text: #Python Libraries 
  import numpy as np
  import pandas as pd
  import pickle
  
  
  diabetes_df = pd.read_csv('archive1.zip')
  #lowering #lowercasing all the column names
  diabetes_df.columns = diabetes_df.columns.str.lower()
  
  #renaming the column name 
  diabetes_df = diabetes_df.rename(columns={'diabetespedigreefunction': 'diabetes_pedigree_function', \
  	'bloodpressure': 'blood_pressure', 'skinthickness': 'skin_thickness'})
  
  # Model Building
  from sklearn.model_selection import train_test_split
  
  # segregating the target variable
  X = diabetes_df.drop(columns='outcome')
  y = diabetes_df['outcome']
  #spliting
  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0)
  
  from sklearn.preprocessing import StandardScaler
  sc = StandardScaler()
  X_train = sc.fit_transform(X_train)
  
  # Creating Random Forest Model
  from sklearn.ensemble import RandomForestClassifier
  classifier = RandomForestClassifier(n_estimators=20)
  classifier.fit(X_train, y_train)
  
  
  # Creating a pickle file for the classifier
  filename = 'model.pkl'
  pickle.dump(classifier, open(filename, 'wb'))