Srinidh-Jilla

Srinidh-Jilla

@Srinidh-Jilla
3
Followers
0
Following
10
Public Repos
0
Private Repos

Language Breakdown

Lines of code distribution across 10 owned repositories

5.0M Total LOC
Jupyter Notebook
4,008,460 lines
80.6%
N/A
JavaScript
591,045 lines
11.9%
N/A
CSS
279,586 lines
5.6%
N/A
Python
68,939 lines
1.4%
N/A
HTML
28,268 lines
0.6%
N/A
Other
37 lines
0.0%
N/A
I

I-Shaped Developer

I-shaped

Specialist — deep expertise in Jupyter Notebook

Jupyter Notebook
JavaScript
CSS
Python
HTML

Collaboration Network

Global Impact visualization

LIVE
srinidh-jilla
0 active collaborators

Repos

10

PRs

0

Growth

+18%

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Coding Streak

Contribution activity over the past year

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Synced via GitHub

Top Repositories

Analyzing-Box-Office-Data-with-Seaborn-plotly-and-Python

In this project, I worked with the The Movie Database (TMDB) Box Office Prediction data set. The motion picture industry is raking in more revenue than ever with its expansive growth the world over. Can we build models to accurately predict movie revenue? Could the results from these models be used to further increase revenue? I tried to answer these questions by the way of exploratory data analysis (EDA) in this project and the next. The statistical data visualization libraries Seaborn and Plotly will be my workhorses to generate interactive, publication-quality graphs.

1 0
Jupyter Notebook
anirudh
0 0
Jupyter Notebook
beta_prototype
0 0
Python
Alpha-Prototype
0 0
Python
Sky-browser
0 0
JavaScript
1st-innings-score-prediction-IPL
0 0
JavaScript
The-FP-NN
0 0
Python
the-FP
0 0
Jupyter Notebook
ESS_project
0 0
Python
COVID19-Data-Analysis-using-Python.

In this Project, I have learned how to preprocess and merge datasets to calculate needed measures and prepare them for an Analysis. In this project I have worked with COVID19 dataset, published by John Hopkins University, which consist of the data related to cumulative number of confirmed cases, per day, in each Country. Also another dataset consist of various life factors, scored by the people living in each country around the globe. I have merged these two datasets to see if there is any relationship between the spread of the the virus in a country and how happy people are, living in that country.

0 0
Jupyter Notebook

Open Source Impact

Contributions to external projects

0 merged PRs

No external contributions found.