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ML Engineer with less than a year in data analytics and predictive modeling.
Aspiring Machine Learning Engineer with strong foundations in Statistics, Probability, Inferential Statistics, and Data Science. Proficient in Python, SQL, Scikit-learn, and Machine Learning with hands-on experience in exploratory data analysis, feature engineering, data preprocessing, predictive modeling, and model evaluation. Experienced in implementing supervised and unsupervised machine learning algorithms on real-world datasets. Passionate about applying machine learning techniques to solve real-world problems and contribute to research-driven projects. Seeking opportunities as a Machine Learning Intern to further develop and apply analytical, predictive modeling, and machine learning skills.
MIT Academy of Engineering
B.Tech · Electronics and Telecommunication Engineering
August 1, 2023 – June 30, 2027
Deloitte Australia
Data Analytics Virtual Internship
January 1, 2026 – June 1, 2026
India
Iris Flower Species Classification using Logistic Regression
January 1, 2026 – June 1, 2026
• Developed a multiclass classification model using Logistic Regression on the Iris dataset. • Performed data preprocessing, exploratory data analysis, and feature selection. • Built a predictive classification model using Scikit-learn and evaluated performance using accuracy score, confusion matrix, and classification metrics.
Flight Price Prediction - EDA & Feature Engineering
January 1, 2026 – June 1, 2026
• Performed exploratory data analysis on airline pricing datasets to identify trends and patterns. • Handled missing values, outliers, and categorical variables using preprocessing techniques. • Applied feature engineering methods to improve data quality and model readiness. • Prepared datasets for predictive modeling through feature engineering and preprocessing. • Conducted statistical analysis and visualization to derive meaningful insights.
Red Wine Quality Analysis
January 1, 2026 – June 1, 2026
• Conducted exploratory data analysis and statistical analysis on wine quality datasets. • Investigated feature relationships using correlation analysis and visualization. • Identified key attributes influencing wine quality.
Google Play Store Dataset Analysis- EDA & Feature Engineering
January 1, 2026 – June 1, 2026
• Performed comprehensive exploratory data analysis on application datasets. • Analyzed installs, ratings, reviews, and category-wise trends. • Conducted data cleaning, preprocessing, and feature engineering. • Generated insights using statistical analysis and visualization techniques.
Customer Churn Prediction using Machine Learning
January 1, 2026 – June 1, 2026
• Built an end-to-end machine learning pipeline to predict customer churn using demographic and service usage data. • Performed data cleaning, exploratory data analysis, feature engineering, missing value treatment, and categorical feature encoding. • Implemented and compared Logistic Regression, Random Forest, and XGBoost models for churn prediction. • Evaluated model performance using Accuracy, Precision, Recall, F1-Score, ROC-AUC, and Confusion Matrix. • Identified key factors contributing to customer attrition through feature importance analysis and data visualization.
Power BI for Data Analytics
Unknown
June 1, 2026 – Present
Deloitte Australia Data Analytics Job Simulation
Forage
June 1, 2026 – Present
Complete Data Science and Machine Learning Bootcamp
Udemy
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects are diverse within the ML/Data Science domain, covering prediction, classification, and EDA. The Deloitte internship and hackathon achievement show initiative and a willingness to apply skills in different contexts. However, all projects are academic, and the experience is limited to a virtual internship, which might indicate a need for more exposure to collaborative, production-oriented environments. The target role is ML Engineer, and the current profile aligns more with a Data Scientist or ML Intern, suggesting a potential gap in engineering-specific skills like MLOps, deployment, or scalable ML systems.
Soft Skills & Operational Fit
The candidate's project descriptions and internship experience suggest an ability to work with data, analyze problems, and present findings. The hackathon achievement indicates a competitive drive and ability to perform under pressure. However, without direct interview data, a comprehensive assessment of soft skills like teamwork, leadership, and adaptability is limited.