Apartment Rent Prediction & Classification Model

Overview

This repository contains a machine learning model designed to predict apartment rental prices based on various features. The model utilizes a dataset with information on apartment listings including features such as number of bathrooms, bedrooms, amenities, location, and more.

Dataset

The dataset used for training and testing the model consists of the following features:

• id: Unique identifier for each apartment listing.
• category: Category of the listing.
• title: Title of the listing.
• body: Description of the listing.
• amenities: Amenities available in the apartment.
• bathrooms: Number of bathrooms in the apartment.
• bedrooms: Number of bedrooms in the apartment.
• currency: Currency used for the price.
• fee: Any additional fees associated with the rental.
• has_photo: Boolean indicating whether the listing has photos.
• pets_allowed: Boolean indicating whether pets are allowed in the apartment.
• price: Rental price.
• price_display: Display of the rental price.
• price_type: Type of the rental price.
• square_feet: Size of the apartment in square feet.
• address: Address of the apartment.
• cityname: City where the apartment is located.
• state: State where the apartment is located.
• latitude: Latitude coordinate of the apartment location.
• longitude: Longitude coordinate of the apartment location.
• source: Source of the listing.
• time: Timestamp of the listing.
• RentCategory: Type of rent price

Usage

1. Data Preprocessing: Before using the model, preprocess the dataset to handle missing values, encode categorical variables, and scale numerical features.
2. Model Training: Train the model using suitable algorithms such as linear regression, decision trees, or neural networks. Tune hyperparameters as necessary.
3. Model Evaluation: Evaluate the trained model's performance using appropriate metrics such as mean absolute error, mean squared error, or R-squared.
4. Prediction: Use the trained model to predict rental prices for new apartment listings based on their features.

Dependencies

Ensure you have the following dependencies installed:

• Python (version 3.x)
• NumPy
• Pandas
• Scikit-learn
• Matplotlib (for visualization)