Low Carbon Technology Trade Analysis

This project explores and analyzes global trade data for low-carbon technology products. The goal is to derive insights from historical trends and statistical analysis while demonstrating professional data analysis techniques.

Project Highlights

• Cleaned and preprocessed raw trade data.
• Conducted exploratory data analysis (EDA) and statistical analysis.
• Visualized trends and correlations in trade patterns.
• Applied hypothesis testing to compare trade values over time.

Motivation

The rise of low-carbon technologies plays a critical role in combating climate change. Analyzing trade patterns helps stakeholders make informed decisions about investment and policy-making.

Workflow

1. Data Cleaning:
   • Handled missing values.
   • Reshaped the dataset for better analysis.
2. Exploratory Analysis:
   • Trends and distribution of trade values over time.
3. Statistical Analysis:
   • Correlation matrix.
   • Hypothesis testing for trade trends.
4. Visualization:
   • Trend charts.
   • Heatmaps for correlation insights.

Technologies Used

• Python (pandas, numpy, matplotlib, seaborn)
• Jupyter Notebook
• Git/GitHub

Files and Structure

|-- data/
|   |-- Trade_in_Low_Carbon_Technology_Products.csv (original data)
|   |-- cleaned_trade_data.csv (cleaned data)
|
|-- notebooks/
|   |-- trade_analysis.ipynb (Jupyter Notebook for analysis)
|
|-- scripts/
|   |-- data_cleaning.py
|   |-- statistical_analysis.py
|   |-- visualization.py
|
|-- requirements.txt
|-- README.md