Machine Learning Engineer with 2 years of practical experience developing predictive models, data preprocessing pipelines, recommendation systems, and ML-powered business applications. Experienced in Python, Scikit-learn, TensorFlow, MLflow, Pandas, SQL, and Azure Machine Learning. Strong understanding of feature engineering, model evaluation, deployment, and MLOps fundamentals.
Expertise & skills
Selected projects
Customer Churn Prediction
Developed a machine learning solution to identify customers likely to discontinue services.
- Collected and cleaned structured datasets.
- Performed feature engineering.
- Trained classification models.
- Compared algorithms using evaluation metrics.
- Tracked experiments with MLflow.
- Deployed prediction APIs using FastAPI.
Sales Forecasting System
Built forecasting models to predict monthly sales trends.
- Prepared historical sales data.
- Developed regression models.
- Optimized hyperparameters.
- Visualized forecasts.
- Improved prediction accuracy through feature selection.
Product Recommendation Engine
Created a recommendation engine for e-commerce users.
- Processed customer behavior data.
- Implemented collaborative filtering.
- Built recommendation APIs.
- Evaluated recommendation quality.
- Improved user engagement through personalized suggestions.
Loan Approval Prediction
Designed a predictive model for loan eligibility analysis.
- Performed exploratory data analysis.
- Handled missing values.
- Built classification pipelines.
- Validated model performance.
- Documented model outputs.
Ways of working
Certifications
- Microsoft Certified: Azure AI Fundamentals (AI-900)
- Machine Learning Specialization
- Python for Data Science – NPTEL
- TensorFlow Developer Fundamentals
Education
- 2024
Master of Technology (M.Tech.) – Artificial Intelligence
Gujarat Technological University (GTU)
- 2022
Bachelor of Engineering (B.E.) – Computer Engineering
Gujarat Technological University (GTU)
Achievements
- Built multiple predictive machine learning solutions.
- Improved model accuracy through feature engineering.
- Deployed ML models as scalable APIs.
- Implemented experiment tracking using MLflow.
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