Welcome to Part 1 of our Heart Stroke Prediction Data Science Project!
In this session we’ll guide you through the initial steps of building a predictive model to identify the risk of heart stroke using data science techniques. This project is ideal for anyone looking to apply their data science skills to a real-world health problem .
🔍 What You’ll Learn:
– Introduction to the Project
– Overview of heart stroke prediction
– Importance and impact of predicting heart strokes
– Understanding the Dataset
– Overview of the dataset used
– Key features and target variable
– Data collection and sources
– Data Preprocessing
– Handling missing values
– Data cleaning and transformation
– Exploratory Data Analysis (EDA) to understand data distribution and relationships
– Feature Engineering
– Creating new features from existing data
– Selecting important features for the model
📊 Tools and Technologies:
-Programming Language: Python
-Libraries: Pandas, NumPy, Matplotlib, Seaborn, Scikit-learn
-Development Environment: Jupyter Notebook or any Python IDE
👥 Who Is This For?
– Aspiring Data Scientists
– Healthcare Data Analysts
– Machine Learning Enthusiasts
– Anyone interested in applying data science to healthcare
💡 Why Watch?
By the end of this session, you’ll have a solid understanding of the initial steps required to preprocess and explore a dataset for a predictive modeling project. This foundational knowledge is crucial for building an accurate and effective heart stroke prediction model.
📣 Feedback and Suggestions:
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