TobilobaOk/Predicting-Nigeria-House-prices

# Predicting-Nigeria-House-prices This documentation outlines the development and evaluation of a Ridge regression model to predict Nigeria house prices using real-life data. The model is evaluated using Mean Absolute Error (MAE) as the primary performance metric. # Problem Statements: • The goal is to create a model that can predict house prices in Nigeria using machine learning (Ridge_Regression_Model) • Real estate agents, buyers, and investors needs have access to market-driven pricing that can assist them in making informed decisions. • The model takes values of property features such as numbers of bedrooms, bathrooms, parking space and various house type to predict apartment prices in Nigeria. # Data Description This dataset contains Houses listings in Nigeria and their prices based on Location and other parameters such as: • bedrooms: number of bedrooms in the houses • bathrooms: number of bathrooms in the houses • toilets: number of toilets • parking space • title: house type # Model Training **Import Statements and Dataset** To begin with, the necessary libraries and modules are imported, and the dataset is loaded into the model. import pandas as pd import seaborn as sns import matplotlib.pyplot as plt import numpy as np from sklearn.model_selection import train_test_split from sklearn.metrics import mean_absolute_error from sklearn.linear_model import LinearRegression, Ridge from sklearn.impute import SimpleImputer from sklearn.pipeline import make_pipeline from category_encoders import OneHotEncoder from sklearn.utils.validation import check_is_fitted from ipywidgets import Dropdown, FloatSlider, IntSlider, interact import streamlit as st import pickle # Data Splitting The independent variables (X) for the model are: "Bedrooms", “Parking Space”, “Title” columns, while the dependent variable (y) was the "Price X (features): Independent Variables y (target): House Price The data is split into training and test sets, with an 80%/20% ratio for trai …