KENNYDGREAT2/Forecast-Lagos-House-Rent

The aim of this project 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. # Forecast-Lagos-House-Rent ### 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 contained 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 Visit the House Rent App here