Initial commit: linear regression for car price prediction

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

1 files changed, 40 insertions, 0 deletions

diff --git a/predict.py b/predict.py
new file mode 100644
index 0000000..3bbcb47
--- /dev/null
+++ b/predict.py
@@ -0,0 +1,40 @@
+import os
+import sys
+
+THETAS_FILE = "thetas.csv"
+
+
+def load_thetas():
+    if not os.path.exists(THETAS_FILE):
+        return 0.0, 0.0
+    with open(THETAS_FILE) as f:
+        lines = f.readlines()
+        return float(lines[0]), float(lines[1])
+
+
+def estimate_price(mileage, theta0, theta1):
+    return theta0 + theta1 * mileage
+
+
+def main():
+    theta0, theta1 = load_thetas()
+    if theta0 == 0.0 and theta1 == 0.0:
+        print(f"Warning: no trained model found ({THETAS_FILE}), using θ0=0 θ1=0")
+    else:
+        print(f"Loaded θ0={theta0}, θ1={theta1}")
+    while True:
+        try:
+            raw = input("\nMileage (km): ")
+            mileage = float(raw)
+            price = estimate_price(mileage, theta0, theta1)
+            print(f"Estimated price: {price:.2f}")
+        except ValueError:
+            print("Please enter a valid number.")
+
+
+if __name__ == "__main__":
+    try:
+        main()
+    except (KeyboardInterrupt, EOFError):
+        print()
+        sys.exit(0)