Ever wonder what happens when you give an LLM the ability to write and execute its own Python code? 🤯 I built StatBot Pro, an Autonomous AI Data Analyst using LangChain, Google Gemini 2.5, and Streamlit. You upload a CSV, ask a question, and the agent writes the Pandas logic, calculates the math, and draws the Matplotlib charts for you in real-time. Watch the video to see it dynamically generate a chart and serve it in a custom UI popup! 👇 #Python #LangChain #Streamlit #DataScience #AI #GenerativeAI Streamlit LangChainPythonGoogleGemini GoogleGemini link: https://lnkd.in/gaar5JPi

Day 26 of My AI & Data Science Journey Today I learned about Lists in Python and explored various list methods that make data handling easier. 🔹 append() – Add elements to a list 🔹 insert() – Insert element at a specific position 🔹 remove() – Remove an element 🔹 pop() – Remove element using index 🔹 sort() – Sort the list 🔹 reverse() – Reverse the list 💡 Key takeaway: Lists are powerful for storing and manipulating data, and understanding their methods helps in writing efficient and clean code. Practiced small exercises to strengthen my understanding. #Python #DataScience #CodingJourney #LearningEveryday #AI

Day 11 of My AI Journey 🚀 Today I started working with data structures in Python. Covered: 👉 Lists and how to store multiple values 👉 Iterating over data using loops 👉 Basic operations like adding, removing, and accessing elements What I worked on: 👉 Built small programs using lists to manage and process data 👉 Practiced combining lists with loops and conditions Key takeaway: 👉 Real-world programs don’t deal with single values — they work with collections of data This step is helping me move closer to handling real datasets and preparing for AI concepts. #Python #AI #LearningInPublic #BuildInPublic

One thing I’ve realized while working on real datasets: EDA is not just about plots. It’s about asking the right questions. Over the past few days, I’ve been analyzing different features from an AI Models dataset — starting with individual columns like intelligence index and price. At first, it felt simple. Just visualize and move on. But the deeper I went, the more I noticed: • Every column tells a different story • Distributions reveal hidden patterns • Even a single feature can raise multiple questions I also realized that: You don’t truly understand data until you analyze it from multiple angles Now moving towards understanding relationships between variables — which is where things get even more interesting. #DataScience #EDA #LearningInPublic #Python #Analytics #dataanalysis

Quick experiment today: analyzing 1,000+ keywords around “shoes”… without spending hours glued to my laptop 💻 I tested what happens when you combine Python + Claude vs using Claude on its own. The difference is clear: Python helps you clean and structure large datasets first… then AI steps in to interpret and accelerate insights on something that actually makes sense. Simple flow: Python to organize the chaos, Claude to add context. The result? A process that could take 2+ hours manually… done in 10–15 minutes ⏱️ And yes, it ran while I watched my Netflix series 😄🍿 Key takeaway: it’s not just about using AI, it’s about automation and using it at the right moment in your workflow. I went from a flat list to spotting search intent, patterns, and opportunities with minimal effort. What’s next? Turning these insights into real business impact… and keep experimenting 🚀 #SEOAutomation #MarketingAnalytics #DataWorkflows

Standard machine learning models are great at predicting what will happen. But in the real world, the most valuable question is often when? ⏱️ Whether you are predicting customer churn, machine failure, or user conversions, treating these as standard classification or regression problems ignores a critical factor: censored data. I just published a new guide: Survival Analysis for Data Scientists: A Practical Guide to Time-to-Event Modeling in Python. If you want to move beyond simple point predictions and start building probability curves over time, this guide is for you. Here is a look at what’s inside: 🔹 The core math behind the survival & hazard functions (kept simple!) 🔹 Why handling "right-censoring" makes or breaks your model 🔹 Building your first Kaplan-Meier estimator 🔹 Implementing the Cox Proportional Hazards model using Python Check out the full article here in the comments! 👇 What is your go-to method for modeling time-to-event data? Let me know below! #DataScience #MachineLearning #Python #SurvivalAnalysis #PredictiveAnalytics #CustomerChurn #DataScientists #TechCareers #AIEngineer

Less noise, more substance 🕊️. We wanted to create a straightforward resource for anyone navigating the worlds of AI, Data Science, and Analytics. These pages are a reflection of our daily work and the lessons we have learned along the way. Take a look through the preview below to see what is available now. Visit us at www.codeayan.com #Codeayan #AI #DataScience #Analytics #MachineLearning #Python #GenerativeAI #AgenticAI #DataDriven #TechCommunity #WebLaunch #Coding #LLM #BigData #BusinessIntelligence #Innovation #DataStrategy #SoftwareDevelopment #TechResources #DigitalGrowth

🚀 Day 18 of My Generative & Agentic AI Journey! Today’s focus was on understanding the return statement in Python functions and how it controls the output of a function. Here’s what I learned: 🔙 Return in Functions: • return is used to send a value back from a function 👉 We can return strings, numbers, or any data type • If we use print instead of return 👉 The function outputs None when we try to store its result • If nothing is returned explicitly 👉 Python automatically returns None 🔢 Types of Returns: • Single value → Function returns one value • Multiple values → Function can return multiple values together • Early return → Function can exit before completing all steps 👉 Useful when a condition is met early 💡 Key takeaway: return makes functions more useful and reusable by allowing them to send results back instead of just displaying output. Understanding this helps in writing cleaner and more functional code 🚀 #Day18 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic

📊 Another step forward in my problem-solving journey! Today, I tackled a Poisson Distribution problem and implemented the solution in Python 🐍 👉 Problem: Find the probability that a random variable ( X = 5 ) given mean ( \lambda = 2.5 ) 💡 What I learned: How to apply the Poisson probability formula in real scenarios Importance of precision (rounding to 3 decimal places) Writing clean, ASCII-only code for platform compatibility ✅ Final Result: 0.067 🧠 Key Insight: Strong fundamentals in probability and statistics are crucial for fields like AI, Machine Learning, and Data Science. Problems like these may seem small, but they build the core intuition needed for advanced concepts. 🚀 Staying consistent and improving every day! #Python #Probability #Statistics #PoissonDistribution #DataScience #MachineLearning #AI #CodingJourney #LearningInPublic link of #Solution :- https://lnkd.in/dKYJeTys

Day 17 of My AI Journey 🚀 Today I focused on bringing multiple concepts together to build more complete programs. Covered: 👉 Combining functions, loops, and data structures 👉 Structuring code into logical steps 👉 Improving readability and organization What I worked on: 👉 Built small end-to-end programs instead of isolated snippets 👉 Focused on writing code that is easier to understand and extend Key takeaway: 👉 Real progress comes from connecting concepts, not just learning them individually This phase is helping me transition from writing scripts to building structured applications. #Python #AI #LearningInPublic #BuildInPublic