Classification and Regression on AIRBNB data

In this repository we will apply some Machine Learning to classify/perform regression on an Airbnb dataset which contains rooms that were available to rent in Madrid, Spain.

Models

The Machine Learning Models we will apply are:

• Naives Bayes
• K-Nearest Neighbors (KNN)
• Decision Trees
• Support Vector Machines (SVM)
  • Classifier:
    • LinearSVC
    • SVC with RBF Kernel
  • Regression:
    • LinearSVR
    • SVR with RBF Kernel

Dataset

The attributes of this dataset are:

• room_type
• price
• latitude
• altitude
• minimum nights
• total reviews
• availability
• calculated_host_listings_count

We will classify room_type based on the other attributes. Also, we will use price as the dependent variable and the other ones as independent while performing regression.

Also, we will be using jupyter notebooks during the process to analyse the data, visualize the results and explain each section all in the same file. We will be performing Bayes, KNN and Decision Trees in the first file and SVM in the second

Preprocessing

Some of the tasks we will do in preprocessing are:

• Distribution analysis

• Correlation Matrix

• Box and whisker plot

• Scale values (StandardScaler)
• Trasform Pipeline (for scaling)
• Removing attributes

Results

The results, analysis and explanation are available in each notebook.

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2024 - rafaroman18