Hi! I'm getting this error for the following Kaggle python exercise... Any thoughts? thanks /tmp/ipykernel_33/3599585355.py:17: UserWarning: FixedFormatter should only be used together with FixedLocator graph.set_yticklabels(new_labels) def prettify_graph(graph): graph.set_title("Results of 500 slot machine pulls") # Make the y-axis begin at 0 graph.set_ylim(bottom=0) # Label the y-axis graph.set_ylabel("Balance") # Bonus: format the numbers on the y-axis as dollar amounts # An array of the values displayed on the y-axis (150, 175, 200, etc.) ticks = graph.get_yticks() # Format those values into strings beginning with dollar sign new_labels = ['${}'.format(int(amt)) for amt in ticks] # Set the new labels graph.set_yticklabels(new_labels) ​ graph = jimmy_slots.get_graph() prettify_graph(graph) graph ​ ​

A while back I posted a thread about boosting my membership sales by 29% using my customer segmentation model. I took it a step further by creating a Dockerfile and a Docker image, and then pushing it to Azure where I created a container. So you can run the app here: http://custseg.dqfqbacuakgthrhj.eastus.azurecontainer.io:5000/ It's not the quickest web app... but with a little patience, you can give it a try and let me know what you think. 😄 For context - if you're not familiar with Kajabi, it's a platform where people can sell their courses online and build an email/customer list. My ML app helps segment their customers into groups for better targeted marketing campaigns (and more revenue!) If you want to read about the guts of the model I built... you can check out my Jupyter Notebook here: https://github.com/nkeblawi/nk-cust-segmentation/blob/main/notebooks/nk-cust-segmentation-report.ipynb My second intent with this is to use this as a portfolio piece to help me get a job as a DS and/or ML-oriented DE 🙂 Thoughts? Comments? Fire away, please!

🚀 Excited to Share My Latest Project! 🚀 Hello Community,I’m thrilled to introduce my latest project – a Streamlit Data Visualization and Analysis App! This tool is designed to automate data summarization, generate insightful questions, and provide visualizations to help users understand their data better. It's a robust solution for data analysts, researchers, business intelligence professionals, and educators. Key Features: Tech Stack: Streamlit: For creating an interactive and user-friendly interface. LIDA (Language Interface for Data Analysis): Powers data summarization, question generation, and visualization. OpenAI GPT-3.5 Turbo: Utilized for generating natural language summaries and questions.Pandas: For data manipulation and analysis. Matplotlib and Seaborn: Used to create comprehensive visualizations. Use Cases:Data Analysts: Quickly gain insights and visual representations of large datasets. Researchers: Automate the analysis process, making it easier to interpret research data. Business Intelligence: Generate detailed reports and visualizations to support decision-making. Educational Purposes: Teach students how to analyze and visualize data effectively. This project leverages advanced technologies to make data analysis more accessible and insightful. I'm incredibly proud of the work that's gone into it and the potential impact it can have.

If you are a developer interested in learning more about Generative AI, "Generative AI for Beginners" is the perfect course for you. With 18 new video lessons, this open source Github repo covers everything from basic concepts to advanced techniques for building Generative AI applications. Each lesson includes both written and short video lessons, making it easy to follow along and learn at your own pace. https://techcommunity.microsoft.com/t5/microsoft-developer-community/generative-ai-for-beginners-full-videos-series-released/ba-p/4183271

I am new to this course, can anyone please tell me if it's worth all the time I am going to invest in it!