Eddiddi/Prediction-of-House-Prices-in-Nigeria

# Prediction-of-House-Prices-in-Nigeria ## Project Overview This project analysis and predicts house prices in Nigeria. ### 1. Exploratory Data Analysis (EDA) - **Notebook:** `1_Exploratory_Data_Analysis.ipynb` - **Description:** This notebook combines Python and SQL to perform exploratory data analysis on the house price dataset. The goal is to understand the distribution of prices across different states, identify trends, and detect any anomalies or outliers. Key steps include data cleaning, visualization, and SQL queries to extract meaningful insights from the data. - **Tools Used:** - Python (Pandas, Seaborn, Matplotlib) - SQL (via `pandasql` or `pysqldf`) ### 2. Model Prediction - **Notebook:** `2_Model_Prediction.ipynb` - **Description:** This notebook focuses on building and evaluating machine learning models to predict house prices based on features such as the state, number of rooms, bathrooms, parking space, and toilets. Several models are tested, including Linear Regression, Ridge Regression, Lasso Regression, Elastic Net, Decision Trees, Random Forest, Gradient Boosting, SVR, and KNN. The models are evaluated based on Mean Squared Error (MSE) and R² Score, and the best model is selected for final predictions. - **Tools Used:** - Python (Scikit-learn, Seaborn, Matplotlib) - Machine Learning Algorithms