Movie Recommendation System using Python and Machine Learning

I built something I'm genuinely proud of this week. 🛠️ Clipper Maker — an AI-powered YouTube clip extractor built entirely in Python. How it works: → Paste any YouTube URL → Whisper AI transcribes the audio with timestamps → librosa scores each moment by energy and speech activity → ffmpeg cuts the top 5–6 clips automatically → Download them individually or as a ZIP What I learned building this: ✅ How to work with audio signals and RMS energy scoring ✅ Running AI models locally with no cloud dependency ✅ Calling command-line tools (ffmpeg) from Python ✅ Building a full web app in pure Python with Streamlit ✅ Structuring a real project with modular, clean code Every line of code written from scratch. Every bug fixed. Every phase tested. This is what learning by building looks like. 💪 🔗 https://lnkd.in/gmqXjgm6 🔗 https://lnkd.in/gWSpczsp #Python #AI #Whisper #BuildInPublic #OpenSource #Developer #SideProject #MachineLearning

I built my first AI Smart Study Assistant using Python and Streamlit. It’s a simple project where I tried to understand how AI apps actually work with a clean user interface. ✨ What it can do: 📄 Summarize text (demo mode) 🧠 Explain topics in simple words ❓ Generate quiz questions 🎨 Simple and interactive web UI 🛠️ Tech used: Python, Streamlit While building this, I understood how user input flows into logic and how AI-based applications are structured. Right now this is a demo version, but I designed it in a way that it can be upgraded later with real AI models and a chat interface. Next step for me is to improve this project further and keep building more AI-based applications. Would love feedback or suggestions 🙌#AI #Python #Streamlit #MachineLearning #LearningByDoing #ArtificialIntelligence #TechJourney #WomenInTech #DataScience Microsoft Google OpenAI https://lnkd.in/eA-xwtqG

🎬 Excited to share my Movie Recommender System project! Built an interactive web app that recommends movies based on similarity using Machine Learning and Streamlit. ✨ Features: • Select any movie from the list • Get instant movie recommendations • Simple and user-friendly interface 🧠 How it works: Movie descriptions are converted into numerical vectors, and a Cosine Similarity Matrix is created to measure similarity between movies. When a movie is selected, the system finds other movies with the highest cosine similarity scores and recommends them. Tech Stack: Python | Pandas | Scikit-learn | Streamlit Learning by building real-world ML applications 🚀 #MachineLearning #Python #Streamlit #AIProjects #StudentDeveloper

🚀 You’ve probably used Python’s print() hundreds of times… But do you really know what it can do? 👀 Most developers only use it for basic debugging — but print() comes with 4 powerful parameters: 👉 sep — control how values are separated 👉 end — control how output ends 👉 file — redirect output anywhere 👉 flush — force real-time output These small features can actually: ✔ Improve your code readability ✔ Replace messy string formatting ✔ Help in logging & ML workflows I recently wrote a complete guide on Medium covering all of this with real examples and practical use cases 👇 🔗 https://lnkd.in/gtW_W8Ry A huge thank you to Javier Armando Jimenez Villafaña and Swapneel Solanki for supporting me throughout this article — for checking the content, verifying the code examples, and providing valuable feedback that helped shape the final version. Really appreciate it! 🙌 I am also on this learning journey myself — exploring Python, AI, and ML one topic at a time. If you found this useful, have questions, or just want to discuss Python, AI, or ML — drop a comment below or DM me directly. I would love to connect and learn together! #Python #Programming #MachineLearning #DataScience #SoftwareDevelopment

🚀 Writing loops in Python for array operations? You’re doing it the hard way ❌ Let’s fix that with NumPy Broadcasting 👇 --- 📌 What is Broadcasting? It allows NumPy to perform operations on arrays of different shapes WITHOUT writing loops 🤯 --- 👀 Example: import numpy as np arr = np.array([1, 2, 3]) result = arr + 10 print(result) ✅ Output: [11 12 13] 👉 NumPy automatically "broadcasts" 10 to match array shape! --- 🔥 Now the real power: arr1 = np.array([[1,2,3], [4,5,6]]) arr2 = np.array([10,20,30]) result = arr1 + arr2 print(result) ✅ Output: [[11 22 33] [14 25 36]] 💡 Smaller array is stretched across rows automatically! --- 📌 Broadcasting Rules (Simple Version): ✔ Shapes must be compatible ✔ Matching from right to left ✔ Dimensions should be equal OR 1 --- 🔥 Why it matters? ✅ No loops → Faster code ✅ Cleaner syntax ✅ Used heavily in ML & Data Science --- ⚠️ Common Mistake: np.array([1,2,3]) + np.array([1,2]) ❌ Error: Shapes not compatible Follow for daily coding clarity 🚀 #Python #NumPy #DataScience #MachineLearning #Coding #Programming #LearnToCode #CodingBlockHisar #Hisar

As I approach graduation in the next two months, I figured I'd start posting some projects that I have completed and currently working on. Here is a data analysis project I completed using Python! I analyzed entertainment industry trends using MovieLens, Netflix, and IMDb datasets to explore patterns in genres, ratings, and content distribution. Some key insights: • Documentary and Film-Noir genres showed the highest average ratings • Comedy and Crime had lower average ratings • Differences exist between user ratings (MovieLens) and broader audience ratings (IMDb) This project involved data cleaning, feature engineering, and cross-platform analysis using Pandas and Jupyter Notebook. Check it out here: https://lnkd.in/gdqkjUjs Looking forward to continuing to build more data projects! #DataScience #Python #DataAnalysis #PortfolioProject #Open

We spend hours fixing bugs… But what if we could predict them before they happen? I’ve been working on a small Machine Learning project in Python—a “Bug Predictor”. Instead of reacting to issues, the model looks at patterns in code history and flags risk early. What it uses: • Git commit history • Code churn (lines added/removed) • File change frequency • Past bug patterns Based on this, it predicts which files are more likely to introduce bugs in future commits. It’s not about 100% accuracy. It’s about giving developers a signal: “Pay extra attention here.” Biggest takeaway: Our code already contains hidden signals—we just don’t use them enough. Still experimenting with improving the model and feature engineering. Curious—would you use something like this in your workflow? #MachineLearning #Python #AI #SoftwareEngineering #DataScience #BuildInPublic

🚀 Day 14 of My Generative & Agentic AI Journey! Today’s focus was on Functions in Python — one of the most important concepts for writing clean and reusable code. Here’s what I learned: 🔁 Avoiding Code Duplication: • Functions help reuse the same logic multiple times • Instead of writing the same code again and again, we can define it once and call it whenever needed 👉 Example use case: Creating a function to handle multiple chai orders by just passing different names and chai types. ⚙️ Handling Complex Tasks: • Broke down a big task into smaller functions • Combined multiple functions to complete a full workflow 👉 Example use case: Building a report system by separating steps like fetching data, filtering it, and summarizing it. 📖 Improving Readability: • Functions make code easier to read and understand • Logic becomes more structured and organized 👉 Example use case: Creating a function to calculate bills instead of writing calculations multiple times. 💰 Real-world Application: • Applied functions to process multiple values using loops 👉 Example use case: Adding VAT to a list of orders using a reusable function. 💡 Key takeaway: Functions help write cleaner, reusable, and scalable code — a must-have skill for real-world development. Moving one step closer to writing production-level Python code 🚀 #Day14 #Python #GenerativeAI #AgenticAI #LearningJourney #BuildInPublic