Developed and deployed a content-based Movie Recommendation System using Python and machine learning techniques. The system recommends similar movies by analyzing metadata such as genres, overview, keywords, cast, and crew. It uses text preprocessing, feature vectorization, and cosine similarity to identify related titles, and is presented through an interactive Streamlit application. This project strengthened my practical understanding of recommendation systems, NLP-based preprocessing, feature engineering, and end-to-end ML application development. Tech stack: Python, Pandas, NumPy, Scikit-learn, NLTK, Streamlit Live App: https://lnkd.in/gyzEeKK9 GitHub: https://lnkd.in/gYFHz2Xd #ArtificialIntelligence #MachineLearning #Python #DataScience #RecommendationSystem #Streamlit #ScikitLearn #GitHub #Projects

🚀 Built a Machine Learning model that predicts house prices. Most people stay stuck in tutorials. I decided to apply it. Used Linear Regression to train on real housing data, evaluated performance, and saved the model for reuse. 📊 Results: • R² Score: 0.58 • MSE: 0.56 Not perfect, but real learning happens here building, testing, improving. Pushed the complete project to GitHub 💻 #BuildInPublic #MachineLearning #AIJourney #Python #DataScience #Consistency #KeepLearning

Day 6 of Solving ML Problems From Scratch: Adam Optimizer Today I worked on implementing the Adam Optimizer from scratch. What I like about Adam is that it combines the benefits of momentum and adaptive learning rates in a very practical way. Instead of taking the same type of step every time, it adjusts based on both past gradients and gradient magnitude, which makes optimization more stable and efficient. While solving this, I got a better understanding of: how momentum helps smooth the update direction how the velocity term adapts the step size why bias correction is important, especially in the early steps how Adam can converge faster than plain SGD in many cases Building these concepts from scratch is helping me understand what is really happening behind the libraries we use every day. It is one thing to call an optimizer in code, but it is very different to actually implement and reason through each update step yourself. Small daily practice like this is making machine learning feel much more intuitive. #MachineLearning #DeepLearning #ArtificialIntelligence #Python #DataScience

Day 2 of learning Machine Learning. Today I worked on a simple linear regression model using Python in Jupyter Notebook. The idea was straightforward: - Input (x): house size - Output (y): price Model used: f(x) = wx + b I understood how: - Training data is structured (x_train, y_train) - Parameters (w, b) define the relationship - The model uses this to make predictions on new inputs Also got hands-on with NumPy and basic plotting using Matplotlib. Still very early, but it's becoming clearer how data is converted into predictions. #MachineLearning #AI #Python #LearningInPublic

Big models aren’t the only way to get better results. Introducing Inferscale 0.1.1—a lightweight Python package that improves LLM outputs using inference-time scaling techniques. No retraining. No massive infrastructure. Just smarter generation. If you're experimenting with LLM quality improvements or trying to reduce costs while maintaining performance, this is worth checking out. GitHub: https://lnkd.in/giq8KJ5g Would love to hear your thoughts and use cases! #LLM #AI #PromptEngineering #MachineLearning #OpenSource #Python #AIDevelopers

🚀 Excited to share my latest project: House Price Prediction an end-to-end Machine Learning pipeline for residential house price prediction. By implementing Scikit-Learn Pipelines and comparing Random Forest vs. Gradient Boosting, I achieved a Mean Absolute Error of ~$16.7k. This project highlights my focus on building clean, reproducible code and robust data preprocessing. Check out the full code and technical breakdown on GitHub: 🔗 [https://lnkd.in/dR-3adKr] #MachineLearning #Python #DataScience #ScikitLearn #Kaggle"

I used to write a lot of clumsy `if` statements just to group data. Checking if a key existed, then initializing a list, then appending. It felt clunky and repetitive. This simple Python trick lets you group any data points by category without boilerplate code, making your data prep for AI/ML much cleaner. It's perfect for aggregating model results by metric or sorting samples by class. 💡 What's your go-to Python trick for cleaning up data operations? #Python #PythonTips #MachineLearning #DataScience #Coding

Sorting lists of dictionaries or objects in Python often means writing small, repetitive lambda functions. There's a cleaner, faster way to grab specific items for sorting or processing. This little trick makes your data operations much more elegant and performant ✨. Do you use `itemgetter` or stick with `lambda` for sorting? Share your preferred method below! #Python #MachineLearning #AI #CodingTips #PythonTips

🎬 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

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐦𝐚𝐝𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐦𝐨𝐫𝐞 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐦𝐞 While exploring datasets in Python recently, I spent some time understanding how correlation works between variables. Using pandas, it’s surprisingly easy to calculate a correlation matrix and see how different columns relate to each other. Sometimes two variables move together strongly, and sometimes there’s almost no relationship at all. What I found interesting is that correlations can quickly highlight patterns that might not be obvious just by looking at raw numbers. Still learning how to interpret these relationships properly, but it’s definitely making the analysis process more insightful. #Python #Pandas #DataAnalytics