Perform univariate analysis on the target variable

[BALaka-18]

Description

Perform Univariate analysis on the target variable of the provided dataset, as has been defined in the README.md file. Support the analysis with appropriate visualizations.

Acceptance Criteria

• Code must be properly formatted.
• All analysis that can be supported with visualization must have corresponding visualizations, else they won't be accepted.
• Conclusions must be written down in a separate Markdown cell, point-wise.

Definition of Done

• All of the required items are completed.
• Approval by 1 mentor.

Time Estimation

1 hour

[soumya997]

Time Estimation
1 hour

@Nibba2018 can you please explain what did you mean by Time Estimation, 1 hour? Is it like we have to solve the issue and make PR with in 1hour?

[BALaka-18]

@soumya997 it is preferable if you do it within 1 hour. Otherwise we'll give you a buffer of 1 hour more.
Since you know what issues are present, you can start preparing beforehand and just come and claim the issue when Hacktoberfest starts.
So I guess 1 hour would be enough to make a PR, as you will already have made the files to commit, right ?

[soumya997]

@BALaka-18 ok.

[soumya997]

I would like to work on this issue, @BALaka-18

[BALaka-18]

I would like to work on this issue, @BALaka-18

Sure, I am assigning it to you. Start working. Remeber, PR must be after Oct 1.

[soumya997]

hey @BALaka-18 @Nibba2018 I need to clarify something,
So, in the contributing.md you mentioned we need to put the accuracy that we got in the file name of the notebook, but for regression, we don't generally calculate accuracy, we measure the performance of the model by calculating the total error let it be r2_score, rmse or mse. So, if can I assume that be saying accuracy you mean the error value.

2nd, If I just convert the .pynb to .py will it be ok?
3rd, which performance matrix should I use (as in the notebook r2_score,mse and ase is mentioned ) while naming the .pynb or .py file?

[Nibba2018]

Hello @soumya997 , sorry for the late response.

1. For regression use R-Squared Score.
2. Yes, that works 👍
3. I guess you mean performance metrics and if that's the case then go ahead with R-Squared Score.

[soumya997]

No problem🤗
Thanks for the response @Nibba2018

[sayantandasgupta]

Excuse me, @BALaka-18 @Nibba2018 , I would like to work on this issue, but I have some questions.
a) Is there a time limit in which I have to make a pull request?
b) I do not have Anaconda or Jupyter Notebook installed in my machine and I do all my work on Google Colab. But the link for the Colab notebook provided opens to the site for the wildfire community or something of the sort, I do not remember the name.
c) Is there any way that I can clone from this repo to Google Colab and Submit my Colab notebook here.
It would be helpful for me to know these. Thank You

[sayantandasgupta]

Oh and I could not understand the part about logarithm transform

[BALaka-18]

Excuse me, @BALaka-18 @Nibba2018 , I would like to work on this issue, but I have some questions.
a) Is there a time limit in which I have to make a pull request?
b) I do not have Anaconda or Jupyter Notebook installed in my machine and I do all my work on Google Colab. But the link for the Colab notebook provided opens to the site for the wildfire community or something of the sort, I do not remember the name.
c) Is there any way that I can clone from this repo to Google Colab and Submit my Colab notebook here.
It would be helpful for me to know these. Thank You

@arka2001
a) There isn't, but to be considered for Hacktoberfest, I would suggest you do it at the earliest because it takes 14 days for your PR to be accepted after we've merged it. We will need time to review your PR as well, right ?

b) We are sorry about the link, we overlooked it. The link will be rectified. Also, you have to work on the Submission_Template.ipynb notebook under the template directory, no direct Colab link will be given.

c) Since you are comfortable working in Colab, which is absolutely fine, you can open GitHub files directly in Colab. Just follow these steps, in case you are unaware :

1. Copy this link : Template_link
2. Open Colab, and switch to the GitHub option instead of the default Recent option.
3. Paste the link from step 1.
4. Open the file it points to (it'll be highlighted in grey).

About submission, just download both the .ipynb and .py versions of the Colab notebook, and push the changes to your forked repo, and create a PR (follow the folder/file structure strictly, as mentioned in the CONTRIBUTING.md)

[sayantandasgupta]

Okay, @BALaka-18 Thank You.