Predicts Stock price data of the fifty stocks in NIFTY-50 index from NSE India.
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, further, the model with the best accuracy is embedded in the web-app developed using streamlit module for the purpose of prediction.
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 |
- 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
- Web-app development using streamlit module from python