Merge pull request #578 from Avdhesh-Varshney/cardio

Cardiovascular Disease Analysis and Prediction Model

Cardiovascular Disease Analysis and Prediction/Dataset/README.md

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+# Cardiovascular Disease Analysis and Prediction Model 
+
+The Dataset used here is taken from the Kaggle database website. You can download the file from the link given here, [Cardiovascular Disease Analysis and Prediction](https://www.kaggle.com/datasets/jocelyndumlao/cardiovascular-disease-dataset)
+
+## About the dataset
+
+There are 14 features and 1000 entries in this dataset.
+
+
+- `patiendid` - **Patient Identification Number**
+- `age` - **Age of the patient**
+- `gender` - **Gender of the patient** - (0 => Female, 1 => Male)
+- `chestpain` - **Chest pain type** - (0 => typical angina, 1 => atypical angina, 2 => non-anginal pain, and 3 => asymptomatic)
+- `restingBP` - **Resting Blood Pressure** - (94-200 in mm HG)
+- `serumcholestrol` - **Serum Cholesterol** - (126-564 in mg/dl)
+- `fastingbloodsugar` - **Fasting blood sugar** - (0, 1 > 120 mg/dl) - (0 => false, 1 => true)
+- `restingelectro` - **Resting electrocardiogram results** - (Value 0: normal, Value 1: having ST-T wave abnormality, and Value 2: showing probable or definite left ventricular hypertrophy by Estes' criteria)
+- `maxheartrate` - **Maximum heart rate achieved** - (71-202)
+- `exerciseangia` - **Exercise induced angina** - (0 => no, 1 => yes)
+- `oldpeak` - **Oldpeak=ST** - (0 - 6.2)
+- `slope` - **Slope of the peak exercise ST segment** - (1-upsloping, 2-flat, 3-downsloping)
+- `noofmajorvessels` - **Number of major vessels** - 0, 1, 2, 3
+- `target` - **Classification** - (0 => absence of heart disease, 1 => presence of heart disease)
+

Cardiovascular Disease Analysis and Prediction/README.md

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+<h1>Cardiovascular Disease Analysis and Prediction</h1>
+
+**GOAL**
+
+The aim of this project is to analyze and predict the disease based on the given dataset.
+
+**DATASET**
+
+https://www.kaggle.com/datasets/jocelyndumlao/cardiovascular-disease-dataset
+
+**DESCRIPTION**
+
+To analyze the dataset of Cardiovascular Disease and build and train the model on the basis of different features and variables.
+
+There are 14 features and 1000 entries in this dataset.
+
+
+### Visualization and EDA of different attributes:
+
+<img alt="heatmap" src="./Images/correlation_heatmap.png">
+
+<img alt="graph" src="./Images/target_correlation.png">
+
+<img alt="graph" src="./Images/age_feature.png">
+
+<img alt="graph" src="./Images/restingrelectro_feature.png">
+
+
+**MODELS USED**
+
+| Model                       | MSE_train | R2_train | MSE_test  | R2_test   |
+|-----------------------------|-----------|----------|-----------|-----------|
+| Random Forest Regression    | 0.002946  | 0.987915 | 0.023432  | 0.903483  |
+| XG Boost Regression         | 0.000002  | 0.999993 | 0.027463  | 0.886879  |
+| Decision Tree Regression    | 0.000000  | 1.000000 | 0.035000  | 0.855834  |
+| Ridge Regression            | 0.063654  | 0.738905 | 0.059288  | 0.755792  |
+| Linear Regression           | 0.063654  | 0.738907 | 0.059305  | 0.755721  |
+| KNN Regression              | 0.094450  | 0.612590 | 0.125600  | 0.482649  |
+| Elastic Net Regression      | 0.180585  | 0.259287 | 0.155394  | 0.359926  |
+
+
+
+**WHAT I HAD DONE**
+
+* Load the dataset which contains 1000 entries in it and having 14 columns in it.
+* Checked for missing values and cleaned the data accordingly.
+* Analyzed the data, found insights and visualized them accordingly.
+* Plotting heatmap using correlation and checking the relation between different features.
+* Found detailed insights of different columns with target variable using plotting libraries.
+* Train the datasets by different models and saves their accuracies into a dataframe.
+
+
+**LIBRARIES NEEDED**
+
+1. Pandas
+2. Matplotlib
+3. Sklearn
+4. NumPy
+5. XGBoost
+6. Sci-py
+7. Seaborn
+
+
+**CONCLUSION**
+
+- Random Forest and XG Boost Regression models show promising performance with lower MSE and higher R2 values.
+- Decision Tree Regression achieved perfect R2 on the training set but performed poorly on the test set, indicating overfitting.
+
+
+**YOUR NAME**
+
+*Avdhesh Varshney*
+
+[![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/avdhesh-varshney-5314a4233/)  [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/Avdhesh-Varshney)
+

Cardiovascular Disease Analysis and Prediction/requirements.txt

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+numpy==1.19.2
+pandas==1.4.3
+matplotlib==3.7.1
+scikit-learn~=1.0.2
+scipy==1.5.0
+seaborn==0.10.1
+xgboost~=1.5.2