TBL stats & predictions

L'Impatto del Basket su Cultura ed Economia Locale

Oltre all'aspetto sportivo puro, il basket ha un impatto significativo sulla cultura ed economia locale turca. Le partite casalinghe attirano migliaia di spettatori nei palazzetti dello sport, generando entrate significative sia per i club che per le città ospitanti. [0]: #!/usr/bin/env python [1]: # coding: utf-8 [2]: # In[1]: [3]: import pandas as pd [4]: import numpy as np [5]: import matplotlib.pyplot as plt [6]: from sklearn.preprocessing import StandardScaler [7]: from sklearn.cluster import KMeans [8]: from sklearn.metrics import silhouette_score [9]: # In[7]: [10]: # Importing the dataset [11]: df = pd.read_csv('Mall_Customers.csv') [12]: X = df.iloc[:, [3,4]].values [13]: # In[8]: [14]: X [15]: # In[9]: [16]: # Using the elbow method to find the optimal number of clusters [17]: wcss = [] [18]: for i in range(1,11): [19]: kmeans = KMeans(n_clusters=i , init='k-means++', max_iter=300,n_init=10 , random_state=0) [20]: kmeans.fit(X) [21]: wcss.append(kmeans.inertia_) [22]: plt.plot(range(1 ,11),wcss) [23]: plt.title('The Elbow Method') [24]: plt.xlabel('Number of Clusters') [25]: plt.ylabel('WCSS') [26]: plt.show() [27]: # In[10]: [28]: kmeans = KMeans(n_clusters=5 , init='k-means++', max_iter=300,n_init=10 , random_state=0) [29]: y_kmeans = kmeans.fit_predict(X) [30]: # In[11]: [31]: # Visualising the clusters [32]: plt.scatter(X[y_kmeans ==0 ,0] , X[y_kmeans ==0 ,1] ,s =100,c='red' ,label ='Cluster1') [33]: plt.scatter(X[y_kmeans ==1 ,0] , X[y_kmeans ==1 ,1] ,s =100,c='blue' ,label ='Cluster2') [34]: plt.scatter(X[y_kmeans ==2 ,0] , X[y_kmeans ==2 ,1] ,s =100,c='green' ,label ='Cluster3') [35]: plt.scatter(X[y_kmeans ==3 ,0] , X[y_kmeans ==3 ,1] ,s =100,c='cyan' ,label ='Cluster4') [36]: plt.scatter(X[y_kmeans ==4 ,0] , X[y_kmeans ==4 ,1] ,s =100,c='magenta' ,label ='Cluster5') plt.scatter(kmeans.cluster_centers_[:,0] , kmeans.cluster_centers_[:,1],s =300,c='yellow', label ='Centroids') plt.title('Clusters of clients') plt.xlabel('Annual Income (k$)') plt.ylabel('Spending Score (1-100)') plt.legend() plt.show() ***** Tag Data ***** ID: 2 description: Clustering the dataset using KMeans and visualizing the resulting clusters. start line: 28 end line: 36 dependencies: - type: Other name: KMeans clustering algorithm parameters and fit_predict method start line: 28 end line: 29 context description: This snippet performs KMeans clustering with the optimal number of clusters determined from the elbow method and visualizes the results. algorithmic depth: 4 algorithmic depth external: N obscurity: 3 advanced coding concepts: 4 interesting for students: 5 self contained: N ************* ## Suggestions for complexity 1. **Dynamic Cluster Coloring**: Instead of hardcoding colors for each cluster (`red`, `blue`, etc.), dynamically generate distinct colors for any number of clusters. 2. **Interactive Plotting**: Convert the static matplotlib plot into an interactive plot using Plotly or Bokeh that allows zooming and hovering to display more information about each data point. 3. **Cluster Quality Metrics**: Implement additional cluster quality metrics like Silhouette Score or Davies-Bouldin Index and visualize these metrics alongside the clusters. 4. **Subspace Clustering**: Modify the code to perform clustering on different subspaces of the data and visualize how clusters change across these subspaces. 5. **Anomaly Detection**: Integrate an anomaly detection mechanism to highlight outliers that don't fit well into any cluster. ## Conversation <|user|>I've got this piece of code that does KMeans clustering and visualizes it [SNIPPET]. I want to make it so that it generates distinct colors dynamically for any number of clusters instead of hardcoding them.<|end|> <|assistant|>Sure! You can use a colormap from `matplotlib` to generate distinct colors dynamically based on the number of clusters. Here's how you can modify your plotting section: python import matplotlib.pyplot as plt import numpy as np # Assuming y_kmeans contains the cluster labels and k is the number of clusters. num_clusters = len(set(y_kmeans)) colors = plt.cm.get_cmap('viridis', num_clusters) for i in range(num_clusters): cluster_points = X[y_kmeans == i] plt.scatter(cluster_points[:,0], cluster_points[:,1], s=100, c=[colors(i)], label=f'Cluster {i+1}') plt.scatter(kmeans.cluster_centers_[:,0], kmeans.cluster_centers_[:,1], s=300, c='yellow', label='Centroids') plt.title('Clusters of clients') plt.xlabel('Annual Income (k$)') plt.ylabel('Spending Score (1-100)') plt.legend() plt.show() This will dynamically generate colors for any number of clusters you have. *** Excerpt *** The most obvious point about this collection is its sheer size – over five hundred pages long and with contributions from an impressive array of scholars – but what makes it remarkable is its cohesion and coherence despite being made up of a range of perspectives on a subject that has been much written about already.[5] The editors have done an excellent job at assembling a wide range of papers which explore key issues within early modern medicine from a variety of perspectives whilst also providing an overview that demonstrates how far scholarship has moved beyond traditional histories of medicine that focus on individual practitioners or institutions to address questions about medical culture.[6] The chapters are organised into five parts which address different aspects of early modern medicine; theoretical debates and institutional structures; spaces and sites; practitioners and patients; textual practices; and finally representation and visual culture.[7] Part one begins with Helen King’s examination of ‘What was ancient medicine?’. This chapter provides a timely reminder that whilst modern scholarship has sought to move beyond simplistic definitions of ‘ancient’ medicine which refer only to Greek medicine derived from Hippocrates and Galen,[8] many contemporaries still viewed it in this way.[9] She goes on to argue that ‘ancient’ medicine was not static but continued to evolve after antiquity through developments such as the rise of Paracelsianism in the early modern period.[10] The second chapter by Wendy Churchill provides a useful overview of early modern anatomical teaching which highlights some important continuities between medieval anatomy courses based around Galen’s On Anatomical Procedures (c.AD150) and those taught later during the Renaissance.[11] She also notes that many universities were slow to adopt new discoveries made by anatomists such as Andreas Vesalius (1514-64) due to their reliance on Galen’s work.[12] Part two opens with John Henderson’s chapter on ‘Medical space’ which considers how doctors negotiated their authority within hospitals alongside other medical practitioners such as surgeons or apothecaries.[13] He argues that these institutions were often sites where different approaches towards healing coexisted uneasily but could also provide opportunities for collaboration between various medical specialists.[14] The next two chapters by Brian Cummings (‘The theology’) and David Cantor (‘Medicine’) examine respectively how religious ideas shaped understandings about health while also influencing medical practice during this period.[15] Both authors emphasise how religious beliefs informed theories about disease causation while also impacting upon treatments offered by physicians.[16] Part three opens with Michael Stolberg’s chapter on ‘Practitioners’ which focuses primarily upon physicians although he also discusses surgeons briefly towards its conclusion.[17] Stolberg argues that there was considerable variation among physicians regarding their training background with some receiving extensive education at university while others learnt their trade through apprenticeship alone.[18] The fourth chapter by Mark Jenner considers how patients negotiated relationships with doctors who often held significant power over them due to their status within society at large rather than simply because they were healers.[19] Jenner argues that although patients could sometimes challenge doctors’ authority by refusing treatment or seeking alternative remedies outside official channels they were ultimately dependent upon them because access to effective medicines was limited without professional assistance.[20] *** Revision 0 *** ## Plan To create an exercise that is as advanced as possible based on the given excerpt: - Integrate advanced medical historical context not explicitly mentioned in the excerpt but relevant to