Machine-learning-Nifty-50predictor-Linear-Regression-Scratch-

Predicts Nifty-50 Closing Price.
In this end-to-end Machine Learning project-tutorial, I have created and trained a model from scratch, using NumPy, that uses the Linear Regression algorithm to predict the Nifty-50 closing price. This problem is in the domain of Time series analysis, but, here it is used to demonstrate application of Linear Regression algorithm.

Understanding the Problem Statement

For this project, I have used the popular WIPRO Nifty-50 dataset for training the model and making predictions.
For the purpose of prediction, only features given in the table below are used. Detailed description about the features is provided within the table.

Features	Description
Prev_Close	Previous Closing Value of Nifty-50
Open	Opening Price For current month
High	Highest price for the month
Low	Lowest Price for the month
Last	Last month's closing price for Nifty-50
VWAP	The volume weighted average price (VWAP) is a trading benchmark used by traders that gives the average price a security has traded at throughout the day, based on both volume and price. It is important because it provides traders with insight into both the trend and value of a security
Volume	Volume is the number of shares of a security traded during a given period of time.
Turnover	Turnover for a particular month
Trades	number of trades during month's period
Deliverable Volume	Deliverable quantity or Deliverable Volume is the quantity of shares which actually move from one set of people to another set of people
%Deliverble	Percentage of deliverable volume

Key Project Takeaways

This project provided hands-on experience in real-time data handling and on the following Machine Learning Techniques:

Data wrangling for preprocessing and cleaning the training and testing data

Normalizing the data

Building an efficient Regression model from scratch using NumPy

Mathematics behind SGD optimization