Scale

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Welcome to IDS-101-13: Thinking Machines!
It's Friday of week 9
Today we're doing something interesting I hope.
- "Can I teach them something cool?" -Shouvik
- An AI primer, pursuant to Week 2 deliverable: "Preliminary Analysis".
- Should be generically good for art people as well, if indirectly.

ML

Whirlwind tour, some review, some novel
Scale: Macronutrients
Linear Regression: Wealth vs Life Expectancy
Polynomial Regression: Wealth vs Life Expectancy

Scale

Suppose we eat food.
FDA keeps track of food because it's the government or something.
Many people eat food for calories, a unit of energy.
Most calories come from protein, carbohydrates or fats.
Proteins and carbohydrates provide 4 calories per gram
Fats provide 9 calories per gram

"Do it for the gram." - me

Scale

     protein_value  fat_value  carbohydrate_value
0             3.47       8.37                4.07
1             4.27       8.79                3.87
2             2.44       8.37                3.57
3             2.44       8.37                3.57
4             2.44       8.37                3.57
..             ...        ...                 ...
307           2.44       8.37                3.57
308           2.44       8.37                3.57
309           2.44       8.37                3.57
310           2.44       8.37                3.57
311           2.44       8.37                3.57

[312 rows x 3 columns]

We probably need to divide by cals/gram.

Scale

df['fat_value'] = df['fat_value'] / 9
df['carbohydrate_value'] = df['carbohydrate_value'] / 4
df['protein_value'] = df['protein_value'] / 4
print(df[:5])

We get... protein_value fat_value carbohydrate_value 0 0.216875 0.103333 0.254375 1 0.266875 0.108519 0.241875 2 0.152500 0.103333 0.223125 3 0.152500 0.103333 0.223125 4 0.152500 0.103333 0.223125
Numbers much closer together.

ML

Whirlwind tour, some review, some novel
✓ Scale: Macronutrients
Linear Regression: Wealth vs Life Expectancy
Polynomial Regression: Wealth vs Life Expectancy

Linear Regression

People with more money might live longer, I don't know.
Sure seems like it'd help.
Is this true of countries?

Linear Regression

I didn't preserve this, but x is gdp and y is life expectancy. 2022_x 2022_y 0 33300.838819 74.992000 1 1642.432039 62.899031 2 352.603733 62.879000 3 1788.875347 57.626176 4 2933.484644 61.929000 .. ... ... 260 3745.560367 72.598000 261 5290.977397 79.524000 263 6766.481254 61.480000 264 1456.901570 61.803000 265 1676.821489 59.391000
Let's take a linear regression.

Linear Regression

# prompt: sklearn linear regression over dataframe "both"

from sklearn.linear_model import LinearRegression

# Prepare the data for linear regression
X = both[['2022_x']]  # GDP per capita in 2022
y = both['2022_y']    # Life expectancy in 2022

# Create and train the linear regression model
model = LinearRegression()
model.fit(X, y)

# Print the model coefficients
print("Coefficients:", model.coef_)
print("Intercept:", model.intercept_)

Linear Regression

We get... Coefficients: [0.00019047] Intercept: 68.69969966270624
Let's plot it.

Oh that is high key not linear.

ML

Whirlwind tour, some review, some novel
✓ Scale: Macronutrients
✓ Linear Regression: Wealth vs Life Expectancy
Polynomial Regression: Wealth vs Life Expectancy

Polynomial Regression

That looked like a little money helped a lot, and a lot more money helped very little.
Lines don't do that, they just 📈
Let's try a parabola - a polynomial of degree 2.

Well actually a parabola will go up then down, so I did degree 3 whoops. # prompt: do polynomial regression of degree 3 over both # Create polynomial features up to degree 3 from sklearn.preprocessing import PolynomialFeatures poly = PolynomialFeatures(degree=3) X_poly = poly.fit_transform(X) # Create and train the polynomial regression model model_poly = LinearRegression() model_poly.fit(X_poly, y) # Print the model coefficients print("Polynomial Coefficients:", model_poly.coef_) print("Polynomial Intercept:", model_poly.intercept_)
Let's see it.

Polynomial Regression

We get... Polynomial Coefficients: [ 0.00000000e+00 7.16975595e-04 -8.57111644e-09 2.87583542e-14] Polynomial Intercept: 65.12557554366506
Let's plot it.

Oh that is high key not polynomial either lol.

ML

Whirlwind tour, some review, some novel
✓ Scale: Macronutrients
✓ Linear Regression: Wealth vs Life Expectancy
✓ Polynomial Regression: Wealth vs Life Expectancy

Scale

Suppose a little bit of something (money) helps a lot.
But a lot doesn't help anymore.
Think: Going from 99 to 100 thousand dollars isn't as impactful as going from 0 to 1.
Usually that's a logarithm.

Linear Regression

We get... Coefficients: [4.54777158] Intercept: 31.58450749077179
Let's plot it.

Yeah that's a log.

Colab Link

Link
If we have time I'll demo the wrangling.

Scale

Fri W3