XGBoost Hyperparameters: Learning Rate, max_depth, gamma

🌱 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

🎯 Regression Capstone Project – Predicting Housing Prices using Machine Learning 🏠📊 Excited to share my latest project where I explored multiple regression models to predict house prices using the USA Housing dataset! 🔍 Project Highlights: Performed data preprocessing and feature selection Implemented and compared several regression models including: 🔹 Linear Regression 🔹 Ridge, Lasso, and ElasticNet 🔹 Polynomial Regression 🔹 Random Forest Regressor 🔹 Support Vector Regressor (SVR) 🔹 KNN, ANN (MLPRegressor), XGBoost, and LightGBM Evaluated models using MAE, MSE, and R² Score Saved trained models as .pkl files for easy deployment 📈 The goal was to identify the most accurate model for predicting house prices and understand the trade-offs between various regression algorithms. 💡 Key Learnings: Importance of proper preprocessing before model training Regularization techniques to prevent overfitting Comparative model evaluation and performance tuning 🔗 Tools & Libraries: Python, scikit-learn, XGBoost, LightGBM, Pandas, NumPy I’m continuously learning and improving in the field of Machine Learning and Data Science — feedback and suggestions are always welcome! 🚀 #MachineLearning #DataScience #RegressionAnalysis #Python #XGBoost #LightGBM #ScikitLearn #CapstoneProject under the guidence of KODI PRAKASH SENAPATI sir github:https://lnkd.in/d7Gu8SRm

🚀 Task 01 Completed: House Price Prediction using Linear Regression 🏡💻 As part of my Machine Learning track, I implemented a Linear Regression Model to predict house prices based on key features like Square Footage, Bedrooms, and Bathrooms. 📊 Tech Stack & Libraries Used: Python 🐍 pandas, numpy scikit-learn (train_test_split, LinearRegression, metrics) ⚙️ Workflow Overview: 1️⃣ Loaded and explored the dataset (house_price_dataset.csv) 2️⃣ Selected independent variables (SquareFootage, Bedrooms, Bathrooms) and dependent variable (Price) 3️⃣ Split data into training and testing sets (80/20 split) 4️⃣ Trained the Linear Regression model 5️⃣ Evaluated performance using Mean Squared Error (MSE) and R² Score 6️⃣ Displayed model coefficients and intercept for better interpretability 📈 Key Metrics: Mean Squared Error (MSE): Measures prediction error R² Score: Indicates how well the model fits the data 💡 This project helped me strengthen my understanding of regression analysis and model evaluation in supervised learning. 🔗 GitHub Repository: SCT_TrackCode_Task01 #MachineLearning #LinearRegression #Python #DataScience #AI #SupervisedLearning #GitHub #MLProjects #LearningJourney

House Price Prediction Project🏠 I'm excited to share my latest machine learning project, where I built a predictive model to estimate house prices based on key features. 🔍 Objective: Analyze the primary factors influencing house prices and develop regression models to predict them accurately. 📊 What I Did: - Cleaned and prepared real‑world housing data where I handled missing values, mixed values, duplicates, and outliers. - Engineered impactful new features like price per square foot, significantly improving model interpretability. - Performed detailed EDA using Seaborn and Matplotlib to identify feature patterns and relationships. - Built Linear Regression, Lasso, and Ridge models, testing and refining each. - Evaluated model performance using the R² Score as a key metric. 🌟 Key Insights & Results: - Initial R² Score: 0.4969 (49.69%). - After feature engineering and outlier handling, I increased my model's R² score from just 0.4969 to an impressive 0.9647, meaning my final model explains over 96% of the variance in house prices. - Careful pre-processing and thoughtful feature construction played a major role in improving prediction accuracy. 🛠️ Tools & Techniques: Python | Pandas | NumPy | Matplotlib | Seaborn | Scikit‑Learn | Pipelines This project strengthened my understanding of how structured feature engineering and robust pre-processing can dramatically improve model performance. Check out the full project on my GitHub for all the details and visual insights! #MachineLearning #DataScience #Python #RegressionAnalysis #FeatureEngineering #HousePricePrediction

Excited to share my latest data science project: a model to predict California house prices using XGBoost! I handled the full machine learning workflow, including: 🔹 Data preprocessing and exploratory data analysis (EDA) 🔹 Training a powerful XGBoost Regressor model 🔹 Evaluating the model's performance The model achieved a solid R-squared score of 0.83 on the test data, showing strong predictive accuracy. This was a fantastic hands-on experience in applying regression techniques. A big thank you to Siddhardhan S for the excellent tutorial that guided this project. You can check out the full Jupyter Notebook and code on my GitHub. All feedback is welcome! #DataScience #MachineLearning #XGBoost #Python #Pandas #ScikitLearn #DataAnalysis #PredictiveModeling #PortfolioProject

From raw data to a 5-part growth strategy. 📈 Just finalized a K-Means Clustering project that segments customers by Annual Income and Spending Score. The result? A clear, actionable framework for targeted marketing. This isn't just a cluster plot; it's a roadmap to increase marketing ROI and customer lifetime value. Key Features: ✔️ Jupyter notebook ✔️ Cleaned and preprocessed dataset. ✔️ Clustering models applied and visualized. ✔️ Initial interpretation of customer groups. Infotact Solutions #DataDriven #CustomerCentric #ROI #MarketingAnalytics #Python #MachineLearning #KMeans #ScikitLearn #DataVisualization #BusinessIntelligence

Reduce churn rate in just a few hours. Can machine learning save your business? Customer data can tell you more than you think. Having data means you know exactly why clients stay and why they churn. You only need a tool that can show you those insights. In Module 3 of the Machine Learning Zoomcamp, I learned how to: - Predict customer churn - Use scikit-learn to implement logistic regression - Understand and apply one-hot encoding Want to read more? Click here: https://lnkd.in/dqaMT99Y #ml-zoomcamp #model #python #scikit-learn #ml #data

ML Zoomcamp - Module 4: Evaluation Metrics for Classification ⚙️ Evaluation metrics for classification measure how well a model predicts categorical outcomes. The confusion matrix forms the basis, showing true positives(TP), true negatives(TN), false positives(FP), and false negatives(FN). Accuracy indicates overall correctness but can be misleading for imbalanced data. Precision measures how many predicted positives are actually correct, while recall shows how many actual positives were identified, the F1-score balances both and is especially useful for uneven datasets. Specificity complements recall by measuring correct negative predictions. The ROC curve and its AUC assess a model’s ability to distinguish between classes, while the Precision-Recall curve and AUC-PR are preferred for highly imbalanced data. In this module, I learned: 🔸Accuracy, precision, recall, F-1 score 🔸ROC curves and AUC 🔸Cross-validation 🔸Confusion matrices 🔸Class imbalance handling Using metrics such as precision, recall, F1-score, and AUC provides a deeper understanding of model performance, enabling data driven decisions, improved reliability, and more effective real world outcomes. #MachineLearning #MachineLearningMetrics #ModelEvaluation #DataScience #PredictiveModeling #Python #Pandas #LearningInPublic Alexey Grigorev DataTalksClub

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?