The dataset used is an anonymized dataset of different properties in Dubai. The dataset contains the size, number of bedrooms, number of bathrooms, neighbourhood name, and building name and the listing price for different properties in Dubai. Dataset is not shared here due to privacy issues.
- Build a Property Price Prediction Model.
a. Explore the dataset, and share with us any insights that you may find which can help you create the price evaluation tool. Please summarise your findings in terms of the relationship between the different features, the price, and feature importance.
b. Build a model which predicts the listing price of the property based on the property’s features
i. Model Input: Features of property
ii. Model Output: Predicted Price
c. How do you evaluate the quality of your results?
d. What are the possible shortcoming & extensions of your approach?
- Build a Property Price Valuation Tool, which would take as input the features of a property and its listing price and determine whether the property is under-priced, fairly priced or overpriced.
a. Implement a program to determine whether a property is underpriced, fairly priced or overpriced. This is your chance to show us the process that you would follow to solve this problem, and how you would model the data.
i. Program Input: Property features and its price
ii. Program Output: Whether the prediction is Underpriced, Fairly Priced, Overpriced
c. How do you evaluate the quality of your results?
d. What are the possible shortcoming & extensions of your approach?