JLPT Data Analysis and Forecasting

Overview

The Japanese-Language Proficiency Test (JLPT) is a standardized test designed to evaluate and certify the Japanese language proficiency of non-native speakers. It is an important tool for assessing Japanese language skills across various levels, ranging from the easiest (N5) to the most advanced (N1). This repository contains a comprehensive analysis of JLPT data, which helps to understand trends, forecast future performance, and evaluate the effectiveness of the test.

Objectives

This analysis aims to:

• Examine trends in the number of JLPT applicants and examinees from 2009 to 2023.
• Forecast future trends in JLPT participation and performance.
• Analyze performance based on scores and passing rates by level and location (Japan and overseas).
• Provide insights into how the JLPT's reach and difficulty have evolved over the years.

Data Sources

The data for this analysis has been gathered from:

• The official JLPT website, which provides historical data on applicants, examinees, and passing rates.
• Additional data sources from relevant Japanese language testing organizations and public datasets.

Repository Contents

• 1984_2023_jlpt_data.csv: Comprehensive dataset for JLPT from 1984 to 2023.
• 2023_examinees_data.csv: Data on examinees published (2023).
• 2023_avg_and_std_scores.csv: Average and standard deviation scores published (2023).
• 2023_analytics_report.pdf: Analytics report from the JLPT website published (2023).
• JLPT_Analysis_and_Forecasting.ipynb: Jupyter notebook for detailed analysis and forecasting specific to JLPT data.

Key Results

• Trend Analysis: A detailed examination of the number of JLPT applicants and examinees over the years.
• Forecasting: Predictions for future JLPT participation and performance based on historical data.
• Performance Analysis: Insights into average scores and passing rates across different levels and locations.

How to Use

1. Clone the Repository:

   git clone https://github.com/yourusername/JLPT-Analysis.git

2. Navigate to the Directory:

   cd JLPT-Analysis

3. Install Required Packages:

   pip install -r requirements.txt

4. Run the Notebook:

   jupyter notebook JLPT_EDA_ML.ipynb

5. Explore and Analyze: Follow the notebook to explore the data, view results, and understand the insights.

Contributing

Contributions are welcome! If you have any suggestions or improvements, please submit a pull request or open an issue.

License

This project is licensed under the MIT License - see the LICENSE file for details.