---
title: "Upload the dataset"
url: https://docs.catalyst.zoho.com/en/tutorials/cancer-detection/upload-dataset/
description: "Create and configure a powerful ML pipeline that can be used to predict the chances of being affected by breast cancer using the Catalyst QuickML service."
last_updated: 2026-10-01T07:03:46Z
service: All Services
type: tutorial
related_links:
  - name: "Data Connectors"
    url: /en/quickml/help/data-connectors/zoho-apps
---

# Upload Dataset

Let's begin by uploading the datasets in Catalyst QuickML using the available dataset [connectors](https://docs.catalyst.zoho.com/en/quickml/help/data-connectors/zoho-apps):

1. Navigate to the QuickML service in the Catalyst console and click **Start Exploring**.

2. In the left panel, navigate to Datasets and click **Import Dataset**.
   An *Import Dataset* pop-up will be displayed. 

   ![cancer-detection-3.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-3.webp)

3. In the *Data Sources* step, scroll down to the *Files* section, and click **Upload File**.

   ![cancer-detection-4.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-4.webp)

4. In the *Data Source Details* step, upload "Cancer_detection_A" dataset that you downloaded and click **Next**.

   ![cancer-detection-5.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-5.webp)

5. In the *Name and Sync Configuration* step, the dataset name will be automatically populated based on the uploaded file. You can modify it if needed. Then, click **Upload**.

   ![cancer-detection-6.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-6.webp)

Now, proceed with uploading the another dataset, "Cancer_detection_B" by repeating the same steps.

![cancer-detection-7.webp](https://catalyst-docs-development.zohostratus.in/images/tutorials/cancer-detection/cancer-detection-7.webp)

Because the datasets in this tutorial are related to health and medical data, here are some key terms to remember:

- **Patient_id**: Unique ID for each patient
- **Patient_name**: Name of the patient
- **Diagnosis**: (M = Malignant / B = Benign)

The dataset also contains information about the breast mass features, such as:

- **Radius**: Average distance from the center to the edge
- **Texture**: Standard deviation of gray-scale values
- **Perimeter**: Perimeter length
- **Area**: Area of the mass
- **Smoothness**: Variation in radius lengths
- **Compactness**: (Perimeter^2 / Area) - 1.0
- **Concavity**: How much the contour is indented
- **Concave Points**: Number of indentations on the contour
- **Symmetry**: How symmetrical the mass is
- **Fractal Dimension**: Measure of the complexity of the shape

For each image, we computed the mean, standard error, and worst (largest of the three largest values) of these features, resulting in a total of 30 features, including the mean radius, radius standard error, and worst radius.