Machine-Learning-Project

Machine Learning Project in the course “Machine Learning ML”. The project is from the module and represents 16 hours of sessions for 2ects. The course “Machine Learning” ML is currently for 4IF (4th year IT department) at INSA Lyon. The subject is in english but we can hand in an assignment in french (so we mix the two languages).

Execution

[commande1 blabla] # permet de blablabla1

[commande2 blabla] # permet de blablabla2

Instructions

For each dataset: write a report in the form of an executable notebook either in R or Python (or even Julia)

• Explore the dataset: chapter 10 poly of course (”SVD et analyse d’un jeu de données”)
• Train different models : use for example: linear regression, ridge regression, kernel ridge regression, nyström approximation for kernel ridge regression. We have to consider at least one additional model (not introduced during the course). For example: random forest, multi-layers perceptron (explain parameters and hyper-parameters and how its hyper-parameters are related to the bias-variance tradeoff)

Dataset

House Sales in King County, USA: This dataset contains information about house sales in King County, Washington, USA. It has 21613 instances and 21 attributes, including the price of each house. The task is to predict the price based on the other features. You can find this dataset on Kaggle (https://www.kaggle.com/datasets/harlfoxem/housesalesprediction). This dataset is relatively large and clean. It has mostly numerical features that are easy to measure and interpret. The target variable (price) is also numerical and continuous.

Dataset attributes

id - Unique ID for each home sold date - Date of the home sale price - Price of each home sold bedrooms - Number of bedrooms bathrooms - Number of bathrooms, where .5 accounts for a room with a toilet but no shower sqft_living - Square footage of the apartments interior living space sqft_lot - Square footage of the land space floors - Number of floors waterfront - A dummy variable for whether the apartment was overlooking the waterfront or not view - An index from 0 to 4 of how good the view of the property was condition - An index from 1 to 5 on the condition of the apartment, grade - An index from 1 to 13, where 1-3 falls short of building construction and design, 7 has an average level of construction and design, and 11-13 have a high quality level of construction and design. sqft_above - The square footage of the interior housing space that is above ground level sqft_basement - The square footage of the interior housing space that is below ground level yr_built - The year the house was initially built yr_renovated - The year of the house’s last renovation zipcode - What zipcode area the house is in lat - Lattitude long - Longitude sqft_living15 - The square footage of interior housing living space for the nearest 15 neighbors sqft_lot15 - The square footage of the land lots of the nearest 15 neighbors

Team

The project is realised by us (students) (group H4221):

• DUBILLOT Elise
• FLANDRE Corentin
• THOMAS Colin