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ML Engineer with less than a year in data analysis & ML model development.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring Machine Learning Engineer with hands-on experience building and evaluating ML models using Python and scikit-learn. Skilled in data preprocessing, exploratory data analysis (EDA), and model optimization. Proficient in Pandas, NumPy, Matplotlib, and Seaborn. Eager to apply practical ML knowledge to solve real-world data challenges.
Shipra College of Computer Science and Technology
Master of Science · Information Technology
August 1, 2024 – June 30, 2026
Shipra College of Computer Science and Technology
Bachelor of Computer Applications
August 1, 2021 – June 30, 2024
Customer Churn Prediction
June 21, 2026 – Present
Built a classification model using Logistic Regression and Random Forest to predict customer churn. Performed data cleaning, feature engineering, and exploratory data analysis on the dataset. Achieved approximately 72% accuracy with improved recall on the minority class.
Retail Weekly Sales Prediction
June 21, 2026 – Present
Developed a regression model for weekly sales forecasting using a retail dataset. Applied preprocessing pipelines and evaluation metrics to validate and improve model performance.
Internship Certificate - Data Science and Machine Learning
Unknown
June 1, 2026 – Present
AI and Machine Learning
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects and education align with an entry-level or junior ML Engineer role, demonstrating an interest in practical applications of machine learning. The pursuit of a Master's degree and certifications suggests a drive for continuous learning. However, the lack of diverse project types beyond standard classification/regression and limited professional experience might indicate a need for more exposure to varied problem domains and team environments to fully assess cultural fit for a senior role.
Soft Skills & Operational Fit
The candidate's resume indicates a proactive approach to learning and applying ML concepts through personal projects and an internship. The professional summary highlights eagerness to solve real-world data challenges. However, without specific psychometric or English test results, it is difficult to assess communication clarity, logical reasoning, work attitude, stress handling, or team collaboration skills.