Praveena S’ Post

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

Machine Learning Graph Data using pygal #machinelearning #datascience #graphdata #pygal Pygal is a simple yet powerful Python library for generating beautiful SVG charts. It allows users to create a wide variety of static and animated visualizations, including bar charts, pie charts, line charts, and radar charts. https://lnkd.in/gn8-hBUW

You spent 45 minutes manually deleting duplicates in Excel last week. I wrote a Python script that does it in 3 seconds. It removes duplicates → clears empty rows → auto-saves the clean file. No manual work. Ever again. #Python #DataAnalytics #Excel #DataCleaning #DataScience

NaNs ruining your analysis? Here’s the quick Pandas trio: use isna() to detect missing values, dropna() to remove incomplete rows, and fillna() to replace gaps with defaults. This tiny example shows all three so you can clean data in seconds.#pandas #python #datascience #dataengineering

Day 1 of Data Structures in Python 🚀 Today I learned the basics of: • Lists • Tuples • Sets • Dictionaries Practiced few basic operations like insert, delete, and search. Understanding how data is stored and accessed is the first step toward better problem-solving. Looking forward to applying these concepts in real problems 🔍 #Python #DSA #LearningJourney #DataStructures

Finally moved Python & AI project from local to live! 🎈 I’ve been experimenting with Python lately and finished building a simple Personal Assistant for my morning routines and productivity. It was fun to step outside .NET and try out some new tools: ⚡ Groq : Really impressed with how fast the responses are. 🐍 Python: Getting more comfortable with the syntax every day. 🖥️ Streamlit: Made it super easy to put a clean UI on top. It’s work in progress, but you can check out V1 here: https://lnkd.in/gwyuaGZt #Python #Streamlit #PersonalProject #BuildingInPublic #WomenInCode

Day 28 / #120DaysOfCode – LeetCode Challenge 🚀 Consistency is building momentum 💯 Today’s focus was on String Processing and Frequency Counting, using Python’s built-in tools for efficient solutions. ✅ Problem Solved: • Most Common Word 💻 Language: Python 📚 Key Learnings: • Used regex (re.findall) to extract words cleanly • Applied Counter to count word frequency efficiently • Learned how to handle case-insensitive strings • Filtered out unwanted words using conditions Clean code + built-in functions = powerful solutions 🚀 Every day improving step by step 💪 🔗 LeetCode Profile: https://lnkd.in/gbeMKcv5 #LeetCode #Python #DSA #Strings #Regex #ProblemSolving #CodingJourney #Consistency #120DaysOfCode

🚢 Completed Titanic Survival Analysis! Tools: Python | Pandas | Seaborn | Matplotlib Key Findings: ✅ Females had higher survival rate ✅ First class passengers survived more ✅ Higher fare = better survival chance GitHub: https://lnkd.in/gTsrns4y #DataAnalysis #Python #Pandas #DataScience

A quick #geopandas cheat sheet on - and if you want to learn all of these (and a lot) more in Python, then: 𝐆𝐞𝐨𝐬𝐩𝐚𝐭𝐢𝐚𝐥 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐄𝐬𝐬𝐞𝐧𝐭𝐢𝐚𝐥𝐬 - 𝐒𝐞𝐜𝐨𝐧𝐝 𝐄𝐝𝐢𝐭𝐢𝐨𝐧 The book: https://lnkd.in/dy-7m_zz Sample: https://lnkd.in/dVP-Ty-Y GeoAI: https://lnkd.in/dDz_zgCH

🐍 Day 116 — Cross Validation Day 116 of #python365ai 🔁 Cross-validation splits data multiple times. Example: from sklearn.model_selection import cross_val_score 📌 Why this matters: Provides more reliable performance estimates. 📘 Practice task: Run cross-validation on a model. #python365ai #CrossValidation #MachineLearning #Python