Python · Machine Learning · Deep Learning

Rock Paper Scissors Detection

2026 Summer Python Course Project Collection covering Python fundamentals, machine learning regression prediction, student behavior data analysis, and YOLO11n-based Rock Paper Scissors gesture recognition.

GitHub Repository Gesture Recognition
Rock Paper Scissors Detection Demo
Project 1

Python Fundamentals Exercises

14 beginner exercises: comprehensive coverage of data types, control flow, functions, lists, dictionaries, and more.

Project 2-1

California Housing Prediction

Random Forest regression for California housing median price prediction, R² = 0.805. Verified that income and geographic location are key influencing factors.

Project 2-2

Student Behavior Analysis

Kaggle real-world dataset analyzing the impact of study time, screen time, etc. on CGPA, with multi-model comparison.

Project 3

YOLO11n Gesture Recognition

🪨📄✂️ Real-time detection of Rock/Paper/Scissors, validation set mAP50 = 0.940, millisecond-level inference.

Project 1

Python Programming Fundamentals

Beginner-level Python programming exercises, zero third-party dependencies

Data Types

  1. 1.1.py — Square of positive integer
  2. 1.2.py — Formatted admission info output
  3. 1.3.py — Right triangle hypotenuse length

Operators & Expressions

  1. 2.1.py — Euclidean distance between two points
  2. 2.2.py — Sum of cubes of digits in a three-digit number
  3. 2.3.py — Letter occurrence count (case-insensitive)

Control Flow & Functions

  1. 3.1-3.2 — Grade level / Character type
  2. 4.1-4.2 — Digit judgment / Factorial sum
  3. 5.1-5.2 — Function encapsulation & math calculations
  4. 6.1-6.2 — List comprehensions / Dictionary mapping
Project 2-1

California Housing Price Prediction — Random Forest Regression

scikit-learn · California Housing Dataset

Dataset

California Housing — scikit-learn built-in dataset

  • Samples: 20,640 block group records
  • Features: 8 (median income, house age, rooms, population, geographic location, etc.)
  • Target: MedHouseVal (median house value, $100K)

Model Evaluation

MSE 0.2552
RMSE 0.5052
R² 0.8053

Top 4 Feature Importance

MedInc (Median Income) > AveOccup (Average Occupancy) > Latitude / Longitude (Geographic Location) > HouseAge

Income level is the strongest predictor for housing prices; the importance of geographic location reflects regional housing price disparities across California.

Python scikit-learn Random Forest Pandas Matplotlib
Project 2-2

Student Digital Behavior and Academic Performance Analysis

Kaggle real-world dataset · Linear Regression vs Random Forest

Dataset

Student Lifestyle and Academic Performance (Kaggle)

  • 12 fields: Age, Branch, Study_Hours, Sleep_Hours, Screen_Time, Gym, Diet, Attendance, Stress, Residence, Internal_Marks, CGPA
  • Target variable: CGPA (Grade Point Average, 0~10)
  • Excluded Internal_Marks to prevent data leakage

Data Cleaning Pipeline

  • Field selection → Deduplication
  • Numeric fields: Median imputation for missing values
  • Categorical fields: Mode imputation
  • Outlier filtering (reasonable ranges for age, study hours, etc.)
  • Categorical variables: One-hot encoding (drop_first)

Visual Analysis (6 Charts)

Screen Time Distribution

Right-skewed distribution; most students spend 3–8 hours on screens daily

Study Hours vs CGPA

Clear positive correlation; students studying 6–8h/day tend to have higher CGPA

Top 3 Feature Importance

Study Hours > Attendance > Screen Time

Python Linear Regression Random Forest Pandas Seaborn EDA
Project 3

Rock Paper Scissors Gesture Recognition — YOLO11n

🪨📄✂️ Ultralytics · PyTorch · Object Detection

Dataset

rock-paper-scissors-sxsw (Roboflow v11)

  • Total 15,874 images
  • Training set 14,966 / Validation set 588 / Test set 320
  • 3 classes: Paper, Rock, Scissors
  • Input size: 640×640

Data Augmentation

7 augmented versions generated per original image:

  • Horizontal flip (50%), random rotation ±12°
  • Shear ±2°, brightness/exposure ±25%
  • Gaussian blur 0~1.5px, salt-and-pepper noise 1%

Training Configuration

ModelYOLO11n (2.58M params)
Epochs50
Batch16
OptimizerAdamW
Training Time~3.07h
GPURTX 5060 (8GB)

Evaluation Results

Validation Set

mAP50 (All) 0.940
mAP50-95 0.697
Precision (P) 0.942
Recall (R) 0.891
Inference Speed < 2.1ms

Test Set

mAP50 (All) 0.934
mAP50-95 0.693
Precision (P) 0.898
Recall (R) 0.920

Per-Class Performance

📄 Paper

Val mAP500.936
Test mAP500.904

🪨 Rock

Val mAP500.943
Test mAP500.951

✂️ Scissors

Val mAP500.940
Test mAP500.947

Application Modes

Single Image Inference

Recognize gestures from any photo; results are annotated with bounding boxes + labels and saved to the output directory.

predict.py

Real-time Camera Detection

Captures from system camera (1280×720) in real-time with millisecond-level per-frame inference; displays FPS in window, press Q to exit.

detect.py

Evaluation Scripts

Validation set evaluation, test set evaluation, dataset download utility — full coverage from training to deployment pipeline.

evaluate.py / test.py
Python PyTorch YOLO11n Ultralytics OpenCV Roboflow CUDA