<jupyter_start><jupyter_text>Importing of the Libraries<jupyter_code>import pandas as pd
import numpy as np
import matplotlib.pyplot as plt<jupyter_output><empty_output><jupyter_text>Reading of the excel file HR dataset into Jupyter .<jupyter_code>data = pd.read_excel ('HRDataset.xlsx')
data<jupyter_output><empty_output><jupyter_text>Clearing of the missing data in salary column.<jupyter_code>from sklearn.impute import SimpleImputer
imputer=SimpleImputer(missing_values=np.nan, strategy='mean')
imputer.fit(data.iloc [:, 1:3])
data.iloc[:, 1:3]=imputer.transform(data.iloc[:, 1:3])
data<jupyter_output><empty_output><jupyter_text>Applying of onehotencoder in Marital Desc column<jupyter_code>from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct=ColumnTransformer(transformers=[('encoder', OneHotEncoder(),[9])], remainder='passthrough')
data=pd.DataFrame(ct.fit_transform(data))
data<jupyter_output><empty_output><jupyter_text>Applying of labelencoding on PerformanceScore.<jupyter_code>from sklearn.preprocessing import LabelEncoder
le=LabelEncoder()
data.iloc[:, 86]=le.fit_transform(data.iloc[:, 86])
data<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.11382799325463744, 'fraction_numerical': 0.00927487352445194, 'mean_word_length': 11.112244897959183, 'pattern_counts': {'":': 0, '<': 21, '<?xml version=': 0, '>': 21, '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': '5', 'dir': 'jupyter-structured-clean-dedup', 'id': '660475', 'license': 'MIT', 'n_tokens_mistral': 422, 'n_tokens_neox': 417, 'n_words': 75, 'path': 'DATA PROCESSING ASSIGNMENT.ipynb', 'repo_name': 'Bamigbola1/Data-preprocessin', 'size': '1186'}