<jupyter_start><jupyter_text><jupyter_code>import numpy as np
import pandas as pd
dataset1 = pd.read_excel("/content/drive/My Drive/DataAnalyticsProblem.xlsx", sheet_name = "Employees who have left")
dataset1["left"] = 1
dataset2 = pd.read_excel("/content/drive/My Drive/DataAnalyticsProblem.xlsx", sheet_name = "Existing employees")
dataset2["left"] = 0
dataset = pd.concat([dataset1, dataset2])
dataset.head()
dataset.shape
dataset.isnull().sum()
from sklearn import preprocessing
l = preprocessing.LabelEncoder()
dataset['dept'] = l.fit_transform(dataset['dept'])
dataset['salary'] = l.fit_transform(dataset['salary'])
dataset.head()
dataset.to_csv('/content/drive/My Drive/final_data_employee_churn.csv')<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.14634146341463414, 'fraction_numerical': 0.01084010840108401, 'mean_word_length': 8.012195121951219, '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': '151721', 'license': 'MIT', 'n_tokens_mistral': 253, 'n_tokens_neox': 245, 'n_words': 45, 'path': 'Pre_Process.ipynb', 'repo_name': 'imdasrj98/Employee-Churn-Prediction', 'size': '738'}