#DATA-EXPLORATORY-ANALYSIS-AND-HYPOTHESIS-TESTING-FOR-INSURANCE-CLAIMS-DATA-PYTHON
Description: In this project, I had datasets of insurance claims by customers and did EDA to get insights on what happened in past period, and check inter-relationship between factors.
What I've done:
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Data manipulation | cleaning - handled missing values, duplicates; categorization & binary encoding.
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Created summaries:- i. amount claimed by the customers, ii. amount claim based on incident cause, iii. claimed insurance for driver related issues and causes etc.
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Data visualization :- i. Claim amount based on gender and segment ii. Comparative analysis which gender had claimed the most iii. Visualize the monthly trend in claims iv. Compare gender-wise claims of fraudulent claims/non-fraudulent claims
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Hypothesis testing ( statiscal test) - compared and determined relationship between multiple factors. i. Any similarity or difference in the amount claimed by males and females / age groups and insurance claims ii. Any relationship between age category and segment / total number of policy claims and the claimed amount iii. Current year has shown a significant rise in claim amounts as compare to last year
Credit Analysis · Matplotlib · Seaborn · pandas · NumPy · Python
I'm an aspiring Data Analyst with a strong foundation in data manipulation and statistical analysis. I thrive on uncovering insights that drive decisions and am passionate about learning.
As a Data Analyst, I specialize in transforming raw data into clear, impactful insights using Python, SQL, and visualization tools like Matplotlib and Power BI. My educational background includes a Post Graduate Diploma in Business Administration in Finance, and I'm currently pursuing a Data Science program at AnalytixLabs.
My technical skills encompass Python, SQL, Statsmodel, and various statistical techniques, with a focus on machine learning libraries like Scikit-Learn and TensorFlow. I'm dedicated to turning data into compelling stories that influence business outcomes.
