Lottery_M6_prediction

Mark Six Lottery Analysis and Prediction

Overview

This project analyzes historical Mark Six lottery data and uses both frequency-based and machine learning methods to predict the next set of winning numbers. Mark Six is a popular lottery game in Hong Kong, where players select 6 numbers from 1 to 49, and 7 numbers are drawn (6 main numbers + 1 extra number). The goal is to explore patterns in the data and generate predictions, though lottery draws are inherently random and predictions are experimental.

Features

• Data Analysis: Frequency of main and extra numbers, distribution of low/high and odd/even numbers.
• Frequency-Based Prediction: Predicts numbers based on their historical frequency.
• Machine Learning Prediction: Uses K-Means clustering to group draws and predict based on cluster centroids.
• Visualization: Bar chart of number frequencies.

Dataset

The dataset contains 62 draws from May 28, 2024, to March 11, 2025, stored in mark_six_history.csv. Key columns include:

• Draw: Draw identifier (e.g., 25/027).
• Date: Date of the draw (e.g., 2025-03-11).
• Winning Number 1, 2, 3, 4, 5, 6: The 6 main numbers drawn.
• Extra Number: The 7th number drawn (bonus number).
• Additional columns: From Last, Low, High, Odd, Even, 1-10, 11-20, 21-30, 31-40, 41-50, prize-related columns (Division 1 Winners, etc.), and Turnover.

Prerequisites

• Python 3.x
• Libraries:
  • pandas: Data manipulation.
  • numpy: Numerical operations.
  • sklearn: Machine learning (K-Means).
  • matplotlib: Visualization.
  • collections: Frequency counting.
  • google.colab: For Colab-specific file uploads (optional).

Install dependencies:

pip install pandas numpy scikit-learn matplotlib
git clone https://github.com/yourusername/mark-six-prediction.git
cd mark-six-prediction

Upload your Mark Six CSV file:
[Uploads mark_six_history.csv]
Uploaded file: mark_six_history.csv
Loaded 62 draws from mark_six_history.csv

Top 10 Most Frequent Main Numbers:
Number 1: 11 times
Number 3: 10 times
Number 15: 9 times
...

Draws per Cluster: Counter({0: 15, 2: 14, 1: 12, 4: 11, 3: 10})

Machine Learning Prediction (K-Means Clustering):
Predicted Main Numbers: [4, 11, 19, 25, 34, 42]
Predicted Extra Number: 22

Frequency-Based Prediction (for comparison):
Predicted Main Numbers: [1, 3, 15, 18, 25, 33]
Predicted Extra Number: 23