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Machine Learning Engineer with less than a year in Data Analytics & Machine Learning
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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 in Electronics and Telecommunication Engineering · Electronics and Telecommunication Engineering
August 1, 2023 – June 30, 2027
Deloitte Australia (Forage)
Data Analytics Virtual Internship
January 1, 2026 – Present
India
Customer Churn Prediction using Machine Learning
June 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.
Red Wine Quality Analysis
June 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.
Flight Price Prediction - EDA & Feature Engineering
June 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.
Google Play Store Dataset Analysis- EDA & Feature Engineering
June 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.
Iris Flower Species Classification using Logistic Regression
June 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.
Deloitte Australia Data Analytics Job Simulation
Forage
June 1, 2026 – Present
Complete Data Science and Machine Learning Bootcamp
Udemy
June 1, 2026 – Present
Power BI for Data Analytics
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects are diverse within the realm of machine learning and data analysis, covering classification, regression, and EDA. The virtual internship with Deloitte and participation in a hackathon demonstrate an initiative to gain practical experience and apply skills in a competitive environment. This indicates a proactive and learning-oriented mindset, which aligns well with a culture of continuous improvement. However, the experience is primarily academic, and exposure to collaborative, production-level environments is limited, which might require adaptation to a professional team setting.
Soft Skills & Operational Fit
The candidate's project descriptions and professional summary indicate a passion for applying machine learning to solve real-world problems and a proactive approach to learning. The virtual internship and hackathon achievement suggest a self-starter attitude and ability to work on practical problems. However, with limited professional experience, the operational fit in a senior role is currently low, requiring significant mentorship and structured guidance.