Mastering ML Pipeline with Python & Data Science

🚀 Day 23 – 100 Days of Python & Data Science Today I practiced data visualization to better understand patterns and relationships in datasets. Visualizing data makes analysis more clear and meaningful. Learning a little more every day 💻✨ #100DaysOfPython #DataScience #Python #LearningJourney 💻GitHub:https://lnkd.in/dUG6qvk5

🚀 Day 21 – 100 Days of Python & Data Science Today, I worked on improving my model evaluation skills — understanding accuracy, confusion matrix, and how to interpret results properly. Not just building models, but learning how to analyze their performance. Step by step growth 💻✨ #100DaysOfPython #DataScience #MachineLearning #Python #LearningJourney 💻GitHub : https://lnkd.in/dUG6qvk5

🚀 Day 24 – 100 Days of Python & Data Science Today I practiced data preprocessing and cleaning — an important step before building any Machine Learning model. Clean data leads to better analysis and better results. Learning and improving every day 💻✨ #100DaysOfPython #DataScience #Python #MachineLearning 💻GitHub:https://lnkd.in/dUG6qvk5

🚀 Day 22 – 100 Days of Python & Data Science Today, I focused on feature selection and understanding how different features impact model performance. Learning that better features = better models. Improving step by step 💻✨ #100DaysOfPython #DataScience #MachineLearning #Python #LearningJourney 💻GitHub: https://lnkd.in/dUG6qvk5

Level up your data stack! From Polars for speed to Great Expectations for quality, here are 8 essential Python libraries every Data Engineer needs to build faster, more resilient pipelines. What’s missing from your toolkit? Drop a comment below with the libraries you think every data engineer should be using! 👇 #DataEngineering #Python #BigData #ETL #ELT #DataStack #Pyspark #SoftwareEngineering

📅 Day 6/30 — Building a “Pages You Might Like” Feature Continuing my 30-day journey into data science, today I built a basic version of the “Pages You Might Like” feature using pure Python. What I worked on today: 📄 Understanding user interests and page data 🔍 Finding patterns based on user preferences ⚙️ Using loops, conditions, and dictionaries to process data 💡 Generating simple page recommendations It was interesting to see how recommendation features can be created using core Python logic without relying on external libraries. ➡️ Next step: exploring more ways to analyze datasets using Python. #LearningInPublic #Python #Anaconda #JupyterNotebook #DataScience #30DaysOfLearning #ProgrammingJourney

📊 What I learned this week in Data Science This week, I explored: • Basics of Python for data analysis • How pandas helps clean and analyze datasets • Why data cleaning is more important than modeling Still learning step by step, but enjoying the process 🚀 #DataScience #Python #LearningInPublic #ComputerEngineering

Data Studio: Transforms 🛠️ One tool for shaping and analyzing your data. Transforms let you clean, join, and reshape raw tables with SQL or Python, and Metabot can write the code for you.

🚀 Day 2 – Data Science Learning Journey Today’s session was all about Matplotlib, one of the most important libraries for data visualization in Python. I explored various functions used to create different types of graphs and plots. It was really interesting to see how raw data can be transformed into meaningful visual insights, making patterns and trends much easier to understand. Every step in this journey is helping me understand how data tells a story through visualization. 📊 #DataScience #Python #Matplotlib #DataVisualization #LearningJourney