Heart Disease Prediction using Machine Learning(DL)

[KamakshiOjha]

Project Title : Heart Disease Prediction using Machine Learning(DL)

Aim: Compare neural network and random forest algorithms to determine the best model for heart disease prediction using accuracy scores.

Approach : I will use neural network and random forest algorithms to implement models, perform exploratory data analysis (EDA), and compare their accuracy scores to find the best-fitted algorithm for heart disease prediction.

Approach for this Project :

1. Problem Understanding and Data Collection
   • Define the Problem: Predict the likelihood of heart disease using patient data.
   • Data Collection: Use the Cleveland Heart Disease dataset from the UCI Machine Learning Repository.
2. Data Preparation Data Loading: Load the dataset using Pandas. Data Exploration: Conduct exploratory data analysis (EDA) to understand data distribution, handle missing values, and identify important features. Data Preprocessing:
   • Normalize or standardize the data.
   • Split the dataset into training and testing sets.
3. Model Building
   • Define the Model Architecture: Utilize Conv1D and dense layers as specified.
   • Compile the Model: Set the optimizer, loss function, and metrics.
   • Train the Model: Fit the model on the training data and validate it with testing data.
4. Model Evaluation
   • Evaluate the Model: Assess performance using metrics like accuracy, precision, recall, and F1-score.

Please assign me this issue under GSSOC.

[KamakshiOjha]

Please assign me this issue under GSSOC.