ramya Vemuri’s Post

Iris Flower Species Prediction using Machine Learning! Excited to share my project where I built a classifier to predict the species of iris flowers using the KNN algorithm. Key highlights: Data Normalization: Preprocessed the data to ensure quality inputs for the model. Model Creation: Trained a KNN model to learn patterns and relationships between features. Performance Evaluation: Achieved an accuracy score of 0.97%, demonstrating the model's effectiveness in predicting iris flower species! This project showcased the power of machine learning in classification tasks and the importance of data preprocessing, model selection, and performance evaluation. Looking forward to applying these skills to more projects and exploring the world of machine learning! #MachineLearning #DataScience #KNN #IrisFlowerClassification #AccuracyScore"

I'm happy to share that our newest publication about Support Vector Models in Massive Data is now available in open access at Elsevier - Decision Anaytics Journal. Another amazing colaboration with Jonatha Pimentel and Raydonal Ospina https://lnkd.in/dDSvyxZP #machinelearning #statistics #datascience #svm #massivedata #bigdata

Exploring Clustering Techniques: K-Means, DBSCAN, and Hierarchical Clustering Link: https://lnkd.in/dq4d-H5S This Colab notebook explores the fundamentals of unsupervised machine learning through clustering techniques: K-Means, DBSCAN, and Hierarchical (Agglomerative) Clustering. Each technique is applied to a dataset, and their respective strengths and weaknesses are examined. Gain insights into how these algorithms group data points and their real-world applications. Dive into the world of clustering and discover its potential in uncovering hidden patterns within your data. #Clustering #UnsupervisedLearning #KMeans #DBSCAN #HierarchicalClustering #MachineLearning #DataScience #ExploratoryAnalysis #ColabNotebook #datamining

Day 12: 𝐊-𝐌𝐞𝐚𝐧𝐬 𝐂𝐥𝐮𝐬𝐭𝐞𝐫𝐢𝐧𝐠 Today, I learned about K-Means Clustering. This unsupervised learning algorithm is widely used for grouping data into clusters based on similarity. - 𝐃𝐞𝐟𝐢𝐧𝐢𝐭𝐢𝐨𝐧:   -K-Means Clustering: An algorithm that partitions a dataset into K distinct, non-overlapping clusters. It works by minimizing the variance within each cluster and assigning data points to the nearest cluster center. 𝐃𝐢𝐬𝐜𝐮𝐬𝐬𝐢𝐨𝐧: Have you applied K-Means Clustering in your projects? What challenges did you face, and how did you overcome them? #DataScience #MachineLearning #ContinuousLearning

🔍 The Evolution of Scientific Discovery: The Four Paradigms of Data Science 📊 Data science has transformed how we approach scientific discovery, progressing through four key paradigms: Empirical – Gathering knowledge through observations and experiments. Theoretical – Developing scientific theories and models. Computational – Leveraging simulations and algorithms to analyze complex systems. Data-Driven – Extracting insights from vast volumes of data. As we advance, data-driven approaches are becoming increasingly critical, unlocking new possibilities in research and innovation. 🚀 #DataScience #ScientificDiscovery #Innovation #MachineLearning #Analytics #BigData

🚀 Day 61 of #180DaysofDataScience 🚀 🔸 Statistics: Today's Adventure: 🔍 What I Explored: Introduction to Normal Distribution: --> A type of continuous probability distribution that describes the likelihood of a random variable taking on a particular value characterized by a symmetric, bell-shaped curve. 📚 Key Takeaways: --> Key Parameters: 1. Mean: Center of the distribution 2. Standard Deviation: Small standard deviation - narrow, tall curve, and large standard deviation - wide, flat curve --> Many real-world phenomena, such as heights, test scores, and measurement errors, follow a normal distribution, making it crucial for data analysis, machine learning, and statistical inference What's Next: 🔮 Upcoming Exploration: --> Tomorrow, I will continue with Normal Distribution #DataScience #180DaysOfData #LearningJourney #TechExploration #DataScienceCommunity #StayCurious

Our latest research paper presents a hybrid approach for tackling the problem of irrelevant features in feature selection process. Feature selection is a significant issue in the machine learning process. Most datasets include features that are not needed for the problem being studied. These irrelevant features reduce both the efficiency and accuracy of the algorithm. Our latest research paper presents a hybrid approach for tackling the problem of feature selection. "An Optimized Hybrid Approach for Feature Selection Based on Chi-Square and Particle Swarm Optimization Algorithms", Data 2024, MDPI. 9(2), 20; https://lnkd.in/dET8eHAw

Thrilled to announce the completion of an ambitious project that harnesses the immense power of machine learning! 🚀 My mission? To plunge into the intricate realm of graph analysis, with a laser focus on clustering driven by local node properties. 🤖📊 🎯 The primary objective is nothing short of groundbreaking: Unravel the complexity of graphs, discerning and grouping nodes with shared structural properties. 🔄🔍 This approach promises to unearth cohorts within a given graph—revealing individuals with strikingly similar characteristics or interests. 🌐✨ 🔮 Brace yourselves for a journey into the cutting-edge, where technology meets innovation. 🧠💡 #MachineLearning #GraphAnalysis #DataScience #Innovation #TechProject #ArtificialIntelligence #BigData

🌺Excited to share my latest project from CipherByte Technologies - Iris Flower Classification using Machine Learning! 🌿🔍 In this video, I walk through how I developed a machine learning model to accurately classify iris flower species based on their sepal and petal characteristics. 📊💻 Watch as I demonstrate the steps involved in data preprocessing, model training, and evaluation, showcasing the power of data science in solving real-world problems. 🚀 Stay tuned for insights gained and lessons learned along the way! #DataScience #MachineLearning #IrisClassification #CipherByteTechnologies #Tech #DataAnalytics #ArtificialIntelligence