👏 Hey everyone! I'm Shristi, a machine learning enthusiast and future data scientist. I'll be showcasing my projects, learning experiences, and perspectives on deep learning and data analysis. Come along with me and let's interact!
#DataScience#MachineLearning#Python
Hey, @X fam,
I recently started the 'Data Decoded' series on #LiNKEDiN and am very excited to get insights from various industries.
You can check out Episode 1- Cosmetic and Beauty Industry breakdown on my LinkedIn- https://t.co/N4nD60AmMh
#DataAnalytics#Insights#DataDriven
🚀Launching "Data Decoded" Series! 📊
Episode 1: Beauty Industry Breakdown 💄✨
Analyzed 12,615 products across 1,753 brands including #Amazon#Myntra#nykaa#Dior#Sephora
Want the full scoop? Follow me on LinkedIn!
Full post drops tomorrow at 10:30 AM. Don't miss it!👀
#DATA
Entering the Corporate World in Your Third Year: A Good or Bad Move?
What are your thoughts on starting internships or entering the corporate world during your college years? Have you had similar experiences or insights?
Let's discuss it here- https://t.co/RARMz0E6Aa
Today's topic: Performance Metrics! 📈✨
Accuracy: Overall correctness.
Precision: True positives vs. predicted positives.
Recall: True positives vs. actual positives.
F1 Score: Balance between precision and recall.
AUC-ROC: Model performance across classification thresholds.
#ML
Today's topic: Naive Bayes! 🌟
Naive Bayes is a simple yet powerful probabilistic classifier based on Bayes' theorem. It's especially effective for text classification and spam detection. Assumes features are independent, hence 'naive'.🧠📊 #MachineLearning#DataScience
Bias: Errors from overly simple models, leading to underfitting.
Variance: Sensitivity to training data noise, leading to overfitting.
Balance is key! ⚖️ #MachineLearning#DataScience#BiasVarianceTradeoff
Today's topic: Overfitting, Underfitting, Bias, and Variance in ML! 🤖📊
Overfitting: Model learns too much noise, great on training data but poor on new data.
Underfitting: Model too simple, poor on both training and new data.
Decision Trees: A versatile model that splits data into branches to make decisions, great for both classification and regression tasks.
Both are powerful tools in the ML toolkit! 🚀 #MachineLearning#DataScience#KNN#DecisionTrees
Today's deep dive: KNN and Decision Tree models! 🌟🤖
KNN (K-Nearest Neighbors): A simple, instance-based learning method that classifies data based on the closest training examples.
Today's topic: Logistic Regression! 📊✨
A powerful algorithm for binary classification tasks, helping to predict outcomes like spam detection, and more.
Check out my GitHub repo for the model! 🚀 #MachineLearning#DataScience#LogisticRegression#GitHub
GitHub link in bio**