Initial commit: linear regression for car price prediction

Training, prediction, and visualization programs using gradient descent
with min-max normalization.

1 files changed, 44 insertions, 0 deletions

diff --git a/visualize.py b/visualize.py
new file mode 100644
index 0000000..90f3e15
--- /dev/null
+++ b/visualize.py
@@ -0,0 +1,44 @@
+import csv
+import os
+import matplotlib.pyplot as plt
+
+DATA_FILE = "data.csv"
+THETAS_FILE = "thetas.csv"
+
+
+def load_data():
+    km = []
+    price = []
+    with open(DATA_FILE) as f:
+        reader = csv.reader(f)
+        next(reader)
+        for row in reader:
+            km.append(float(row[0]))
+            price.append(float(row[1]))
+    return km, price
+
+
+def load_thetas():
+    if not os.path.exists(THETAS_FILE):
+        return None, None
+    with open(THETAS_FILE) as f:
+        lines = f.readlines()
+        return float(lines[0]), float(lines[1])
+
+
+def main():
+    km, price = load_data()
+    plt.scatter(km, price)
+    theta0, theta1 = load_thetas()
+    if theta0 is not None:
+        x_line = [min(km), max(km)]
+        y_line = [theta0 + theta1 * x for x in x_line]
+        plt.plot(x_line, y_line, color="red")
+    plt.xlabel("Mileage (km)")
+    plt.ylabel("Price")
+    plt.title("Car price vs mileage")
+    plt.show()
+
+
+if __name__ == "__main__":
+    main()