Krishn Singh’s Post

Diving deep into my latest ML project! 📊 Leveraging the power of PandasProfiling to gain valuable insights into my dataset. This incredible tool has helped me uncover hidden patterns, identify potential issues, and make data-driven decisions. #DataScience #MachineLearning #PandasProfiling #DataExploration

Predicting the age of abalone Hello everyone, I've completed my second project that is "Predicting the age of Abalone" which is a regression project. I came across this on kaggle as a playground competition and decided to build a project on this while taking part in the competition. In this project, I've followed the workflow I learnt during the "Complete AI, ML and DS Bootcamp" by ZTM. I first imported the data then visualised it and then tried a few baseline models and selected the model that performed better and tuned its hyperparameters. After that I used Optuna to tune the hyperparameters of RandomForestRegressor and also hyperparameters of XGBRegressor. This is my second competition I've taken part on kaggle and I got a rank of 994 😅 and I'm learning from these competitions. Please check out my project on GitHub and the link is in the comments. Can anyone help me with the questions below:- 🔸How do I perform EDA better(especially the visualisations part)? 🔸How to select which hyperparameters to include while tuning and how to choose values to try for each hyperparameter?

Predicting the age of abalone Hello everyone, I've completed my second project that is "Predicting the age of Abalone" which is a regression project. I came across this on kaggle as a playground competition and decided to build a project on this while taking part in the competition. In this project, I've followed the workflow I learnt during the "Complete AI, ML and DS Bootcamp" by ZTM. I first imported the data then visualised it and then tried a few baseline models and selected the model that performed better and tuned its hyperparameters. After that I used Optuna to tune the hyperparameters of RandomForestRegressor and also hyperparameters of XGBRegressor. This is my second competition I've taken part on kaggle and I got a rank of 994 😅 and I'm learning from these competitions. Please check out my project on GitHub and the link is in the comments. Can anyone help me with the questions below:- 🔸How do I perform EDA better(especially the visualisations part)? 🔸How to select which hyperparameters to include while tuning and how to choose values to try for each hyperparameter?

In Episode 2 of AI@Work, we share the framework we will use to cover our video series journey. Then we jump into the first topic: Using AI and ML for customer segmentation. You can watch the episode here: https://lnkd.in/gaYZ8KQQ Previous episode can be watched here: https://lnkd.in/ghyRWf6i #data #analytics #ai #ml #datascience #designedanalytics #deeplearning

Learn by Doing. ✅

🌟🌟 Starting in Less than 24 hours. #8pm EAT See you #today for Day 1/3 of our #MachineLearning Bootcamp. Join the attendees before the space is locked. Register here if yet to register> https://lnkd.in/deUKYkTx We are excited to have you onboard! See the Schedule below: Day 1: Setting the Ground (Understanding Machine Learning) Day 2: Delving into Hands-on with Machine Learning Basics Day 3: Diving Deeper with ML/AI Concepts #MachineLearning #ArtificialIntelligence

"Excited to share my latest achievement CodSoft! 🎬🔮 I've developed a machine learning model that predicts movie genres based on plot summaries. Using techniques like TF-IDF and classifiers like Naive Bayes, Logistic Regression, and Support Vector Machines, I've unlocked insights into movie categorization. 📽️✨ #MachineLearning #MovieGenres #DataScience"

🚀 Exciting News: Image-Text Classification! 🖼️📝 👋 Thrilled to share my latest project - an exploration into the world of memes using advanced image-text classification. Using machine learning techniques, I've developed a model that can analyze memes, categorizing them with remarkable accuracy. But here's the best part: I've made the code available for anyone eager to delve into meme analysis. You can find it here: [https://lnkd.in/eiwEy3Wd].  #MemeAnalysis #DataScience #MachineLearning

🚀 Day 2 Update: Continuing My 5-Day ML Learning Journey! 📈 Another productive day in my 5-day machine learning adventure, and today's focus was on exploring the power of logistic regression! 🌟 📚 Day 2 Recap: Simple Logistic Regression Model 📊 Today, I delved deeper into the world of machine learning by diving into simple logistic regression. It's incredible to see how this model can be used for binary classification tasks, predicting outcomes as either 0 or 1 with probabilities! 💡