• 🎓 1st Year Computer Science & Engineering Student
• 🤖 Passionate about Machine Learning & Deep Learning
• 🔥 Self-learned ML, DL, DSA — no coaching, no shortcuts
• 🏗️ Building real projects from scratch and deploying them
• 📚 Currently learning: CNN, NLP, Flask
• 💡 Believer in: “Build it. Break it. Learn it.”
• 📍 From Kashmir — proving location is no barrier

ANN Regression | PyTorch | R² = 0.934

• Predicts hourly electrical energy output of a Combined Cycle Power Plant
• 9,568 samples | 4 features | StandardScaler + Adam optimizer
• R² Score: 93.42% — strong generalization on test set
• Full pipeline: preprocessing → training → validation → best model saving

ANN Multiclass Classification | PyTorch | Accuracy = 94.44%

• Classifies 7 varieties of date fruits from 34 morphological features
• 898 samples | LabelEncoder | CrossEntropyLoss
• Test Accuracy: 94.44% — smooth loss convergence over 100 epochs
• Clean PyTorch pipeline with proper evaluation loop

Scikit-Learn | Algorithms | Real World Projects

• End-to-end implementations of core ML algorithms
• Linear/Logistic Regression, KNN, SVM, Naive Bayes, Decision Trees
• Random Forest, XGBoost, AdaBoost, PCA, Cross Validation
• Built both from scratch and using Scikit-Learn

Pandas | Matplotlib | Seaborn | Web Scraping

• Data analysis projects including COVID-19 dataset visualization
• Web scraping using BeautifulSoup
• Real world EDA with actionable insights

current_focus = {
    "learning"  : ["CNN", "NLP", "Flask"],
    "building"  : "Text Summarization App",
    "practicing": "DSA daily",
    "goal"      : "ML Internship by 2nd year"
}

Machine Learning          ████████████████████  100%
Deep Learning (ANN)       ████████████████░░░░   80%
Deep Learning (CNN/NLP)   ████████░░░░░░░░░░░░   40%
DSA                       ██████░░░░░░░░░░░░░    30%
Flask / Backend           ████░░░░░░░░░░░░░░░░   20%

"Kashmir — built everything from scratch, one commit at a time." 🚀