# The most basic example of Linear Regression

Just for fun, I wanted to learn how to do linear regression and here’s the example I come up with.

Let’s say you have a historical data of 1000 people who dined in your restaurant and left a tip. This is going to be perfect data because I generated. In the real world you will not find something like this.

If you don’t understand Linear Regression like me before I wrote this post, I recommend you to read this basic linear regression..

The idea is that you have two variables. In this case, it’s **tips** and **total amount of bill**. You should explore the data by plotting the graph of these two variables. From my generated data you will get something like this.

You can clearly see that there’s a strong correlation between the amount of tip and meal.

Now if you can find the slope of the graph and intercept you should be able to use the formula.

```
Y = MX + C
M = slope of the graph
C = Intercept
```

If you’re lazy to look at my notebook.

Then you can run this code.

```
import pandas as pd
import numpy as np
from scipy import stats
total_bills = np.random.randint(100, size=1000)
tips = total_bills * 0.10
x = pd.Series(tips, name='tips')
y = pd.Series(total_bills, name='total_bills')
df = pd.concat([x, y], axis=1)
slope, intercept, r_value, p_value, std_err = stats.linregress(x=total_bills, y=tips)
predicted_tips = (slope * 70) + intercept
```

The result is $7 which corresponds to the 10% tip.

Til next time,

noppanit
at 00:00