Time-series forecasting is a critical component in the field of machine learning, especially when dealing with data that changes over time, such as stock prices. Unlike conventional models that treat observations independently, time-series models recognize and utilize the chronological order of data. This approach is essential for understanding non-stationary data…

Today I am going to show you step by step how you can forecasting any stock, in our case boeing stock using deep learning. Let’s start coding: The first step would be creating the mydatabse in mongoDB and loading the json api file which is unstructred data into database and…

Today i am going to show you, how you can use machine learning and computer vision to understand sign language. Let’s start coding: # Import libraries import numpy as np import pandas as pd import matplotlib.pyplot as plt from pathlib import Path import os.path from sklearn.model_selection import train_test_split from tensorflow.keras.preprocessing.image import ImageDataGenerator import tensorflow as tf from sklearn.metrics import…

A Project to identify arbitrage opportunities between two stock exchanges trading the same stock. The algorithm searches for the possibility of a mismatch and trades on it. …

In this article, I will use XGBRegressor from XGBoost library to predict future prices of stocks using technical indicators as features. Let’s start coding: import os import numpy as np import pandas as pd import xgboost as xgb import matplotlib.pyplot as plt from xgboost import plot_importance, plot_tree from sklearn.metrics import mean_squared_error from sklearn.preprocessing import MinMaxScaler from sklearn.model_selection import…

Today I am going to show you how you can forecast stock prices using lstm. Let me show you how you can do it. import numpy as np import pandas as pd from keras.models import Sequential from keras.layers import LSTM,Dense,Dropout,Flatten,Input import matplotlib.pyplot as plt from keras.layers import LSTM,Dense,Dropout,MaxPooling1D,TimeDistributed,Conv1D from keras.models import load_model from keras.models import Model from sklearn.model_selection…

Forecasting stock with 1D Convolution long short term memory. Let’s start: import numpy as np import pandas as pd from keras.models import Sequential from keras.layers import LSTM,Dense,Dropout,Flatten,Input import matplotlib.pyplot as plt from keras.layers import LSTM,Dense,Dropout,MaxPooling1D,TimeDistributed,Conv1D from keras.models import load_model from keras.models import Model First we import necessary libraries such as…

In today’s fast-paced financial markets, day trading has emerged as a popular and lucrative strategy for those seeking short-term gains. With the rapid advancements in technology and the growing influence of artificial intelligence, traders are now leveraging the power of neural networks and deep learning to optimize their trading decisions…

In this article, we delve into the fascinating world of stock price prediction using time series analysis. — As stock prices fluctuate over time, understanding their patterns and trends becomes crucial for making informed investment decisions. We will explore various techniques, such as moving averages, linear regression, seasonal plots, and lag embeddings, to model and predict stock prices. Throughout the article, we will use a real-world dataset to…