add: R2 Score to get confidence

confidence.py

@@ -0,0 +1,44 @@
+import pandas as pd
+'''
+Class Confidence:
+    Calculate the confidence using the R-Square score
+    https://en.wikipedia.org/wiki/Coefficient_of_determination
+'''
+
+class Confidence:
+    def __init__(self, thetas_path="thetas.csv", data_path="datasets/data.csv"):
+        self.theta_path = thetas_path
+        self.data_path = data_path
+        self.data = []
+        self.theta0 = 0
+        self.theta1 = 0
+        self.get_thetas()
+        self.get_data()
+    def get_thetas(self):
+        try:
+            with open(self.theta_path, 'r') as file:
+                data = pd.read_csv(file)
+                self.theta0 = data["theta0"].iloc[0]
+                self.theta1 = data["theta1"].iloc[0]
+        except:
+            print("! Warning, no trained model has been found")
+
+    def get_data(self):
+        try:
+            with open(self.data_path, 'r') as file:
+                self.data = pd.read_csv(file)
+        except:
+            print("! Warning, no data has been found")
+
+    def estimate_price(self, mileage):
+        return self.theta0 + (self.theta1 * mileage)
+
+    def get_confidence(self):
+        predicted_price = []
+        for data in self.data["km"]:
+            predicted_price.append(self.estimate_price(data))
+        avg_price = sum(self.data["price"]) / len(self.data["price"])
+        ss_tot = sum((y - avg_price) ** 2 for y in self.data["price"])
+        ss_res = sum((y - y_hat) ** 2 for y, y_hat in zip(self.data["price"], predicted_price))
+        r2 = 1 - (ss_res / ss_tot)
+        return r2