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Stock Price Prediction using Regression & LSTM


                                                 AI , ML and deep learning have been trasforming finance and investing. “AI is to trading what fire was to othe caveman!”. AI powered trading could potentially redice the risk and maximise the returns. Many companies are leveraging AI in trading.

                                          In this project we will train the Ridge Regression model and deep neural networks to predict future stock prices. AL/ML model will be trained using historical stock price data along with the volume of transactions. and also we use a type of neural network known as Long Short-term Memory Network(LSTM)

Required Libraries:

import pandas as pd
import as px
from copy import copy
from scipy import stats
import matplotlib.pyplot as plt
import numpy as np
import plotly.figure_factory as ff
from sklearn.linear_model import LinearRegression
from sklearn.svm import SVR
from sklearn.model_selection import train_test_split
from sklearn.metrics import r2_score
from tensorflow import keras

Collab Link: Click Here
**Note: we pridicted the prices by using both methods i.e. rigid regression and LSTM**


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