<jupyter_start><jupyter_text>Car Evaluation Dataset Labels: Car acceptability Class Values: unacceptable, acceptable, good, vgood<jupyter_code>#BreastCancer
from RadViz.main import RadViz3D
from RadViz.main import RadViz2D
import pandas as pd
import time
from sklearn import preprocessing
import plotly.io as pio
pio.renderers.default='iframe'
data= pd.read_csv('car_evaluation.csv')
data1 = data.iloc[:,:-1] 
data2 = data['decision']

# label_encoder object knows how to understand word labels. 
label_encoder = preprocessing.LabelEncoder()


for f in data1.columns:
    if (data1[f].dtype == 'object'):
        data1[f]= label_encoder.fit_transform(data1[f])   
        
data1 = pd.DataFrame(data=data1)
data2 = pd.DataFrame(data=data2)
data_final = pd.concat([data2,data1],axis=1)
data_final.head()

y=data_final['decision']
X=data_final.drop(['decision'], axis=1)
BPs=10000
time_start = time.time()
RadViz3D(y,X,BPs)
print( 'RadViz3D done! Time elapsed: {} seconds'.format(time.time()-time_start))
BPs=1000
time_start = time.time()
RadViz2D(y,X,BPs)
print( 'RadViz2D done! Time elapsed: {} seconds'.format(time.time()-time_start))<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.1331615120274914, 'fraction_numerical': 0.02577319587628866, 'mean_word_length': 6.327044025157233, 'pattern_counts': {'":': 0, '<': 5, '<?xml version=': 0, '>': 5, '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': '1', 'dir': 'jupyter-structured-clean-dedup', 'id': '302986', 'license': 'MIT', 'n_tokens_mistral': 443, 'n_tokens_neox': 425, 'n_words': 90, 'path': 'Car Evaluation dataset/.ipynb_checkpoints/Car Evaluation-Jupyter file-checkpoint.ipynb', 'repo_name': 'elewah/RadViz-Plotly-Examples', 'size': '1164'}