Yup i used it instead to keep each plot as individual plots instead of plt.close
I finish the Tennis Ace
A Codecademy ‘challenging’ project from the Data Science course Machine Learning section, supervised machine learning (linear regression model)
I have completed Tennis Ace Challenge Project.
I have learned quite a lot. Thanks Codecademy!
My code is below, which quite similiar to the hints
Nice. These defining functions explore my mind~Thanks~
Hi, just a quick Question:
I tried to automate searching for the best variable with this code:
for col in df.columns[1:]: features_train, features_test, outcome_train, outcome_test = train_test_split(df[[col]], df[['Winnings']], train_size = 0.8, random_state = 6) lin_reg = LinearRegression() lin_reg.fit(features_train, outcome_train) print(col) print(lin_reg.score(features_test, outcome_test))
does anyone have an idea how to do this for a 2 or n variable linear model?
Would love to get some ideas!
Continuing the discussion from Tennis Ace Challenge Project (Python):
This is how I did this project. I used .corr() method to find out which features have the strongest relationship with different outcomes. I also plotted those features which have the larger values in correlation matrix against the different outcomes. You can check my code here:
Find my solution here
Hello, here is my solution
Such a good example. Than you so much for sharing it! I learned a lot.
Here is the link to my solution for the Tennis Ace challenge project: GitHub
This was a fun and light project that I found closely corresponds to the lessons.
Any feedback is appreciated.
I am uploading my code here. Check it out!
Any feedback will be so appreciated!
Check out my project for linear regression.
any feedback is much appreciated.
Hey everyone! Just finished tennis ace project ! If you want to take a look here is my solution
Here’s my code! Any feedback is welcome
Here is my code for the Tennis Ace Challenge Project.
I want to share with you my project of Machine Learning & Tennis
I hope you like it