ml_packages_in_R.rmd

# (PART) Machine Learning and More {-}

# Low-key ML packages in R

## Vedant Kumar and Siddhant Kumar

### Nuralnet Package in R

```{r}
library("neuralnet")
library(quantmod) 
```

```{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)
```

```{r}
# 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
```

```{r}
# plot neural network
plot(nn)
```

```{r}
#prepare a test data
weight=c(130,90)
height=c(130,185)
df_test=data.frame(weight,height)
```

```{r}
## Prediction using neural network
Predict=compute(nn,df_test)
prob = Predict$net.result
pred = ifelse(prob>0.5, 1, 0)
print(pred)
```

#Part 1 - quantmod Package in R

```{r}
#download the price data for Apple Inc.
getSymbols("AAPL")
```

```{r}
#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")

head(AAPL)

```

```{r}
#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)} )
```

```{r}

#View the data in line and candlesticks charts
names(stocks) <- c("AAPL", "GOOG")

head(stocks$AAPL)

chart_Series(AAPL)

chartSeries(stocks$AAPL,
            type="line",
            subset='2017',
            theme=chartTheme('white'))


```

```{r}
chartSeries(stocks$AAPL,
            type="candlesticks",
            subset='2017-05',
            theme=chartTheme('white'))

```

```{r}
#calculation of some common financial metrics
seriesHi(stocks$AAPL)
```

```{r}
seriesLo(stocks$AAPL)
```