enamablanky/Wazobia-Real-estate-Project

Wazobia Real Estate Limited is a prominent real estate company operating in Nigeria. However, they have been facing challenges in accurately predicting the prices of houses in the current market. To overcome this hurdle, Wazobia Real Estate Limited is seeking the expertise of data scientists like you to develop a robust predictive model. # Wazobia-Real-estate-Project --- # **Project Title:** **Housing Price Prediction Using Machine Learning** --- ## **1. Project Overview** This project focuses on **predicting housing prices** based on features such as location, number of rooms, size, and amenities. The goal is to develop a machine learning model that can estimate house prices with high accuracy, enabling better decision-making for buyers, sellers, and real estate investors. --- ## **2. Problem Statement** Housing prices are influenced by multiple factors, including **location, size, number of bedrooms, and additional amenities**. Accurately predicting prices is challenging due to data variability and correlations among features. The objective is to: 1. **Analyze factors** affecting house prices. 2. Build a **predictive model** to estimate prices based on input features. 3. Evaluate model performance to ensure reliability and scalability. --- ## **3. Dataset Description** - **Features:** 1. **Location** – Geographical area or city sector. 2. **Size (Square Footage)** – Total area of the property. 3. **Number of Bedrooms** – Count of bedrooms in the house. 4. **Number of Bathrooms** – Count of bathrooms. 5. **Amenities** – Features such as garden, pool, or parking. 6. **Year Built** – Construction year of the property. 7. **Price** – Target variable representing the house price. - **Data Cleaning Tasks:** - Handling **missing values** in features like size and amenities. - Converting **categorical data** (location) to numerical values using encoding. - Detecting and removing **outliers** in features like price and size. --- ## **4. Approach & Methodology** ### **Step 1: Data Exploration and Preprocessing** - **Exploratory Data Analysis (EDA):** - Visualized relationships between features using **scatter plots**, **pair plots**, and **histograms**. - Identified correlations with **heatmaps** to find the most influential factors. - **Data Cleaning:** - Handled missing data with **imputatio …