π B.E. in Instrumentation and Electronics Engineering (Jadavpur University, 2027)
π― Aspiring Machine Learning Engineer | Passionate about Data Science & Deep Learning
π Currently building projects & aiming for ML internships at top tech companies
- Languages: Python, Java, MATLAB
- Libraries & Frameworks: NumPy, Pandas, Scikit-learn, TensorFlow, Keras
- Tools: Git, Jupyter, VS Code, Linux (CLI & Bash)
- Visualization: Matplotlib, Seaborn, Plotly
- Core Areas: Data Analysis, Machine Learning, Deep Learning, DSA
Tech: Python, Scikit-learn, SMOTE, GridSearchCV
- Built classification models to identify key attrition factors (e.g., workload, salary)
- Achieved 97% accuracy using a tuned Random Forest model
- Proposed retention strategies using model insights
Tech: TensorFlow/Keras, CNN, Hyperparameter Tuning
- Built and tuned a CNN achieving 63.5% accuracy on test set
- Used data augmentation, Dropout, and Conv2D layers to improve generalization
- π Coursera: Mathematics for Machine Learning and Data Science β Andrew Ng (3-course series)
- π§ NPTEL: Data Structures and Algorithms (Java) β IIT KGP (Elite-Silver, 76%)
- π NPTEL: Introduction to ML β IIT M (Elite-Top 5%, 70%)
- π§ Email: sahilsharma130605@gmail.com
- π GitHub: SahilSharma1306
I'm passionate about blending core electronics knowledge with modern AI systems β working on hybrid projects soon!