<jupyter_start><jupyter_text>Importing the ailuropoda_melanoleuca(Giant Pandas) dataset<jupyter_code>#Importing the dataset for Giant Pandas
giantp = pd.read_csv('Compara.95.protein_default.homologies.tsv.gz', compression='gzip', header=0, sep='\t', quotechar='"')
giantp.head(10)<jupyter_output><empty_output><jupyter_text>Data Preprocessing<jupyter_code>X = giantp.copy()
X = X.drop(['identity','homology_identity','dn','ds','wga_coverage'],axis=1)

# Label Encoding
for c in giantp.columns[giantp.dtypes == 'object']:
    X[c] = X[c].factorize()[0]
# fill NANs
X = X.fillna(-999)

X['l_goc'] = X['goc_score'].map(lambda x: x>10 )
y = X.l_goc
X = X.drop('l_goc',axis=1)
X.head(10)<jupyter_output><empty_output><jupyter_text>Fitting the data<jupyter_code># Fitting the classifier with the dataset
rf = RandomForestClassifier()
rf.fit(X,y)<jupyter_output><empty_output><jupyter_text>Visualization<jupyter_code>#Visualizing the data
import matplotlib.pyplot as plt
plt.plot(rf.feature_importances_)
plt.xticks(np.arange(X.shape[1]),X.columns.tolist(),rotation=90)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.16117216117216118, 'fraction_numerical': 0.016483516483516484, 'mean_word_length': 9.027522935779816, 'pattern_counts': {'":': 0, '<': 17, '<?xml version=': 0, '>': 18, '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': '4', 'dir': 'jupyter-structured-clean-dedup', 'id': '651345', 'license': 'MIT', 'n_tokens_mistral': 455, 'n_tokens_neox': 437, 'n_words': 62, 'path': 'giantpandas.ipynb', 'repo_name': 'ABHIJIT-13/giant_pandas', 'size': '1092'}