🚗 New Car Purchase Prediction using Flask Excited to share my latest project where I combined Machine Learning with Web Development to build a simple prediction app! Project Overview: This web application predicts whether a person will purchase a car based on key inputs like Age and Salary. Technologies Used: ✔️ Python ✔️ Flask ✔️ Scikit-learn ✔️ NumPy & Pandas ✔️ HTML Model Used: ✔️ Decision Tree How it works: ✔️ User enters Age and Salary ✔️ Data is processed by the Flask backend ✔️ Machine Learning model makes prediction ✔️ Result is displayed instantly Key Learning: This project helped me understand how to: ✔️ Integrate ML models with web applications ✔️ Handle user input through forms ✔️ Deploy models in real-world scenarios GitHub : [ https://lnkd.in/gsEcvXHc ] #MachineLearning #Flask #Python #DataScience #AI

I built a web app to make data analysis accessible for beginners Try it here: https://lnkd.in/gTgGrczZ No prior knowledge needed Upload any dataset and get insights in minutes What it does • Cleans data and handles missing values • Detects outliers with clear metrics • Generates visual insights automatically • Builds and compares models • Explains results simply for non technical users Best experienced on a computer Current limitations • 200 MB file size limit • Large datasets may take longer to process • Joins require exact column matches • UI still evolving Built with Python and Streamlit, combining logic with AI assistance Open to feedback #DataScience #MachineLearning #Streamlit

🚀 Excited to share my latest project! I’ve built a **Credit Risk Prediction App** using **Machine Learning + Streamlit** that predicts whether a loan applicant is a *Good* or *Bad* credit risk. 🔍 Key Highlights: • Built using XGBoost Classifier • Interactive web app with Streamlit • Real-time predictions based on user input • Clean and simple UI This project helped me strengthen my skills in: 👉 Data preprocessing 👉 Model building 👉 Deployment with Streamlit 📌 Check out the project on GitHub: https://lnkd.in/dNtZHgwY #MachineLearning #DataScience #Streamlit #Python #AI #Projects #LearningJourney 

In my last post, I shared my House Price Prediction app — built using Linear Regression. Linear Regression powers my app. But under the hood, every prediction is a decision. Here's how This post: Decision Tree → asks questions → outputs "High price" (a category) Same goal. Completely different thinking. 🌳 How it works A Decision Tree splits data using yes/no questions. Each question = a node Each final answer = a leaf No multiplication. No weights. Just logic. 🏠 My house price tree — step by step: Area > 1500 sq ft? ├── YES → Prime location? │ ├── YES → 🟢 High price (₹80L+) │ └── NO → 🟡 Medium-high (₹55–75L) └── NO → Age < 10 years? ├── YES → 🟠 Medium (₹35–55L) └── NO → 🔴 Low price (below ₹35L) 3 questions. 4 answers. That's it. The model learns these splits automatically from data. No manual rules. No equations. Enter house details → follow the branches →answer #DecisionTree #MachineLearning #HousePricePrediction #DataScience #Python #ScikitLearn How a decision tree predicts house prices

Stop using Pandas for everything. I just published a full breakdown of 7 Python libraries that are redefining how developers build in 2026 with install commands + real code examples for each. Here's the quick cheat sheet: ⚡ Polars → 10x faster than Pandas for big data 📄 MarkItDown → Converts PDFs/Word docs into AI-ready Markdown 🤖 Smolagents → Build your first AI agent in 10 lines 🧑✈️ GPT Pilot → An AI that writes entire features, not just autocomplete 📱 Flet → Build web + mobile + desktop apps in pure Python 🛡️ Pyrefly → Catch bugs BEFORE you run your code (Meta-built) 🌐 Morphik-Core → AI that understands images and videos, not just text You don't need to learn all 7 today. Pick the one that solves YOUR problem right now. Working with data? → Polars Building an app? → Flet Curious about agents? → Smolagents The full blog (with code examples for every library) is linked in the comments 👇 Which of these are you already using? Drop it below 🔽 #Python #AI #MachineLearning #Programming #Developer #TechIn2026 #AITools #OpenSource #PythonDeveloper #CodingTips

I built a complete 𝗨𝘀𝗲𝗱 𝗖𝗮𝗿 𝗣𝗿𝗶𝗰𝗲 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗼𝗿 from scratch, creating a full end-to-end pipeline that handles everything from raw data to a live application. Instead of relying on a pre-built dataset, I identified a unique problem and built my own data source using web scraping. My goal was to move beyond tutorials and mimic a real-world 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝗰𝗲 workflow.  • 𝗦𝗰𝗿𝗮𝗽𝗶𝗻𝗴: Automated data collection to get real-time market prices.  • 𝗣𝗿𝗲𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴: Cleaning messy web data into a machine-learning-ready format.  • 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴: Training a robust regressor to find the patterns.  • 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: Building a Flask web app to make the model accessible to anyone. The Workflow: 𝗦𝗰𝗿𝗮𝗽𝗲 𝗗𝗮𝘁𝗮 → 𝗖𝗹𝗲𝗮𝗻 & 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 → 𝗧𝗿𝗮𝗶𝗻 𝗠𝗼𝗱𝗲l → 𝗗𝗲𝗽𝗹𝗼𝘆 #MachineLearning #DataScience #Python #Flask #WebScraping #PortfolioProject Check out the full documentation and code on GitHub: https://lnkd.in/gAZp4iKq

Every journey begins with a single step — and here’s mine. I’ve built a Code Debugger App using Streamlit as part of my learning path in Data Science and Machine Learning. While it’s a simple project, it helped me understand how to turn logic into an interactive tool. 🔍 What I learned from this project: Building interactive apps with Python Structuring problem-solving logic Handling and analyzing code inputs Creating user-friendly interfaces 🌐 Live App: https://lnkd.in/gkKkyJtc 💡 My goal is to move toward more advanced projects like: Data analysis & visualization Machine learning model integration AI-powered tools This is just the beginning — more exciting projects coming soon! I’d really appreciate your feedback and suggestions 🙌 #DataScience #MachineLearning #Python #Streamlit #LearningJourney #CSE #AI #Projects

🚀 From Jupyter Notebook to Live ML Web App! I recently completed a project predicting the age of abalones using the UCI Abalone dataset. While the data analysis was done in Python, I wanted to make the insights accessible through a live interactive interface. 🛠️ The Technical Side: Performed deep EDA using Seaborn and Matplotlib to understand physical feature correlations. Trained and optimized a Random Forest Regressor to achieve the best prediction accuracy. Deployed the front-end visualization using Lovable to create an interactive "Viz Studio." 🔗 Check out the live app here: https://lnkd.in/dqgSp7hP 📂 Github Code: https://lnkd.in/dwUPKrSe This project helped me bridge the gap between backend machine learning models and frontend user experience. Feedback is always welcome! #DataScience #MachineLearning #Python #DataAnalytics #PortfolioProject #RandomForest

I recently had a very interesting problem to solve: how to safely store and parse user inputted formulas for our analytics platform. At first glance, it sounds simple. Take a formula, evaluate it, return a number. Something like: (meta.spends + (ads.spends - 50)) * 100 My first instinct was the obvious one: just evaluate the string. That idea lasted about 30 seconds. Because the moment you try to do this in a real system, things get complicated very quickly: • Users can input anything which is a security risk • Formulas need to be reusable and debuggable • Metrics depend on other metrics so ordering matters • You need control over functions and allowed operations I explored existing options, but they were either unsafe (like "eval"), too limited or too heavy for what we needed. I realized we had to build a lightweight execution engine, so here's what we ended up doing: 1. Parse the formula into an Abstract Syntax Tree (AST) 2. Evaluate it in a controlled environment (no arbitrary execution) 3. Extract dependencies to understand which variables are required 4. Support custom functions like SUM, AVG, etc. But the most interesting realization came later. Formulas are not isolated, they form a graph. One metric depends on another, which can depend on another in turn. Suddenly, this becomes a dependency problem. Which means: • You need to resolve execution order • You need to detect cycles • You need to think like a query planner, not just a parser What started as "just evaluate a formula" turned into designing a small, safe, and extensible computation layer. And this is something I have noticed repeatedly. The interesting problems are not always the big ones. They are the ones that look simple enough to ignore, until you try to build them properly. #engineering #systemdesign #python #analytics #backend #softwaredevelopment