project data wrangling

dataset.docx

Home >Computer Science homework help >project data wrangling

What is Data Analytics?

Part 1: Choosing a Dataset:

I choose a dataset from the Kaggle. The name of my dataset is Stroke Prediction Dataset and my dataset consists of the following specifications:

1. It has 12 columns.

2. It has 5110 rows.

3. It is labeled data.

Part 2: Dataset Background

Summary

According to World Health Organization (WHO), the stroke disease ranks on second number which causes deaths. Stroke may cause the deaths of many people, it is a disease in which blood is interrupted when blood is supply in the part of brain. It prevents the tissue of brain to get oxygen and nutrients from the environment. The cells of brain begin to die in seconds. A stoke is the medical emergency, it requires urgent treatment and the symptoms of stroke are high blood pressure, diabetes and heart disease etc. These are the major risk factors which is responsible for stroke attack. We use these risk factors to predict the stroke in machine learning. We can determine from the patient heath record whether the patient is suffered from stroke or not. This dataset helps us to determine whether the patient is suffered from stroke or not.

This data comes from the electronic records of the patients that are released by Mckinsey & company. The electronic record of the patients such as he/she is suffered from heart disease, hypertension etc. predicts that the patient has stroke or not.

Many people suffer in the disease of stroke but they don't know that they suffer in stroke. Using the machine learning algorithms, we can tell the patients that he/she suffer in stroke disease. These predictions help patients to know that in which disease they suffer? Are they suffer in stroke or not? In this way, we can solve the problems of the patients and we can solve the problems of the people also. We can solve the problems of World.

Part 3: Dataset Info

This dataset contains the records of 5110 patients and 12 fields. It consists of 11 input attributes and 1 output attribute. There are 11 input attribute that is: id, age, gender, hypertension (binary attribute: 0 means patient does not have hypertension, 1means patient has hypertension), heart disease (binary attribute: 0 means patient does not have any heart disease, 1means patient has a heart disease), marital_status, work_type, residence_type, average_glucose_level, body_mass_index (BMI) and smoking_status of the patient. The 12 output attribute is stroke that predicts whether the patient had a stroke or not.

The following table shows column name, Data Type and numeric or categorical types of data.

Column Name	Data Type	Numeric/Categorical
Id	Int64	Numeric
Gender	Object	Categorical
Age	Float64	Numeric
Hypertension	Int64	Numeric
Heart Disease	Int64	Numeric
Marital_Status	Object	Categorical
Work_Type	Object	Categorical
Residence_Type	Object	Categorical
Average_Glucose_Level	Float64	Numeric
Body_Mass_Index	Float64	Numeric
Smoking_Status	Object	Categorical
Stroke	Int64	Numeric

Part 4: Import your Dataset

We use Jupyter Notebook to import this dataset. We follow some steps to import the dataset.

1. First of all, we import the pandas library that is as follows:

2. We import this dataset by using the function pd.read_csv( ). The screenshot of the following code is as follows:

3. We print the dataset using the head function that is as follows:

4. Now, we describe the information of the dataset using the info() function. The code of the following function is as follows:

5. The, we describe the shape of the dataset using the function that is as follows: