Lagos House Rent Prediction

The Contributors to this project are:

• Kehinde Olalekan
• Babajide Alao
• Onabanjo Micheal
• Paul Adegbite
• Innocent Alinta

Statement of Purpose

Knowing the rate of inflation in the country at the moment, one needs to be well-informed or kept abreast of the rent prices of properties in various locations in Lagos state, Nigeria. With this project, the aim is to build a machine learning model that helps users to predict the rent price of properties in their chosen locations across Lagos State, Nigeria.

Data Description

The data used in the course of this project was scraped from a real estate website. The scraped data contains over 141,000 observations and 7 features.

Repository Files

The following is a detailed description of the files in the repository.

• Data cleaning - Jupyter notebook of the cleaning process
• Data Scraping - Jupyter notebook of the web scraping process using Beautiful Soup
• Data Wrangling - EDA - Jupyter notebook containing visuals and analysis done on the dataset
• Machine Learning Prediction - Jupyter notebook of the machine learning model
• final_xgboost_model - Pickle file of the machine learning model used in creation of the streamlit app
• model_data.csv - Csv file generated after cleaning the dataset
• newhousing.csv - Csv file of the observation scraped from the website using Beautiful Soup
• newlagosrent.csv - Csv file generated after the scraped file was first cleaned using Microsoft Excel
• app.py - Python file used to create the streamlit app

Tools Used

• Beautiful Soup for Scraping of the data
• Pandas for Accessing and manipulation of the data
• Matplotlib and Seaborn for Visualization and generating insights
• XGBoost for creating a gradient-boosted regression model.
• Streamlit for creating a frontend application
• Microsoft Excel For initial stage of data cleaning

Read the project explanation here

Visit the House Rent App here