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EDAV Fall 2021 Mon/Wed Community Contributions

48 Low-key ML packages in R

48.1 Vedant Kumar and Siddhant Kumar

48.1.1 Nuralnet Package in R 

# creating training data set
weight=c(120,80,55,100,75,60)
height=c(150,165,155,175,145,170)
obese=c(1,0,0,1,1,0)

df=data.frame(weight,height,obese)
# fit neural network
nn=neuralnet(obese~weight + height,data=df, hidden=3,act.fct = "logistic",
             linear.output = FALSE)

# - obese~weight + height, Placed is label(dependent variable) and 
#weight and height are features (independent variable).
# - df is dataframe,
# - hidden = 3: represents single layer with 3 neurons.
# - act.fct = "logistic" is the activation function
# plot neural network
plot(nn)
#prepare a test data
weight=c(130,90)
height=c(130,185)
df_test=data.frame(weight,height)
## Prediction using neural network
Predict=compute(nn,df_test)
prob = Predict$net.result
pred = ifelse(prob>0.5, 1, 0)
print(pred)
##      [,1]
## [1,]    1
## [2,]    0

#Part 1 - quantmod Package in R

#download the price data for Apple Inc.
getSymbols("AAPL")
## [1] "AAPL"
#add parameters to the getSymbols() function and view the data using the head() function.
getSymbols("AAPL",
           from = "2016/12/31",
           to = "2018/12/31",
           periodicity = "daily")
## [1] "AAPL"
head(AAPL)
##            AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
## 2017-01-03   28.9500   29.0825  28.6900    29.0375   115127600      27.33247
## 2017-01-04   28.9625   29.1275  28.9375    29.0050    84472400      27.30188
## 2017-01-05   28.9800   29.2150  28.9525    29.1525    88774400      27.44072
## 2017-01-06   29.1950   29.5400  29.1175    29.4775   127007600      27.74664
## 2017-01-09   29.4875   29.8575  29.4850    29.7475   134247600      28.00078
## 2017-01-10   29.6925   29.8450  29.5750    29.7775    97848400      28.02902
#Using financial data from multiple organisations using lapply()
stocks <-lapply(c("AAPL", "GOOG"), function(x) {getSymbols(x, 
                                                             from = "2016/12/31", 
                                                             to = "2018/12/31",
                                                             periodicity = "daily",
                                                             auto.assign=FALSE)} )
#View the data in line and candlesticks charts
names(stocks) <- c("AAPL", "GOOG")

head(stocks$AAPL)
##            AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
## 2017-01-03   28.9500   29.0825  28.6900    29.0375   115127600      27.33247
## 2017-01-04   28.9625   29.1275  28.9375    29.0050    84472400      27.30188
## 2017-01-05   28.9800   29.2150  28.9525    29.1525    88774400      27.44072
## 2017-01-06   29.1950   29.5400  29.1175    29.4775   127007600      27.74664
## 2017-01-09   29.4875   29.8575  29.4850    29.7475   134247600      28.00078
## 2017-01-10   29.6925   29.8450  29.5750    29.7775    97848400      28.02902
chartSeries(stocks$AAPL,
            type="line",
            subset='2017',
            theme=chartTheme('white'))
chartSeries(stocks$AAPL,
            type="candlesticks",
            subset='2017-05',
            theme=chartTheme('white'))
#calculation of some common financial metrics
seriesHi(stocks$AAPL)
##            AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
## 2018-10-03   57.5125   58.3675   57.445    58.0175   114619200      56.12624
seriesLo(stocks$AAPL)
##            AAPL.Open AAPL.High AAPL.Low AAPL.Close AAPL.Volume AAPL.Adjusted
## 2017-01-03     28.95   29.0825    28.69    29.0375   115127600      27.33247