text: # Data Preprocessing
  
  # Importing the libraries
  import numpy as np
  import matplotlib.pyplot as plt
  import pandas as pd
  
  # Import comet_ml in the top of your file
  from comet_ml import Experiment
  
  experiment  = Experiment(api_key="Dz2W3DAahv0OvSAERUfhA5b7I")
  
  # Importing the dataset
  dataset = pd.read_csv('Data.csv')
  X = dataset.iloc[:, [0,1,2]].values
  y = dataset.iloc[:, 3].values
  
  # GeT to know your data
  dataset.describe()
  dataset.info()
  
  # Check if missing values
  dataset.isnull().values.any()
  
  # Check number of NaNs
  dataset.isnull().sum().sum()
  
  # Taking care of missing data
  from sklearn.preprocessing import Imputer
  imputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)
  imputer = imputer.fit(X[:, 1:3])
  X[:, 1:3] = imputer.transform(X[:, 1:3])
  
  # Encoding categorical data
  # Encoding the Independent Variable
  from sklearn.preprocessing import LabelEncoder, OneHotEncoder
  labelencoder_X = LabelEncoder()
  X[:, 0] = labelencoder_X.fit_transform(X[:, 0])
  onehotencoder = OneHotEncoder(categorical_features = [0])
  X = onehotencoder.fit_transform(X).toarray()
  # Encoding the Dependent Variable
  labelencoder_y = LabelEncoder()
  y = labelencoder_y.fit_transform(y)