Background: You are working as an analyst for a real estate company. Your company wants to build a machine learning model to predict the selling prices of houses based on a variety of features on which the value of the house is evaluated.
Objective: The task is to build a model that will predict the price of a house based on features provided in the dataset. The senior management also wants to explore the characteristics of the houses using some business intelligence tool. One of those parameters include understanding which factors are responsible for higher property value - $650K and above. The questions have been provided later in the document for which you can use tableau.
Data: The data set consists of information on some 22,000 properties. The dataset consisted of historic data of houses sold between May 2014 to May 2015. These are the definitions of data points provided: (Note: For some of the variables that are self explanatory, no definition has been provided)
- Id: Unique identification number for the property.
- date: date the house was sold.
- price: price of the house.
- waterfront: house which has a view to a waterfront.
- condition: How good the condition is (overall). 1 indicates worn out property and 5 excellent.
- grade: Overall grade given to the housing unit, based on King County grading system. 1 poor ,13 excellent.
- Sqft_above: square footage of house apart from basement.
- Sqft_living15: Living room area in 2015(implies - some renovations). This might or might not have affected the lotsize area.
- Sqft_lot15: lotSize area in 2015(implies - some renovations).
We encourage you to thoroughly understand your data and take the necessary steps to prepare your data for modeling before building exploratory or predictive models. Since this is a classification model, you can use linear regression for classification for building a model. You are also encouraged to use other models in your project including KNN, decision trees for regression. To explore the data, you can use the techniques that have been discussed in class. Some of them include using the describe method, checking null values, using matplotlib and seaborn for developing visualizations. The data has a number of categorical and numerical variables. Explore the nature of data for these variables before you start with the data cleaning process and then data pre-processing (scaling numerical variables and encoding categorical variables). You will also use tableau to visually explore the data further.
Use different models to compare the accuracies and find the model that best fits your data. You can use the measures of accuracies that have been discussed in class. Please note that while comparing different models, make sure you use the same measure of accuracy as a benchmark.