Data Analysis With Python (Heart Disease Dataset)

In this project, I conducted an in-depth analysis of a Heart Disease dataset using Python to uncover key insights and factors contributing to heart disease. The goal was to explore patterns, visualize relationships, and build a foundation for predictive analysis to assist in early detection and prevention.

Key Achievements:

Data Cleaning and Preprocessing: I cleaned the dataset by handling missing values, detecting outliers, and standardizing data. I also performed feature scaling and encoding for categorical variables to ensure compatibility with machine learning models.

Exploratory Data Analysis (EDA): Using libraries like Pandas, Matplotlib, and Seaborn, I performed an exploratory data analysis to visualize relationships between various features (e.g., age, cholesterol levels, blood pressure) and the presence of heart disease. This included histograms, box plots, and pair plots.

Statistical Insights: I conducted statistical analysis, including correlation analysis and hypothesis testing, to identify the most significant factors contributing to heart disease. Features like cholesterol levels, age, and exercise-induced angina were found to have strong correlations with heart disease occurrence.

Predictive Modeling: I applied machine learning algorithms like Logistic Regression and Random Forest to build a predictive model based on the dataset. This helped in assessing the likelihood of heart disease based on patient data.

Model Evaluation: The models were evaluated using metrics such as accuracy, precision, recall, and the ROC curve, ensuring they provide reliable predictions for heart disease risk.

Through this project, I demonstrated my expertise in data analysis, statistical modeling, and machine learning, using Python to extract actionable insights from medical data and build predictive models for heart disease risk assessment.