Introduction:

The housing market reflects the economy, and housing price ranges are of great interest to buyers and sellers. The purpose of this project is to predict house prices using explanatory variables covering many aspects of residential housing. This project aims to build a regression model that will estimate the house price accurately based on its features.

For that we've to form a regression model that's able to accurately estimate the value of the house given the options and to reduce the distinction between expected and actual rating (RMSE). Almost all aspects of residential homes in Ames, Iowa, USA areexplained by 79 variables (features).

User Requirements

• Hardware requirements:
  1. Operating system- Windows 7,8,10
  2. Processor- Dual core 2.4 GHz (i5 or i7 series Intel processor or equivalent AMD)
  3. RAM - 4GB
• Software Requirements:
  1. Python 3.8
  2. PyCharm
  3. PIP
  4. Jupyter Notebook
  5. Anaconda
  6. Chrome
  7. Cloud Environment to Deploy Model

Unresolved Issues:

Due to lack of technical requirements we will be assuming the ideal parameters of the XGBoost Regressor.

Analysis of Work Done and Design

Goals:

• To develop an efficient and effective model which predict the sale price for each house.
• To achieve good accuracy by minimizing the difference between predicted and actual rating (RMSE/MSE)

Data & Attributes:

We have used a data set uploaded by Dean De Cock on Kaggle for a competition. You can download the data set from this link https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques/data It consists of four files, namely as:

• data_desription.txt
• sample_submission.csv
• test.csv
• train.csv

The dataset contains 2919 records (rows) and 80 features (columns) (including ‘SalePrice’ column of the train dataset). data_description.txt consists of the information about all the features listed in the train and test csv files. These features are:

FEATURE	DESCRIPTION
MSSubClass	The type of the house involved in the sale of the property
MSZoning	The general zoning classification of the sale of the property
LotFrontage	Linear feet ofstreet connected to the house
LotArea	Lot size in square feet
Street	Type of road access to the house
Alley	Type of alley access to the house
LotShape	General shape of the house
LandContour	House flatness
Utilities	Type of utilities available
LotConfig	Lot configuration
LandSlope	House Slope
Neighborhood	Locations within Ames city limits
Condition1	Proximity to various conditions
Condition2	Proximity to various conditions (if there is more than one)
BldgType	House type
HouseStyle	House style
OverallQual	Overall quality of material and finish of the house
OverallCond	Overall condition of the house
YearBuilt	Construction year
YearRemodAdd	Remodel year (if no remodeling nor addition, same as YearBuilt)
RoofStyle	Roof type
RoofMatl	Roof material
Exterior1st	Exterior covering on house
Exterior2nd	Exterior covering on house (if there is more than one material)
MasVnrType	Type of masonry veneer
MasVnrArea	Masonry veneer area in square feet
ExterQual	Quality of the material on the exterior
ExterCond	Condition of the material on the exterior
Foundation	Foundation type
BsmtQual	Basement height
BsmtCond	Basement Condition
BsmtExposure	Refers to walkout or garden level walls of the property
BsmtFinType1	Rating of basement finished area
BsmtFinSF1	Type 1 finished square feet
BsmtFinType2	Rating of basement finished area (if there are multiple types)
BsmtFinSF2	Type 2 finished square feet
BsmtUnfSF	Unfinished basement area in square feet
TotalBsmtSF	Total basement area in square feet
Heating	Heating type
HeatingQC	Heating quality and condition
CentralAir	Central air conditioning
Electrical	Electrical system type
1stFlrSF	First floor area in square feet
2ndFlrSF	Second floor area in square feet
LowQualFinSF	Low quality finished square feet in all floors
GrLivArea	Above-ground living area in square feet
BsmtFullBath	Basement full bathrooms
BsmtHalfBath	Basement half bathrooms
FullBath	Full bathrooms above ground
HalfBath	Half bathrooms above ground
Bedroom	Bedrooms above ground
Kitchen	Kitchens above ground
KitchenQual	Kitchen quality
TotRmsAbvGrd	Total rooms above ground (excluding bathrooms)
Functional	Home functionality
Fireplaces	Number of fireplaces
FireplaceQu	Fireplace quality
GarageType	Garage location
GarageYrBlt	Year garage was built in
GarageFinish	Interior finish of the garage
GarageCars	Size of garage (in car capacity)
GarageArea	Garage size in square feet
GarageQual	Garage quality
GarageCond	Garage condition
PavedDrive	How driveway is paved
WoodDeckSF	Wood deck area in square feet
OpenPorchSF	Open porch area in square feet
EnclosedPorch	Enclosed porch area in square feet
3SsnPorch	Three season porch construction space in sq. feet
ScreenPorch	Screen porch area in square feet
PoolArea	Pool area in square feet
PoolQC	Pool quality
Fence	Fence quality
MiscFeature	Miscellaneous feature
MiscVal	Value of miscellaneous feature
MoSold	Sale month
YrSold	Sale year
SaleType	Sale type
SaleCondition	Sale condition

Code is uploaded and accessible for knowledge purpose!

Conclusions:

With the increase in the demand for the houses and lack of proper evaluation of the prices of those housed, the demand for automated prediction models is increased. The House Price Prediction system which effectively determines the worthiness of the house using a variety of features is useful not only for real-estate companies but also for individual buyers who are on the lookout for their dream home. The proposed system will help to determine the near-accurate price of the houses.

Bibliography:

• Kaggle - https://www.kaggle.com/
• Google - https://www.google.com/
• Matplotlib - https://matplotlib.org/
• Seaborn - https://seaborn.pydata.org/
• Scikit-learn - https://scikit-learn.org/stable/
• Analytics Vidya - https://www.analyticsvidhya.com/blog/2020/12/improve-predictive-model-scorestacking-regressor/