📊 Experiment 12 – Random Forest Classifier In this experiment, I explored the Random I explored how the model splits data into branches based on key features and how it reaches a final decision at each leaf node. It was exciting to visualize the tree structure and learn how pruning can help improve model accuracy. This practical gave me a better understanding of how decision trees handle classification problems in a simple yet powerful way. 📁 GitHub: https://lnkd.in/eTtC53qu 🎓 Guided by: Ashish Sawant #MachineLearning #RandomForest #DataScience #Python #AI #Coding #Learning #JupyterNotebook #CSE #PRMCEAM

📊 Experiment 9: K-Nearest Neighbors (KNN) Classifier In this practical, I implemented the K-Nearest Neighbors (KNN) algorithm for classification tasks. I understood how the model predicts new data points by checking their closest neighbors in the dataset. It was an insightful hands-on experience to understand distance-based learning in action. 📁 GitHub: [https://lnkd.in/dFff8cPb] 🎓 Guided by: Ashish Sawant #MachineLearning #KNN #Python #AI #DataScience #Analytics #StudentLearning #CSE #PRMCEAM

Excited to share our recent work on Random Forest, a robust ensemble learning technique that enhances prediction accuracy and reduces overfitting by combining multiple decision trees. Key Highlights: Boosting accuracy through ensemble methods Reducing variance using bootstrap sampling Understanding feature importance for better insights Implemented using Python, leveraging libraries like Scikit-learn and TensorFlow Random Forest is one of the most versatile algorithms in machine learning — effective for both classification and regression tasks, and a great step toward mastering ensemble models! Team Members: M ARUN KUMAR REDDY (RA2311026010328) B THARUN SUJITH (RA2311026010348) B VENKATA ANIL KUMAR (RA2311026010353) A POOJA SAMANVITHA (RA2311026010363) P VENU GOPALA KRISHNA (RA2311026010368) #MachineLearning #RandomForest #ArtificialIntelligence #DataScience #Python #TeamWork #AI #DeepLearning #MLProjects #CSEAIML

🚀 Exploring the Power of Support Vector Machine (SVM)! Thrilled to share my latest DSS practical on Support Vector Machine (SVM) — one of the most powerful and versatile algorithms in Machine Learning for both classification and regression tasks. 💡 In this experiment, I implemented the SVM model using Python (Scikit-learn) and explored how different kernel functions — linear, polynomial, and RBF — influence model performance, accuracy, and decision boundaries. 📊 Guided by Ashish Sawant Sir. ✨ 🔗 GitHub: https://lnkd.in/ez_NstrZ 📁 Google Drive: https://lnkd.in/ezXFx_py 🔗 #MachineLearning #Python #SVM #AI #DataScience #CSE #Engineering #DSS #MLProject

🤖 Performed Practical on K-Nearest Neighbors (KNN) Algorithm Implemented and understood the working of KNN, a powerful supervised learning algorithm used for both classification and regression tasks. Learned how distance metrics and the value of k influence model accuracy and performance. 🔗 GitHub Repository: https://lnkd.in/gsPj_hxs 🧑🏫 Under the Guidance of: Ashish Sawant #MachineLearning #DataScience #KNN #Python #AI #MLAlgorithms #LearningJourney #GitHub

After implementing an SVM in my last lab, this experiment (EXPO: 11) focuses on the Decision Tree Algorithm. I used sklearn on the same heart dataset to build a predictive model. It's fascinating to contrast the two approaches. While the SVM finds a mathematical boundary, the Decision Tree builds an intuitive, flowchart-like model that's highly interpretable. You can see in the visualization exactly how it splits the data based on features like 'cp', 'ca', and 'age' to make a decision. Skills: Machine Learning, Decision Trees, Python, Scikit-learn, Pandas, Data Visualization #MachineLearning #DecisionTree #DataScience #Python #ScikitLearn #InterpretableAI #StudentProject #EmergingTech

🚀 Heart Disease Prediction using KNN I recently built a machine learning model to predict heart disease using the K-Nearest Neighbors (KNN) algorithm. The project involved cleaning the dataset, handling missing values, scaling features, and training the model using Scikit-learn. ✅ Achieved good accuracy and insights using: Confusion Matrix Accuracy Score Classification Report Tools used: Python, Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn Luminar Technolab #MachineLearning #DataScience #KNN #Python #HeartDiseasePrediction

🌳 Experiment 11 – Decision Tree Analysis In this practical, I implemented the Decision Tree algorithm to classify data and evaluate model performance. Learned how tree-based models make predictions through dataset preparation, training, and testing. 💻 GitHub: [https://lnkd.in/dFff8cPb] 🎓 Guided by: Ashish Sawant #MachineLearning #DecisionTree #DataScience #Python #AI #Coding #CSE #PRMCEAM