How to Predict Netflix Stock Price using Machine Learning in R

Model Used for Netflix Stock Price Prediction

Executing and Checking the Model Summary

Step 1: Importing the required libraries

Below is the list of external and internal libraries and packages, that we will be requiring for this R Machine Learning Project:

Package	Uses
smooth	Smoothing techniques and forecasting models for time series analysis.
forecast	Used for forecasting time series data.
xts	Used for handling and manipulating time series data.
imputeTS	Used functions to handle missing values in time series data
fpp2	Provides datasets and additional forecasting tools
tseries	Used for functions for time series analysis, including tests for stationarity.
ggfortify	Used for easy visualization of time series objects
ggplot2	A popular package for creating complex and customizable plots in R.
quantmod	This package provides tools to fetch financial market data, analyze, and visualize it.

#Install and load libraries
#Smoothing techniques for time series analysis.
install.packages("smooth")
library(smooth)

# Used for forecasting time series data.
install.packages("forecast")
library(forecast)

#Used for handling and manipulating time series data
install.packages("xts")
library(xts)

#handle missing values in time series data
install.packages("imputeTS")
library(imputeTS)

#provides datasets
install.packages("fpp2")
library(fpp2)

#functions for time series analysis
install.packages("tseries")
library(tseries)

#visualization of time series objects 
install.packages("ggfortify")
library(ggfortify)

#customizable plots in R
install.packages("ggplot2")
library(ggplot2)

# fetch financial market data
install.packages("quantmod")
library(quantmod)

Step 2: Loading the Netfix Stock Price Dataset

Here we install and load the required libraries, based on the choice of mode of dataset (as discussed above).

Loading dataset from Finance websites

# Loading the required data
df = read.csv("/content/NFLX.csv") #if you use external data set

Loading dataset from CSV file

# Here we use getSymboles() function for collect the data from Yahoo finance
getSymbols('NFLX', from = '2002-01-01', to = '2024-01-01')
df = NFLX

# View dataset
head(df)

Output:

           NFLX.Open NFLX.High NFLX.Low NFLX.Close NFLX.Volume NFLX.Adjusted
2002-05-23  1.156429  1.242857 1.145714   1.196429   104790000      1.196429
2002-05-24  1.214286  1.225000 1.197143   1.210000    11104800      1.210000
2002-05-28  1.213571  1.232143 1.157143   1.157143     6609400      1.157143
2002-05-29  1.164286  1.164286 1.085714   1.103571     6757800      1.103571
2002-05-30  1.107857  1.107857 1.071429   1.071429    10154200      1.071429
2002-05-31  1.078571  1.078571 1.071429   1.076429     8464400      1.076429

Step 3: Checking the dimension and missing values of our data

Here we measure the dimension of the dataset and check the missing values.

# Check the dimension of the dataset
dim(df)

# Check the missing values of all the columns of the dataset
colSums(is.na(df))

Output:

[1] 5439    6

    NFLX.Open     NFLX.High      NFLX.Low    NFLX.Close   NFLX.Volume NFLX.Adjusted 
            0             0             0             0             0             0

Step 4: Taking the summary of the data

We check the summary of the data and get the basic idea of the dataset.

# Checking the summary of the data
summary(df)

Output:

     Index              NFLX.Open          NFLX.High           NFLX.Low       
 Min.   :2002-05-23   Min.   :  0.3779   Min.   :  0.4107   Min.   :  0.3464  
 1st Qu.:2007-10-16   1st Qu.:  4.1143   1st Qu.:  4.1936   1st Qu.:  4.0400  
 Median :2013-03-13   Median : 33.9957   Median : 34.5543   Median : 33.5100  
 Mean   :2013-03-11   Mean   :132.3833   Mean   :134.4291   Mean   :130.2730  
 3rd Qu.:2018-08-04   3rd Qu.:255.3800   3rd Qu.:261.5600   3rd Qu.:249.5550  
 Max.   :2023-12-29   Max.   :692.3500   Max.   :700.9900   Max.   :686.0900  
   NFLX.Close        NFLX.Volume        NFLX.Adjusted     
 Min.   :  0.3729   Min.   :   285600   Min.   :  0.3729  
 1st Qu.:  4.1214   1st Qu.:  5922600   1st Qu.:  4.1214  
 Median : 33.9600   Median : 10018000   Median : 33.9600  
 Mean   :132.4029   Mean   : 15907149   Mean   :132.4029  
 3rd Qu.:255.1150   3rd Qu.: 18833300   3rd Qu.:255.1150  
 Max.   :691.6900   Max.   :323414000   Max.   :691.6900

Step 5: Plotting the data

We will use chartSeries() function from the quantmod package in R, typically used for financial and stock market data visualization. type = ‘auto’, it automatically selects an appropriate chart type based on the data provided.

chartSeries(df, type = 'auto')

Output:

Predicting Stock Prices in R

Now we will Check that the data is stationary or not by visualize the data.

ggplot(df, aes(x = NFLX.Close))+
  geom_density(alpha = 0.5, fill = "blue") +
  geom_histogram(aes(y = ..density..), 
                 color = "black", 
                 fill = "lightgray", 
                 bins = 30, alpha = 0.4) +
  labs(title = "Density and Histogram of Close Price",
       x = "Close Price",
       y = "Density") +
  theme_minimal()

Output:

Predicting Stock Prices in R

Clearly the data is not normally distributed which implies it is a non-stationary data.

Step 6: Model building

We take out the data frame consist of closing price and then split our data df.close consist of closing price of stock in a 80:20 ratio where 80% is the training purpose and remaining for test or validation purpose.

We will split the data in train and test and now we will use arima model to Predicting Stock Prices.

# df.close is just name of the data frame consist of closing price you can take 
df.close = df[,4] # just taking the 4th column i.e. Close price

# Train test split
df.close.train = df.close[1:(0.8*length(df.close))]

df.close.test = df.close[(0.8*length(df.close)):length(df.close)]

Step 7: Model Fitting

# df.close.arima is just a name convention 
df.close.arima = auto.arima(df.close.train,
                            seasonal = T,
                            stepwise = T,
                            nmodels = 100,
                            trace = T,
                            biasadj = T)

Output:

 Fitting models using approximations to speed things up...

 ARIMA(2,1,2) with drift         : 21853.71
 ARIMA(0,1,0) with drift         : 21847.69
 ARIMA(1,1,0) with drift         : 21848.52
 ARIMA(0,1,1) with drift         : 21847.56
 ARIMA(0,1,0)                    : 21847.87
 ARIMA(1,1,1) with drift         : 21848.77
 ARIMA(0,1,2) with drift         : 21849.32
 ARIMA(1,1,2) with drift         : 21850.01
 ARIMA(0,1,1)                    : 21847.64

 Now re-fitting the best model(s) without approximations...

 ARIMA(0,1,1) with drift         : 21849.74

 Best model: ARIMA(0,1,1) with drift

Netflix Stock Price Prediction & Forecasting using Machine Learning in R

Recently, many people have been paying attention to the stock market as it offers high risks and high returns. In simple words, “Stock” is the ownership of a small part of a company. The more stock you have the bigger the ownership is. Using machine learning algorithms to predict a company’s stock price aims to forecast the future value of the company stock. Due to some factors or elements stock price is dynamic and volatile and predicting it is more challenging.

Table of Content

DataSet Used for Netflix Stock Price Prediction
Model Used for Netflix Stock Price Prediction
How to Predict Netflix Stock Price using Machine Learning in R

Step 1: Importing the required libraries
Step 2: Loading the Netfix Stock Price Dataset
Step 3: Checking the dimension and missing values of our data
Step 4: Taking the summary of the data
Step 5: Plotting the data
Step 6: Model building
Step 7: Model Fitting

Executing and Checking the Model Summary

Checking Accuracy of Netflix Stock Price Prediction Model
Performance Comparison on Netflix Stock Price Prediction Model on Training vs Test Data Set

Predict Netflix Stock Price

Calculate Test accuracy score

Tags:

#R Projects #AI-ML-DS #R Machine Learning

Model Used for Netflix Stock Price Prediction

Executing and Checking the Model Summary

How to Predict Netflix Stock Price using Machine Learning in R

Step 1: Importing the required libraries

Step 2: Loading the Netfix Stock Price Dataset

Step 3: Checking the dimension and missing values of our data

Step 4: Taking the summary of the data

Step 5: Plotting the data

Step 6: Model building

Step 7: Model Fitting

Netflix Stock Price Prediction & Forecasting using Machine Learning in R

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