Tab 3 is live — and this one gets into the real groundwork of any ML pipeline! 🧹 After exploring the data in Tabs 1 & 2, Tab 3 handles end-to-end Data Preprocessing: • Train / Validation / Test split with a dynamic slider • Stratified splitting with a fallback for small class sizes • One-hot encoding for categorical features • Standard scaling for numerical features • Class balance check — with optional SMOTE for imbalanced datasets Clean data in, better models out. 🚀 More tabs coming soon! #DataScience #MachineLearning #DataPreprocessing #SMOTE #Streamlit #Python #FeatureEngineering #BuildingInPublic #DataAnalytics #OpenToWorkhashtag

Data Cleaning is only half the battle. Are you Engineering your features? In Step 2 of the Machine Learning pipeline, many beginners stop at data cleaning. While removing NaNs and dropping irrelevant rows is essential, the real magic happens during Feature Engineering. While working on my recent Price Prediction project, I realized that the raw data rarely tells the full story. To build a high-performing model, you have to create features that capture the "why" behind the numbers. I focused on three key areas for this preprocessing script: 📈 Moving Averages: Capturing trends over time. 📉 Volatility: Accounting for market fluctuations and risk. 🕒 Lag Features: Giving the model a "memory" of previous price points. Clean data gets you a working model. Engineered features get you a winning model. Check out the snippet of my preprocessing logic below! 👇 #MachineLearning #DataScience #Python #FeatureEngineering #PredictiveAnalytics

𝐂𝐫𝐚𝐜𝐤𝐞𝐝 𝐭𝐡𝐞 𝐂𝐨𝐝𝐞 𝐨𝐧 𝐇𝐨𝐮𝐬𝐞 𝐏𝐫𝐢𝐜𝐞 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐨𝐧! I just wrapped up a deep dive into Predictive Modeling using the classic California Housing Dataset. Beyond just fitting a model, I focused on clean data visualization and resolving distribution skews to ensure high-performance results. 𝐊𝐞𝐲 𝐇𝐢𝐠𝐡𝐥𝐢𝐠𝐡𝐭𝐬:  𝐀𝐥𝐠𝐨𝐫𝐢𝐭𝐡𝐦: Linear Regression  𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧: Modernized EDA using Seaborn histplot & probplot  𝐓𝐞𝐜𝐡 𝐒𝐭𝐚𝐜𝐤: Python, Scikit-learn, Pandas, NumPy  𝐕𝐞𝐫𝐬𝐢𝐨𝐧 𝐂𝐨𝐧𝐭𝐫𝐨𝐥: Managed via a clean, professional GitHub workflow. Check out the full implementation and clean repository in first comment below! #MachineLearning #DataScience #AIEngineering #Python #GitHub #LinearRegression #HousePricePrediction

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

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

From raw data to meaningful insights! Just wrapped up a hands-on project exploring multiple linear regression—diving into data cleaning, visualization, feature relationships, and building predictive models. It’s always rewarding to see how patterns emerge when the right techniques are applied. Model Performance: • MSE: 8108.57 • MAE: 73.80 • RMSE: 90.05 • R² Score: 0.759 • Adjusted R²: 0.599 Key takeaways: • The power of visualization in understanding data relationships • Importance of feature selection and assumptions in regression • Turning numbers into actionable insights Continuously learning, building, and growing in the data space Dataset: https://lnkd.in/gDNUVVMc #DataScience #MachineLearning #Python #DataAnalysis #LearningJourney

🚀 Day 29 – LeetCode Journey Today’s problem: Combine Two Tables ✔️ Used Pandas merge() to join datasets ✔️ Applied left join to retain all records from the primary table ✔️ Selected only required columns for clean output 💡 Key Insight: Understanding how to work with dataframes and joins is essential for real-world data analysis. Using merge() makes combining structured data simple and efficient. This problem strengthened my skills in Pandas, data manipulation, and SQL-like operations in Python. From algorithms to data handling — growing every day 📊🔥 #LeetCode #Day29 #Pandas #DataAnalysis #Python #ProblemSolving #CodingJourney #100DaysOfCode

Why settle for one algorithm when you can have both? 🎬 I just wrapped up a project building a Hybrid Movie Recommendation System to tackle one of the biggest challenges in ML: balancing user behavior with item characteristics. While Collaborative Filtering is great for finding what "people like you" watched, it often fails with new movies. By integrating Content-Based Filtering, I built a system that stays smart even when data is sparse. Key Highlights: User-User & Item-Item Similarity: Leveraged collaborative filtering for deep personalization. Content Logic: Analyzed metadata to ensure niche favorites don't get lost. The Hybrid Edge: Combined both models to significantly reduce the "Cold Start" problem and improve recommendation diversity. Tech Stack: Python | Pandas | NumPy | Scikit-learn Check out how I simulated a real-world streaming engine using the MovieLens dataset! 🔗 GitHub link - https://lnkd.in/dFmPK4WD #MachineLearning #DataScience #Python #RecommendationSystems #BuildingInPublic

🚀 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 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