Python Movie Recommendation System with Scikit-learn and Streamlit

I built my own simple RAG (Retrieval-Augmented Generation) system using React, Flask, and Python: ✨ Features: A landing page where I added a block of information. That information is saved as a PDF and extracted using PyMuPDF. Users can ask questions, and the system retrieves answers using Chroma DB vectorization and chunking. 📹 Check out the demo video below 👇 This project was a great hands-on way to understand RAG workflows, vector databases, and PDF text extraction. #Python #ReactJS #Flask #RAG #ChromaDB #PyMuPDF #AI #WebDevelopment

🚀 Day 12/100: Mastering Python Loops & range() Today’s coding session was all about automating repetition! I dove deep into for loops and the range() function to control iterations efficiently. Key Takeaways: 🔹 Used range(start, stop, step) to generate precise number sequences. 🔹 Leveraged for loops to iterate through lists and strings. 🔹 Practiced break and continue to manage loop flow.  Loops are absolute game-changers for automating repetitive tasks and data processing. 💡  #100DaysOfCode #Python #LearningToCode #DataScience #Automation #ProgrammingBasicsCodegnan

𝐀 𝐬𝐦𝐚𝐥𝐥 𝐭𝐡𝐢𝐧𝐠 𝐢𝐧 𝐩𝐚𝐧𝐝𝐚𝐬 𝐭𝐡𝐚𝐭 𝐬𝐚𝐯𝐞𝐝 𝐚 𝐥𝐨𝐭 𝐨𝐟 𝐭𝐢𝐦𝐞 While working with a dataset in Python today, I came across something simple but very useful — value_counts() in pandas. Instead of writing multiple filters or loops just to see how frequently different values appear in a column, value_counts() gives a quick frequency breakdown instantly. For example, if you want to see how many records belong to each category, city, or product type, one line can show the entire distribution. It’s a small function, but it makes exploring a new dataset much faster. Slowly realizing that data analysis is really about knowing these small but powerful tools. #Python #Pandas #DataAnalytics #LearningJourney

Unlocking the Power of Strings in Python! 🐍✨ Today’s focus on my Python journey was all about understanding and manipulation—specifically, Strings. It’s incredible how much logic depends on effectively handling text data! Here are my key takeaways from today's deep dive: ✂️ String Slicing: Mastering the [start:stop:step] syntax. It feels like precision surgery for text data—extracting exactly what you need, whether it's a prefix, a suffix, or a reversed substring. 🚫 String Immutability (Mutation): A crucial realization! You can’t change a string in place. Trying to do word[0] = 'C' will throw an error. Understanding this forces you to think correctly about creating new modified strings instead of trying to mutate existing ones. 🛠️ String Methods: My toolbox just got a lot bigger. I explored powerful built-in functions like: .strip() for cleaning up whitespace. .replace() for quick swaps. .split() and .join() for converting between strings and lists. .upper(), .lower(), .capitalize() for formatting. Understanding these fundamentals is making my code cleaner and more efficient. Every day is a step closer to building complex applications! #Python #CodingJourney #Strings #DataManipulation #SoftwareDevelopment #ContinuousLearning #WebDev #Backend #ProgrammingFundamentals #CleanCode #LearningToCode

Sharpened my Python fundamentals today by diving deep into Dictionaries and Sets. Here’s what I practiced: • Creating dictionaries and sets • Understanding key-value data structures • Dictionary methods and operations • Set methods and real-world use cases • Hands-on practice problems to strengthen concepts Building strong foundations one concept at a time. Consistency > intensity. #Python #CodingJourney #LearningPython #DataStructures #Upskilling #TechSkills

🚀 Built a Movie Recommendation System using Collaborative Filtering! I recently developed a movie recommendation system that suggests movies based on user preferences and similarity patterns. The model analyzes user–movie interactions to uncover hidden patterns and deliver personalized recommendations. 👉 Simply select a movie, and the system instantly suggests similar movies you’re likely to enjoy! ✨ Key Features: • Collaborative filtering–based recommendations • User–movie similarity analysis • Instant top movie suggestions • Clean and interactive web interface 🎥 Check out the demo to see it in action! 💻 Tech Stack: Python | Scikit-learn | Pandas | NumPy | Streamlit 🔗 GitHub Repository: https://lnkd.in/g-d3DwSa 🔗 Live Demo: https://lnkd.in/dnigN_7e #MachineLearning #RecommenderSystem #Python #AIProject #DataScience #Streamlit #CollaborativeFiltering #AIML

Day7 of #30DayChartChallenge Theme: Multiscale Category: Distributions Tool: Python Data Source: python scikit-learn Datasets I worked with a few features from a machine learning dataset and plotted their distributions. At first, everything sits on different ranges. One stretches far, another stays tight, another somewhere in between. It looks fine, but comparing them like that is off. After scaling, they fall into the same range. Now the comparison actually makes sense. It’s a small step in most workflows, but seeing it visually makes the difference clearer. #30DayChartChallenge #python #Dataviz #Datascience

🎬 Building my first Movie Recommendation System. Explored a dataset with 1.4M+ rows and found heavy missing values in key features like genres, keywords, and overview. My approach: dropping missing data to keep recommendations meaningful. What would you do? Drop or fill? Let me know your thoughts in the comments 👇 #MachineLearning #Python #DataScience

The Basics of Strings Focus: String Properties & Indexing Day 3 was all about Strings. I thought I knew what a "word" was, but Python sees things a bit differently. 🧐 What clicked today: Case Sensitivity: Python is picky! Variable and variable are two different citizens. Slicing: String Methods: Indexing: Learning that counting starts at 0 was a bit of a brain-bender at first, but it makes so much sense now. Immutability: This was the big "Aha!" moment—knowing that once a string is created, you can’t just reach in and change a character. You have to build a new one. It’s these small details that make the difference between code that runs and code that crashes. 💻 #Strings #CodingJourney #PythonLearning #ContinuousGrowth #PythonDay3

🎬 Built a Movie Recommendation System using Python & ML Using content-based filtering to recommend movies based on similarity — here's a quick breakdown how it works: 1. TF-IDF Vectorizer converts movie descriptions into vectors. 2. Cosine Similarity measures how similar two movies are. 3. Random Forest classifier validates the results. Results: ◈ 16 movies ◈ 4 genres ◈ 97% model accuracy ◈ Toy Story & Finding Nemo topped similarity at 0.61 ◈ The Godfather & Goodfellas closely matched at 0.58 A great way to understand how Netflix-style recommendations work under the hood. Open to feedback and questions! 👇 #MachineLearning #Python #DataScience #RecommendationSystem #BuildInPublic #dataanalytics #datascience #mlproject #datanalyst #datascientist #scikitlearn