Netflix Stock Price Prediction with Machine Learning

Netflix is one of the most popular OTT streaming platforms. It offers a vast collection of television series and films and owns its productions known as Netflix Originals. People who are highly active in stock market investments always keep an eye on companies like Netflix because of its popularity. If you are one of them who would like to learn about how to predict the stock prices of Netflix with machine learning, this article is for you. In this article, I will take you through the task of Netflix stock price prediction with Machine Learning using Python.

Netflix Stock Price Prediction with Machine Learning

To predict the stock prices of Netflix with machine learning, I will be using the LSTM neural network as it is one of the best approaches for regression analysis and time series forecasting. So here, I will start by importing the necessary Python libraries and collecting the latest stock price data of Netflix:

import pandas as pd
import yfinance as yf
import datetime
from datetime import date, timedelta
today = date.today()

d1 = today.strftime("%Y-%m-%d")
end_date = d1
d2 = date.today() - timedelta(days=5000)
d2 = d2.strftime("%Y-%m-%d")
start_date = d2

data = yf.download('NFLX', 
                      start=start_date, 
                      end=end_date, 
                      progress=False)
data["Date"] = data.index
data = data[["Date", "Open", "High", "Low", "Close", "Adj Close", "Volume"]]
data.reset_index(drop=True, inplace=True)
print(data.tail())
           Date        Open        High  ...       Close   Adj Close    Volume
3442 2022-02-01  432.959991  458.480011  ...  457.130005  457.130005  22568100
3443 2022-02-02  448.250000  451.980011  ...  429.480011  429.480011  14346000
3444 2022-02-03  421.440002  429.260010  ...  405.600006  405.600006   9905200
3445 2022-02-04  407.309998  412.769989  ...  410.170013  410.170013   7782400
3446 2022-02-07  410.170013  412.350006  ...  402.100006  402.100006   8228000

[5 rows x 7 columns]

The above dataset is collected using the yfinance API in Python. You can learn more about it from here. Now let’s visualize the stock price data of Netflix by using a candlestick chart as it gives a clear picture of the increase and decrease of stock prices:

import plotly.graph_objects as go
figure = go.Figure(data=[go.Candlestick(x=data["Date"],
                                        open=data["Open"], 
                                        high=data["High"],
                                        low=data["Low"], 
                                        close=data["Close"])])
figure.update_layout(title = "Netflix Stock Price Analysis", 
                     xaxis_rangeslider_visible=False)
figure.show()
Netflix stock price prediction

Now let’s have a look at the correlation of all the columns with the Close column:

correlation = data.corr()
print(correlation["Close"].sort_values(ascending=False))
Close        1.000000
Adj Close    1.000000
High         0.999837
Low          0.999831
Open         0.999643
Volume      -0.395022
Name: Close, dtype: float64

Training LSTM for Netflix Stock Price Prediction

Now I will train the LSTM neural network model for the task of Netflix stock price prediction using Python. Here I will first split the data into training and test sets:

x = data[["Open", "High", "Low", "Volume"]]
y = data["Close"]
x = x.to_numpy()
y = y.to_numpy()
y = y.reshape(-1, 1)

from sklearn.model_selection import train_test_split
xtrain, xtest, ytrain, ytest = train_test_split(x, y, 
                                                test_size=0.2, 
                                                random_state=42)

Now I will prepare the LSTM neural network architecture:

from keras.models import Sequential
from keras.layers import Dense, LSTM
model = Sequential()
model.add(LSTM(128, return_sequences=True, input_shape= (xtrain.shape[1], 1)))
model.add(LSTM(64, return_sequences=False))
model.add(Dense(25))
model.add(Dense(1))
model.summary()
Model: "sequential"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 lstm (LSTM)                 (None, 4, 128)            66560     
                                                                 
 lstm_1 (LSTM)               (None, 64)                49408     
                                                                 
 dense (Dense)               (None, 25)                1625      
                                                                 
 dense_1 (Dense)             (None, 1)                 26        
                                                                 
=================================================================
Total params: 117,619
Trainable params: 117,619
Non-trainable params: 0
_________________________________________________________________

Now here’s how we can train an LSTM neural network model for predicting the stock prices of Netflix using Python:

model.compile(optimizer='adam', loss='mean_squared_error')
model.fit(xtrain, ytrain, batch_size=1, epochs=30)
Epoch 1/30
2757/2757 [==============================] - 26s 8ms/step - loss: 7604.0146
Epoch 2/30
2757/2757 [==============================] - 21s 8ms/step - loss: 631.5478
Epoch 3/30
2757/2757 [==============================] - 21s 8ms/step - loss: 464.4248
Epoch 4/30
2757/2757 [==============================] - 21s 8ms/step - loss: 372.2728
Epoch 5/30
2757/2757 [==============================] - 23s 8ms/step - loss: 393.3465
Epoch 6/30
2757/2757 [==============================] - 23s 8ms/step - loss: 388.6576
Epoch 7/30
2757/2757 [==============================] - 24s 9ms/step - loss: 256.3762
Epoch 8/30
2757/2757 [==============================] - 24s 9ms/step - loss: 233.4001
Epoch 9/30
2757/2757 [==============================] - 24s 9ms/step - loss: 277.1848
Epoch 10/30
2757/2757 [==============================] - 24s 9ms/step - loss: 271.6196
Epoch 11/30
2757/2757 [==============================] - 21s 8ms/step - loss: 314.4778
Epoch 12/30
2757/2757 [==============================] - 22s 8ms/step - loss: 310.0940
Epoch 13/30
2757/2757 [==============================] - 22s 8ms/step - loss: 230.9971
Epoch 14/30
2757/2757 [==============================] - 22s 8ms/step - loss: 320.0016
Epoch 15/30
2757/2757 [==============================] - 21s 8ms/step - loss: 266.8751
Epoch 16/30
2757/2757 [==============================] - 22s 8ms/step - loss: 184.3275
Epoch 17/30
2757/2757 [==============================] - 23s 8ms/step - loss: 187.5371
Epoch 18/30
2757/2757 [==============================] - 23s 8ms/step - loss: 262.0464
Epoch 19/30
2757/2757 [==============================] - 23s 9ms/step - loss: 124.5728
Epoch 20/30
2757/2757 [==============================] - 22s 8ms/step - loss: 235.3674
Epoch 21/30
2757/2757 [==============================] - 22s 8ms/step - loss: 210.1229
Epoch 22/30
2757/2757 [==============================] - 22s 8ms/step - loss: 149.8269
Epoch 23/30
2757/2757 [==============================] - 22s 8ms/step - loss: 137.0508
Epoch 24/30
2757/2757 [==============================] - 22s 8ms/step - loss: 182.6988
Epoch 25/30
2757/2757 [==============================] - 22s 8ms/step - loss: 139.5222
Epoch 26/30
2757/2757 [==============================] - 23s 8ms/step - loss: 174.5475
Epoch 27/30
2757/2757 [==============================] - 23s 9ms/step - loss: 139.0385
Epoch 28/30
2757/2757 [==============================] - 23s 8ms/step - loss: 135.3190
Epoch 29/30
2757/2757 [==============================] - 21s 8ms/step - loss: 122.6136
Epoch 30/30
2757/2757 [==============================] - 21s 8ms/step - loss: 182.0294
<keras.callbacks.History at 0x7f0057f21790>

Now let’s test the model by giving the inputs according to the features that we used to train this model for predicting the final results:

import numpy as np
#features = [Open, High, Low, Adj Close, Volume]
features = np.array([[401.970001, 427.700012, 398.200012, 20047500]])
model.predict(features)
array([[408.57364]], dtype=float32)

So this is how we can train an LSTM neural network model for the task of Netflix stock price prediction with machine learning using Python.

Summary

So this is how we can use machine learning to predict the stock prices of Netflix. Netflix is one of the most popular OTT streaming platforms. People who are highly active in stock market investments always keep an eye on companies like Netflix because of its popularity. I hope you liked this article on the task of Netflix stock price prediction with machine learning using Python. Feel free to ask valuable questions in the comments section below.

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Aman Kharwal

Coder with the ♥️ of a Writer || Data Scientist | Solopreneur | Founder

Articles: 1297

2 Comments

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  1. Hello and many thanks for the lesson. I just faced with a problem and hope you could help me to resolve it.

    I run the code properly. Just when I want to fit the mode, I receive the following error:

    “ValueError: Error when checking input: expected lstm_13_input to have shape (4, 1) but got array with shape (1, 4)”

    Could you please why this problem happens, and how I can resole it.

    Many thanks for your guidance.