Welcome to the data repository! This README provides a brief overview of the datasets included in this collection. Each dataset is valuable for various analyses and machine learning tasks. For more detailed information, you can refer to the links and references provided below.

Datasets

1. California Housing Data File Name: california_housing_data*.csv Description: This dataset contains information about housing in California from the 1990 US Census. It includes features such as the median house value, median income, and other demographic and geographic details. Source: Google Developers - California Housing Data Description
2. MNIST Data File Name: mnist_*.csv Description: This dataset is a small sample of the MNIST database, which contains handwritten digit images. Each image is labeled with the corresponding digit, making it useful for image classification tasks. Source: MNIST Database - Wikipedia Further Information: Yann LeCun's MNIST Page
3. Anscombe's Quartet File Name: anscombe.json Description: Anscombe's quartet is a set of four datasets that have nearly identical simple descriptive statistics, yet appear very different when graphed. It was originally described in a seminal paper by Francis Anscombe. Source: Anscombe's Quartet - Wikipedia Original Reference: Anscombe, F. J. (1973). 'Graphs in Statistical Analysis'. American Statistician. 27 (1): 17-21. JSTOR 2682899. Usage California Housing Data: Useful for regression analysis, exploring housing trends, and predicting house prices. MNIST Data: Ideal for image classification tasks, training machine learning models, and benchmarking algorithms. Anscombe's Quartet: Useful for teaching and demonstrating the importance of graphical data representation and the limitations of statistical summaries. Preparing Data To use these datasets, ensure you have the necessary tools to handle CSV and JSON file formats. Common libraries such as Pandas in Python can be used to load and process these datasets.