https://www.machinehack.com/course/predicting-house-prices-in-bengaluru/
- 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.
- Area_type – describes the area
- Availability – when it can be possessed or when it is ready(categorical and time-series)
- Location – where it is located in Bengaluru (Area name)
- Size – in BHK or Bedroom (1-10 or more)
- Society – to which society it belongs
- Total_sqft – size of the property in sq.ft
- Bath – No. of bathrooms
- Balcony – No. of the balcony
- Price – Value of the property in lakhs(INR)
- Contains all the features and target variable.
- Contains 13,321 records.
- Contains all the features.
- Contains 1,481 records.
With the given 9 features(categorical and continuous) build a model to predict the price of houses in Bengaluru.
- Root-Mean-Squared-Error (RMSE) between the logarithm of the predicted price value and the logarithm of the observed sales price.
- Taking logs means that errors in predicting expensive houses and cheap houses will affect the result equally.
.
├── code
│ └── Data_preparation_and_Analysis.ipynb
├── input
│ ├── sample_submission.xlsx
│ ├── test.csv
│ └── train.csv
├── README.md
└── submissions