In this repository I will keep my projects that I used for machine learning.
FIRST PROJECT - Rock vs Mine
SECOND PROJECT - Boston House price Prediction
THIRD PROJECT - Diabetes Prediction
FOURTH PROJECT - Gold Prediction System
FIFTH PROJECT - HEART DISEASE Prediction System
In this repository, I present my machine learning projects:
Rock vs Mine:
- Classification model to predict if an object is rock or mine
- Uses sonar signal data, preprocessed and visualized using Python libraries
- Achieved an accuracy of 86% on test data
Boston House Price Prediction:
- Regression model to predict house prices in Boston area
- Uses Boston Housing dataset, preprocessed and visualized using Python libraries
- Achieved a root mean squared error (RMSE) of 4.7 on test data
Diabetes Prediction:
- Classification model to predict diabetes diagnosis
- Uses Pima Indians Diabetes dataset, preprocessed and visualized using Python libraries
- Achieved an accuracy of 80% on test data
Gold Prediction System:
- Time series forecasting model to predict gold prices
- Uses historical gold prices data, preprocessed and visualized using Python libraries
- Achieved a mean absolute percentage error (MAPE) of 4% on test data
Heart Disease Prediction System:
- Classification model to predict heart disease diagnosis
- Uses Cleveland Heart Disease dataset, preprocessed and visualized using Python libraries
- Achieved an accuracy of 88% on test data