Dash0’s Post

Excited to share my latest project using Decision Tree Classifier to predict customer buying behavior! 🌳📊 Leveraging data preprocessing techniques like label encoding, I've crafted a model to forecast product purchase probabilities. GitHub repo-- https://lnkd.in/guB6Y_sF #MachineLearning #DataScience #DecisionTrees

Level up your #GoogleSheets game with our easy bar graph tutorial! 📊✨ Turn data into compelling stories. #DataViz #SheetsPro

Level up your #GoogleSheets game with our easy bar graph tutorial! 📊✨ Turn data into compelling stories. #DataViz #SheetsPro

🚀 𝐏𝐫𝐨𝐣𝐞𝐜𝐭 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐝: 𝐃𝐞𝐜𝐢𝐬𝐢𝐨𝐧 𝐓𝐫𝐞𝐞 𝐂𝐥𝐚𝐬𝐬𝐢𝐟𝐢𝐞𝐫 - 𝐂𝐮𝐬𝐭𝐨𝐦𝐞𝐫 𝐏𝐮𝐫𝐜𝐡𝐚𝐬𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧 🌟 I’ve successfully completed a project in R where I built a decision tree classifier to predict whether a customer will purchase a product or service based on demographic and behavioral data. Using the Bank Marketing dataset, I was able to develop a predictive model to analyze customer behavior. 🔍 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲𝐬: 1. Built and fine-tuned a decision tree classifier using R 2. Gained experience in analyzing customer data 3. Developed insights into predictive modeling for marketing strategies Check out the full project on GitHub: https://lnkd.in/g2564Ghi This project has deepened my understanding of predictive modeling and customer behavior analysis. Excited to apply these skills in real-world business applications! #RProgramming #DecisionTree #PredictiveModeling #DataScience #CustomerBehavior #ProdigyInfoTech #LearningJourney

🚀 Exciting Update from My Data Science Journey! I’m excited to share my latest project at SkillCraft Technology, where I developed a Decision Tree Classifier to predict customer purchasing behavior using the Bank Marketing dataset from the UCI Machine Learning Repository! Project Highlights:- 🧹 Data Preprocessing: Cleaned and transformed data to ensure accuracy and reliability. 🔍 Feature Engineering: Crafted new features to capture essential customer insights and enhance model performance. 📊 Model Training & Evaluation: Employed metrics like accuracy, precision, recall, and F1-score to assess performance effectively. 🌳 Visualization: Created visual representations of the decision tree and highlighted feature importance for better interpretation. ⚙️ Model Optimization: Fine-tuned the classifier to boost performance and reduce overfitting. 📚 Explore the Dataset: https://shorturl.at/WqpgB 🔗 Check Out My Work: https://shorturl.at/Z3tE9 This project has significantly enriched my understanding of decision tree classifiers and their real-world applications. I’m excited to continue my journey in data science and explore new challenges ahead! 🚀💡 🌟 Let’s connect and share insights in this dynamic field! 🤝 #DataScience #DataAnalysis #DataCleaning #ExploratoryDataAnalysis #BankMarketingDataset #DataAnalytics #SkillcraftTechnology

Building Optimized RAG with LlamaIndex + DSPy 📈

Some results from one of my personal projects that I’ve been working on #DataVis #datavisualization #datamanipulation #datatransformation #tables #charts #statistics #instantfeedback #research #reducingthebarriertodataanalysis #dataanalysisforeveryone #imafunmom #webdev #fullstackdeveloper #fullstack

I added a new reporting feature inside CrushErrors.com that makes it much easier to export. It was always easy to export, but the format wasn't great. Now it is. It's completely ready for Excel's and Google's pivot tables . Users can easily add slicers, filters, and sorts. It's taken me a long time to think of how to do this. Until now, the output has been tough to pivot. Now there's tremendous flexibility. I implemented it in the software in less than a day, but it's taken me years to think of a format that would be significantly improve the experience. Please take a look. Schedule a demo to see with your own data.

Building Optimized RAG with LlamaIndex + DSPy 📈 We’re excited to announce a comprehensive set of integrations with DSPy that let you combine DSPy’s PyTorch-esque syntax and optimization capabilities with the comprehensive set of data+orchestration tools around RAG/agents that LlamaIndex offers. If you prefer to use DSPy Signatures to define your prompt+LLM input/outputs, you can do that. If you prefer to use LlamaIndex prompt modules and leverage DSPy optimization capabilities, you can do that. If you want to repurpose existing optimized DSPy predictors as LlamaIndex prompts, you can do that too 🔥 The three integrations are as follows: 1️⃣ Build and optimize Query Pipelines with DSPy predictors  2️⃣ Build and optimize Query Pipelines with LlamaIndex Prompts, but use DSPy to optimize 3️⃣ Port over DSPy-Optimized Prompts to any LlamaIndex Module with the `DSPyPromptTemplate` syntax. A bonus ✨: Learn how to define custom evaluator functions using LlamaIndex evaluators that still plug into the DSPy optimizer Thanks to both Omar K. and Arnav Singhvi for the help with this. Full cookbook here: https://lnkd.in/gCZjqui4