<jupyter_start><jupyter_text>Gene Expression Clustering<jupyter_code>gen_exp = pd.read_csv('gene_exp_small.csv')
gen_exp = gen_exp.iloc[:, 2:-1]
gen_exp.head()
from sklearn.cluster import KMeans
Kmean = KMeans(n_clusters=2)
Kmean.fit(gen_exp)
labels = Kmean.labels_
from sklearn.metrics import davies_bouldin_score, silhouette_score
print(davies_bouldin_score(gen_exp, labels)) 
print(silhouette_score(gen_exp, labels))<jupyter_output>2.935152999156101
0.08382327446484648<jupyter_text>PDB ID Clustering<jupyter_code>pdb = pd.read_csv('pdb_ids_enc.csv')
pdb = pdb.iloc[:, 1:]
pdb.head()
Kmean = KMeans(n_clusters=2)
Kmean.fit(pdb)
labels = Kmean.labels_
print(davies_bouldin_score(pdb, labels)) 
print(silhouette_score(pdb, labels))<jupyter_output>1.5044464212729516
0.3311776896849029
{'fraction_non_alphanumeric': 0.1197452229299363, 'fraction_numerical': 0.0929936305732484, 'mean_word_length': 9.767123287671232, 'pattern_counts': {'":': 0, '<': 7, '<?xml version=': 0, '>': 7, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '351950', 'license': 'MIT', 'n_tokens_mistral': 387, 'n_tokens_neox': 340, 'n_words': 44, 'path': 'individual_clustering.ipynb', 'repo_name': 'piyushmishra12/IMC', 'size': '785'}