Project Overview Developed a clinical data analytics and predictive machine learning project to identify key risk factors for stroke prediction. Combined healthcare domain knowledge with data science workflows to enable early risk detection using real patient datasets. ​Key Insights & Features ​Data Cleaning & Clinical EDA: Processed missing values and conducted exploratory data analysis on critical medical indicators including BMI, blood glucose levels, and hypertension status. ​Predictive Machine Learning: Trained a Random Forest Classifier model to evaluate patient risk factors and predict stroke occurrence. ​Model Performance: Achieved a predictive model accuracy of 94% in identifying high-risk clinical profiles. ​Tools & Technologies ​Programming & Libraries: Python (Pandas, NumPy, Scikit-learn) ​Algorithms & Techniques: Random Forest Classifier, Feature Engineering, Exploratory Data Analysis (EDA)