Ayyodeji/Nigeria-House-Price-Prediction

House Price Prediction in Nigeria # Nigeria House Price Prediction This repository contains a Jupyter Notebook for predicting house prices in Nigeria. The notebook implements various machine learning algorithms, including K-Nearest Neighbors (KNN), Naive Bayes, and Support Vector Machines (SVM) for classification. The goal of this project is to analyze the given dataset and build models to predict whether a house price is above or below the median price. ## Project Overview In this project, the Nigeria House Price Prediction.ipynb notebook explores and preprocesses the house price dataset. It then builds and evaluates different classification models, including KNN, Naive Bayes, and SVM, to predict whether a house price is above or below the median price. The project aims to provide insights into the factors influencing house prices in Nigeria and develop accurate prediction models. ## Dataset The dataset used in this project is provided as `nigeria_houses_data.csv`. It contains information about house features, such as the number of bathrooms, bedrooms, toilets, and parking spaces, along with the corresponding house prices. The notebook preprocesses the dataset by transforming the features and splitting the dataset into training and testing sets. ## Repository Structure - `Nigeria House Price Prediction.ipynb`: The Jupyter Notebook containing the code for data preprocessing, model building, and evaluation. - `nigeria_houses_data.csv`: The dataset file used for house price prediction. ## Usage To use this project, follow these steps: 1. Clone the repository: git clone github.com cd Nigeria-House-Price-Prediction 2. Open the Jupyter Notebook: jupyter notebook 3. Open the provided Jupyter Notebook `Nigeria House Price Prediction.ipynb`. 4. Follow the instructions in the notebook to explore the dataset, preprocess the data, build and evaluate the KNN, Naive Bayes, and SVM models. ## Dependencies The project requires the following depende …