final push var.1

README.md

@@ -1,5 +1,23 @@
-# Netflix AI Greenlight Challenge
+# group member !
+Vincent kuo
+Iris Su
+Sean Shen
+Jeannie LAN
+# The changes include:
+Our analysis is available under the netflix-writer.ipynb exercise;
+temp contains temporary files and Vincent's presentation notes, which can be ignored;
+the team final project is the presentation PDF.
+
+
+
+
+
 
+
+
+
+
+# Netflix AI Greenlight Challenge
 [![License: GNU AGPL v3](https://img.shields.io/badge/License-AGPL_v3-blue.svg)](https://www.gnu.org/licenses/agpl-3.0)
 [![Python](https://img.shields.io/badge/Python-3.13-blue?logo=python)](https://www.python.org/)
 [![Pydantic](https://img.shields.io/badge/Pydantic-2.13-blue?logo=pydantic)](https://docs.pydantic.dev/)
@@ -116,4 +134,4 @@ pip --version # you should see pip 25.3.x
 Please report bugs to the [GitHub Issues Page](https://github.com/netflix-writers/netflix/issues)
 for this project.
 
-ask for Lawrence.
+ask for Lawrence.r

bad_example.py

@@ -0,0 +1,12 @@
+import numpy as np
+import pandas as pd
+
+def compute_average(values):
+    total = 0
+    for i in range(len(values)):
+        total = total + values[i]
+    average = total / len(values)
+    return average
+
+result = compute_average([1, 2, 3, 4, 5])
+print(result)

bad_number.txt

@@ -0,0 +1 @@
+not a number

good_number.txt

@@ -0,0 +1 @@
+42

messy_example.py

@@ -0,0 +1,6 @@
+def compute_average(values):
+    total = 0
+    for i in range(len(values)):
+        total = total + values[i]
+    average = total / len(values)
+    return average