About Me

EDA on World Countries Data

This project, "World Countries Data Analysis", aims to analyze and visualize various socio-economic and demographic indicators across countries to uncover patterns and insights.

1. Data Acquisition:

   Data is gathered from global sources such as the World Bank or United Nations, covering metrics like GDP, population, education, etc.

2. Data Cleaning and Preparation:

   The dataset is cleaned by handling missing values, correcting inconsistencies, and formatting it for analysis.

3. Exploratory Data Analysis (EDA):

   Statistical summaries and visualizations are used to explore distributions and relationships among variables.

4. Visualization:

   Charts and graphs are created to display trends and comparisons across countries and regions.

5. Statistical Analysis:

   Quantitative techniques are applied to understand and support findings from the data.

6. Insights and Conclusions:

   Findings are summarized to derive insights that could inform global development strategies or future research.

Tools Likely Used:

• Python (Pandas, Matplotlib, Seaborn)
• Jupyter Notebook

Potential Outcomes Include:

• Identification of economic growth factors in different regions.
• Understanding disparities in education and healthcare metrics.
• Recognizing trends in population growth and associated challenges.

UPI-Data-Scrap-Viz

Easily visualize and analyze your Google Pay and Paytm transaction data through insightful graphs and charts — all in one place. This project helps you understand your digital transaction history by converting your raw Google Pay and Paytm data into clear and informative visualizations using Python.

• Data Scraping:

  Utilizes Python's BeautifulSoup library to extract transaction data from Google Pay and Paytm HTML files. The script parses the HTML structure to locate and retrieve relevant transaction details such as date, amount, and transaction type.

• Data Cleansing:

  Processes the raw scraped data to remove duplicates, handle missing values, and standardize formats. For example, dates are converted into a uniform format, and invalid or incomplete entries are filtered out.

• Data Transformation:

  Transforms the cleaned data into a structured format using Pandas. This includes creating a DataFrame, categorizing transactions (e.g., income, expense), and calculating summary statistics like total expenditure and income.

• Data Visualization:

  Generates insightful visualizations using Matplotlib and Seaborn. Examples include bar charts for monthly expenses, pie charts for category-wise spending, and line graphs for transaction trends over time.

Build Back Better – ASEAN Data Science Explorer 2020

This project was developed as part of the ASEAN Data Science Explorers (ADSE) 2020 competition, an initiative by the ASEAN Foundation and SAP that empowers youth across Southeast Asia to harness the power of data for social impact.

📖 Project Overview

Our project, Build Back Better, was created under the theme of UN Sustainable Development Goal (SDG) 8: Decent Work and Economic Growth. The aim was to address the severe economic challenges faced by ASEAN nations in the wake of the COVID-19 pandemic and propose data-driven solutions to help rebuild and strengthen ASEAN’s economy.

🔍 Key Insights & Contributions

• Conducted a comprehensive data analysis of ASEAN economies, focusing on sectors most affected by the pandemic such as tourism, manufacturing, and employment.
• Highlighted the disproportionate impact on vulnerable groups, including youth and low-skilled workers, who faced rising unemployment and reduced income opportunities.
• Identified gaps in digital adoption and innovation as barriers to economic resilience.
• Proposed policy recommendations and strategic interventions such as digital upskilling, promoting inclusive entrepreneurship, and fostering sustainable industries to accelerate recovery.
• Visualized insights through interactive and compelling data-driven storytelling, making the findings accessible and actionable.

🏆 Achievements

Our project was selected as a Top 10 National Finalist in the ADSE 2020 competition, standing out among numerous entries for its impactful analysis and innovative solutions.

🌏 Vision

Through this project, we aimed to demonstrate how data science and analytics can drive sustainable recovery and long-term growth in ASEAN economies, while ensuring inclusivity and resilience in the face of future challenges.

🙌 Acknowledgments

• ASEAN Foundation & SAP for organizing the ASEAN Data Science Explorers 2020.
• All mentors, peers, and collaborators who supported the development of this project.