The task is to create a predictive model for house prices using the available independent variables. The primary goal is to help the management gain insights into how house prices are influenced by various factors. With this understanding, the management can make informed decisions to optimize their strategies and focus on areas that offer the highest returns on investment.
The model will provide valuable information on the relationships between different variables and house prices, enabling the management to identify key drivers of pricing dynamics in the housing market. By leveraging this knowledge, the firm can adapt its business approach to suit market conditions and seize opportunities in a new market.
Overall, the predictive model acts as a powerful tool for the management to make data-driven decisions, anticipate market trends, and set competitive pricing strategies. It empowers the firm to gain a competitive advantage and thrive in the real estate industry by maximizing profits and effectively navigating the complexities of the housing market.
- This Module is implementation of House Price Prediction using Regularization technique. i.e. Lasso Regression and Ridge Regression.
- build a regression model using regularisation in order to predict the actual value of the prospective properties and decide whether to invest in them or not.
- Which variables are significant in predicting the price of a house, and
- How well those variables describe the price of a house.
- Data Description
- Data has details about the House, i.e. basic house details, locality , neighbourhood, Amenities and Area along with Price.
- There are total 1460 rows and 81 variables
- Data contains mixed variables, few categorical and few numerical.
- ['GrLivArea', 'Functional_Typ', 'OverallQual_Very Good', 'CentralAir_Y', 'MSSubClass_1-STORY PUD (Planned Unit Development) - 1946 & NEWER', 'Neighborhood_Somerst', 'TotalBsmtSF', 'Exterior2nd_Wd Sdng', 'MSSubClass_1-STORY 1946 & NEWER ALL STYLES', 'GarageType_Attchd'] plays important role in describing price of the house
- If the home functionality is typical, then the price of house will increase by 1.09 to 1.10 times
- If the overall material and finish of the house is Very Good or Excellent, the price of house will increase by 1.09 to 1.10 times
- If Somerst is a nearby location, then the price of house will increase by 1.04 to 1.05 times
- sklearn
- pandas
- numpy
- matplotlib
- seaborn
Created by kritipawar@github.com - feel free to contact me!