6 Exercise Checklist

This checklist should help you keep track of your exercises. Remember that you have to hand in satisfactory solutions to at least two-thirds of the exercises. If you’re part of the beginner track, this refers to two-thirds of part A (EDA) only. If you’re part of the advanced track, you have to hand in at least two-thirds of both individual parts A and B. Hence, you cannot hand in 100 percent of the first part and only 50 percent of the second one. You’ll need more than 66% in each one for a certificate. After all, you’re not that advanced if you only did half of it, right?

Part 1: Exploratory Data Analysis (Beginner + Advanced Tracks)

  1. Visualize available apartments

    1. Load the calendar data set and get an overview of it

    2. Transform data in columns price, available, and date

    3. Show the number of available Airbnb apartments in a simple line plot

    4. Create a calendar plot to visualize the availability of Airbnb listings

  2. Correlation between price and availability

    1. Calculate the Correlation Coefficient between available and price

    2. Visualize Correlation

  3. Distribution of Prices

    1. Calculate Mean and Standard Deviation for each neighbourhood

    2. Visually compare the price distribution for the, on average, most expensive neighbourhood with the least expensive one

  4. Analysis of Listing Reviews

    1. Compute the average number of reviews per apartment

    2. Merge with the listings data set to extract the location of listings

    3. Plot the location of 200 most reviewed apartments (e.g. with barplot)

  5. Maps

    1. Map the 200 most frequently reviewed apartments

    2. Create a new map extending its functionality by adding a pop up window

    3. Create a heatmap

  6. BE CREATIVE

    1. Surprise us with one or more plots of your choice!

Part 2: Price Prediction Using Statistical Methods (motivated Beginner + Advanced Tracks)

  1. Visualize feature correlations in a correlation matrix/heatmap

  2. Regression

    1. Simple regression model using one variable

    2. Improve your model using more features

  3. Improve your model

    1. Train new models using more advanced methods