PRANAV GUNDU

Data Analyst skilled in analytical tools such as Matplotlib and Seaborn, with a focus on interpreting complex datasets. Expertise in data cleaning, preprocessing, and exploratory data analysis to identify key factors influencing credit card defaults. Findings presented to stakeholders facilitated strategic decision-making and improved outcomes.

 Online Retail 

Utilized the Online Retail II dataset from the UCI Machine Learning Repository, which contains transactional data from a UK-based online retail store, spanning approximately one year, and the analysis focused on sales forecasting Key activities included :

• Performed data cleaning and preprocessing to handle missing values, outliers, and inconsistencies
• Conducted exploratory data analysis (EDA) to understand customer behavior, identify sales patterns, and gain insights into product performance .
• Visualized findings using visualization tools like Matplotlib and Seaborn to effectively communicate insights, such as identifying high-value customer segments, predicting future sales trends, or uncovering product associations .
• The project aimed to provide actionable recommendations for targeted marketing campaigns .

Default of credit card clients :

Utilized the "Default of credit card clients" dataset from the UCI Machine Learning Repository, which contains information on default payments, demographic factors, credit data, history of payment, and bill statements of credit card clients in Taiwan from April 2005 to September 2005. The analysis focused on predicting credit card defaults. Key activities included:

• Performed data cleaning and preprocessing to handle missing values, outliers, and inconsistencies.
• Conducted exploratory data analysis (EDA) to understand the relationships between various factors (e.g., demographic information, credit history) and the likelihood of default.
• Visualized findings using tools like Matplotlib and Seaborn to effectively communicate insights, such as the key factors influencing default risk and the performance of different prediction models.
• The project aimed to provide insights that could help financial institutions in Taiwan to better understand and manage credit risk.