In this project, we aim to build a machine learning model to predict whether a house will sell or not based on a set of features. This is a binary classification problem where the target variable is a binary variable indicating whether the house sold or not. We will analyze and visualize the data to gain insights into the features that influence house sales.
The dataset used in this project contains information about various houses including the number of rooms, hospital beds, hotel rooms, parks, rainfall, the location of the house, and other important features. The dataset also includes a binary variable indicating whether the house sold or not.
We will use a supervised learning approach to train a binary classification model using various machine learning algorithms such as logistic regression, decision trees, and random forests. We will evaluate the performance of each model using metrics such as accuracy, score. The best-performing model will be selected and used for prediction.
The trained machine learning model will be used to predict whether a house will sell or not based on the given set of features. The results will be presented in the form of a interactive widgets in jupyter notebook that can be used by technical audience. The end output will help real estate agents and homeowners to make informed decisions about selling their properties.
