Heart Disease prediction using 5 algorithms
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Updated
Sep 14, 2023 - Jupyter Notebook
Heart Disease prediction using 5 algorithms
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Various data structure implementations in Python
This repo is the Machine Learning practice on NHANES dataset of Heart Disease prediction. The ML algorithms like LR, DT, RF, SVM, KNN, NB, MLP, AdaBoost, XGBoost, CatBoost, LightGBM, ExtraTree, etc. The results are good. I also explore the class-balancing (SMOTE) because the original dataset contains only 5% of patient and 95% of healthy record.
Practice dataset for regression or classification modelling
MDBOT (Graduation Project)
model to predict the survival in the Titanic disaster, with 98.8 accuracy.
This is python for DS and ML bootcamp
Panads,Numpy, Scikit learn, Keras, ML Libraries
Decision tree algorithm demonstration
This is the second project in University of Tehran College of Engineering AI course.
Diabetes Dataset This dataset is originally from the N. Inst. of Diabetes & Diges. & Kidney Dis.
This project implements a Disease Prediction System using various machine learning algorithms to predict potential diseases based on user-provided symptoms. The system utilizes a Django web framework to provide a user-friendly interface for inputting symptoms and viewing the predicted disease.
Machine Learning algorithms implemented from scratch. Different ML algorithms implemented from scratch. Namely, Multipile Linear Regression and Logistic Regression KNN and Naive Bayes Recrusive Decision Trees Bagging and Boosting (Ada Boost) Yarwosky's Algorithm and Agglomerative Hierarchical Clustering
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