House Price Prediction

Introduction

This project aims to predict house prices using machine learning,The project is divided into several phases, each addressing a different aspect of the AI development process.

Design Thinking (Phase 1)

• Design Thinking Document (AI_Phase1.pdf)

Machine Learning (Phase 2)

• Machine Learning Document (AI_Phase2.pdf)

Devlopment phase 1

Model Building (Phase 3)

• Model Building (AI_Phase3.ipynb)

Devlopment phase 2

Testing and evaluation (Phase 4)

• Model testing and evaluation (AI_Phase4.docx)

Final submission

Documentation (Phase 5)

• Project Documentation (AI_Phase5.docx)

Code file

• Complet code file(AI_Phase5.ipynb)

Requirements

Before running the project code, make sure you havej uputer notebook and the following packages installed:

pip install pandas numpy scikit-learn xgboost matplotlib seaborn

If you dont have the jupyter notebook you can use a anaconda framework that has a jupyter notebbok .

how to run code

Before you need to clone our repositary

Clone this GitHub repository:

it is done by following command.

git clone https://github.com/gokul5557/IBM_project.git

Then navigate to the directory.

Navigate to the project directory:

cd IBM_project

Open the Jupyter Notebook for the code phase:

jupyter notebook AI_Phase5.ipynb

if you have anaconda framework then open anaconda prompt then type following command.

jupyter notebook

This command open the jupyter notebook on your browser with the current directory

Follow the instructions in the notebook to run the code.

you can run the code by pressing "ctrl + Enter" or you can run it by pressing the run button at the top below title bar .

Final Documentation

For complete project documentation, refer to the Project Documentation (AI_Phase5.docx) file. That is the final document that has full detail about our project. with code and output and screen shot of output of our code.