House_Price_Predictor

This project is website to predict the price of a house based on given features' values

Components:

Supervised Regression Model:

• Read dataset and choose most important features using Pandas. Dataset is from kaggle.
• Dataset was splitted into 3 parts:
  • train set will be used to train all models
  • validate set will be used to evaludate each model, and choose the best one
  • test set will be used only once at the end, for the final model chosen
• Using scikit-learn and xgboost libraries, several models where created
• Train each model on the data
• Save models scores over the validation subset, choose best one, then test it using testing subset.
• Save best model as pkl file using pickle library

Prediction:

Using Flask, a simplistic page was created, that inputs house features like area, number of bedrooms & bathrooms... and then predicts the price of the house based on the best model saved.

HTML & CSS files can be found inside /templates and /static respectively

Usage

Below you find all the necessary steps to use or try the project:

• Download Python
• Download this project
• Open the command line inside the project folder to install the required libraries:

  pip install -r requirements.txt

• Run app.py
• Insert the details of the house you wanna estimate its price, then click PREDICT

It is important to note that the dataset is for a specific area, it might not be actually accurate or beneficial for you in the real world.