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Data Science with less than a year in Python & Machine Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Entry-level Data Analyst/Machine Learning Engineer with a strong foundation in Python, SQL, Data Analysis, Machine Learning, Power BI, and Advanced Excel. Passionate about transforming data into meaningful insights and applying analytical and machine learning techniques to solve real-world business problems. Eager to contribute to organizational growth while continuously enhancing technical expertise.
Prof. Ram Meghe Institute of Technology & Research
Bachelor of Engineering (B.E.) · Computer Science & Engineering
N/A – June 30, 2025
Shivaji Secondary and Higher Secondary Vidyalaya
12th (Higher Secondary Certificate – HSC)
N/A – May 31, 2021
Codenera Pvt. Ltd.
AI Intern
September 1, 2025 – Present
India
Stock Price Prediction using Machine Learning
June 24, 2026 – Present
Developed predictive model using Python and supervised learning algorithms on 5+ years of historical stock data. Performed comprehensive data cleaning and feature engineering using Pandas and NumPy. Implemented multiple regression models (Linear Regression, Decision Trees) achieving 85%+ accuracy. Created interactive visualizations using Matplotlib to present trends and performance metrics.
Cultural Fit Analysis
The candidate's projects and internship align well with a Data Science role, demonstrating a clear interest and foundational skills in the field. The project diversity is limited to one personal project, and the experience is an internship, which is typical for an entry-level candidate. The breadth of skills covers core data science areas, suggesting a good initial fit for a data-centric team.
Soft Skills & Operational Fit
The candidate lists problem-solving, analytical thinking, quick learner, communication, and teamwork as soft skills. The project and internship descriptions indicate an ability to work with data and implement technical solutions. However, without specific examples or interview data, the depth of these soft skills and their operational fit cannot be fully assessed.