Cardiovascular-disease-prediction

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Cardiovascular-disease-prediction

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The objective of this project is to develop a machine learning model that can predict the risk of Cardiovascular diseases (CVDs) in individuals based on their health data.

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# [Cardiovascular disease prediction](https://github.com/ShilpaAjitheks/Machine_Learning_Pro/blob/master/Cardiovascular_Disease_Prediction_/cardiovascu.ipynb)
![](https://d112y698adiu2z.cloudfront.net/photos/production/software_photos/002/508/249/datas/gallery.jpg)

<h3>CONTEXT:</h3>
Cardiovascular disease (CVD) is a general term used to describe heart or blood vessel diseases. Blood flow to the heart, brain or body can be reduced because of a blood clot build-up of fatty deposits inside an artery, leading to the artery hardening and narrowing .Cardiovascular diseases (CVDs) are the leading cause of death worldwide, accounting for approximately 31% of all deaths globally. Early diagnosis and prevention of CVDs are crucial.

<h3>AIM:</h3>
Objective of this project is to develop a machine learning model that can predict the risk of Cardiovascular diseases (CVDs) in individuals based on their health data.

1. To explore and visualize the dataset.
2. To preprocess and clean the dataset.
3. To build a machine learning model to predict the risk of CVDs.
4. To evaluate the performance of the model.
5. To fine-tune the model to improve its performance.
6. To deploy the model and make predictions on new data.

<h3>SOURCE OF THE DATASET USED:</h3>
The dataset used for this project is the “Cardiovascular Disease dataset” from Kaggle. The dataset contains 70,000 records of patients aged 30-70 years, and it includes 11 features such as age, gender, blood pressure, cholesterol levels, smoking status, and more. The target variable is a binary variable indicating whether the patient has CVD or not.

<h3>FEATURES:</h3>

01. age        :			age in days
02. gender     :			Gender | 1: female, 2: male
03. height     :			Height-(cm)
04. weight     :			Weight-(kg)
05. ap_hi      :			Systolic blood pressure-(norm:120 hg)
06. ap_lo      :			Diastolic blood pressure-(norm:80 hg)
07. cholesterol:			Cholesterol | type-1: normal, 2: above normal, 3: well above normal|
08. gluc       :			Glucose | type-- 1: normal, 2: above normal, 3: well above normal |
09. smoke      :			Smoking |type- 0: do 1: dont do
10. alco       :			Alcohol intake|type- 0: do 1: dont do 
11. active     :			Physical activity |type- 0: do 1: dont do
12. cardio     :			Presence or absence of cardiovascular disease

<h2>Guideline to Access Project</h2>

Cardiovascular_Disease_Prediction_ folder inside this Repository(Cardiovascular-disease-prediction) contains cardiovascu.ipynb notebook.You people can simply access the notebook and use.You can simply fork repository to yours and clone.
Python(.py) file of the same also is provided.
cardio_train.csv is our dataset in csv format.

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