<jupyter_start><jupyter_text><jupyter_code>from google.colab import drive
drive.mount('/content/gdrive')
import pandas as pd

import io
pip install pandas 
from google.colab import files
uploaded = files.upload()

df = pd.read_csv(io.BytesIO(uploaded['WA_Fn-UseC_-Telco-Customer-Churn (1).csv']))
df.head()
!pip install pycaret
df.shape
data = df.sample(frac=0.95, random_state=786)
data_unseen = df.drop(data.index)
data.reset_index(inplace=True, drop=True)
data_unseen.reset_index(inplace=True, drop=True)
print('Data for Modeling: ' + str(data.shape))
print('Unseen Data For Predictions: ' + str(data_unseen.shape))
from pycaret.classification import *
exp_clf101 = setup(data = data, target = 'Churn')
best_model = compare_models()<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.13178294573643412, 'fraction_numerical': 0.012919896640826873, 'mean_word_length': 6.380952380952381, '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': '385302', 'license': 'MIT', 'n_tokens_mistral': 292, 'n_tokens_neox': 285, 'n_words': 56, 'path': 'Telecom_Colab.ipynb', 'repo_name': 'mattnikiel/pycaret', 'size': '774'}