was yug

"Passionate coder exploring the tech world 💻 | Learning programming, solving problems, and building projects 🚀| Sharing my journey to grow with the community"

[was yug @was_yug]

Learned SQLite today 🗄️🐍
Explored how Python interacts with databases using SQLite.
Practiced creating tables, inserting records, querying data, and performing CRUD operations ⚙️
@Krishnaik06
#Python #SQLite #ML #CodingJourney

[was yug @was_yug]

Studied Gradient Boosting today 🤖📈
Unlike Bagging, it builds trees sequentially — each one correcting the errors of the last
Implemented both GradientBoostingRegressor and GradientBoostingClassifier in sklearn ⚙️
@ApnacollegeX @ShradhaKhapra_
#MachineLearning #AI #DS #Coding

[was yug @was_yug]

Explored Ensemble Learning today 🤖🌲
Learned Bagging, Boosting, Random Forest, Bagging Classifier & Regressor.
Understanding how combining multiple models improves performance and reduces overfitting 📈
@ApnacollegeX @ShradhaKhapra_
#MachineLearning #AI #DataScience #Coding

[was yug @was_yug]

Back after a short break, explored pointers in C++ 🔧
Learned about reference operators (&) and dereference operators (*)
Understanding how memory is accessed and modified directly.
Low-level concepts getting clearer ⚙️
@ApnacollegeX @ShradhaKhapra_
#CPP #DSA #Coding

[was yug @was_yug]

Kicked off DSA with C++ — following @striver_79 🚀
Starting from the basics: syntax, loops, I/O. Excited to see where this goes 💪
#DSA #CPP #Coding #CodingJourney

[was yug @was_yug]

Learned web scraping today — BeautifulSoup + Requests 🕸️
Scraped real book titles & prices from the web with pure Python.
Data collection is the first step of every DS project 📷
@codewithharry
#ML #AI #DataScience #Coding #CodingJourney #DS

[was yug @was_yug]

Solved the "Coders of Bangalore" DS challenge by @codewithharry
Analyzed raw Instagram data — max followers, max posts & more using pure Python, no pandas 🐍
Hands-on logic building hits different 💡
#ML #AI #DataScience #Coding #CodingJourney

[was yug @was_yug]

Just completed all CodeChef practice problems up to 500 difficulty rating 🏆
Every problem taught me something new about logic building & problem solving 💡
Consistency >> everything 🔥
#CodeChef #CompetitiveProgramming #ProblemSolving #DSA #Coding

[was yug @was_yug]

Learned Support Vector Machines today — both SVC & SVR 🧠
Hyperplanes, margins, epsilon-insensitive tubes & cost functions — SVM is elegant once it clicks💡
@ApnaCollegeX @ShradhaKhapra_
#Machinelearning #AI #DataScience #Coding #CodingJourney

[was yug @was_yug]

Took a quick overview of DSA — starting with Time Complexity 📊
O(1) < O(logn) < O(n) < O(n²)
Small intro course for now, will go deeper from a dedicated source soon 🔥
@Hiteshdotcom @ChaiCodeHQ
#DSA #Coding #CodingJourney

[was yug @was_yug]

Built a Customer Purchase Predictor on e-commerce data 🛒
Learned RandomSearchCV & GridSearchCV — 25,920 fits to find the best model 🔍
Accuracy 87.2% | F1 67.4%
Hyperparameter tuning is not optional 💡
@ApnaCollegeX @ShradhaKhapra_
#ML #AI #DataScience #Coding #CodingJourney

[was yug @was_yug]

Built a Decision Tree classifier on the Titanic dataset 🚢🌳
Pre & post pruning + hyperparameter tuning with ccp_alpha
Accuracy: 83.2% | Precision: 85.4%
Visualizing the tree hits different 👀
@ApnacollegeX @ShradhaKhapra_
#ML #AI #DataScience #Coding #CodingJourney

[was yug @was_yug]

Learned Decision Trees today 🌳Entropy, Gini Impurity, Information Gain — how a tree actually decides to split.
Also covered Pruning: pre & post — because overfitting is real 😅
@ApnacollegeX @ShradhaKhapra_
#ML #AI #DataScience #Coding #CodingJourney

[was yug @was_yug]

Improved House Price Prediction R² from 77% to 80% 🏠📈
Key lesson: removing outliers + fixing skewed data makes a real difference.
Clean data > complex models 💡
#ML #AI #DataScience #Coding #CodingJourney

[was yug @was_yug]

Built a House Price Prediction system on a real dataset 🏠
Raw model: R² 65% → After log transform & feature engineering: R² 77%
EDA + feature engineering isn't optional — it's everything 💡
#ML #AI #DataScience #Coding #CodingJourney

[was yug @was_yug]

Built a Loan Approval System using Logistic Regression & Naive Bayes on a real dataset 🏦
LR: Accuracy 87.5% | Precision: 78.1% NB: Accuracy 87.5% | Precision: 81%
First real-world ML project. Just getting started 🔥
#ML #AI #DataScience #Coding #CodingJourney

[was yug @was_yug]

Just coded Logistic Regression from scratch in Python — no sklearn, pure numpy 🧮
Sigmoid, Gradient Descent, weight updates — built every piece manually.
#ML #AI #DataScience #Coding #CodingJourney

[was yug @was_yug]

Just coded Linear Regression from scratch in Python — no sklearn, pure math 🧮
Gradient Descent, weight updates, bias — built it all manually using numpy.
Understanding the math behind ML hits different 🔥
#MachineLearning #Coding #DS #AI

[was yug @was_yug]

Learned KNN today — also explored hyperparameter tuning with GridSearchCV & Cross Validation 🔍
Accuracy: 91.8% | Precision: 93.5% | Recall: 90.6% | F1: 92%
Best K = 7. Small tweak, big difference 💡
#KNN #ML #DataScience #CodingJourney #Coding

[was yug @was_yug]

Learned Naive Bayes today & implemented GaussianNB on a real heart disease dataset 🫀
Accuracy: 86.8% | Precision: 0.9 | Recall: 84.3% | F1: 0.87
Simple probabilistic algorithm, surprisingly powerful results. Slowly falling in love with ML 🤍
#ML #DataScience #Coding #AI