scikit-learn 1.7: new features, improvements, and more

🚀 House Price Prediction with 13 ML Models + Flask 🚀 Just completed a full-stack regression pipeline that trains, evaluates, and deploys 13 machine learning models — 🔧 Tech Stack:   Python · pandas · scikit-learn · XGBoost · LightGBM · Flask · pickle 📊 Highlights: - Trains 13 regression models on USA housing data   - Logs performance metrics (MAE, MSE, R²) to CSV   - Saves each model as a `.pkl` file for deployment   - Flask app with dropdown model selection   - Predicts house prices from user input   - Displays results and model comparison table 🔗 GitHub Repo:https://lnkd.in/dX5vCmf6 🙏 Huge thanks to my mentor KODI PRAKASH SENAPATI SIR for the guidance and feedback throughout this build. #MachineLearning #FlaskApp #RegressionModels #PythonDeveloper #PortfolioProject #DataScience #XGBoost #ScikitLearn #GitHub #LinkedInLearning

🚀 New Notebook Alert: Mastering the apply() Method in Pandas Just published a fresh notebook on Kaggle where I dive into one of the most powerful tools in the pandas library: the apply() method. Whether you're transforming data, applying custom functions, or optimizing your workflow, apply() is a game-changer for any data scientist. 🔍 In this notebook, I: Break down how apply() works with rows and columns Show practical examples using real-world football player data Compare apply() with alternatives like map() and lambda functions Highlight performance tips and common pitfalls 📘 Check it out here: https://lnkd.in/dkx97pu4 💬 I’d love your feedback—what’s your favorite use case for apply()? And if you’re just getting started with pandas, feel free to reach out. Let’s grow together! #DataScience #Python #Pandas #Kaggle #LearningByDoing #PortfolioBuilding #LinkedInLearning

🚀 Top 3 XGBoost Hyperparameters You Must Know 1️⃣ Learning Rate (η) What it does: Scales how much each new tree contributes to predictions. Effect: Smaller → slower learning, less overfitting, more trees needed Larger → faster learning, may overfit Example: learning_rate = 0.1 → each tree adjusts predictions by 10% of its weight 2️⃣ max_depth What it does: Maximum depth of each tree (max splits along a path). Effect: Smaller → simpler trees, less overfitting Larger → more complex trees, captures more patterns but may overfit Example: max_depth = 3 → a path can have at most 3 splits → each path can “use” up to 3 features 3️⃣ gamma (γ) What it does: Minimum loss reduction required to make a split. Effect: Higher → fewer splits, simpler trees → less overfitting Lower → more splits → more complex trees Example: gamma = 1 → split only if it improves loss by at least 1 💡 Pro Tip: Start with default values, tune max_depth & gamma first to control complexity, then adjust learning_rate for gradual improvement. #DataScience #Python #SQL #Statistics #InterviewPrep #MachineLearning #Analytics

Excited to share the next step in my machine learning portfolio! I have developed a machine learning application for house price prediction, complete with an interactive user interface. This project is an end-to-end Automated Valuation Model (AVM) for the Seattle housing market. The process involved data analysis, training a Linear Regression model with Scikit-learn, and building a functional front-end with @Streamlit. The application allows users to input property attributes—such as square footage, bedrooms, and location—to generate a price estimate. Technical Stack: Language: Python Libraries: Pandas, Scikit-learn Framework: Streamlit for the web application This project demonstrates a complete workflow from data processing to a functional application prototype. The source code and detailed analysis are available on my GitHub. Source Code: https://lnkd.in/ddVRywEt #MadeWithStreamlit #Streamlit #DataScience #MachineLearning #Python #PropTech #PredictiveAnalytics #PortfolioProject #ScikitLearn #MachineLearningEngineer #Pandas #Portfolio #DataAnalytics #FullStackDataScience

🌱 Beginners, Start Your Machine Learning Journey Like This 👇 If you’re just getting started with Machine Learning, don’t overthink it. Begin simple. Here’s a perfect first project every beginner should try: 📘 Dataset: Iris Dataset — small, clean, and easy to understand. 🧠 Algorithm: K-Nearest Neighbors (KNN) ⚙️ Steps to follow: Load the dataset using pandas. Drop unnecessary columns (like Id). Split your data into features (X) and target (y). Train your model with KNeighborsClassifier from scikit-learn. Predict and check your accuracy on both train and test sets. 💡 Example result: Train Accuracy: 97.32% Test Accuracy: 97.36% This simple workflow teaches you the complete ML pipeline — data preparation, training, testing, and evaluation — without being overwhelming. Every ML expert once started with this small but powerful exercise. So open your Jupyter Notebook, load the Iris dataset, and take your first step toward Machine Learning today! 🚀 Dataset : https://lnkd.in/gs3jEJ4N #MachineLearning #Python #DataScience #Beginners #KNN #IrisDataset #LearningJourney

Pandas DateTime Handling (ETL Time Intelligence) Dates are everywhere — logs, trades, transactions — and Pandas gives us powerful tools to parse, manipulate, and analyze time-based data. Quick Tip (ETL Insight): ▪️Use pd.to_datetime() to standardize mixed date formats. ▪️Use .dt accessor to extract date parts (year, month, weekday). ▪️Always store timestamps in UTC for global consistency. ▪️Great for time-based aggregations, window functions, and trend analysis. #Python #Pandas #DateTime #TimeSeries #ETL #DataCleaning #DataEngineering #BigData #100DaysOfCode

💎 Hidden Gems in NumPy: 7 Functions Every Data Scientist Should Know 🚀… Think you’ve mastered NumPy? Wait till you see these underrated power tools hiding in plain sight 👇 1️⃣ np.where() – Replace loops with elegant, vectorized conditional logic. Filtering and labeling made simple. 2️⃣ np.clip() – Instantly keep values within range. Perfect for taming outliers and noisy data. 3️⃣ np.ptp() – Get the peak-to-peak range in one line. Fast measure of variability. 4️⃣ np.percentile() – Pinpoint thresholds, detect outliers, and track KPIs like a pro. 5️⃣ np.unique() – Clean your data and count duplicates effortlessly. ✨ These compact tools can save hours of preprocessing time—and make your analytics pipeline shine. 💬 What’s your favorite “hidden gem” NumPy function? Drop it below 👇 #NumPy #Python #DataScience #Analytics #MachineLearning #CodingTips

When .describe() in pandas lies to you! We all love using .describe() for quick data summaries… But sometimes, it can be misleading  The problem: When your dataset has mixed types — numbers, categories, dates — .describe() can behave oddly. Numeric columns stored as object (e.g., "25", "30") get skipped. Encoded categories (1, 2, 3) are treated as continuous numbers. include='all' adds noise instead of clarity. The fix: Clean your data types before describing! df['age'] = df['age'].astype('int64') df['city_code'] = df['city_code'].astype('category') df.describe(include=['int64', 'float64']) Now you’ll only see true numerical summaries — no confusion, no clutter. 💡 Pro tip: Always check df.dtypes before trusting .describe(). Good data types = good insights. #DataScience #Python #Pandas #EDA #DataCleaning #MachineLearning #Analytics

Forecasting time series is central to predictive analytics. This post on skforecast gives practical insight. As I grow my skills in ML and statistics, I’m curious how this tool handles non-stationarity or irregular data. Thoughts?

Tree-based models like Gradient Boosting are excellent for prediction but they hit a limit when it comes to extrapolation. Without intervention, forecasts flatten at the edges and lose sight of underlying trends. At DataBased Solutions, we design models that see beyond their training data. The real innovation lies in bridging that gap with the right data transformations and model design. How are you addressing extrapolation in your forecasting pipelines?