https://testmlmodel.herokuapp.com/

Predicting House Prices in Bengaluru

Data:

• The train and test data will consist of various features that describe that property in Bengaluru.
• Each row contains fixed size object of features. There are 9 features and each feature can be accessed by its name.

Features:

1. Area_type – describes the area
2. Availability – when it can be possessed or when it is ready(categorical and time-series)
3. Location – where it is located in Bengaluru (Area name)
4. Size – in BHK or Bedroom (1-10 or more)
5. Society – to which society it belongs
6. Total_sqft – size of the property in sq.ft
7. Bath – No. of bathrooms
8. Balcony – No. of the balcony

Target variable:

• Price – Value of the property in lakhs(INR)

Train dataset:

• Contains all the features and target variable.
• Contains 13,321 records.

Test dataset:

• Contains all the features.
• Contains 1,481 records.

Problem statement:

• With the given 9 features(categorical and continuous) build a model to predict the price of houses in Bengaluru.