Subachandran murugesan’s Post

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📊 Day 50 of #100DaysOfML 🌐: Setting the Stage for Data Harmony - An Exploration of Sets! 🚀 Hello LinkedIn community! 👋 On Day 50 of my #100DaysOfML journey, let's embark on a journey into the realm of sets—a fundamental concept that lays the foundation for effective data manipulation and analysis. Join me as we explore the art of bringing order and structure to our datasets! 📈🔍 🌟 Delving into Sets: 1️⃣ Definition: Unveiled the essence of sets, collections of unique elements that enable us to organize and manage data efficiently. 🧑💻 Sets are the building blocks of order in the data world. 2️⃣ Set Operations: Navigated through fundamental set operations—union, intersection, and difference—unleashing the power to combine, filter, and extract distinct elements. 🔄 Set operations empower us to sculpt datasets to fit our analytical needs. 3️⃣ Set Cardinality: Ventured into the concept of set cardinality, understanding the count of elements within a set. 📊 Cardinality is the compass that guides us through the magnitude of data collections. 🚀 Like expert architects, mastering sets allows us to design robust and structured foundations for our data structures. 💡 Key Takeaway: Sets provide the organizational framework for effective data handling, offering a systematic approach to managing and analyzing information. 👉 Ready to build a structured foundation for your data! Stay tuned for more insights, challenges conquered, and the continued exploration of data organization. 🌐🔍 #SetsExploration #DataOrganization #SetOperations #DataStructures #ProgrammingJourney #100DaysOfML #MachineLearningChallenge #ProgrammingProgress #TechLearning #ContinuousLearning #DataAnalysis #PythonCoding #TechSkills #CodingLife #AIInsights #DataInsights #ProgrammingCommunity #DataManipulation #StructuredData #TechInnovation #PythonTips #ExploreData #DataHarmony #DataScienceFoundations #TechWisdom #StatisticalInsights #DataOrganizationSkills

🚀 Day 3 of the 50-Day Challenge: Building a Churn Prediction System! 🚀 🌟 Today, we're diving into customer retention strategies with a powerful churn prediction system trained on a Kaggle dataset. This project is all about identifying customers likely to cancel their subscription, helping businesses make informed decisions. 💪 🔍 Project Highlights: 🧠 Data Preprocessing: Learn the crucial steps of handling missing values, encoding categorical variables, and scaling numerical features to prepare the data for modeling. 📊✨ 🤖 Model Building: Utilize a machine learning model to predict customer churn. Explore different algorithms and select the best-performing one for accurate predictions. 🧠💡 📉 Model Evaluation: Evaluate the model using appropriate metrics and visualizations to understand its performance and make necessary improvements. 📈🔍 🌟 Feature Importance: Analyze the importance of various features in predicting churn, helping to understand key factors influencing customer behavior. 🔑📊 🔥 Ready to predict customer churn and enhance business decisions? Dive into the tutorial and let's build something impactful together! 💥 🔗 GitHub Repository link - https://lnkd.in/dMSXb5J7 Dataset Link: https://lnkd.in/d87WVyHk 🙌 Join the conversation! Have you worked on churn prediction before? Share your insights below! ⬇️ Tried different machine learning models for churn prediction? Tell us your tips and tricks! 📣 Reposting this? Don't forget to tag us! 🔄 Let's make the most of this challenge and learn together! 🎓✨ #50DayChallenge #DeepLearning #MachineLearning #ChurnPrediction #CustomerRetention #DataScience #AI #ArtificialIntelligence #TechInnovation #TechTrends #TechCommunity #LearningJourney #Python #NeuralNetworks #TechEducation #DataScientists #JupyterNotebook #ModelBuilding #FeatureImportance #DataPreprocessing #TechLearning

Day 7 of #100_days_of_machine_learning Today, I delved into the intricate development lifecycle of implementing machine learning models for software products. Firstly, I focused on framing the problem, identifying clients' needs, costs, and choosing the best-fit model. Gathering data involved diverse sources like CSVs, APIs, and web scraping, emphasizing the importance of data warehouses and ETL processes to maintain consistency. Data preprocessing, including handling duplicates, missing values, outliers, and scaling, was crucial before diving into exploratory data analysis (EDA) to understand patterns. Feature engineering and selection optimized the input variables for model efficiency. The heart of the process lay in training, evaluating, and selecting models, leveraging ensemble learning for enhanced performance. Deploying the model into production environments marked a significant milestone, followed by rigorous testing, including A/B testing for customer feedback. Optimization strategies encompassed backups for models and data, load balancing, and retraining models to counter efficiency decline termed "model rotting." Understanding these steps is pivotal for a successful machine learning-driven software product, ensuring continual improvement and adaptation. #100_days_of_machine_learning

🛸 Excited to share that I've completed the Data Engineer track on DataCamp, where I focused on mastering data engineering principles! 🔍 Throughout this journey, I delved deep into: - ETL and ELT workflows for seamless data integration. - Advanced SQL techniques for complex querying and manipulation. - Python programming with pandas for thorough data analysis and cleaning. - Effective data visualization methods to convey insights. - Software engineering best practices for writing clean, maintainable code. - Version control using Git to track and manage project changes. - Orchestrating data pipelines with Apache Airflow for automated workflow management. - Leveraging cloud platforms like Snowflake for robust data warehousing solutions. 🌐 This career track was part of two main tracks on DataCamp: - Data Engineer in Python - Associate Data Engineer with SQL In total, I completed 21 courses and worked on 5 projects, providing me with a comprehensive understanding of data engineering principles and techniques. 🚀 Happy learning, and here's to applying these skills to exciting data challenges ahead! #DataEngineering #DataCamp #SQL #Python #DataVisualization #Git #CloudComputing #ApacheAirflow #CareerGrowth #LifelongLearning

A Decision Tree Classifier is a machine learning algorithm used for classification tasks. It builds a tree-like model by recursively partitioning the dataset based on significant features. Each node of the tree represents a decision based on feature values, leading to leaf nodes that predict the outcomes. Decision trees are interpretable, easy to understand, and effective in capturing complex relationships in data. They are widely used for pattern recognition, classification, and decision-making due to their simplicity and versatility. Here, I applied Decision Tree Classifier to the Digits dataset. The journey commenced with data wrangling, preparing the dataset for the classifier. Following a strategic train and test split, the Decision Tree was summoned, undergo training . The model was then tested, and accuracy was meticulously assessed through a comprehensive evaluation involving a confusion matrix and classification report. To offer a glimpse into the model's decision-making logic, a captivating visualization of the decision tree was crafted, revealing the secrets behind its predictions. Github :- https://lnkd.in/d7sM_nGt #machinelearning #dataanalysis #datascience #decisiontree #supervisedlearning

There is a lot more stuff to be done than to create models locally on jupyter notebook and making them production ready. Here are the key take aways from this course: - Designing an End to End ML Solution - Model Training, Monitoring and Logging - Feature Store and Model registeries - Packaging and Containerization - CI/CD Pipelines for deployement - Data Drift, Feedback loop & Retraining - Model Serving #datacamp #endtoend #machinelearningengineer #machinelearning #datascience #mlops

Day 23 of 100 Days of Learning with I4G. #DataCamp #Ingressive4Good I visualized data in plots and figures to expose underlying patterns and generate insights. Good visualizations are crucial since they help you communicate findings to others and dig deeper to understand why certain trends are observed. In this course, I worked on creating different types of visualizations (line plots, bar plots, error plots, scatterplots, histograms), adding data to an axes object, plotting time series data, annotating the visualizations, saving the visualizations, and automating them.

🚀 Introducing StreamViz: Your Interactive Data Classification and Visualization Tool! 🚀 I'm thrilled to share the launch of my latest project, StreamViz, now live on Streamlit Cloud! This application makes it easy to upload datasets, clean and encode data, visualize insights, and apply classification algorithms—all through an interactive and user-friendly interface. 🌟 Key Features: Data Upload: Seamlessly upload your CSV or Excel files and get a preview of your data. Data Cleaning and Encoding: Effortlessly encode categorical variables and select target and input features. Classification: Apply various classification models like Logistic Regression, SVM, Decision Tree, KNN, and Random Forest. Evaluate their performance with accuracy metrics and confusion matrices. Visualization: Generate a variety of plots including pie charts, bar plots, heatmaps, distribution plots, violin plots, and box plots to gain insights into your data. 🔧 Try It Out: You can explore and interact with StreamViz directly on Streamlit Cloud. Click the link below to get started: 🔗 https://lnkd.in/dDQHXYa7 📈 Who Can Benefit?: Data Scientists: Quickly test and visualize different models and datasets. Students and Educators: An excellent tool for learning and teaching data science concepts. Business Analysts: Easily derive insights from data without deep programming knowledge. 👥 Get Involved: StreamViz is open-source, and contributions are welcome! Feel free to fork the repository, suggest improvements, or submit pull requests. Let’s collaborate to make data science more accessible! 🔗 GitHub Repository: https://lnkd.in/d5xaYi2j Special thanks to everyone who has supported this project. Your feedback and contributions are invaluable! #DataScience #MachineLearning #Streamlit #OpenSource #DataVisualization #AI #Python #DataAnalysis