[Dropbysometime]

Why do Random Forests Work? Understanding Tree Ensembles as Self-Regularizing Adaptive Smoothers https://journals.plos.org/plosone/article?id=10.1371/journal...

Found the GitHub repository code https://github.com/AzazHassankhan/Machine-Learning-based-Tra...

Made some changes from line 9 to 70 . Usee yfinance instead of alpaca Replace all code with code below until line# 70

import plotly.offline as pox

import plotly.graph_objs as go

import numpy as np

import talib as tl

import matplotlib.pyplot as plt

import pandas as pd

import numpy as np

import talib as ta

from sklearn.model_selection import train_test_split

from sklearn.metrics import

accuracy_score,classification_report

#import alpaca_trade_api as tradeapi

#from alpaca_trade_api import TimeFrame, TimeFrameUnit

from sklearn.ensemble import RandomForestClassifier

from sklearn.preprocessing import StandardScaler

import seaborn as sns

from matplotlib.pyplot import figure

from statsmodels.tsa.stattools import adfuller

from sklearn.svm import SVC

from sklearn.neighbors import KNeighborsClassifier

from sklearn.linear_model import LogisticRegression

from sklearn.ensemble import AdaBoostClassifier

import yfinance as yf

from datetime import datetime

symb = "TSLA"

start = datetime(2021, 10, 18, 9, 30, 0)

end = datetime(2021, 10, 18, 10, 30, 0)

df =yf.download("TSLA", period="1mo",interval ="15m")

next=df.copy()

next.tail()

df['close']=df['Close']

df['high']=df['High']

df['low']=df['Low']

df['open']=df['Open']

df['volume']=df['Volume']