Welcome to my Data Analyst portfolio! This repository showcases my skills in data cleaning, data wrangling, exploration, and visualization using various tools. Through several case studies using open-source datasets, I demonstrate my ability to derive insights and present data in a meaningful way.
- Programming Languages: Python, SQL
- Libraries/Packages: Pandas, Numpy, Matplotlib, Seaborn
- Database: MySQL, PostgreSQL
- Visualization Tools: Tableau, Matplotlib, Seaborn, Looker Studio
- Others: Jupyter Notebook, Google Colab, Visual Studio Code
- Dataset 1: Customers-Dataset
- Dataset 2: Booking-Hotel-Dataset
This project is to analyze the performance of a leading eCommerce marketplace. With key aspects: customer growth, product quality, and payment types, to create a comprehensive performance report that aids in strategic decision-making.
- Objective: To analyze to analyze the performance of a leading eCommerce marketplace.
- Techniques: Data Wrangling
- Outcome: The analysis provided a comprehensive overview of the eCommerce marketplace's performance by examining key aspects such as customer growth, product quality, and payment types. It identified significant trends and patterns in each area, offering valuable insights into overall business performance. These findings support strategic decision-making by highlighting strengths and areas for improvement, enabling the company to enhance its operations and better meet market demands..
Case Study 2: Data-Driven Insights: Visualizing Hotel Booking Patterns for Strategic Decision-Making
This project visualizes hotel booking data to identify important patterns and trends. The analysis helps understand customer behavior and factors affecting hotel business performance.
- Techniques: To identify booking patterns, seasonal trends, and factors influencing cancellations to support hotel business strategies.
- Techniques: Using exploratory data analysis to discover patterns and trends, data cleaning to improve data quality, and data visualization with tools like Tableau or Matplotlib to illustrate findings.
- Outcome: Insights into booking trends, customer preferences, and cancellation factors that can be used to improve hotel marketing strategies and operations.