This repository contains a Jupyter Notebook demonstrating a practical example of data science and machine learning for heart disease classification.

Obese-tree is a GitHub repository showcasing the application of a Support Vector Machine (SVM) model to estimate obesity levels based on eating habits and physical condition. Explore the code, data, and Jupyter notebooks to learn how SVM can be used for predictive modeling in the context of health and wellness.

Build machine learning model to predict whether a house will sell or not based on a set of features. The results will be presented in the form of interactive widgets in jupyter notebook for technical audience that can be used to make informed decision about selling their properties.

In this series of notebooks, we will dive into each step of the data analysis process of a data set with some information about a list of cars and several attibutes, including their prices. So essentially we will develop a model to predict cars price.

This repository contains different machine learning classification algorithms based on different data. All notebooks include Data Preparation (data fetch, filling missing values, feature engineering), Exploratory Data Analysis (using various visualization techniques), Pre-Processing, Building a Machine Learning Model, Model Evaluation.

Machine Learning notebooks for refreshing concepts.

Classifying badminton strokes based on accelorometer and gyroscope sensor data attached to player's wrist. An end-to-end Machine Learning project, from data collection and preprocessing to final model evaluation.