Sahil Alam’s Post

My first ML project is live on GitHub. Built a Random Forest model trained on 1,460 real house sales that predicts sale prices with a Mean Absolute Error of ~$17,000. Used SHAP values to explain which features drive predictions — turns out overall quality and living area matter most. Tech used: Python, pandas, scikit-learn, SHAP https://lnkd.in/gC4DhQbg #DataScience #MachineLearning #Python #Portfolio

Still Googling Pandas syntax every time you work on a project? . . . . I created a one-page Pandas Cheat Sheet covering the most used commands: read_csv() • groupby() • merge() • fillna() • drop_duplicates() Save this before your next project Which topic should I cover next: NumPy / Statistics / ML Metrics ? #Pandas #Python #DataAnalytics #DataScience #MachineLearning #Analytics #InterviewPreparation

Are Matplotlib abstractions helping—or getting in the way? Let’s ask Cameron Riddell! In this week’s Cameron’s Corner, Cameron looks at the layers of abstraction in Matplotlib and how they shape the way we write plotting code. While higher-level interfaces can make things faster to write, they can also obscure what’s actually happening underneath. Learn: ✅ How Matplotlib’s abstraction layers are structured ✅ When higher-level APIs simplify your workflow ✅ Why dropping down a level can sometimes give you more control Read here: https://lnkd.in/gVJKvErq Do you prefer high-level plotting tools or working closer to Matplotlib’s core? Let us know how you approach it 👇 #Python #Matplotlib #DataViz #CameronsCorner

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

I understood NumPy better when I applied it to real data 👇 Learning concepts is one thing… But using them on actual data is different. So I tried a simple example: 👉 Dataset: list of student marks Task: Add 5 bonus marks to every student Using Python list: - needed a loop - more lines of code Using NumPy: - converted list → array - added 5 in a single step That’s it. What I realized: NumPy is not just about syntax. It’s about handling data efficiently at scale. Even a small example made it clear: - less code - faster execution - cleaner logic Now I’m focusing more on applying concepts, not just learning them. If you're learning NumPy, try this: 👉 Take any small dataset and apply operations on it That’s where real understanding begins. What’s one concept you learned but haven’t applied yet? #NumPy #Python #DataScience #DataEngineering #MachineLearning #CodingJourney #TechLearning

Most people use Pandas for EDA. 𝗩𝗲𝗿𝘆 𝗳𝗲𝘄 𝘂𝘀𝗲 𝗶𝘁 𝗲𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆. That’s the difference between spending hours exploring data and getting insights in minutes. Over time, one thing has stood out to me: It’s not just about the insights - it’s about how efficiently you get there. I’ve put together a quick reference: 📊 10 Pandas EDA Tricks that help: • Write cleaner, more readable code • Speed up analysis • Build more reliable workflows 📌 Attached is a cheat sheet for easy reference. 𝗙𝗼𝗿 𝗮 𝗱𝗲𝘁𝗮𝗶𝗹𝗲𝗱 𝗯𝗿𝗲𝗮𝗸𝗱𝗼𝘄𝗻: 🔗 https://lnkd.in/gv6_TmUD What’s one Pandas tricks you use that saves you the most time? #DataAnalytics #DataScience #Python #Pandas #EDA

Most Python workflows rely on heuristics. They’re quick, intuitive, but usually not optimal. A simple greedy approach might get you a solution, but it often leaves efficiency, performance, and cost savings on the table. GAMSPy brings algebraic modeling into Python, so you can express constraints and objectives directly and solve for a true optimum. At PyConDE & PyData 2026, Justine Broihan and Muhammet Soyturk will walk through this using a classic operations example, and then extend it into machine learning. They'll cover: 🔸 How optimization compares to rule-based heuristics and 🔸 How it can be used to test ML models (e.g. minimal changes needed to trigger misclassification) 🔸 The Art of the Optimal: A Pythonic Approach to Complex Decision-Making 📍 April 14 · 16:30 📍 Platinum (2nd Floor) If you're building decision-making systems in Python, this is worth a look. More details 👉 https://lnkd.in/dyifGdVi #PyConDE #PyData #Optimization #GAMSPy #GAMS #Python

𝐔𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 𝐜𝐨𝐫𝐫𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬 𝐦𝐚𝐝𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐢𝐬 𝐦𝐨𝐫𝐞 𝐢𝐧𝐭𝐞𝐫𝐞𝐬𝐭𝐢𝐧𝐠 𝐟𝐨𝐫 𝐦𝐞 While exploring datasets in Python recently, I spent some time understanding how correlation works between variables. Using pandas, it’s surprisingly easy to calculate a correlation matrix and see how different columns relate to each other. Sometimes two variables move together strongly, and sometimes there’s almost no relationship at all. What I found interesting is that correlations can quickly highlight patterns that might not be obvious just by looking at raw numbers. Still learning how to interpret these relationships properly, but it’s definitely making the analysis process more insightful. #Python #Pandas #DataAnalytics

If you're working with data, mastering NumPy is non-negotiable. 📊 From array creation to linear algebra, this cheat sheet is a quick reminder of how powerful NumPy really is. Whether you're cleaning data, running statistical analysis, or building models — these functions are your daily toolkit. Save this for later… your future self will thank you. 😉 #DataScience #Python #NumPy #DataAnalytics #MachineLearning

📊 My First Machine Learning Project — CGPA vs Salary Prediction! I built a Linear Regression model in Python that predicts student salary packages based on CGPA. 🔍 What I did: ✅ Exploratory Data Analysis ✅ Trained a Linear Regression model ✅ Evaluated predictions with % error ✅ Visualized the regression line 🔧 Tools: Python | Pandas | Scikit-learn | Matplotlib 🔗 Full project on GitHub: https://lnkd.in/dEtZaUdm #MachineLearning #Python #DataScience #LinearRegression #FirstProject