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import numpy as np # linear algebra | |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) | |
from sklearn.metrics import roc_auc_score | |
from sklearn.preprocessing import LabelEncoder, StandardScaler | |
from sklearn.compose import ColumnTransformer | |
from sklearn.pipeline import make_pipeline | |
from sklearn.pipeline import Pipeline | |
from lightgbm import LGBMClassifier | |
from xgboost import XGBClassifier | |
from category_encoders import OneHotEncoder |
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#Install packages | |
install_github("petermeissner/wikipediatrend") | |
install_github("twitter/AnomalyDetection") | |
library(devtools) | |
library(Rcpp) | |
library(wikipediatrend) | |
library(AnomalyDetection) | |
#load data |
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import pandas as pd | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import os | |
import time | |
from numpy import newaxis | |
import math | |
from sklearn.metrics import mean_squared_error | |
import statsmodels.api as sm | |
from statsmodels.tsa.api import ExponentialSmoothing, SimpleExpSmoothing, Holt |
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#xgboost explainer | |
#library(devtools) | |
#install_github("AppliedDataSciencePartners/xgboostExplainer") | |
library(xgboost) | |
library(xgboostExplainer) | |
#getting data | |
set.seed(123) | |
data(agaricus.train, package='xgboost') |
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from datetime import date, timedelta | |
import pandas as pd | |
import numpy as np | |
from sklearn.metrics import mean_squared_error | |
from keras.models import Sequential | |
from keras.layers.core import Dense, Dropout, Activation | |
from keras.layers import LSTM | |
from keras import callbacks | |
from keras.callbacks import ModelCheckpoint |
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#Load data | |
import pandas as pd | |
white = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv", sep=';') | |
red = pd.read_csv("http://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-red.csv", sep=';') | |
#define Target | |
red['type'] = 1 | |
white['type'] = 0 | |
wines = red.append(white, ignore_index = True) |
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import PyPDF2 | |
def read_pdfs(pdf_file_name): | |
pdf = PyPDF2.PdfFileReader(open(pdf_file_name,'rb')) | |
num_pages = pdf.numPages | |
count = 0 |
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library(data.table) | |
#download data | |
flights <- fread("https://raw.githubusercontent.com/wiki/arunsrinivasan/flights/NYCflights14/flights14.csv") | |
flights | |
#Subset | |
flights[origin == "JFK" & month == 6L] # by column | |
flights[1:2] #by row |
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library(RODBC) | |
library(tm) | |
library(wordcloud) | |
library(ggplot2) | |
library(ROracle) | |
a <- read.csv(file = 'C:/Users/eye1/Desktop/text.csv') | |
names(a) <- 'feedback' | |
narrative <- a$feedback | |
str(narrative) |
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library(caret) | |
set.seed(1) | |
data<-read.csv(url('https://datahack-prod.s3.ap-south-1.amazonaws.com/train_file/train_u6lujuX_CVtuZ9i.csv')) | |
preProcValues <- preProcess(data, method = c("medianImpute","center","scale")) | |
library('RANN') | |
data_processed <- predict(preProcValues, data) |
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