mohneesh7

mohneesh7

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

mohneesh7

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:

mohneesh7

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

mohneesh7

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)