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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') | |