Help visualizing and print out

[Larz60+]

This has an output, not checking accuracy. You forgot to specify delimiter, so pandas assumed a comma. Actual was a tab.
I printed out the data frame, you can remove that statement.
I am missing something in my python install that's needed for running ( lzma module ).
I don't think this will show up on your app:

# from IPython.core.debugger import set_trace
import pandas as pd
import numpy as np
import os
import matplotlib.pyplot as plt
import time

# Assert starting direstory is script directory
os.chdir(os.path.abspath(os.path.dirname(__file__)))

plt.style.use(style="seaborn")
 
df = pd.read_csv("LTCUSDHour.csv", sep='\t')

print(df)
 
df = df [["Close"]].copy()
#print(df.head()
 
df["Target"] = df.Close.shift(-1)
df.dropna(inplace=True)
#print(df.head())
 
# Train Test Split
def train_test_split(data, perc):
    data = data.values
    n = int(len(data) * (1 - perc))
    return data [:n], data [n:]
train, test = train_test_split(df, 0.2)
 
X = train[:, :-1]
Y = train[:, -1]
 
 
from xgboost import XGBRegressor
 
model = XGBRegressor(objective=r"reg:squarederror", n_estimators=1000)
model.fit(X, Y)
 
test[0]
 
val = np.array(test[0, 0]).reshape(1, -1)
 
pred = model.predict(val)
 
#to Predict
def xgb_predict(train, val):
    train = np.array(train)
    X, Y = train[:, :-1], train[:, -1]
    model = XGBRegressor(objective=r"reg:squarederror", n_estimators=1000)
    model.fit(X, Y)
 
    val = np.array(val).reshape(1, -1)
    pred = model.predict(val)
    return pred[0]
 
xgb_predict(train, test[0, 0])
 
from sklearn.metrics import mean_squared_error
 
def validate(data, perc):
    predictions = []
    train, test = train_test_split(data, perc)
    history = [x for x in train]
    for i  in range(len(test)):
        test_X, test_Y = test[i, :-1], test[i, -1]
        pred = xgb_predict(history, test_X[0])
        predictions.append(pred)
        history.append(test[i])
 
    error = mean_squared_error(test[:, -1], predictions, squared=False)
    return error, test[:, -1], predictions
 
 
time
rmse, Y, pred = validate(df, 0.2)
 
print(rmse)

Output:
UserWarning: Could not import the lzma module. Your installed Python is incomplete. Attempting to use lzma compression will result in a RuntimeError.
                           Local time     Open  High  Low     Close        Volume
0    04.01.2021,00:00:00.000 GMT+0700  151 155   149  153    1.4649           NaN
1    04.01.2021 01:00:00.000 GMT+0700      153   154  150  152.0000  1.272900e+00
2    04.01.2021 02:00:00.000 GMT+0700      152   153  150  152.0000  1.008200e+00
3    04.01.2021 03:00:00.000 GMT+0700      152   157  152  155.0000  1.346800e+00
4    04.01.2021 04:00:00.000 GMT+0700      155   158  155  157.0000  9.463000e-01
..                                ...      ...   ...  ...       ...           ...
305  16.01.2021 17:00:00.000 GMT+0700      147   149  146  148.0000  1.383900e-06
306  16.01.2021 18:00:00.000 GMT+0700      148   151  147  150.0000  6.740000e-07
307  16.01.2021 19:00:00.000 GMT+0700      150   153  147  150.0000  1.301100e-06
308  16.01.2021 20:00:00.000 GMT+0700      150   152  148  149.0000  1.032900e-06
309  16.01.2021 21:00:00.000 GMT+0700      149   149  147  149.0000  8.830000e-07

[310 rows x 6 columns]
/media/captainkirk/Data-3TB/projects/T-Z/T/TryStuff/venv/lib/python3.8/site-packages/xgboost/data.py:104: UserWarning: Use subset (sliced data) of np.ndarray is not recommended because it will generate extra copies and increase memory consumption
  warnings.warn(
2.7550179373488026