Project Contributors

Ansh gathered all the important data from a website called Kaggle. He ensured the data was accurate by identifying and correcting any anomalies or errors. Ansh then taught the computer to make predictions about house prices using a method called Linear Regression. Additionally, he analyzed the data to discover interesting insights about houses and prices, extracted important features to enhance data efficiency, and performed Exploratory Data Analysis (EDA). To make the predictions accessible, Ansh built a special website component using a Flask server.

Arman made our project look appealing on the internet! He created a cool home page where you can see our predictions and interact with them. Arman also designed a webpage to inform people about our project and ensured it's user-friendly for anyone accessing it on their computers. Additionally, he created a page on our website to introduce the team members who worked on the project. Arman gathered helpful information and resources and developed a webpage for those interested in learning more about predicting real estate prices. He's the one who made everything look good and work smoothly.