Heart Disease Classification using SVM, k-Nearest Neighbors (KNN), and Random Forest

[ayesha-119]

💥 Proposal

I propose implementing multiple machine learning algorithms, including Support Vector Machines (SVM), k-Nearest Neighbors (KNN), and Random Forest, for heart disease classification. These algorithms are known for their effectiveness in handling classification tasks and can provide complementary insights into the dataset.

### Steps:

Dataset: dataset is available on the UCI Machine Learning Repository and contains various clinical attributes for heart disease classification. the link to dataset: https://archive.ics.uci.edu/ml/datasets/heart+Disease

Preprocessing: Handle missing values, normalize numerical features, and encode categorical variables if necessary.

Model Implementation: a. SVM: Use scikit-learn or a similar library to implement the SVM algorithm. Configure hyperparameters (kernel type, regularization parameter) and train the model on the preprocessed dataset. b. KNN: Implement the KNN algorithm using scikit-learn. Experiment with different values of k (number of neighbors) and explore distance metrics for optimal performance. c. Random Forest: Use the scikit-learn library to implement the Random Forest algorithm. Adjust hyperparameters like the number of trees, maximum depth, and feature subsampling to optimize the model.

Model Evaluation: Assess the performance of each algorithm using metrics like accuracy, precision, recall, and F1-score. Compare the results of SVM, KNN, and Random Forest to determine their individual strengths and weaknesses.

Documentation: Document the implementation steps, including dataset preprocessing, algorithm configurations, and performance evaluation results for each algorithm.

[ayesha-119]

@elucidator8918 @khusheekapoor I want to work on this project, I am a girl's script summer of code contributor. Please assign it to me.

[Kota-Karthik]

@elucidator8918 @khusheekapoor I want to work on this project, I am a gssoc'23 contributor. Please assign it to me.

[khusheekapoor]

@ayesha-119 - you can go ahead! We are assigning you 21 days for this project, after which it will be assigned to someone else if not completed. All the best! Name the file as: algorithm_dataset.ipynb and link it in the readme of the labeled directory as algorithm - dataset.

@lcs2022026 - since we are following the first-come-first-serve policy, we will not be able to assign you this issue. However, you can create another issue and use the same algorithms on a different dataset.

[UditSharma9999]

I'm a contributor for GSSoC '23, I want to work on this issue. Please assign me.

[impeccable16]

i want to work on this issue