Unlocking Insights with Data Visualization using Python

Day 7 of my 100 Days of Code challenge 🚀 Today I built a Movie Recommendation System using Python, Pandas, Scikit-learn, and Streamlit 🎬 This project recommends similar movies based on genre using content-based filtering and cosine similarity. What I learned from this project: How recommendation systems work at a basic level How to convert text data into vectors using CountVectorizer How to use cosine similarity to find similar items How to deploy a Streamlit app How to debug a real deployment issue related to file paths It was a simple project, but it gave me a practical understanding of how recommendation logic works behind the scenes. 🔗 Live Demo: https://lnkd.in/dW8bTgzE 💻 GitHub Repo: https://lnkd.in/g6mgQ7qY Every small project is helping me understand concepts better and build confidence step by step. #100DaysOfCode #Python #MachineLearning #DataScience #Streamlit #ScikitLearn #AI #LearningInPublic #CodingJourney

🚀 Day-66 of #100DaysOfCode 📊 NumPy Practice – Image Matrix Manipulation Today I simulated a grayscale image using NumPy and performed a simple brightness adjustment. 🔹 Concepts Practiced ✔ Random matrix generation ✔ Array arithmetic operations ✔ Pixel value clipping using np.clip() ✔ Understanding image data as matrices 🔹 Key Learning Images in computer vision are essentially NumPy matrices, where each element represents a pixel intensity. NumPy makes it easy to manipulate these values efficiently. Exploring how NumPy connects with image processing and computer vision 📸✨ #Python #NumPy #DataScience #ComputerVision #MachineLearning #100DaysOfCode

🚀 Ridge Regression Visualized! Created an interactive dashboard with 9 visualizations that demystify L2 regularization - from 3D loss landscapes to real housing predictions. Built with Python, scikit-learn & Matplotlib. Check out the coefficient paths in the carousel! 👇 #DataScience #AI #Python

One Pandas Cheat Sheet to rule them all. I'm sharing my go-to guide for mastering data manipulation in Python. If you want to level up your Data Science workflow, this is for you. - Clean data faster - Master indexing & filtering - Simplify aggregations Comment "SHEET" below and I’ll DM you the complete version! #AI #DataScience #PythonProgramming #CodingTips

Time series forecasting in Python can help you predict future trends. In this course, you'll learn what time series data is and how to break it down into its key components. Then you'll build baseline models, learn important forecasting techniques like ARIMA and seasonal ARIMA, evaluate your models, & more. https://lnkd.in/gzrtnBdV

New day — new good-to-know-and-repeat. Today I revisited a tutorial on ARIMA models for time-series forecasting. Not the newest method — but still one of the most useful to understand. A quick reminder for anyone working with data: • Fancy models don’t replace strong fundamentals. • Forecasting starts with understanding your data — trends, seasonality, stationarity. • Sometimes simple and interpretable models outperform complex ones. In a world full of deep learning hype, it’s good to occasionally come back to the classics. ARIMA is one of them. What forecasting methods do you keep coming back to? 📊

I built a simple dashboard using Python, Seaborn, and Matplotlib to explore the famous Iris dataset. 🔍 Key insights: • Clear separation between species using petal measurements • Sepal features show more overlap across species • Distribution plots help highlight patterns and variability Tools used: • Python • Seaborn • Matplotlib This is part of my journey in Data Science and Data Visualization. #DataScience #Python #DataVisualization #Seaborn #MachineLearning #Portfolio

📈 Matplotlib Explained (Visualization Library) Matplotlib is used to create basic plots. 🔹 Important Functions: ✔ plot() → Line chart ✔ bar() → Bar chart ✔ scatter() → Scatter plot ✔ hist() → Histogram ✔ title() → Add title ✔ xlabel(), ylabel() → Axis labels 💡 Visualization helps to understand data easily. #Matplotlib #DataVisualization #Python

Starting your ML journey? Begin with the fundamentals 🎯 Day 1 tip: Master these before diving into algorithms: ✅ Python basics (variables, loops, functions) ✅ NumPy & Pandas for data manipulation ✅ Linear algebra & calculus concepts ✅ Statistics & probability Remember: Strong foundations = Better ML models The quality of your features determines your model's ceiling. Garbage in, garbage out! #MachineLearning #LearningJourney #Python #DataScience

Day 6 of #LearnInPublic Today I worked on the problem: First Non-Repeating Element in an Array. I implemented two approaches in Python: 1️⃣ Hash Map Approach (Using defaultdict) • Count frequency of each element • Traverse again to find the first element with frequency = 1 Time Complexity: O(n) Space Complexity: O(n) 2️⃣ Brute Force Approach • Compare every element with the rest of the array Time Complexity: O(n²) Space Complexity: O(1) Key takeaway: Using a hash map trades extra space for a significant improvement in time complexity. Small daily improvements compound over time. #Python #DataStructures #Algorithms #LearnInPublic #CodingJourney