Performed EDA: Handled Missing Values and Outliers in Stock Price Data

[RB137]

I have handled the data quality issues in the stock price dataset and fixed the problems related to missing values and outliers mentioned in issue #137 :

Fixes: #137

Handling Missing Values:

Used forward fill (ffill) to fill missing values in the Open, High, Low, Close, Adj Close, and Volume columns, ensuring data continuity for time-series analysis.

Outlier Treatment:

Applied the Interquartile Range (IQR) method to detect and cap outliers in the stock price columns, preventing distortion from extreme values and creating a more reliable dataset.
These fixes enhance the dataset's quality, making it ready for further analysis and predictive modeling.

Before:

After:

Added documentation to enhance code readability :

[github-actions]

Ensure the PR matches the requirements mentioned in the Contribution guide. The maintainer might get in touch to enusre quality. Thanks for your time

[RB137]

I’ve handled missing values and outliers and added some documentation.
Have a look at it, review it, and kindly accept my PR! @rohitinu6 @Mayureshd-18 @jvedsaqib

[Mayureshd-18]

Looks good to me!

[RB137]

@Mayureshd-18 Thanks for the approval! Please add the gssoc-ext, hacktoberfest, hacktoberfest-accepted, and suitable level labels, and kindly merge it to close the issue. @rohitinu6

[RB137]

@rohitinu6 @Mayureshd-18
Thanks for approving. The issue seems fixed, so please merge my PR and close the issue #137.

[RB137]

@rohitinu6 @Mayureshd-18
Also, please label it as level 2, as it matches the difficulty level of data refinement. and outliers handling

[Mayureshd-18]

@RB137 The assigned level 1 is appropriate based on the root objective which is the refinement of code. Higher levels are reserved for new algos and procedures.
Thanks for understanding! Merging!

[github-actions]

🎉🎉 Thank you for your contribution! Your PR #148 has been merged! 🎉🎉