add: Initial commit

data.csv

@@ -0,0 +1,25 @@
+km,price
+240000,3650
+139800,3800
+150500,4400
+185530,4450
+176000,5250
+114800,5350
+166800,5800
+89000,5990
+144500,5999
+84000,6200
+82029,6390
+63060,6390
+74000,6600
+97500,6800
+67000,6800
+76025,6900
+48235,6900
+93000,6990
+60949,7490
+65674,7555
+54000,7990
+68500,7990
+22899,7990
+61789,8290

model.py

@@ -0,0 +1,74 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+class Model:
+    def __init__(self, data_name="data.csv", learning_rate=0.01, epochs=2000):
+        with open(data_name, 'r') as file:
+            self.data = pd.read_csv(file)
+            self.km = [self.data["km"][i] for i in range(len(self.data))]
+            self.price = [self.data["price"][i] for i in range(len(self.data))]
+            self.learning_rate = learning_rate
+            self.epochs = epochs
+            self.theta0 = 0
+            self.theta1 = 0
+            self.km_min = 0
+            self.km_max = 0
+            self.cost_history = []
+            self.m = len(self.data)
+            self.normalize_values()
+    def __estimate_price(self, mileage):
+        return self.theta0 + (self.theta1 * mileage)
+
+    def estimate_price(self, mileage):
+        return self.__estimate_price((mileage - self.km_min) / (self.km_max - self.km_min))
+
+    def normalize_values(self):
+        self.km_min = np.min(self.km)
+        self.km_max = np.max(self.km)
+        for i in range(len(self.km)):
+            self.km[i] = (self.km[i] - self.km_min) / (self.km_max - self.km_min)
+    
+    def calculate_thetas(self):
+        tmp_t0 = 0
+        tmp_t1 = 0
+        cost = 0
+        for i in range(self.m):
+            pred = self.__estimate_price(self.km[i])
+            tmp_t0 += pred - self.price[i]
+            tmp_t1 += (pred - self.price[i]) * self.km[i]
+            cost += (pred - self.price[i]) ** 2
+        self.cost_history.append(cost / (2 * self.m))
+        return (tmp_t0, tmp_t1)
+
+    def train(self):
+        for epoch in range(self.epochs):
+            tmp_t0, tmp_t1 = self.calculate_thetas()
+            self.theta0 -= self.learning_rate * tmp_t0
+            self.theta1 -= self.learning_rate * tmp_t1
+        pass
+    def visualize(self):
+        km_range = np.linspace(min(self.data["km"]), max(self.data["km"]), 100)
+        predicted_prices = self.theta0 + (self.theta1 * (km_range - self.km_min) / (self.km_max - self.km_min))
+        fig, (regression, cost) = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
+
+        regression.scatter(self.data["km"], self.data["price"], color='blue', label='Données')
+        regression.plot(km_range, predicted_prices, color='red', label='Regression line')
+
+        regression.set_xlabel("Kilometers (km)")
+        regression.set_ylabel("Price (Euros)")
+        regression.set_title("Linear Regression")
+        regression.legend()
+        regression.grid(True)
+
+        cost.plot(range(1, len(self.cost_history) + 1), self.cost_history, color='green')
+        cost.set_title("Coût vs Itérations")
+        cost.set_xlabel("Itérations")
+        cost.set_ylabel("Coût")
+        cost.grid(True)
+        plt.show()
+        pass
+
+first_model = Model()
+first_model.train()
+first_model.visualize()