Python can make you rich in the stock market!
Step 1:- Let's Make Necessary Imports
import quandl
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
Step 2:- Get Amazon stock data
amazon = quandl.get("WIKI/AMZN")
print(amazon.head())
Step 3:- Get only the data for the Adjusted Close column
amazon = amazon[['Adj. Close']]
print(amazon.head())
Step 4:- Predict for 30 days; Predicted has the data of Adj. Close shifted up by 30 rows
forecast_len=30
amazon['Predicted'] = amazon[['Adj. Close']].shift(-forecast_len)
print(amazon.tail())
Step 5:- Drop the Predicted column, turn it into a NumPy array to create dataset
x=np.array(amazon.drop(['Predicted'],1))
#DataFlair - Remove last 30 rows
x=x[:-forecast_len]
print(x)
Step 6:- Create a dependent dataset for predicted values, remove the last 30 rows
y=np.array(amazon['Predicted'])
y=y[:-forecast_len]
print(y)
Step 7:- Split datasets into training and test sets (80% and 20%)
x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.2)
Step 8:- Create SVR model and train it
svr_rbf=SVR(kernel='rbf',C=1e3,gamma=0.1)
svr_rbf.fit(x_train,y_train)
Step 9:- Get the score
svr_rbf_confidence=svr_rbf.score(x_test,y_test)
print(f"SVR Confidence: {round(svr_rbf_confidence*100,2)}%")
Step 10:- Create a Linear Regression model and train it
lr=LinearRegression()
lr.fit(x_train,y_train)
Step 11:- Get score for Linear Regression
lr_confidence=lr.score(x_test,y_test)
print(f"Linear Regression Confidence: {round(lr_confidence*100,2)}%")
Thanks !!