# Problem Statement

The problem we're tackling here is simple to understand yet challenging to solve: **How can we most accurately predict the selling price of a house based on a variety of features such as its size, location, number of bedrooms, etc.?**

In formal terms, we're aiming to build a predictive model that maps features to a house's selling price . Mathematically, this can be represented as:

For the purpose of this project, we'll assume that you have access to a dataset that contains information about house sales, including various features (e.g., square footage, number of bedrooms, neighborhood) and the actual selling price .

Example Code: Importing the Dataset

To get started, let's assume you're using Python and the pandas library to import your dataset.

# Importing the necessary libraries

import pandas as pd

# Loading the dataset

df = pd.read_csv('house_prices.csv')

# Display the first few rows of the DataFrame

df...