Dendritic Datasets Crate

This crate contains all the preloaded datasets for dendritic. Most of these datasets come from kaggle and have been converted to parquet files to work with the apache arrow crate. Dendritic does not support any known dataframe libraries at the moment. Dendritic works with anything that can be converted to it NDArray<f64> data structure.

Datasets

• Diabetes: Diabetes dataset for binary classification tasks.
• Iris: Iris flowers dataset for multi class classification tasks
• Breast Cancer: Breast Cancer diagnosis for binary classification
• Alzhiemers: Alzhiemers diagnosis data amongst adults
• Customer Purchase: Customer purchase data for multi class classification
• Boston Housing: Boston housing data for regression tasks
• Student Performance: Student test scores for regression tasks
• Airfoil Nooise: Airfoil noise data for regression tasks

Disclaimer

The dendritic project is a toy machine learning library built for learning and research purposes. It is not advised by the maintainer to use this library as a production ready machine learning library. This is a project that is still very much a work in progress.

Example Usage

This is an example of using any of the preloaded datasets for dendritic

use dendritic_datasets::iris::*;
use dendritic_datasets::breast_cancer::*;
use dendritic_datasets::diabetes::*;
use dendritic_datasets::alzhiemers::*;
use dendritic_datasets::customer_purchase::*;
use dendritic_datasets::student_performance::*;
use dendritic_datasets::airfoil_noise::*;

fn main() {

    // Examples of loading the datasets
   let diabetes = "../dendritic-datasets/data/diabetes.parquet";
   let (x_train, y_train) = load_diabetes(diabetes).unwrap();

   let breast_cancer = "../dendritic-datasets/data/breast_cancer.parquet";
   let (x_train, y_train) = load_breast_cancer(breast_cancer).unwrap();

   let iris_data = "../dendritic-datasets/data/iris.parquet";
   let (x_train, y_train) = load_iris(iris_data).unwrap();

   let alz = "../dendritic-datasets/data/alzheimers.parquet";
   let (x_train, y_train) = load_alzhiemers(alz).unwrap();

   // Refer to crate docs for loading other datasets
}