Project Title

Heart Disease Prediction Using Machine Learning with Python

Domain

Python | Artificial Intelligence (AI) | Machine Learning (ML) | Healthcare Analytics | Data Science | Predictive Analytics

Project Level

Beginner to Intermediate

Project Objective

Heart disease is one of the leading causes of death worldwide. Early detection and diagnosis can significantly improve treatment outcomes and save lives. The objective of this project is to build an intelligent Machine Learning model capable of analyzing patient health parameters and predicting whether a person is at risk of heart disease.

Students will learn the complete Machine Learning workflow, including data collection, preprocessing, exploratory data analysis, feature engineering, model training, testing, evaluation, and prediction. This project provides practical exposure to Artificial Intelligence, Healthcare Analytics, and Predictive Modeling concepts used in modern medical decision-support systems.

Note: This project is intended for educational purposes only and should not be used as a substitute for professional medical diagnosis.

Session Access Link

Project Training Session

Session Link: 

Click here to Watch your uploaded session

Learning Outcomes

After successful completion of this project, students will be able to:

• Understand the fundamentals of Machine Learning.
• Learn how disease prediction systems work.
• Understand classification algorithms and predictive analytics.
• Perform data cleaning and preprocessing.
• Conduct exploratory data analysis (EDA).
• Work with healthcare datasets.
• Handle numerical and categorical medical data.
• Train and evaluate machine learning models.
• Measure model performance using different evaluation metrics.
• Build a real-world heart disease prediction system.
• Improve analytical and problem-solving skills.

Programming Language & Technologies Used

Programming Language

• Python 3.x

Machine Learning Libraries

• Scikit-learn
• NumPy
• Pandas

Data Visualization Libraries

• Matplotlib
• Seaborn

Development Platforms

• Google Colab
• Jupyter Notebook
• VS Code (Optional)

Data Storage

• CSV Files
• Google Drive

Project Workflow

Phase 1: Dataset Collection

Students will collect a Heart Disease Dataset containing:

• Age
• Gender
• Chest Pain Type
• Resting Blood Pressure
• Cholesterol Level
• Fasting Blood Sugar
• Resting ECG Results
• Maximum Heart Rate Achieved
• Exercise-Induced Angina
• ST Depression
• Slope of Peak Exercise ST Segment
• Number of Major Vessels
• Thalassemia
• Heart Disease Status (Present/Absent)

Dataset Format:

• CSV File

Phase 2: Data Preprocessing

Students will perform:

• Removal of duplicate records
• Handling missing values
• Data cleaning
• Encoding categorical variables
• Feature selection
• Outlier detection and treatment
• Data normalization/scaling

Purpose:

To improve data quality and model performance.

Phase 3: Exploratory Data Analysis (EDA)

Students will analyze the dataset using:

• Summary Statistics
• Correlation Matrix
• Histograms
• Box Plots
• Count Plots
• Heatmaps
• Feature Relationship Analysis

Benefits:

• Understand patient health trends.
• Identify factors influencing heart disease.
• Detect anomalies and patterns in medical data.

Phase 4: Feature Engineering

Students will prepare data for machine learning using:

• Label Encoding
• One-Hot Encoding
• Feature Scaling
• Correlation-Based Feature Selection

Benefits:

• Improves model efficiency.
• Enhances prediction accuracy.
• Reduces irrelevant features.

Phase 5: Model Building

Students will train Machine Learning models such as:

Logistic Regression

• Widely used for medical classification problems.
• Fast and interpretable model.

Decision Tree Classifier

• Easy to understand and visualize.
• Helps identify important risk factors.

Random Forest Classifier

• Ensemble learning technique.
• Provides better prediction accuracy.

Support Vector Machine (SVM)

• Effective for healthcare classification tasks.
• Performs well with structured datasets.

K-Nearest Neighbors (KNN)

• Simple and effective classification algorithm.
• Useful for model comparison.

Phase 6: Model Evaluation

Students will evaluate model performance using:

• Accuracy Score
• Confusion Matrix
• Precision
• Recall
• F1-Score
• ROC-AUC Score

Expected Accuracy:

80% – 95% (depending on dataset quality, preprocessing, and model selection)

Phase 7: Prediction System

Students will create a system where users can:

• Enter patient health information.
• Click Predict.
• Receive output:
  
  • Heart Disease Detected
  • No Heart Disease Detected

Example:

Input:

• Age: 55
• Cholesterol: 240
• Blood Pressure: 150

Output:

• High Risk of Heart Disease

or

• Low Risk of Heart Disease

Final Project Deliverables

Each student must submit:

Source Code

• Python Files (.py)
• Jupyter Notebook (.ipynb)

Dataset

• CSV Dataset Files

Documentation

• Project Report (PDF)

Screenshots

• Dataset Import
• Data Cleaning
• Exploratory Data Analysis
• Model Training
• Accuracy Results
• Prediction Results

Presentation

• PPT (Minimum 10 Slides)

Additional Files

• Trained Model (.pkl file)
• Output Results

Google Drive Submission Process

Step 1:

Create a Google Drive Folder.

Folder Name Format:

WineQualityPrediction_StudentName

Step 2:

Upload all project files.

Step 3:

Share the folder with:

support@corporatewebsolutions.in

Permission:

Viewer Access

Step 4:

Copy the shared Google Drive link.

Step 5:

Submit the link through the Google Form.

Status Report Submission Form

Google Form Link: https://forms.gle/RNLyVNgnsbeuffP27

Important Instructions

• All assignments must be completed and submitted on time.

• Students must submit original work only.

• Copying projects from online sources is strictly prohibited.

• Every student must maintain a project progress report.

• All project files must be uploaded to Google Drive before final submission.

• Ensure the shared folder is accessible before submitting the link.

• Late submissions may lead to reduced marks.

• Students should attend all project guidance sessions.