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Data Science with less than a year in Machine Learning and Data Analysis.
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
MCA student with hands-on academic project experience in data analysis and machine learning, seeking an entry-level or internship opportunity in the IT sector. Ability to work with datasets, solve analytical problems, and communicate findings clearly. Eager to learn, adapt quickly, and contribute meaningfully to a team.
Aurora's PG College
Master of Computer Applications (MCA)
August 1, 2024 – Present
Vasista Degree College
Bachelor's in Computer Science · Computer Science
August 1, 2021 – June 30, 2024
Keshav Memorial Junior College
Intermediate
N/A – Present
Bharathi Vidya Bhavan High School
SSC
N/A – May 31, 2019
Crime Data Analysis Using Machine Learning
June 22, 2026 – Present
Analyzed multi-year historical crime datasets to identify crime trends and high-risk zones, supporting data-driven decision-making. Applied clustering and classification techniques to detect crime hotspots and patterns across regions. Performed data cleaning and preprocessing on raw crime records using Pandas and NumPy to prepare data for modeling. Built heatmaps and trend visualizations to present crime patterns in an easily interpretable format.
Cultural Fit Analysis
The candidate's academic background and single project demonstrate an interest in data science. The project's focus on crime data analysis shows an ability to apply technical skills to real-world problems. However, the lack of diverse projects or professional experience limits the assessment of broader cultural fit, adaptability, and collaboration in a professional setting. The target role of Data Science aligns with the candidate's stated objective and academic pursuits.
Soft Skills & Operational Fit
The candidate lists strong analytical and logical thinking, attention to detail, problem-solving, good communication, ability to work independently, and team collaboration as core skills. These are foundational for a Data Science role, but their practical application and depth need to be validated through interviews, especially given the lack of professional experience.