Machine Learning Engineer with less than a year in Python & Predictive Models
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Motivated ComputerScience graduate with practical experience in Python, SQL, and Machine Learning. Experienced in developing, evaluating, and deploying predictive models. Eager to contribute analytical and technical skills in an entry-level Ai/ ML role and software developer role.
Amity University Madhya Pradesh
B.Tech · Computer Science Engineering
August 1, 2021 – June 30, 2025
Sunware Technologies
Machine learning Developer
January 1, 2025 – May 1, 2025
India
Technohacks Edutech
Data Science Intern
July 1, 2023 – August 1, 2023
India
Heart Disease Prediction System
June 21, 2026 – Present
Technologies: Python, Flask, Scikit-learn, Pandas, NumPy Compared Logistic Regression, RandomForest, SVM, and KNN models to identify best-performing classifier. Performed feature scaling and systematic model evaluation. Deployed final model using Flask for real-time web-based prediction.
Loan Default Prediction System
June 21, 2026 – Present
Technologies: Python, Scikit-learn, Pandas, XGBoost Developed predictive models using Logistic Regression, Random Forest, and XGBoost. Applied feature engineering and missing value handling to enhance robustness. Evaluated models using Accuracy, Precision, Recall, F1-score, and ROC-AUC.
Python (Basic)
HackerRank
June 1, 2026 – Present
Python for Data Science
Lernx
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects and internships align well with an entry-level Machine Learning Engineer role, demonstrating a clear interest and foundational skill set in the domain. The diversity of projects (heart disease, loan default) shows an ability to apply ML techniques across different problem spaces. The listed skills and technologies are relevant to the target role, indicating a good initial cultural fit for a data-driven, technically focused team.
Soft Skills & Operational Fit
The candidate's project descriptions and experience indicate a structured approach to problem-solving and an understanding of the ML lifecycle. The academic projects and internships suggest a proactive learning attitude and ability to work on defined tasks. However, without specific behavioral assessment data, it's difficult to fully assess soft skills like teamwork, leadership, or stress handling.