Machine Learning Zoomcamp 2026

Homework 5: Deploying Machine Learning Models Statistics

Distribution of scores and reported study time for this homework.

Submissions

416

Median total score

6

Average total score

6

Score distribution

All values are points.

Questions score

Min
1
Median
6.0
Max
6
Q1
6.0
Avg
5.5
Q3
6.0

Learning in public score

Min
-
Median
0.0
Max
7
Q1
0.0
Avg
0.5
Q3
0.0

Total score

Min
1
Median
6.0
Max
14
Q1
6.0
Avg
6.1
Q3
6.0

Time distribution

All values are hours reported by students.

Lectures

Min
0.0
Median
4.0
Max
72.0
Q1
3.0
Avg
6.6
Q3
7.0

Homework

Min
0.0
Median
2.0
Max
62.0
Q1
1.8
Avg
4.0
Q3
4.5

Question breakdown

Correctness and answer distribution per question.

1. uv version

416 / 416 correct (100.0%)

Answer Count
0.13.5 106
uv 0.13.5 22
0.13.6 21
uv 0.13.5 (x86_64-unknown-linux-gnu) 17
0.13.1 17
0.12.23 13
0.10.11 11
0.13.4 8
0.12.19 8
0.12.13 8
uv 0.13.5 (46776a95d 2026-10-21 x86_64-pc-windows-msvc) 7
0.13.0 7
0.12.21 5
0.12.5 5
0.11.32 5
0.12.17 5
0.12.7 4
uv 0.13.6 (x86_64-unknown-linux-gnu) 4
uv 0.13.6 4
0.1.0 3
uv --version 3
uv 0.12.5 3
0.12.18 3
uv 0.12.19 3
0.11.21 3
0.11.16 3
uv 0.12.10 3
uv 0.12.23 (x86_64-unknown-linux-gnu) 3
0.38.0 3
uv 0.9.13 2
0.12.10 2
uv 0.11.20 (x86_64-unknown-linux-gnu) 2
0.12.1 2
uv 0.13.5 (Homebrew 2026-10-22 aarch64-apple-darwin) 2
0.12.14 2
0.12.9 2
uv 0.10.11 2
0.11.28 2
0.12.12 2
0.11.26 2
0.13.7 2
uv 0.13.7 (x86_64-unknown-linux-gnu) 2
uv 0.13.7 2
uv 0.13.4 (x86_64-unknown-linux-gnu) 2
uv 0.13.3 (502f93ca6 2026-10-19 x86_64-pc-windows-msvc) 2
uv 0.13.4 2
uv 0.13.1 (62faed335 2026-10-13 x86_64-pc-windows-msvc) 2
0.11.7 2
0.4.20 2
uv 0.13.1 (x86_64-unknown-linux-gnu) 1
0.7.22 1
uv 0.11.24 1
0.12.15 1
0.12.11 1
0.12.16 (761ff1379 2026-09-17 x86_64-pc-windows-msvc) 1
precisa ser obtida executando uv --version. 1
uv 0.12.19 (x86_64-unknown-linux-gnu) 1
uv 0.11.21 1
uv 0.12.21 (7af826859 2026-09-29 x86_64-unknown-linux-gnu) 1
uv 0.12.24 (x86_64-unknown-linux-gnu) 1
uv 0.11.24 (5e04460c0 2026-06-23 x86_64-pc-windows-msvc) 1
uv 0.13.0 (x86_64-unknown-linux-gnu) 1
uv 0.10.9 1
uv 0.13.1 1
0.9.16 1
uv 0.10.12 1
uv 0.13.3 (x86_64-unknown-linux-gnu) 1
0.3.15 1
0.11.29 1
uv 0.12.17 1
uv 0.12.18 (x86_64-unknown-linux-gnu) 1
0.9.17 1
uv 0.12.10 (x86_64-unknown-linux-gnu) 1
Setup check only not graded 1
0.11.8 1
0.8.22 1
0.11.3 1
0.5.31 1
0.11.17 1
0.8.2 1
uv 0.13.7 (316c827e0 2026-11-03 x86_64-apple-darwin) 1
0.9.26 1
0.13.3 1
0.11.30 1
uv 0.12.18 (x86_64-unknown-linux-musl) 1
0.10.2 1
uv 0.11.6 1
uv 0.9.6 1
0.12.24 1
uv 0.11.11 (x86_64-unknown-linux-gnu) 1
uv 0.11.26 (396ef7ce4 2026-06-30 x86_64-pc-windows-msvc) 1
0.13.5 — setup check, not graded 1
3.14.2 1
uv 0.12.3 1
uv 0.13.5 (x86_64-unknown-linux-gnu 1
uv 0.11.23 1
uv 0.12.15 1
uv 0.12.21 (7af826859 2026-09-29 x86_64-pc-windows-msvc) 1
uv 0.13.4 (7456b60bc 2026-10-20 x86_64-pc-windows-msvc) 1
uv 0.10.8 1
uv 0.11.0 1
uv 0.11.28 (Homebrew 2026-07-07 aarch64-apple-darwin) 1
uv 0.10.10 (8c730aaad 2026-03-13) 1
0.10.8 1
uv-0.13.5 1
0.10.0 1
uv 0.11.8 1
uv 0.10.3 1
uv 0.12.19 (Homebrew 2026-09-24 aarch64-apple-darwin) 1
0.11.19 1
0.12.8 1
uv 0.12.15 (x86_64-unknown-linux-gnu) 1
3.13.16 1
0.11.18 1
uv 0.11.15 (3cffe97c2 2026-05-18 x86_64-pc-windows-msvc) 1
uv 0.11.7 (9d177269e 2026-04-15 aarch64-apple-darwin) 1

2. Scikit-Learn version

396 / 416 correct (95.2%)

1 1.6.1 16 (3.8%)
2 1.7.2 396 (95.2%)
3 1.8.0 2 (0.5%)
4 2.0.0 1 (0.2%)

3. Conversion probability (loaded model)

366 / 416 correct (88.0%)

Answer Count
0.533 345
0.532994 6
0.769799 6
0.5329944186432507 4
0.770 3
0.553 2
0.5329 2
0.532 2
.533 2
0,533 2
0.53299442 2
Conversion probability: 0.533 2
0.77 2
0.554 1
0.534 1
precisa executar o modelo. 1
predict.py 1
Loaded-model probability: 0.533 1
0.5336 1
0.53 (2.d.p) 1
0.527 1
0.533({'conversion_probability': 0.532994}) 1
0.3257561103397851 1
0.38 1
0.769 1
0.533, True 1
0.6638181262086477 1
{"lead_source": "paid_ads", "number_of_courses_viewed": 2, "annual_income": 79276.0} 1
{'conversion_probability': 0.769799, 'conversion': True} 1
0.952 1
0,532994 1
0.533 (0.532994) 1
0.758 1
"conversion_probability": 0.5329944186432507, "conversion": true 1
0.55 1
0.333 1
0.566 1
na 1
0.513 1
'conversion_probability': 0.532994, 'conversion': True 1
import pickle with open('pipeline_v1.bin', 'rb') as f_in: pipeline = pickle.load(f_in) client = { 'lead_source': 'organic_search', 'number_of_courses_viewed': 4, 'annual_income': 80304.0, } result = pipeline.predict_proba(client)[0, 1] print(round(result, 3)) 1
{'conversion_probability': 0.532993, 'conversion': True} 1
0.767 1
0.727 1

4. Conversion probability (API)

347 / 416 correct (83.4%)

Answer Count
0.770 275
0.77 36
0.769799 24
0.769 8
0,770 4
0.534 3
{"conversion_probability":0.769799,"conversion":true} 3
0.533 3
0.770, true 3
0.7697 2
0.5340417283801275 2
.770 2
{'conversion_probability': 0.769799, 'conversion': True} 2
conversion_probability: 0.769799 conversion: true conversion_probability rounded to 3 decimal places: 0.770 1
course-lead-conversion-api 1
precisa chamar a API. 1
{ "conversion_probability": 0.77, "conversion": true } 1
/predict 1
conversion_probability: 0.77, conversion :true 1
0.77 (2.d.p) 1
0.822 1
0.770 , true 1
0.770({"conversion_probability":0.769799,"conversion":true} 1
0.3257561103397851 1
0.85 1
0.770; conversion: true 1
0.770 (after rounding off) 1
{"conversion_probability":0.770,"conversion":true} 1
{conversion_probability":0.769799,"conversion":true} 1
Conversion probability: 0.770 1
0.760 1
0.77,true 1
0.6638181262086477 1
{"conversion_probability":0.770,"conversion":true}% 1
{"lead_source": "organic_search", "number_of_courses_viewed": 4, "annual_income": 80304.0} 1
API probability: 0.770 1
0.952 1
0.69 1
{"conversion_probability": 0.770, "conversion": true} 1
0.770 True 1
0,769799 1
"conversion_probability": 0.770, "conversion": True 1
0.758 1
'conversion_probability': 0.769799, 'conversion': True 1
0.333 1
0.918 1
na 1
{ "conversion_probability": 0.770, "conversion": true } 1
{"conversion_probability":0.77,"conversion":true} 1
0.475644 1
conversion_probability":0.769799,"conversion":true 1
0.757 1
"conversion_probability": 0.770, "conversion": true 1
0.5329944186432507 1
Response body Download { "conversion_probability": 0.769798, "conversion": true } 1
{ "conversion_probability": 0.770, "conversion": true } 1
0.727 1

5. Python base image

403 / 416 correct (96.9%)

1 python:3.9-slim-bullseye 1 (0.2%)
2 python:3.11.15-slim-bookworm 403 (96.9%)
3 python:3.12-slim-bookworm 4 (1.0%)
4 python:3.13.10-slim-bookworm 7 (1.7%)

6. Conversion probability (container)

359 / 416 correct (86.3%)

Answer Count
0.770 283
0.77 39
0.769799 24
0.769 10
{'conversion_probability': 0.769799, 'conversion': True} 3
0,770 3
.770 2
{"conversion_probability":0.770,"conversion":true} 2
0.757 2
0.534 1
{"conversion_probability":0.769799,"conversion":true} 1
0.760 1
0.77, True 1
0.918 1
na 1
0.556 1
0.7 1
0.59 1
0.3573298975790752 1
0.77 (2.d.p) 1
0.822 1
0.770 ({'conversion_probability': 0.769799, 'conversion': True}) 1
0.3257561103397851 1
0.85 1
0.770; conversion: true 1
0.7698 1
precisa executar o container. 1
0.9933071490756734 1
0.6638181262086477 1
0.734 1
Expected probability (container not verified): 0.770 1
{'conversion_probability': 0.770, 'conversion': True} 1
python:3.11.15-slim-bookworm 1
0.769799 ≈ 0.77 1
0,769799 1
0.754 1
0.758 1
The container returned a conversion probability of 0.769799, which is 0.770 rounded to 3 decimal places. 1
'conversion_probability': 0.769799, 'conversion': True 1
0.333 1
conversion = True 1
{ "conversion_probability": 0.770, "conversion": true } 1
{'conversion_probability': 0.77, 'conversion': True} 1
0.99 1
"status":"ok","model_sha256":"1646bbdcd38d4f044da6b630c5b332c93a314245a8c21929011c42de51f629f1","feature_defaults_sha256":"1694f842b908fac9f53c0cc19be6d91f934befc63af23cc32fefafde69acd0cd" 1
q4_predict.py=0.533 ; q6_test.py=0.770 1
"conversion_probability": 0.770, "conversion": true 1
{'conversion_probability': 0.769798, 'conversion': True} 1
0.533 1
0.727 1

Calculated: 9 November 2026, 13:02