Welcome back! It's currently the end of week 3 of my Lambda School journey and I thought I'd provide an update on the data set we used two weeks ago. This week, we'll look at some additional visualizations of the data plus a simple linear regression model (which uses some of the pre-course work for the course).

First, let's clean up the data a bit.

```
video_game_ratings = video_game_ratings.dropna(subset=['User_Score', 'Critic_Score', 'Year_of_Release'])
video_game_ratings = video_game_ratings[video_game_ratings['User_Score'] != 'tbd']
video_game_ratings['User_Score_Numeric'] = pd.to_numeric(video_game_ratings['User_Score'])
video_game_ratings['Critic_Score_Numeric'] = pd.to_numeric(video_game_ratings['Critic_Score'])
```

Next, let's look at some scatterplots to see if we can identify a relationship between the variables we are interested in.

We see that there is a slight positive relationship between user and critic scores and global sales. This is what we would expect.

Before we actually do the linear regression, let's be sure to check for correlations between our two variables.

It looks like there is a pronounced positive correlation between user score and critic score (which is what we would expect). We can confirm this by calling `.corr()`

on the required feature set:

Since we don't want to do a multiple linear regression with correlated features, we'll do individual regressions of global sales on both critic score and user score.

For a regression on user score, we get a `beta_0`

of `-0.11201624`

and a `beta_1`

of `0.01213527`

. For our training data, we get a mean absolute error of `0.762`

, while for our test data, we get a mean absolute error of `0.8202`

.

For a regression on critic score, we get a `beta_0`

of `-1.54676995`

and a `beta_1`

of `0.03283348`

. For our training data, we get a mean absolute error of `0.745`

, while for our test data, we get a mean absolute error of `0.8095`

.

We see from our models that there is (as expected) a positive relationship between both critic and user scores and global sales. We also notice that critic score is the better predictor of the two features.

And that's it! You can find the completed Google Colab notebook here. Hope you enjoyed this dive into simple linear regression. See you next time!

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