Optimizing Large Datasets with NumPy Memory-Mapped Arrays

Our team applied Descriptive, Predictive, and Prescriptive Analytics to the Car Crashes dataset using Pandas, Seaborn, and Scikit-learn. We built a Multiple Linear Regression model and visualized key predictors like speeding and alcohol involvement. The project enhanced our skills in data visualization, model evaluation, and collaborative analytics. Dr. Pritpal Singh Link to the main worksheet: https://lnkd.in/g4MR_t-B #DataScience #MachineLearning #Python #TeamWork #AnalyticsProject #RoadSafety #PredictiveAnalytics #Visualization

🚀 Day 14: Exploratory Data Analysis (EDA) in Action Today was all about applying EDA on real datasets to uncover insights. 📊 Lesson 1: Hands-on with Cars Dataset Cleaned and explored data using Pandas Looked at distributions, correlations, and key statistics 📊 Lesson 2: EDA Assignment Practiced identifying trends Detected missing values, duplicates, and outliers Learned how EDA guides the next steps in analysis or modeling EDA feels like being a detective of data — asking the right questions and letting the data reveal its story. #Day14 #Python #EDA #Pandas #DataScience #DataCleaning #WomenInTech #MachineLearning

Day 4 — Data Science Learning Journey Today, I began exploring one of the most fascinating parts of Statistics for Data Science — Probability Probability helps us measure uncertainty and make data-driven predictions — something that powers almost every machine learning algorithm. Here’s what I learned today -Sample Space: All possible outcomes of an experiment -Events: Specific outcomes we’re interested in -Addition Rule: Probability of A or B happening -Multiplication Rule: Probability of A and B happening together Next, I’ll dive deeper into Conditional Probability and Bayes’ Theorem, which are key concepts for Data Science and Machine Learning. #DataScience #Probability #Statistics #Python #MachineLearning

𝐓𝐡𝐞 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 𝐓𝐡𝐚𝐭 𝐖𝐢𝐥𝐥 10𝐱 𝐘𝐨𝐮𝐫 𝐒𝐩𝐞𝐞𝐝. The truth about data work? It's not the fancy models; it's the 20% of foundational commands you use 80% of the time. And that little moment of doubt when you need to quickly reshape an array, calculate covariance, or nail a complex multi-condition filter... that's where all the time goes. I got fed up with bouncing between Stack Overflow and my IDE just to recall the syntax for 𝘯𝘱.𝘭𝘪𝘯𝘴𝘱𝘢𝘤𝘦 or 𝘥𝘧.𝘥𝘵.𝘥𝘢𝘺. So, I compiled this single-page, 𝐡𝐢𝐠𝐡-𝐢𝐦𝐩𝐚𝐜𝐭 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐡𝐞𝐚𝐭 𝐒𝐡𝐞𝐞𝐭 —specifically targeting the commands that separate the beginners from the power users. This isn't your standard, fluffy list. This is the condensed power you need for: - 𝐋𝐢𝐧𝐞𝐚𝐫 𝐀𝐥𝐠𝐞𝐛𝐫𝐚: Essential functions for ML foundations. - 𝐓𝐢𝐦𝐞 𝐒𝐞𝐫𝐢𝐞𝐬 𝐌𝐚𝐬𝐭𝐞𝐫𝐲: All the dt accessor methods (year, month, day) in one spot. - 𝐃𝐞𝐞𝐩 𝐀𝐠𝐠𝐫𝐞𝐠𝐚𝐭𝐢𝐨𝐧: Mastering 𝘨𝘳𝘰𝘶𝘱𝘣𝘺, 𝘢𝘨𝘨, and the critical pivot table for reporting. The goal is simple: Stop searching. Start doing. Found this helpful? 🔃𝐒𝐡𝐚𝐫𝐞 𝐢𝐭 #DataScience #Python #NumPy #Pandas #Productivity #CareerGrowth #MachineLearning

𝗘𝘃𝗲𝗿𝘆 𝗱𝗮𝘁𝗮 𝘀𝗰𝗶𝗲𝗻𝘁𝗶𝘀𝘁 𝗸𝗻𝗼𝘄𝘀 𝘁𝗵𝗲 𝗳𝗲𝗲𝗹𝗶𝗻𝗴: the model is perfect, the data is loaded, but then... you hit run. And you wait. ☕️ My recent project was a Monte Carlo Stock Simulation, calculating 100,000 future price paths. It was a beautiful financial model, but it had a silent killer: the Python for loop. The loop was supposed to calculate 25.2 million daily returns. The Nightmare: I timed the initial run. The Python loop method took 1 minute and 13 seconds. Over a minute of wasted time, just watching the cursor spin, waiting for the interpreter to sequentially check 25.2 million individual steps. The Hero: I realized the answer wasn't better hardware; it was a better approach: NumPy Vectorization. I replaced the nested loops with a single line of code, using the power of Ufuncs (np.cumsum, np.exp) to process the entire array at once. The Victory: The optimized version took just 1.19 seconds. That's not just faster—it's 62x FASTER! We turned an agonizing minute of waiting into an instant result, all by shifting the work from slow Python to optimized C code. This carousel walks you through the entire story: from the slow code (the killer) to the single-line solution (the hero). Swipe through to see the exact code comparison and how we crushed that 62x speed barrier! 👇 #DataStorytelling #Python #NumPy #Vectorization #CodingTips #DataScience

📈 Multiple Linear Regression | Geometric Intuition & Code 💻 After learning Simple Linear Regression, I took the next step — building a Multiple Linear Regression (MLR) model to predict house prices using multiple factors: 🏠 House Size (sq ft) 🛏 Bedrooms ⏳ Age of the House 🧠 What I Learned How multiple features together affect predictions Geometrically, MLR fits a plane (or hyperplane) — not just a line — in higher dimensions How to interpret coefficients, intercept, and R² score to measure performance 💻 Tools Used Python | Pandas | NumPy | Matplotlib | Scikit-Learn 🔗 Check out my complete notebook here: 👉https://lnkd.in/d54KJM6n Every project adds one more layer to my understanding of Machine Learning fundamentals and brings me closer to mastering Data Science. 🚀 #MachineLearning #DataScience #Python #LinearRegression #MultipleLinearRegression #GitHub #LearningByDoing #AI #WomenInTech #DataAnalytics #CareerGrowth

📊 Strengthening My Data Science Skills with NumPy (Thanks to @codewithharry!) As I dive deeper into data science, I’ve been exploring the power of NumPy — and I must say, it’s an incredible tool for efficient numerical computation. Today, I worked with: Multi-dimensional arrays Reshaping and broadcasting Fast, vectorized operations And understood how NumPy uses contiguous memory to boost performance All of this is part of the amazing Data Science course by Code with Harry — it’s beginner-friendly, super clear, and packed with practical examples. Highly recommend it to anyone starting or brushing up their foundations. This journey is about consistent learning, and every small step feels rewarding. 🚀 #DataScience #Python #NumPy #CodewithHarry #LearningInPublic #TechJourney #MachineLearning #StudentDeveloper

𝗔 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 𝗕𝘆 𝗔𝗺𝗮𝘇𝗼𝗻! 📈 In the past years, foundation models have been extensively utilized in time series forecasting, with models like TimeGPT and TimesFM gaining significant attention. Chronos is a forecasting model family introduced by Amazon, that is based on language model architecture. Chronos models have been trained on a large corpus of time series datasets, as well as synthetic data generated with Gaussian processes. Chronos-2 has just been released, achieving the best performance on fev-bench and GIFT-Eval amongst pretrained models. Check the links below for more information and follow me for regular data science content! 𝗖𝗵𝗿𝗼𝗻𝗼𝘀 𝗚𝗶𝘁𝗵𝘂𝗯 𝗽𝗮𝗴𝗲: https://lnkd.in/dvQ6D6EA 𝗟𝗲𝗮𝗿𝗻 𝗠𝗟 𝘄𝗶𝘁𝗵 𝗣𝘆𝗖𝗮𝗿𝗲𝘁📚: https://lnkd.in/dyByK4F #datascience #python #deeplearning #machinelearning

Amazon’s new Chronos family of forecasting models applies language model architecture to time series prediction. A major step forward in AI-driven forecasting! Trained on both real and synthetic data, Chronos-2 now leads benchmarks like fev-bench and GIFT-Eval among pretrained models. Thank you, Giannis Tolios, for sharing this insightful update. #DataScience #TimeSeries #AI #MachineLearning

🎯 End-to-End ML in Action: Bank Marketing Prediction A quick hands-on project to refresh core ML workflow — from raw data to evaluated models. Goal: Predict if a client subscribes to a term deposit. Stack: Python · Scikit-learn · XGBoost Steps: Data cleaning · Feature engineering · Model tuning · Evaluation Top performer: ✅ XGBoost (F1 = 0.77 · AUC = 0.91) Key drivers: Longer calls & higher balances → higher conversion. A simple yet complete ML pipeline — perfect practice for model building, comparison, and explainability. github: https://lnkd.in/dD53_dqk #MachineLearning #DataScience #MLProjects #Python #XGBoost