Rent Prediction Flask App

This project is a Flask web application that predicts apartment rent prices based on input features such as the number of bedrooms, bathrooms, and location. The app uses a pre-trained machine learning model (Random Forest Regressor) to make predictions, with the backend written in Python and the model built using scikit-learn.

Features

• Predict rent prices for apartments based on the following factors:
  • Number of Bedrooms
  • Number of Bathrooms
  • Number of Toilets
  • Apartment Status: Newly Built, Furnished, and/or Serviced
  • City
• Simple and intuitive web interface for users to input data.
• Scalable Flask backend that processes the data and returns predictions.

Technologies Used

• Flask: Web framework for building the API.
• Python: Backend programming language.
• scikit-learn: Machine learning library used to train the model.
• Pandas: Data manipulation and analysis library.
• NumPy: Library for numerical operations.
• Jinja: Template engine for rendering HTML.

Setup Instructions

Prerequisites

Before you begin, ensure you have the following installed on your machine:

• Python 3.x
• pip

Installation

1. Clone the repository:

   git clone https://github.com/Tonycrux/rent_calculator.git
   cd rent_calculator