Course: MMAI 5040 Business Applications of Artificial Intelligence
Netflix Rooms is a machine learning recommender system that clusters users based on shared interests and demographics. We leveraged K-Means, Principle Component Analysis (PCA), and t-distributed stochastic neighbourhood embedding (t-SNE) to form the clusters and Decision Trees for interpretation. Afterwards, we created a “Community Persona” for the cluster with the strongest differences to provide the user with an example of how this recommender system could work once deployed.
Netflix Rooms’ objective is to connect similar users together through this proposed virtual movie theater experience to provide an element of human-connection to streaming. The aim is to improve the well-being of Netflix users, while also providing the company with a clear return on investment and a competitive edge.
To see our full project proposal, refer to the following.
No additional packages need to be installed, as we used Pandas and Numpy, both of which are pre-installed on Google Colab.
For additional information regarding package and library installations via Google Colab, please see here.
from sklearn.cluster import KMeans
KM_clusters = KMeans(n_clusters=20, init='k-means++').fit(XC)
KM_clustered = XC.copy()
KM_clustered = pd.DataFrame(KM_clustered)
KM_clustered.loc[:,'Cluster'] = KM_clusters.labels_
from sklearn.decomposition import PCA
WCSS = []
for i in range(1, 21):
kmeans_pca = KMeans(n_clusters = i, init = 'k-means++', random_state=42)
kmeans_pca.fit(scores_pca)
WCSS.append(kmeans_pca.inertia_)
kmeans_pca = KMeans(n_clusters=20, init = 'k-means++', random_state=42)
kmeans_pca.fit(scores_pca)
from sklearn.manifold import TSNE
tsne = TSNE(verbose=1, perplexity=50)
X_embedded = tsne.fit_transform(X_scaled.to_numpy())
from sklearn import tree
clf = DecisionTreeClassifier(max_depth=3, random_state=42)
clf.fit(X_train, y_train)
Google Colaboratory. (n.d.). packages_and_modules.ipynb. Google Colaboratory (Colab). Retrieved May 21, 2022, from https://colab.research.google.com/github/bebi103a/bebi103a.github.io/blob/master/lessons/03/packages_and_modules.ipynb