Implementing Linear Regression from Scratch with NumPy

🚀 Built a House Price Prediction Model using Machine Learning In this project, I implemented: ✅ Linear Regression ✅ Ridge Regression ✅ Lasso Regression 📊 Compared model performance using RMSE & R² score 📉 Observed how regularization reduces overfitting Key Learning: Lasso helped in feature selection by shrinking some coefficients to zero. #MachineLearning #Python #DataScience #FinalYearProject

Recently worked on implementing XGBoost for a machine learning problem and explored how gradient boosting improves model performance through regularization and tree-based learning. XGBoost is powerful for handling structured data and reducing overfitting while maintaining high accuracy. Excited to keep experimenting and optimizing models 🚀 #XGBoost #MachineLearning #DataScience #Python #MLModels #LearningByDoing

Built a regression model to predict movie ratings using features like genre, director, and actors. This project helped me understand data preprocessing, feature engineering, and regression techniques in Machine Learning. #MachineLearning #Python #DataScience #Regression #ScikitLearn #MLProject #Codesoft

🚀 Excited to share my Machine Learning – Supervised Learning Algorithms repository! From Linear Regression to Naive Bayes, I’ve implemented key supervised learning algorithms with Python. Aimed at anyone looking to learn or explore ML practically. Check out the full code here: 👉 https://lnkd.in/gKyyN9E2 💡 Feedback and contributions are welcome! Let’s learn and grow together. #MachineLearning #Python #AI #ML #DataScience #SupervisedLearning #GitHub #OpenSource

🚀 Improving Model Performance with Ensemble Learning Explored the power of ensemble techniques by implementing a Bagging Regressor with Decision Trees to improve regression performance. By training multiple models on different bootstrapped samples and aggregating their predictions, bagging helps reduce variance, prevent overfitting, and produce more stable results compared to a single model. This project reinforced how ensemble methods can significantly enhance model robustness and predictive accuracy in real-world machine learning problems. . . . . #EnsembleLearning #Bagging #MachineLearning #ScikitLearn #Python #DataScience #MLProjects

🤖 Hands-on with Machine Learning using LDA! Implemented Linear Discriminant Analysis on the Iris dataset, evaluated the model using accuracy, classification report, and confusion matrix, and tested predictions on new input data. Learning how theory turns into working models through practice. #MachineLearning #LDA #Python #DataScience #LearningJourney “Practice is where concepts turn into confidence.”

Polynomial Regression in Action 📈 Linear models fail when relationships are non-linear. So I implemented Polynomial Regression to capture the true pattern between temperature 🌡️ and ice-cream sales 🍦. This visualization clearly shows how higher-degree features help model curved trends that linear regression can’t. 🔗 GitHub Repository: https://lnkd.in/g_EsSpxP Learning by building 🚀 #MachineLearning #PolynomialRegression #DataVisualization #LearningByDoing #Python #AI

💻 Day 9: Advanced Sorting Today I learned and implemented the following sorting techniques: 🔹 Merge Sort • Divide and Conquer approach • Recursive implementation • Efficient for large datasets 🔹 Recursive Bubble Sort 🔹 Recursive Insertion Sort 🧠 Key takeaways: • Recursion simplifies complex logic into smaller subproblems • Merge Sort offers better time complexity compared to basic sorting algorithms • Understanding recursion deeply helps in mastering advanced algorithms #striversa2zdsasheet #problemsolving #leetcode #LearningInPublic #DSA #SortingAlgorithms #Recursion #Python

#Day36 of my second #100DaysOfCode I focused more on understanding model quality and reliability today. ML: • Learned about VIF (Variance Inflation Factor) and how it’s used to test multicollinearity between features • Understood why multicollinearity can negatively impact model interpretation • Studied model simplification techniques • Explored Bayesian Information Criterion (BIC) and how it helps balance model fit vs complexity Good day for strengthening statistical intuition behind ML models. #WomenWhoCode #MachineLearning #DataScience #ModelSelection #Python #LearningInPublic