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# A.Importing Libraries -->
import pandas as pd
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix

# Models Used -->
from sklearn.ensemble import RandomForestClassifier
from sklearn.tree import DecisionTreeClassifier

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# LIBRARIES USED -->
import nltk
import string
import pandas as pd
from nltk.corpus import stopwords

# REQUIRED DOWNLOADS -->
nltk.download('punkt')
nltk.download('stopwords')
from sklearn.model_selection import train_test_split

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// Libraries Used --> 
#include <LiquidCrystal.h> 
#include <HCSR04.h>

// Pins Used --> 
#define Trig 6
#define Echo 7
#define MA 7
#define Y_LED 8

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# libraries used -->
import pandas as pd
import numpy as np
from scipy import stats
import re

# get list of university towns -->
def get_list_of_university_towns():
    
    # Open and read files -->

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# libraries used --> 
import numpy as np
import pandas as pd
from sklearn.datasets import load_breast_cancer
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import precision_score
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

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def date_extractor():
    # imports -->
    import pandas as pd
    import re
    from calendar import month_name
    import dateutil.parser
    from datetime import datetime
    
    # import medical notes -->
    doc = []

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def blight_model():
    import pandas as pd
    import numpy as np
    from sklearn.neural_network import MLPClassifier
    from sklearn.preprocessing import MinMaxScaler
    from sklearn.svm.libsvm import predict_proba
    
    # training data
    train_data = pd.read_csv('train.csv', encoding = 'ISO-8859-1')