I’m excited to share one of my recent projects — a Movie Recommendation System built using Machine Learning, Python, and Streamlit! This project recommends movies similar to the one a user selects, based on their descriptions and features from The Movie Database (TMDb). It was an amazing opportunity to combine both my data science and web development skills. 💡 What I worked on: Built a content-based recommendation model using scikit-learn Processed and analyzed movie data with Pandas and NumPy Integrated the TMDb API to fetch real-time movie posters and details Designed an interactive and visually appealing Streamlit frontend to display recommendations horizontally 🎯 Key Learnings: This project helped me understand how machine learning models can be connected with real-world data and deployed through simple web apps. I also learned how to work with APIs, handle data preprocessing, and improve user interfaces. 🔗 Check out the project on GitHub: https://lnkd.in/gaTDvTJA #MachineLearning #Python #DataScience #Streamlit #RecommendationSystem #AI #ArtificialIntelligence #TMDB

Week 6 of my AI & Data Science journey 🚀 This week, I explored Flask, a lightweight yet powerful Python web framework that plays a key role in deploying machine-learning models and data applications. Key learnings: Building and structuring Flask applications Handling routes, templates, and dynamic URLs Managing GET and POST requests Connecting Flask with machine-learning scripts for model deployment Understanding REST API basics for real-world AI projects Learning Flask bridges the gap between development and deployment — turning data-science scripts into full-fledged interactive apps. 📂 Notes & Assignments: https://lnkd.in/gp2ZQGgQ #Python #Flask #AI #MachineLearning #DataScience #WebDevelopment #LearningJourney

🎬 Movie Recommender System — Built with Streamlit & Machine Learning! Excited to share my latest project — a Movie Recommendation System that helps users find movies similar to their favorites! 🍿 🔍 How it works: The system takes a selected movie as input and recommends similar movies using a content-based filtering algorithm. It leverages machine learning to analyze movie features and find the best matches. 🧠 Tech Stack: Python 🐍 Streamlit (for the interactive web interface) Pandas, NumPy, Scikit-learn (for ML logic) Pickle (for model storage) TMDB API (for fetching movie posters 🎥) Try it live:https://lnkd.in/dQKu6XUq #MachineLearning #DataScience #Python #Streamlit #MovieRecommendation

Excited to share a recent side project — a mini CodeRabbit clone built using Python, FastAPI, LangChain, and LangGraph. live: https://lnkd.in/gSP_sxec github: https://lnkd.in/gBpRDP63 Key highlights: Clones GitHub PRs Parses code using Tree-sitter to build ASTs and semantic graphs with networkx Enriches context for LLM-driven analysis Uploads processed data to S3 Triggers multi-agent AI code reviews via Redis/RQ The goal was to explore how AI agents can collaborate to review pull requests intelligently by combining static analysis with contextual reasoning. This project reflects my ongoing interest in developer tools, AI automation, and building practical, self-hostable AI review systems. #Python #FastAPI #LangChain #LangGraph #AI #DevTools #CodeReview #LLM #NEXTJS

🚀 Just launched my new project — a Spam Detection Web App! I built an end-to-end Machine Learning system that classifies messages as Spam or Not Spam in real time. Here’s what I worked on: 🧹 Data Cleaning & Preprocessing: Lowercasing, stopword removal, lemmatization 🔍 Feature Extraction: TF-IDF vectorization 🤖 Model Training: Linear SVC, Multinomial Naive Bayes, and Logistic Regression ⚙️ Optimization: Tuned model hyperparameters with Optuna 🌐 Deployment: Integrated the trained model into a Flask-based web app that shows both prediction and confidence score 💻 Tech Stack: Python, scikit-learn, Optuna, Flask, Pandas, NumPy 🔗 Project Link: https://lnkd.in/gnvw8beU Excited to share this as part of my journey in building ML-powered applications. Feedback and suggestions are always welcome 🙌 #MachineLearning #Python #DataScience #Flask #Optuna #SpamDetection #MLProjects #OpenSource

💡 Never thought a small idea could turn into something this cool in just an hour! With just some basic Python knowledge (and a big shoutout to Claude AI for the assist 😄), I built “Screen Solver Pro” - a desktop app that can: 🎯 Capture any part of your screen 🔠 Extract the text using OCR 🤖 Instantly analyze or solve it using AI It’s made with: 🧰 Python, PyQt5, MSS, Pillow, Pytesseract, Requests, Pyperclip, NumPy 🧠 Ollama for running LLMs locally ⚡ In my case, I connected it with the Deepseek cloud model for quicker results. Super handy for solving on-screen quizzes, understanding code snippets, or breaking down tricky text , all with a single click. #Python #AI #ClaudeAI #Ollama #Automation #Innovation #DesktopApp #Deepseek

💻 Handwritten Digit Recognizer using KNN 🚀 Excited to share my latest ML project — a Handwritten Digit Recognizer built using the K-Nearest Neighbors (KNN) algorithm! 🧠 Tech Stack: Python 🐍 scikit-learn OpenCV Streamlit (for the web app interface) 🎯 About the Project: This app takes your handwritten digit as input (drawn on canvas) and predicts the correct digit using a KNN classifier trained on the Digits dataset from scikit-learn. 🔗 Try it here: 👉 https://lnkd.in/gXpC8RWM GitHub repo: https://lnkd.in/gMc3z3GN A small step in exploring Machine Learning and Model Deployment! ✨ #MachineLearning #KNN #Streamlit #AI #DataScience #Python #MLProjects

👀 Do you know how much time you actually spend in front of your device? 📊 Visualizing My Screen Time in Real-Time (Python Demo – Part 2) In my last post, I shared how I built a Python demo that uses face detection to track how long I spend in front of my computer 👀💻 Now, I wanted to see that data come to life. So I started saving the recorded time into a CSV file, and built a small real-time visualization to monitor it. Here’s what this new version does 👇 🧾 Stores the sitting time data automatically in a CSV file. 🐍 Uses matplotlib to plot a live chart of total time spent. 👀 Integrates watchdog to detect any changes in the CSV — and updates the chart in real time whenever new data is added. It’s a small but satisfying step toward a smarter tool to help visualize and understand our daily screen habits. Next up: I’m thinking about adding notifications or daily summaries. Would you find that helpful? #Python #Matplotlib #Watchdog #DataVisualization #HealthTech #AI #Productivity

💡 ML Quiz Time! Let’s test your Machine Learning instincts today 😎 Here’s a quick Python snippet — can you guess the output without running it? 👇 from sklearn.linear_model import LinearRegression import numpy as np X = np.array([[1], [2], [3], [4]]) y = np.array([2, 4, 6, 8]) model = LinearRegression() model.fit(X, y) print(model.predict([[5]])) 🤔 Think carefully… Is it 10.0 exactly? Or something slightly different? Why? 👉 Drop your answer in the comments before scrolling! Let’s see who gets it right without executing the code. Hint: consider how scikit-learn handles float conversions and intercepts 👀 🔥 I’ll reveal the correct output and a short explanation in my next post! Follow me for more such fun ML quizzes, mini tutorials, and real-world data science challenges. 💬 So, what do you think the output will be? #MachineLearning #DataScience #Python #AI #CodingQuiz #MLforEveryone