Machine Learning Engineer
Aspiring Machine Learning Engineer with hands-on experience in Python, SQL, and machine learning frameworks. Skilled in supervised and unsupervised learning, EDA, model deployment, and data visualization. Proven ability to deliver impactful ML projects with quantifiable results.
Purpose: Predict customer responses to marketing campaigns to optimize targeting and improve response rates.
Tools Used: Python, Pandas, Scikit-learn, Matplotlib.
Achieved 92% accuracy in predicting customer responses.
Reduced model training time by 25% through optimized preprocessing pipelines.
GitHub Repo: Link
Purpose: Classify different types of traffic signs to assist in autonomous driving systems and improve road safety.
Tools Used: Python, TensorFlow, Keras.
Classified 43 categories of traffic signs using a CNN model.
Improved dataset augmentation pipeline, increasing model robustness by 20%.
GitHub Repo: Link
Participated in the NASA Space Apps Challenge, developing a seismic event detection model handling over 5,000 data points.
Reduced false positive rate by 15% through fine-tuning threshold metrics.
Tools: Python, Scikit-learn, Matplotlib.
Completed a virtual job simulation focused on data science tasks for British Airways, including web scraping, data analysis, and presentation of insights.
Participated in AI-focused summer camp, gaining hands-on experience in machine learning and AI development through projects and workshops.
Completed intensive training program in AI and Machine Learning at ITI, covering topics from basics to advanced model deployment.