"Predict house prices with precision using Linear Regression. Leverage EDA for insightful data exploration. A data-driven solution for informed real estate decisions."
Dataset - Features(Columns) Description
'price': The price of the house (target variable).
'area': The area or size of the house in square feet.
'bedrooms': The number of bedrooms in the house.
'bathrooms': The number of bathrooms in the house.
'stories': The number of stories or floors in the house.
'mainroad': Categorical variable indicating whether the house is located near the main road or not.
'guestroom': Categorical variable indicating whether the house has a guest room or not.
'basement': Categorical variable indicating whether the house has a basement or not.
'hotwaterheating': Categorical variable indicating whether the house has hot water heating or not.
'airconditioning': Categorical variable indicating whether the house has air conditioning or not.
'parking': The number of parking spaces available with the house.
'prefarea': Categorical variable indicating whether the house is in a preferred area or not.
'furnishingstatus': The furnishing status of the house (e.g., unfurnished, semi-furnished, fully furnished).