Jupyter Notebook of Machine Learning House Price Prediction

This repository is containing Jupyter Notebook file that can make easy to explore and understand what we do and what we process to our datasets. The datasets in this repository is gathered from Kaggle by Cam Nugent

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

Prequisites packages:

• Python 3
• Visual Studio Code with Extensions Installed
  • Python
  • Jupyter Notebook

Preparation

1. Create your own Virtual Environment using Python 3 VEnv Module

   python3 -m venv MyWorkspace

2. Clone this repository as src directory under MyWorkspace directory that created by Python 3 Virtual Environment

   cd MyWorkspace
   git clone https://github.com/dimaskiddo/jupyter-price-prediction-house.git src

3. Activate the Virtual Environment and Update PIP

   source bin/activate
   pip install --no-cache-dir --upgrade pip setuptools wheel

4. Install Dependencies Library for Data Science

   pip install --no-cache-dir numpy pandas scikit-learn matplotlib seaborn

5. Install Jupyter Notebook Kernel

   pip install --no-cache-dir ipykernel ipython

Exploration

1. Open the Visual Studio Code with MyWorkspace directory and then select src/Notebook.ipynb file

2. At the top of Visual Studio Code select Jupyter Notebook Kernel to Current Python Environment

3. After some auto adjustment made by Visual Studio Code you can start or run Python code from the Jupyter Notebook