NegasiHaile / Data-Science-Projects

Explore my diverse collection of projects showcasing machine learning, data analysis, and more. Organized by project, each directory contains code, datasets, documentation, and resources. Dive in to discover insights and techniques in data science. Reach out for collaborations and feedback.

Data Science Projects

Welcome to my Data Science Projects Repository! This repository contains a collection of my data science projects, showcasing my skills and expertise in the field. Each project demonstrates different aspects of data analysis, machine learning, and visualization.

Projects

1. Breast Cancer Prediction
   • Description: The project predicts the diagnosis (M = malignant, B = benign) of the Breast Cancer
   • Technologies Used: The notebooks uses Decision Tree Classification and Logistic Regression
   • Results: The logistic regression gave 97% accuracy and decision tree gave 93.5% accuracy
2. Red Wine Quality Prediction
   • Description: The project predicts the quality of the wine in the value 0 or 1. 1 for good quality and 0 for bad quality
   • Technologies Used: The notebooks uses logistic regression, support vector machine, decision tree and knn
   • Results: The logistic regression model performs the best with accuracy of 86.67%
3. Heart Stroke Prediction
   • Description: The project predicts the risk of heart stroke on studying the person's demographics and medical info
   • Technologies Used: The notebooks uses logistic regression, support vector machine, decision tree and knn
   • Results: The logistic regression, SVM and KNN performs the best with 93.8 % accuracy
4. House Price Prediction
   • Description: The project predicts the house price after studying the variables such as location, area, bredroom, bathroom count and many more.
   • Technologies Used: The notebooks uses Linear Regression, Ridge Regression and Random Forest Regressor
   • Results: The Random Forest Regressor performed best with accuracy of 87.89%
5. Titanic Survival Prediction
   • Description: The project predicts the survival during the titanic disaster based on socio-economic measures
   • Technologies Used: The notebooks uses Descision Tree Classifier
   • Results: The Decision Tree Classifer performed well on the test dataset with an accuracy of 89.5%
6. Diamond Price Prediction
   • Description: The project predicts the price (in US dollars) of the diamonds based on their features
   • Technologies Used: The notebooks uses Descision Tree Regressor and Random Forest Regressor
   • Results: The Decision Tree Regresor performed well on the test dataset with an accuracy of 96%
7. Diabetes Prediction
   • Description: The project predicts the diabetes status of a patient based on the provided dataset features
   • Technologies Used: Random Forest, Decision Tree, XgBoost classifier, and Support Vector Machine (SVM) are used.
   • Results: The Decision Tree RegresorRandom Forest method performed well on the test dataset with an accuracy of 76%