diff --git a/run_all_regressions.py b/run_all_regressions.py
index e1f9329..ceed036 100644
--- a/run_all_regressions.py
+++ b/run_all_regressions.py
@@ -3,6 +3,7 @@ import pandas as pd
 from sklearn.metrics import mean_squared_error, r2_score
 from matplotlib import pyplot as plt
 import numpy as np
+from sklearn.preprocessing import PolynomialFeatures
 
 
 def k_days_into_future_regression(X, y, k, n0):
@@ -46,6 +47,20 @@ def standard_lr(x, y):
   return regr.intercept_, regr.coef_, r2, mse
 
 
+def poly_regression(x, y, degree):
+  # Polynomial regression with nth degree, gives back rmse and r2
+  polynomial_features = PolynomialFeatures(degree=degree)
+  x_poly = polynomial_features.fist_transform(x)
+
+  model = linear_model.LinearRegression()
+  model.fit(x_poly, y)
+  y_poly_pred = model.predict(x_poly)
+
+  rmse = np.sqrt(mean_squared_error(y, y_poly_pred))
+  r2 = r2_score(y, y_poly_pred)
+  return rmse, r2
+
+
 def run_all_linears():
 
     # Reads in the neccessary csv file
@@ -71,6 +86,17 @@ def run_all_polynomials():
     for i in range(4, 11):
         for j in range(1, 11 - i):
             mat = df[[df.columns[i], df.columns[i + j]]].values
+            for d in range(2, 5):
+                rmse, r2 = poly_regression(mat[:, 0], mat[:, 1], d)
+                plt.figure(figsize=(6, 6))
+                plt.xlabel(df.columns[i])
+                plt.ylabel(df.columns[i + j])
+                plt.title('r2: ' + str(r2) + 'degree: ' + str(d))
+                plt.scatter(mat[:, 0], mat[:, 1])
+                plt.savefig('wellness_poly_regressions/' + df.columns[i] + '_vs_' + df.columns[i + j] + '_' + str(d) + '_degree.png')
+                print(df.columns[i] + '_vs_' + df.columns[i + j] + '_degree_' + str(d) + '_r2=' + str(r2) + '_rmse=' + str(rmse))
+                plt.close()
 
 
 run_all_linears()
+run_all_polynomials()