socratesk

####  This REFERENCE file contains frequently used R codes  ####

# To detach a library that is already loaded in R Environment, without restarting R console-
detach("package:mlr", unload=TRUE)

# To install single package
install.packages("mlr")

# To install multiple packages
install.packages(c("mlr", "xgboost"))

socratesk

# This is a sample model to demonstrate how a Machine Learning model 
# can be implemented in Production as a REST API and how it can be consumed

# Import libraries and packages
from sklearn import svm, datasets
import pickle 
import numpy as np

# Load Sample data
iris = datasets.load_iris()

socratesk

# import Flask class from the flask module
from flask import Flask

import numpy as np
import pickle

# Create Flask object to run
app = Flask(__name__)

@app.route('/')

socratesk

# import libraries
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor

# load data file
train = pd.read_csv('50-Startups.csv')

# perform one-hot encoding for categorical variable
trainDummies = pd.get_dummies(train['State'], prefix = 'state')

socratesk

import pandas as pd

country = ['NZ', 'BR', 'US']
onehotDF = pd.get_dummies(country)
print(onehotDF)

socratesk

import pandas as pd
import numpy as np

platformDF = pd.DataFrame({'id' : [101, 102, 103, 104, 105, 106], 
                           'platform' : ['Android', 'Android', 'iOS', np.nan, 'Android', 'iOS']})

platformDF.platform = platformDF.platform.fillna('NaN')
onehotDF = pd.get_dummies(platformDF)
print(onehotDF)

socratesk

import pandas as pd

gadgetDF = pd.DataFrame({'gadgetId' : [101, 102, 103, 104, 105], 
                           'gadgetName' : ['Apple_iPhone_6', 
                                           'Apple_iPad_3', 
                                           'Samsung_Galaxy_S8', 
                                           'Samsung_Galaxy_S9', 
                                           'Google_Pixel_3']})

dummyDF = gadgetDF['gadgetName'].apply(lambda x: pd.Series(x.split('_')))

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import pandas as pd

incExpDF = pd.DataFrame({'id' : [101, 102, 103, 104, 105],
                         'familyCnt' : [2, 4, 3, 3, 5],
                         'totalInc' : [68000, 72000, 34000, 44000, 52000],
                         'totalExp' : [48000, 66000, 33000, 41000, 50000]})

incExpDF['incPerPerson'] = incExpDF['totalInc'] / incExpDF['familyCnt']
incExpDF['expPerPerson'] = incExpDF['totalExp'] / incExpDF['familyCnt']
incExpDF['savingsPerPerson'] = incExpDF['incPerPerson'] - incExpDF['expPerPerson']

socratesk

# Initalize webcam. 0 starts built-in camera
cap = cv2.VideoCapture(0)

# Specify HSV range of Tennis Ball
ballHSVLower = np.array([25, 75, 85])
ballHSVUpper = np.array([50, 220, 255])

while True:
    # Read captured webcam frame
    _, frame = cap.read()

socratesk

# import libraries
import numpy as np
import pandas as pd
import eli5

from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import train_test_split
from eli5.sklearn import PermutationImportance

# load data file