Cardiovascular Disease Prediction

Overview

This project focuses on leveraging data-driven techniques to predict the likelihood of cardiovascular disease occurrence in patients based on their medical records and health history. By applying data preprocessing, exploratory data analysis, and machine learning models, this system aims to provide valuable insights and predictions to aid in early disease detection and prevention. Through a combination of data analysis and predictive modeling, the project strives to enhance healthcare decision-making and improve patient care outcomes.

Features

• Data Preprocessing: Cleaned and prepared the dataset for analysis.
• Outlier Removal: Detected and handled outliers in the data.
• Descriptive Statistics: Generated statistical summaries to understand the data.
• Exploratory Data Analysis: Explored data patterns and relationships.
• Target Variable and Independent Variables Visualization: Visualized relationships between variables.
• Label Encoding: Encoded categorical variables for modeling.
• Correlation Matrix Heatmap: Visualized feature correlations.
• Cardiovascular Disease Prediction: Implemented predictive models.
  • Decision Tree Classifier
  • Random Forest Classifier
  • Logistic Regression
• Model Evaluation: Assessed model performance using appropriate metrics.

Usage

1. Data Preparation: Ensure your dataset is properly formatted with the features listed above.

2. Data Analysis: Utilize data visualization and statistical techniques to gain insights into the data.

3. Data Preprocessing: Clean and preprocess the data, handling missing values and encoding categorical features as needed.

4. Exploratory Data Analysis: Explore data patterns and relationships to inform model selection.

5. Model Building: Choose and train a suitable machine learning algorithm (e.g., Random Forest, Decision Tree, Logistic Regression) using the prepared data.

6. Model Evaluation: Assess your models' performance using relevant metrics such as accuracy, precision, recall, and F1-score.

7. Prediction: Deploy your trained model to predict the occurrence of cardiovascular disease in new patients.