U.S.A Apartments Rental Prediction and Classification with Machine Learning

This project explores the use of Machine Learning (ML) and Deep Learning (DL) models to predict apartment rental prices in the U.S. based on various features. In addition to regression-based price prediction, the project also includes a classification task that categorizes apartments into three pricing tiers: low, average, and high.

The dataset used is well-researched and has been analyzed by many others. While I drew inspiration from existing analyses (especially from https://www.kaggle.com/code/harleenkaurvt1930/apartment-rent-data-regression-classification/notebook), this project also incorporates novel approaches and more advanced techniques—most notably, the implementation of Neural Networks for both regression and classification tasks and the use of optimization algorithms.

Overview of the Project

• Objectives:

  • Perform EDA to find insights about features that affect the rental price of the listings.
  • Filter, and transform the data.
  • Create Pre-processing pipelines to transform the data and make them suitable for machine learning models.
  • Dimensionality Reduction.
  • Feature Engineering.
  • Perform Regression models with the objective to predict the price.
  • Perform Classification models to classify the listings.
  • Optimization of the models.
  • Regression and Error Analysis

• Methodology:

  • Data Preperation
  • Data exploration and preprocessing
  • Regression Analysis
  • Classification Analysis
  • Interpretation and meaningful insights

Findings and more in depth analysis

All that is included in the report file.

How to Run the Code

1. Download the dataset from the kaggle link that is in the Dataset Description file.
2. Download the notebook and run it in Google Colab or IDE of your preference.
3. Run this command to install all the necessary packages if you don't have them already.

   pip install numpy pandas matplotlib seaborn scikit-learn xgboost tensorflow prettytable scipy ydata-profiling