Abstract:
In the financial operations, many factors play a decisive role. Price and demand play an
essential role among them, since they are the key determinants of the financial activities.
Demand is not statically placed. In high-range prices, it is marked by unpredictable
fluctuations. The principal determinant of market volatility is this form of fluctuation. We
now have intelligent machines that can find the lessons from data in this age of artificial
intelligence. Data insights can be obtained using machine learning techniques for
prediction purposes. Prediction can be a successful way of eliminating market uncertainty.
We try to find techniques for the machine learning in our work to help us predict the future
demand for products at any business. Our work is based on the raw data from the website
of Kaggle. Machine Learning has various prediction algorithms. We use gradient boosting,
neural networking (MLP regression), linear regression, SVM, Decision Tree, regression
random, forest regression to find the solution. In order to achieve the optimum accurately,
we have compared the accuracy in terms of efficiency
