Upload Disease-Classification-5Targets.zip

This dataset is structured for a multi-class classification machine learning task. The goal is to predict one of five possible diseases based on a set of seven physiological and symptomatic features. The data is entirely numeric, clean (containing no missing values), and ready for model training.

<h3>Data Structure</h3>
<b>Entries:</b> 1,041 rows <br/>
<b>Columns:</b> 8

Column Breakdown
Disease (Target Variable)

Type: Integer (int64)

Description: This is the target column you would try to predict. It contains numerical codes from 0 to 4, representing five distinct diseases.

Temperature (Feature)

Type: Integer (int64)

Description: A numerical value representing the patient's body temperature.

Pulse Rate (Feature)

Type: Float (float64)

Description: The patient's pulse rate.

L.A Pain (Feature)

Type: Integer (int64)

Description: A binary indicator for Lower Abdomen Pain (likely 10 for 'yes' and 0 for 'no').

U.A Pain (Feature)

Type: Integer (int64)

Description: A binary indicator for Upper Abdomen Pain (likely 10 for 'yes' and 0 for 'no').

Vomiting Feeling (Feature)

Type: Integer (int64)

Description: A binary indicator for the symptom of vomiting (likely 10 for 'yes' and 0 for 'no').

Yellowish Urine (Feature)

Type: Integer (int64)

Description: A binary indicator for the symptom of yellowish urine (likely 10 for 'yes' and 0 for 'no').

Indigestion (Feature)

Type: Integer (int64)

Description: A binary indicator for the symptom of indigestion (likely 10 for 'yes' and 0 for 'no').

Disease-Classification-5Targets.zip

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