📊 Day 13 | PCA (Dimensionality Reduction) 📉📊 Today, I explored Principal Component Analysis (PCA). PCA is used to reduce the number of features while preserving important information. This helps in: ✔ Reducing complexity ✔ Improving model performance ✔ Visualizing high-dimensional data I applied PCA using Python to transform data into fewer dimensions 💻 This helped me understand how large datasets can be simplified without losing key insights. #MachineLearning #PCA #DataScience #LearningInPublic #Python

Master Python predictive modeling with scikit-learn and create accurate models that drive business success with this comprehensive guide https://lnkd.in/gWZEs6Vr #PythonPredictiveModeling Read the full article https://lnkd.in/gWZEs6Vr

𝗗𝗮𝘆 𝟮 | 𝗨𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴 𝗵𝗼𝘄 𝗣𝘆𝘁𝗵𝗼𝗻 𝗵𝗮𝗻𝗱𝗹𝗲𝘀 𝗱𝗮𝘁𝗮 Today’s learning was focused on how data is stored and used in Python, which is an important base for data analysis. 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱: 💠 Variables and assigning values 💠Data types such as int, float, string, and boolean 💠Using type() to check and understand data types I tried a few small examples to see how different data types behave. Even though this topic looks simple, it is clear that everything in programming depends on how well we handle data. Taking time here feels important before moving forward. #PythonBasics #DataTypes #DataAnalysis #LearningInPublic #CodingJourney

Day 3/30 – Python Series 🚀 Topic: Slicing (Why | What | How) Mastering slicing is a small step that creates a big impact in data processing. From extracting data efficiently to writing cleaner code, it’s a must-know for every Data Engineer. Let’s keep building. 💻 #Python #DataEngineering #LearningInPublic #100DaysOfCode #CodingJourney #TechSkills #FutureEngineer

50% of Python Pandas users do this: df[df['customer_age'] > 50][['cust_id', 'cust_age', 'address']] Instead this : df.loc[df['customer_age'] > 50, ['cust_id', 'cust_age', 'address']] So Which one is better? While Both yield same results Most people stop at: “just use loc, it’s cleaner.” The REAL difference is 1. One indexing operation 2. Row and column selection in a single step 3. No intermediate DataFrame creation 4. Direct reference to the original dataset If your transformation has business meaning, don’t let it be split across implicit steps. Make it explicit. Make it atomic. That’s what .loc really enforces. . . . . . . . . #Python #Pandas #DataEngineering #DataScience #CodeNewbie

🚀 Matrix Multiplication: Code Implementation (Data Structures And Algorithms) This Python code illustrates how to perform matrix multiplication. The function takes two matrices as input and returns their product. It ensures that the matrices are compatible for multiplication (number of columns in the first matrix equals the number of rows in the second). The algorithm iterates through the rows of the first matrix and the columns of the second matrix to compute each element of the resulting matrix. Understanding the nested loops and the dot product calculation is key to understanding matrix multiplication. #Algorithms #DataStructures #CodingInterview #ProblemSolving #professional #career #development

📊 Comparing Two Outlier Removal Approaches in Python When cleaning datasets, how you remove outliers matters more than you think. I recently compared two common strategies: 1️⃣ Column-wise removal – Drop outliers sequentially, one column at a time. 2️⃣ Dataset-level removal – Flag all outliers across the entire dataset first, then remove them together. 🔍 What I found: The column-wise approach changes the IQR bounds after each removal, causing many non‑outlier rows to be wrongly filtered out (545 → 365 rows). The dataset-level approach respects original distributions, removes only true outliers (545 → 463 rows), and avoids over‑cleaning. ✅ Takeaway: Always identify outliers globally before removing them – your data will thank you. 📁 Used Python, pandas, IQR method, and a housing dataset. 🔗 Full code & notebook: https://lnkd.in/gheGYYEz #DataScience #Python #OutlierDetection #DataCleaning #Pandas #MachineLearning

An exercise to help build the right mental model for Python data. - Solution: https://lnkd.in/e55Pr8nJ - Explanation: https://lnkd.in/ebPVvnhx - More exercises: https://lnkd.in/eQSdJdaW The “Solution” link visualizes execution and reveals what’s actually happening using 𝗺𝗲𝗺𝗼𝗿𝘆_𝗴𝗿𝗮𝗽𝗵: https://lnkd.in/e3sUM7wG

Learn how to build a recommendation system with Python and machine learning, including data collection, preprocessing, and model selection https://lnkd.in/g-FccWQn #BuildingARecommendationSystemWithPython Read the full article https://lnkd.in/g-FccWQn

Just wrapped an energizing session teaching data loaders for local LLMs in Python! We mapped out Text Loaders pulling PDFs, JSONs, CSVs, and TXTs into strings or key-value trees via #LangChain, Image Loaders converting to binary formats, plus #OCR magic on pure-image #PPTX files using Python’s pptx loader—and Pandas crushing CSV/XLS flows. Total game-changer for building rock-solid AI pipelines! Tell me how do you think about texts on whiteboard? #Python #AI #LocalLLM #whiteboardKnowledge