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import pandas as pd | |
import plotly.graph_objects as go | |
from scipy.signal import savgol_filter | |
data = pd.read_csv('DOGE-USD.csv', index_col=0, parse_dates=True) | |
data.head() | |
# Savitzky-Golay filter | |
y_filtered = savgol_filter(data.High, 11, 2) |
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import streamlit as st | |
import pandas as pd | |
import numpy as np | |
from sklearn.svm import SVC | |
from joblib import dump, load | |
from sklearn.model_selection import train_test_split | |
# from sklearn.metrics import ConfusionMatrixDisplay | |
from sklearn.metrics import plot_confusion_matrix | |
from sklearn.metrics import precision_score, recall_score, f1_score |
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from math import sqrt | |
def distance(a, b): | |
return sqrt((a[0] - b[0]) ** 2 + (a[1] - b[1]) ** 2) | |
def point_line_distance(point, start, end): | |
if (start == end): | |
return distance(point, start) | |
else: |
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import tensorflow.keras as keras | |
from tensorflow.keras.datasets import mnist | |
from tensorflow.keras.models import Sequential | |
from tensorflow.keras.layers import Dense, Dropout, LSTM | |
from tensorflow.keras.optimizers import Adam | |
from tensorflow.compat.v1.keras.layers import CuDNNLSTM | |
#Importing the data | |
(X_train, y_train),(X_test, y_test) = mnist.load_data() # unpacks images to x_train/x_test and labels to y_train/y_test | |
#Normalizing the data |
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