Community Hub</strong></p>