House Price Prediction using Machine Learning

This project aims to predict house prices using machine learning techniques. We will utilize a dataset containing various features of houses and their corresponding sale prices. By building and training machine learning models, we will predict house prices with the best possible accuracy.

• Project Overview
• Dataset
• Project Structure
• Getting Started
• Data Preprocessing
• Exploratory Data Analysis
• Model Building
• Model Evaluation
• Results
• Usage
• Contributing
• License

In this project, we will:

• Utilize machine learning algorithms to predict house prices.
• Perform data preprocessing and feature engineering to prepare the data.
• Analyze the dataset through exploratory data analysis.
• Train and evaluate different machine learning models.
• Choose the best model and fine-tune its hyperparameters for optimal performance.

We will be using the 'house_price_prdiction' dataset for this project. The dataset contains various features such as the number of bedrooms, bathrooms, square footage, and more, as well as the corresponding sale prices. You can download the dataset from the provided link.

The project structure is organized as follows:

• data/: Contains the dataset file(s).
• notebooks/: Jupyter notebooks for data preprocessing, exploratory data analysis, and model building.
• models/: Saved machine learning models.
• src/: Custom Python modules and scripts.
• README.md: This README file.
• requirements.txt: List of project dependencies.

1. Clone this repository:

git clone https://github.com/your-username/house-price-prediction.git
cd house-price-prediction