Episode Descriptions

This project contains an exploratory data analysis (EDA) of the OECD PISA dataset using Gemini in Google Colab. This project is featured on YouTube.

Part 2: Data Visualization and Analysis In the second episode, we dive deeper into data analysis by creating various visualizations to uncover insights and trends. We explore different ways to visualize the data, such as histograms, bar plots, scatter plots, pair plots, and heatmaps. Additionally, we analyze trends, gender disparities, and relationships between economic indicators and test scores. This episode showcases the power of AI in transforming raw data into meaningful visualizations without writing any code. Part 1: Data Loading and Preprocessing In the first episode of our AI-driven data analysis series, we introduce the datasets and demonstrate how to load and preprocess them using Google Gemini and Colab. We cover the initial steps of data analysis, including loading the datasets, renaming columns for consistency, and handling missing values. This episode sets the foundation for the comprehensive data analysis to follow.

Files

• OECD_PISA_data.csv: The dataset.
• OECD_PISA_Data_Analysis.ipynb: The Jupyter notebook containing the EDA (Part 1).
• Episode 1: Data_Analysis_with_Gemini.ipynb: : The Jupyter notebook containing the continued EDA (Part 1)
• Episode 2: Data_Visualisation_with_Gemini.ipynb: The Jupyter notebook containing the continued EDA and visualizations (Part 2)

YouTube Gemini Colab OECD PISA Data Analysis

Episode 2:

Episode 1:

How to Use

1. Clone the repository.
2. Open the Jupyter notebooks in Google Colab.
3. Run the cells to perform the analysis.

Repository Setup

Step 1: Clone the Repository

Open your terminal or command prompt and run the following command:

git clone https://github.com/YOUR_USERNAME/YouTube-Gemini-Colab-OECD-PISA-Data-Analysis.git