Heart Disease Prediction Using Machine Learning (Logistic Regression) 🫀💻 📌 Project Overview This project predicts the risk of heart disease in patients based on various health parameters using Logistic Regression. The model is trained on the Framingham Heart Study dataset, which contains features such as age, cholesterol levels, blood pressure, smoking status, and diabetes history.

🗂️ Dataset Dataset Name: Framingham Heart Study(file attached) Source: Kaggle Target Variable: TenYearCHD (0 = No Heart Disease, 1 = Risk of Heart Disease) 🔧 Technologies Used Google Colab (for cloud-based model training) Python Libraries: Pandas, NumPy, Scikit-Learn, Matplotlib, Seaborn 📊 Machine Learning Model Used Logistic Regression (Binary Classification Model) 🛠️ Installation & Usage 1️⃣ Open in Google Colab Click the button below to open the notebook: 2️⃣ Load the Dataset in Google Colab from google.colab import files uploaded = files.upload() 3️⃣ Install Dependencies !pip install pandas numpy scikit-learn matplotlib seaborn 4️⃣ Run the Model import pandas as pd from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score

Load the dataset

df = pd.read_csv("framingham.csv")

Data Preprocessing

df.fillna(df.mean(), inplace=True)

Splitting into features and target

X = df.drop("TenYearCHD", axis=1) y = df["TenYearCHD"]

Train-test split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Logistic Regression Model

model = LogisticRegression() model.fit(X_train, y_train)

Predictions & Accuracy

y_pred = model.predict(X_test) accuracy = accuracy_score(y_test, y_pred) print(f"Model Accuracy: {accuracy * 100:.2f}%") 📈 Model Performance Logistic Regression Accuracy: ~85% Model trained on Google Colab using scikit-learn. 📌 Future Improvements Tune hyperparameters for better performance. Experiment with other models (Random Forest, SVM, Deep Learning). Deploy the model using Flask/Streamlit for real-world use. 📩 Contact GitHub: Vishalchau2003