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 plotly.express 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**