TransBorder Freight Data Analysis

This README outlines the steps implemented in the Python script and the tools used for the analysis in this project.

Overview

The project analyzes TransBorder Freight Data using the CRISP-DM framework. The analysis involves data cleaning, exploration, transformation, and visualization of freight transportation data.

Tools Used

The following tools and libraries were used in this project:

• Python: Primary programming language for the analysis.
• Pandas: For data manipulation and cleaning.
• NumPy: For numerical computations.
• Matplotlib and Seaborn: For data visualization.
• Jupyter Notebook: To organize and execute the Python code interactively.

Steps in the Python Script

1. Data Loading

• Import necessary libraries.
• Load monthly TransBorder Freight datasets into Pandas DataFrames.
• Merge these monthly datasets into yearly datasets for efficient analysis.

2. Data Cleaning

• Handle missing values by either filling or removing them based on context.
• Rename columns for consistency and readability.
• Filter out irrelevant data entries.

3. Data Transformation

• Perform type conversions (e.g., converting columns to appropriate data types).
• Create additional features or columns that provide meaningful insights (e.g., aggregating freight data by region).

4. Exploratory Data Analysis (EDA)

• Generate summary statistics (mean, median, etc.) for key variables.
• Create visualizations to understand trends and patterns, such as:
  • Line plots for freight trends over time.
  • Bar charts for top trading partners.
  • Heatmaps for correlation analysis.

5. Insights and Interpretation

• Analyze the visualizations and statistical outputs to identify trends and anomalies.
• Summarize key findings in text and visual form.

6. Export Results

• Save cleaned and transformed datasets to new CSV files.
• Export generated visualizations as image files for reporting.

How to Run the Script

1. Ensure you have Python installed on your system (version 3.7 or higher).
2. Install the required Python libraries using pip:

   pip install pandas numpy matplotlib seaborn

3. Open the Jupyter Notebook file in your preferred editor (e.g., JupyterLab, VSCode).
4. Execute each cell sequentially to perform the analysis.
5. Review the outputs, including visualizations and exported files.

Notes

• Ensure all raw datasets are stored in the appropriate directory as specified in the script.
• Modify file paths and other configurations based on your system setup.

Contact

For questions or issues, please reach out.