Analyzing Titanic Dataset with Python and Pandas

DAY 30/30 TO LEARN PYTHON FOR DATA ANALYSIS Understanding data using GroupBy in Pandas 📊 Analyzed the Titanic dataset to see how passengers are distributed across different classes using: 👉 groupby() + count() 💡 Insight: Most passengers were in 3rd class Fewer passengers in 1st and 2nd class Also learned: ✔️ count() ignores missing values ✔️ GroupBy helps in summarizing data quickly Small insights like these help build strong analytical thinking 🚀 #Python #DataScience #Pandas #DataAnalysis #MachineLearning #AI #DataAnalytics #LearnPython #CodingJourney #100DaysOfCode #BeginnerDataScientist #GroupBy #DataPreprocessing #TechLearning #Analytics

Day 27 |Data Fellowship 🚀 Moving into a new chapter today: Aggregating DataFrames in Pandas! 🐼 It’s one thing to look at rows of data, but it’s another to actually summarize them to see the big picture. Learning how to calculate means, medians, and sums to turn messy data into actual insights. 📊 #DataScience #Python #Pandas #DataCamp #Day27 #Lumbinitechmonth

🐍📈 Math for Data Science In this learning path, you'll gain the mathematical foundations you'll need to get ahead with data science #python #learnpython

Day 70 of the #three90challenge 📊 Today I started learning Pandas — one of the most powerful libraries for data analysis in Python. After working with NumPy arrays, Pandas takes things further by making data easier to organize, analyze, and manipulate. What I explored today: • Introduction to Series and DataFrames • Loading data into Pandas • Viewing and understanding dataset structure • Basic operations on tabular data Example thinking: NumPy works with arrays. Pandas works with real-world datasets. Example: import pandas as pd data = {"Name": ["A", "B", "C"], "Age": [25, 30, 22]} df = pd.DataFrame(data) print(df) This is where data starts to feel structured and analysis-ready. From numerical operations → to real data analysis 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #Pandas #DataAnalytics #LearningInPublic #Consistency #Upskilling

Data Cleaning is where real data science begins. One of the simplest yet most powerful steps? dropna() Missing data can silently break your analysis. Clean data = Better insights = Smarter decisions. Start simple. Stay consistent. Build strong foundations. #DataScience #Python #DataCleaning #BeginnerFriendly #CodingJourney #AI #MachineLearning

As part of my continuous learning journey in Python, Data Analysis, and Artificial Intelligence (AI), I documented and published my Python Libraries notes on GitHub. These notes cover key libraries: NumPy for numerical computing, Pandas for data manipulation and analysis, Matplotlib and Seaborn for data visualization and creating meaningful insights from data. 💻 Python Libraries Notes 🔗 HTML version: https://lnkd.in/dUV83GYF 🔗 PDF version: https://lnkd.in/deJvpWPi Continuing to build my skills in Data Analysis and AI by learning and sharing knowledge. 🚀 #Python #DataAnalysis #ArtificialIntelligence #NumPy #Pandas #DataVisualization #LearningJourney

 I am currently learning Python and SQL and building my skills in AI and Data Science. I have created this GitHub repository to practice and upload my learning journey. 🔗 GitHub Link: https://lnkd.in/g9NiMjCi #python #sql #AI #machinelearning #datascience

𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗲 𝗠𝗟 𝗠𝗼𝗱𝗲𝗹𝘀 𝘄𝗶𝘁𝗵 𝗬𝗲𝗹𝗹𝗼𝘄𝗯𝗿𝗶𝗰𝗸! 📊 Yellowbrick is a Python library that provides useful visualizations for machine learning models. For example, regression models can be visualized with a prediction error plot or Cook's distance, whereas ROC/AUC curves and the confusion matrix are suitable for classification models. Furthermore, Yellowbrick can be installed by itself, or alternatively used with the PyCaret library that integrates its functionality. Have you ever utilized Yellowbrick to visualize machine learning models? Visit the links below for more information, and make sure to follow me for regular data science content! 𝗬𝗲𝗹𝗹𝗼𝘄𝗯𝗿𝗶𝗰𝗸 𝘄𝗲𝗯𝘀𝗶𝘁𝗲: https://lnkd.in/enK2fQ2D 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝗮𝗻𝗱 𝗙𝗼𝗿𝗲𝗰𝗮𝘀𝘁𝗶𝗻𝗴: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning