Pratiksha Yadav’s Post

Continuing with Seaborn - I explored some commonly used plots that make data analysis easier 🔹 Countplot → shows frequency of categories 🔹 Heatmap → shows correlation between variables 🔹 Pairplot → helps understand relationships between multiple features 💡 What I learned: Visualization is not just about charts — it’s about choosing the right way to represent data. Each plot answers a different question. Learning something new every day. #Python #Seaborn #DataVisualization #DataAnalytics #LearningJourney

Today, I focused on working with NumPy arrays. Building a solid foundation for data manipulation and analysis. Here’s what I practiced: 🔹 Created a 1D array with values from 1 to 15 🔹 Built a 2D array (3×4) filled with ones 🔹 Generated a 3×3 identity matrix 🔹 Explored key array properties like shape, type, and dimensions 🔹 Converted a regular Python list into a NumPy array This session helped me better understand how data is structured and handled in numerical computing. Getting comfortable with arrays is definitely a crucial step toward more advanced data analysis and machine learning tasks. Looking forward to building on this momentum 💡 #AI #MachineLearning #Python #NumPy #DataAnalysis #M4ACE

Common mistakes I learned to avoid in data visualization 📊 While practicing, I realized that creating charts is easy… but creating the right chart is what matters. Here are some mistakes to avoid :- ❌ Using wrong chart types ❌ Overloading charts with too much data ❌ Ignoring labels and titles ❌ Poor color choices ❌ Not focusing on the story behind data 💡 My takeaway: A good visualization should be simple, clear, and meaningful. Because the goal is not just to show data — 👉 it’s to communicate insights. Learning and improving every day #DataVisualization #Seaborn #Python #DataAnalytics #LearningInPublic

20 ML algorithms and their real-world use cases. One cheat sheet i wish i had when i started. I spent months confusing random forest with decision trees and had no clue when to use xgboost vs lightgbm. So i made this for myself. Save this and share this with someone who's into data analytics. #machinelearning #datascience #algorithms #python #dataanalyst

🚀 Hands-on Machine Learning Project: Decision Tree Classifier Recently, I worked on a small but insightful project where I implemented a Decision Tree Classifier using Python and Scikit-learn. 📊 What I did: Created a structured dataset with features like Age, Salary, and Experience Applied data preprocessing techniques Built and trained a Decision Tree model Evaluated performance using Confusion Matrix & Classification Report Visualized patterns using Seaborn 📈 Key Learnings: How Decision Trees split data based on feature importance Importance of handling data properly before modeling Understanding evaluation metrics like precision, recall, and F1-score 💡 This project helped me strengthen my fundamentals in machine learning and model evaluation. 🔗 I’ll be sharing the GitHub repository soon! #MachineLearning #DataScience #Python #ScikitLearn #DecisionTree #DataAnalytics #LearningJourney

📊 Not everything in data science is a finished project most of it is exploration. This is a small snapshot from my Jupyter Notebook while working through a project. At this stage, it’s not about perfect results it’s about: • Understanding the data • Trying different approaches • Visualizing patterns • Making sense of what’s happening underneath What looks like simple code on the screen is actually a process of trial, error, and discovery. 💡 Key takeaway: Before insights come confusion. Before clarity comes experimentation. Every notebook is just a record of how thinking evolves through data. #DataScience #Python #JupyterNotebook #DataAnalytics #LearningInPublic

Stop scrolling if you’ve ever wondered how people actually predict the future with data. I’ve been learning ARIMA forecasting recently, and I mapped out a simple roadmap that made everything click for me. It starts with getting comfortable in Python - Pandas for wrangling, Matplotlib for visualising. Then you move into the core ideas: stationarity, ACF, PACF, and how they shape the model. After that, it’s about building the ARIMA model, validating it properly, and using it to make real‑world predictions. What I enjoy most is how it turns raw, messy data into insights you can genuinely act on. Still learning, but enjoying the process 🚀 #DataScience #TimeSeries #ARIMA #Python #LearningJourney

Ever feel like something as simple as a scatter plot shouldn’t be this stressful? I built this visualization using Matplotlib, and honestly, it took more effort than I expected. Not because it’s complex but because I’m still getting comfortable with the tool. What I’m learning is this: Data Science isn’t just about concepts. It’s about translating ideas into code and that part takes practice. This plot shows the relationship between property area and price, and even though it looks simple, it represents progress. Small wins matter. If you’re learning too and feel stuck sometimes, you’re not alone. Keep building. #DataScience #Python #Matplotlib #LearningInPublic #AnalyticsJourney

📊 Turning Data into Visual Stories with Matplotlib & Seaborn Recently, I’ve been exploring data visualization using Matplotlib and Seaborn in Python, and it’s been an insightful experience. 🔹 Matplotlib gives full control over plotting and is great for building customized visualizations from scratch. 🔹 Seaborn, built on top of Matplotlib, makes it easier to create beautiful and informative statistical graphics with minimal code. What I’ve learned: ✔️ Choosing the right chart makes data more understandable ✔️ Visualization helps uncover patterns and trends quickly ✔️ Clean and simple design improves data storytelling From line charts to heatmaps, these tools make data analysis more meaningful and impactful. Looking forward to applying these skills in real-world data projects! #Python #DataVisualization #Matplotlib #Seaborn #DataScience #LearningJourney

Master Figures, Lines & Arrows in Matplotlib! The matplotlib module can plot geometric figures such as rectangles, circles, and triangles. These figures can then illustrate mathematical, technical, and physical relationships. This blog post demonstrates the creative options of matplotlib through three examples by illustrating the Pythagorean theorem: a gear representation, a pointer diagram, and a current-carrying conductor in a homogeneous magnetic field. #Python #DataViz #Matplotlib #CodeMagic #RheinwerkComputingBlog Dive in now and transform your graphs! https://hubs.la/Q04byPg90