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Data Analyst with less than a year in SQL, Python, and data visualization.
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Data Analyst with academic and project experience in SQL, Python, and data visualization. Built and deployed analytical solutions including retail sales reporting, customer churn prediction, and insurance claim forecasting. Strong foundation in data cleaning, exploratory analysis, and statistical modeling, with hands-on exposure to PostgreSQL, MySQL, and Power BI. Skilled in transforming raw datasets into actionable insights and eager to contribute to business decision-making through data-driven solutions.
IPCS Calicut
Diploma in Python & Data Science
January 1, 2024 – January 1, 2025
Priyadarshini College
B.Sc. Mathematics · Mathematics
January 1, 2021 – January 1, 2024
Insurance Claim Prediction
January 1, 2026 – June 1, 2026
Built a scikit-learn pipeline (preprocessing + model training) using the insurance.csv dataset. Developed a Streamlit app for real-time insurance claim prediction with single and batch input options. Automated training workflow with train.py, generating reusable model artifacts (insurance_model.joblib). Improved claim prediction accuracy and reduced manual analysis effort by 65%. Processed 500+ insurance records with interactive UI and CSV batch upload.
View ProjectTelecom Churn Prediction
January 1, 2026 – June 1, 2026
Built a scikit-learn pipeline (preprocessing + model) using the Telco Customer Churn dataset. Developed a Streamlit app for real-time single and batch churn prediction with evaluation/retraining options. Automated churn analytics workflows, enabling HR/Business teams to monitor customer retention risks. Improved churn classification accuracy and reduced manual analysis effort by 70%. Processed 1,000+ customer records with interactive dashboards and CSV batch uploads.
View ProjectRetail Sales Analytics
January 1, 2026 – June 1, 2026
Designed and implemented a relational database (retail_sales) to store 1,000+ transactional records including customer demographics, product categories, and sales metrics. Built optimized SQL queries in psql for category-wise aggregation, monthly trend analysis, and time-based shift classification (Morning/Afternoon/Evening). Automated reporting workflows, reducing manual reporting effort by 70% through schema inspection and query optimization. Delivered professional sales summaries and dashboards for HR/business teams to track performance and product trends. Demonstrated end-to-end database management: schema creation, query development, and integration with PostgreSQL for analytics.
View ProjectCustomer Shopping Behavior Analysis
January 1, 2026 – June 1, 2026
Cleaned and analyzed a dataset of 3,900 transactions using Python (pandas, NumPy) for EDA and feature engineering. Designed and executed SQL queries (PostgreSQL/MySQL/SQL Server) to uncover revenue trends, customer segments, and product preferences. Built an interactive Power BI dashboard showcasing revenue by category, subscription behavior, age group contributions, and shipping type comparisons. Automated reporting workflows and created a Gamma PPT to present insights and business recommendations to stakeholders. Delivered actionable findings: identified loyal customers (80%), highlighted top products per category, and recommended strategies to boost subscriptions and loyalty programs.
View ProjectOCI Data Science Professional Certification
Oracle Badge
June 1, 2026 – Present
Diploma in Python and Data Science
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity (telecom, retail, insurance) and use of various tools (SQL, Python, Power BI, Streamlit, scikit-learn) suggest adaptability and a willingness to explore different domains and technologies. The focus on delivering actionable insights aligns well with a data-driven culture. However, the lack of team-based projects or professional experience limits the assessment of collaboration and broader cultural integration.
Soft Skills & Operational Fit
The candidate demonstrates initiative through personal projects and active GitHub contributions. The project descriptions indicate an ability to translate technical work into business value (e.g., 'reduced manual analysis effort by 70%'). However, without direct work experience, the operational fit in a team environment and stress handling capabilities are unassessed.