Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
using linear regression
Pull Request for PyVerse 💡
Requesting to submit a pull request to the PyVerse repository.
Issue Title
predicting the future stock price based on the historical data using linear regreassion
Info about the Related Issue
What's the goal of the project?
--> the goal of the project is to reduce the risk in the loss in stock market
Describe the aim of the project.
--> predicting the future prices helps in investing thoughtfully
Name
Please mention your name.
Shaista Attar
GitHub ID
Please mention your GitHub ID.
Shaistaattar42
Email ID
Please mention your email ID for further communication.
[email protected]
Identify Yourself
Mention in which program you are contributing (e.g., WoB, GSSOC, SSOC, SWOC).
I am contributing in GSSOC program
Closes
Enter the issue number that will be closed through this PR.
Closes: #558
Describe the Add-ons or Changes You've Made
Give a clear description of what you have added or modified.
Describe your changes here.
Type of Change
Select the type of change:
How Has This Been Tested?
Describe how your changes have been tested.
--> Data Integrity Checks: Ensured that the dataset was correctly loaded, with no missing values in essential columns such as Date, Open, High, Low, Close, and Volume.
--> Feature Selection Validation: Verified that the selected features (Open, High, Low, Close, Volume) had the appropriate data types and ranges.
-->Target Variable Check: Confirmed that the target variable (Target column) was correctly shifted to represent the next day's closing price, ensuring alignment between the features and labels..
Checklist
Please confirm the following: