https://github.com/carolinerocks/data-visualization-canada-china-trade
https://github.com/carolinerocks/data-visualization-canada-china-trade
data-visualization jupyter-notebook pyhton
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/carolinerocks/data-visualization-canada-china-trade
- Owner: Carolinerocks
- Created: 2024-12-14T06:11:06.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-12-20T22:48:22.000Z (over 1 year ago)
- Last Synced: 2024-12-30T21:22:33.983Z (over 1 year ago)
- Topics: data-visualization, jupyter-notebook, pyhton
- Language: Jupyter Notebook
- Homepage:
- Size: 3.34 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Visualization - Canada China Trade
**1. Introduction**
This project aims to analyze the factors influencing individual medical insurance costs using statistical modeling techniques. By iteratively building regression models—from additive to interaction and higher-order models—the analysis explores the impacts of key predictors on insurance charges while ensuring statistical soundness.
**2. Languages and Tools**
- Language: Python
- Analysis Tools:
- Statistical Modeling (Multiple Linear Regression)
- Diagnostics: Residual plots, Breusch-Pagan tests, Shapiro-Wilk tests
- Data Visualization: ggplot2 (for plots such as scatter plots, interaction visualizations)
**3. Analytical Methods**
- Additive Models: Capturing main effects of predictors such as age, BMI, smoking status, and children.
- Interaction and Higher-Order Models: Including interaction terms and polynomial features to identify complex non-linear relationships.
- Diagnostics: Ensuring assumptions of linear regression, including linearity, homoscedasticity, and normality of residuals, are satisfied.
**4. Conclusion**
This project provides a comprehensive analysis of Canada-China trade relations over the past decade, identifying key trends, challenges, and growth opportunities. The study reveals that trade dynamics are shaped by Canada’s reliance on Chinese manufacturing, China’s demand for Canadian natural resources, and external factors like geopolitical events and the COVID-19 pandemic.
Using ARIMA models, the project effectively forecasts future trade trends, offering valuable insights for policymakers and investors. The analysis suggests opportunities to diversify trade, reduce sector-specific reliance, and strengthen economic cooperation. Canadian investors are encouraged to focus on natural resources, particularly agriculture and mining, while also exploring local and high-tech manufacturing investments to reduce import dependency. Additionally, the growing e-cigarette and alternative tobacco markets present promising investment opportunities.