Global Terrorism Analysis Using Python

ello Guys !

I recently completed a data engineering project focusing on global wealth data using PostgreSQL and Python. Here’s how I approached it:

Data Collection and Preparation: Used datasets (Wealth-AccountsCountry.csv, Wealth-AccountData.csv, Wealth-AccountSeries.csv) to gather information on countries, wealth indicators, and series topics.

Database Setup: Established a PostgreSQL database (accounts) and created tables (accountscountry, accountsdata, accountsseries) to store country details, wealth indicators over years, and series metadata.

Data Loading and Transformation: Imported CSV data into Pandas DataFrames for initial exploration, renamed columns, selected relevant features, and formatted data for PostgreSQL compatibility.

Database Operations: Connected Python to PostgreSQL using psycopg2, executed SQL commands to create tables, define relationships (FOREIGN KEY constraints), and inserted cleaned data.

Exploratory Data Analysis (EDA): Utilized Pandas, Matplotlib, and Seaborn for EDA, examining country distributions (Short Name, Currency Unit) and analyzing indicators by topic and series codes.

Visualization and Insights: Presented insights through bar plots and pie charts, highlighting top countries by metrics (Short Name), currency unit distributions, and indicator counts by topic and series codes.

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link Github : https://lnkd.in/dFiswtWn

hashtag#DataEngineering hashtag#SQL hashtag#Python hashtag#PostgreSQL hashtag#DataAnalysis hashtag#DataVisualization