Scaling Data with Pandas Grouping and Sorting Techniques

Can we predict a stroke before it happens? 🧠 I recently finished a project using the Healthcare Stroke Dataset to build a prediction tool from scratch. Instead of using high-level libraries, I built the Logistic Regression model using only NumPy to truly understand the math behind the predictions. Key Highlights: Data Cleaning: Handled imbalances and missing values using Pandas. Feature Engineering: Created custom features like "Age-Glucose" interaction to improve model sensitivity. Deployment: Built a live dashboard with Streamlit so users can interact with the model in real-time. make sure to remove the space when you copy the link Check out the app here: [https://lnkd.in/e3knnnXe] #DataAnalytics #MachineLearning #Python #HealthTech #DataScience

Python Data Visualization Quick Guide V1.0 📊 What’s inside: • Distribution plots (Histogram, KDE, Box, Violin) • Categorical analysis (Bar, Count, Pie) • Relationship plots (Scatter, Regression, Bubble) • Time series visualizations (Line, Area) • Multivariate exploration (Heatmaps, Pairplots) • Hierarchical charts (Sunburst, Treemap) • Geographic maps with Plotly • Faceting and subplot layouts • A Visualization Selection Guide to help choose the right chart quickly 🔗 Notebook link: https://lnkd.in/daHNQpdq I’d love to hear your feedback and suggestions for improving it further. #Python #DataScience #DataVisualization #EDA #MachineLearning #Plotly #Seaborn #Matplotlib

🚀 Day 4 – Data Science Learning Journey Today’s session reinforced key statistical fundamentals, strengthening concepts that form the backbone of data analysis. Along with theory, I explored Seaborn, a powerful Python library for statistical data visualization. Using the tips.csv dataset, I performed several visualizations to understand patterns, relationships, and distributions in the data. It’s fascinating to see how statistics and visualization together turn raw data into meaningful insights. Looking forward to learning more as the journey continues. 📊 #DataScience #Statistics #Seaborn #Python #DataVisualization #LearningJourney

🚀 Day-70 of #100DaysOfCode 📊 NumPy Practice – Finding Top K Elements Today I worked on finding the top 3 largest elements in a NumPy array. 🔹 Concepts Practiced ✔ Array sorting using np.sort() ✔ Array slicing ✔ Extracting top values from datasets 🔹 Key Learning Finding top-K elements is a common task in data analysis, ranking systems, and machine learning, where identifying the most significant values is important. Step by step improving my NumPy and data manipulation skills 🚀 #Python #NumPy #DataScience #PythonProgramming #100DaysOfCode #LearningJourney

The "Big 5" of Python for Data Science 🐍 If you are just starting in Data Science, the sheer number of libraries can feel overwhelming. But if you master these five, you can handle 90% of most data projects. Pandas: Your go-to for data cleaning and exploration. NumPy: The powerhouse for numerical operations. Matplotlib: Great for basic, customizable plotting. Seaborn: Elevates your visuals for statistical analysis. Scikit-learn: The gold standard for implementing Machine Learning. Mastering the tools is the first step toward solving real-world business problems with data. Which of these do you use most in your daily workflow? Let’s discuss below! 👇 #DataScience #Python #DataAnalytics #MachineLearning #TechTips #GradeLearner

📅 Day 9/30 — NumPy Indexing & Slicing Continuing my 30-day journey into data science, today I explored how to efficiently access and manipulate data using NumPy arrays. What I worked on today: 🔢 Accessing elements using indexing (including negative indexing) ✂️ Extracting data using array slicing 🔁 Selecting elements using step slicing 🎯 Using index arrays to pick specific elements 🧠 Applying boolean masking to filter data based on conditions It was interesting to see how NumPy provides powerful ways to quickly access, modify, and filter data, which is very useful when working with large datasets. ➡️ Next step: exploring more advanced NumPy operations and applying them to real-world data. #LearningInPublic #Python #DataScience #NumPy #30DaysOfLearning #ProgrammingJourney

Are your dataset variables secretly plotting behind your back? 👀 Before building any Machine Learning model, you need to know exactly how your features interact. Some are best friends, others are total strangers, and a few are just repeating the exact same story. How do you spot them instantly? Enter: The Correlation Matrix. 🔴🔵 It's not just a pretty heatmap—it's the ultimate lie detector for your data. Check out the post below to learn how to decode it in seconds! 👇 #DataScience #MachineLearning #DataAnalysis #Python #DataViz #Analytics #ScikitLearn #Coding #BigData #TechTips #ArtificialIntelligence #DataScientist #Statistics #EDA

Before learning this, I thought analysis was just about running models. But now I understand something important. If your data is messy, your results will be messy. Missing values. Duplicates. Typos. Wrong formats. Real world data is rarely perfect. Dropping null values with dropna() can help, but it must be done carefully. If you remove too much data, you might create bias. Data cleaning is not the exciting part, but it is the foundation. Clean data leads to reliable insights. And reliable insights build trust. #DataCleaning #DataScience #Python #Pandas #DataPreparation #CleanData #DataAnalysis #DataQuality #MachineLearning #DataInsights #LearnDataScience #Analytics

In this project, I performed data cleaning, visualization, and statistical exploration to better understand feature relationships such as sepal length, sepal width, petal length, and petal width across different species. Using Python libraries like Pandas, Matplotlib, and Seaborn in Google Colab, I generated insights through summary statistics and visual plots. This exercise strengthened my understanding of data preprocessing, visualization techniques, and pattern identification — key steps before building any machine learning model. #DataScience #EDA #Python #MachineLearning #GoogleColab #IrisDataset

🚀 Day 13 of Sharing My Data Science & Machine Learning Journey  Understanding the "Spread" – Measures of Dispersion Yesterday we found the center; today we find out how much the data deviates from it! 📊 In Data Science, knowing the average (Mean) isn't enough. You need to know if your data points are clustered closely together or scattered far apart. This is where Measures of Dispersion come in. #DataScience #MachineLearning #Statistics #MeasureOfDispersion #Python #LearningJourney