mathdatech/python-scripts
1
0
Fork 0
mirror of https://github.com/metafy-social/python-scripts.git synced 2024-11-23 20:11:10 +00:00
Code Issues Packages Projects Releases Wiki Activity

Merge pull request #310 from NebulaAnish/master

Browse Source 
Added Linear Regression file to find linear relation between two rela…
This commit is contained in:
Bartick Maiti 2022-10-10 22:31:52 +05:30 committed by GitHub
commit 28e4fcdb98
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
5 changed files with 5681 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

284
scripts/Linear Regression/Linear Regression.ipynb Normal file
View File

File diff suppressed because one or more lines are too long

24
scripts/Linear Regression/README.md Normal file
View File

@ -0,0 +1,24 @@
# Linear Regression Model
A linear regression model used written in python language in Jupyter Notebook to help find linear relation between two variables.
## Tech Stack 🛠
- Python
- Pandas
- Matplotlib
- sklearn
## Features ⚡
- Find a line that optimally describes the relation between the given data
- Find slope and intercept of the line to re-plot it anywhere
## Steps to use 👀
- Save a data.csv file in the same folder as the project and name it "data.csv" [lowercase]
- csv file must have only two columns with names col_1 and col_2 where col_1 is feature and col_2 is the target variable.
PS: Have a look at the example code in the folder which is used to find relation between budget and revenue of the movie
<hr>

334
scripts/Linear Regression/example implementation/Linear Regression.ipynb Normal file
View File

File diff suppressed because one or more lines are too long

5035
scripts/Linear Regression/example implementation/cost_revenue_clean.csv Normal file
View File

File diff suppressed because it is too large Load Diff

4
scripts/Linear Regression/requirements.txt Normal file
View File

@ -0,0 +1,4 @@
- Python 3.6 or higher
- Anaconda Installation
or can be used by uploading it to the online jupyter notebook version