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Launch PlatformOur enhanced prediction script uses machine learning to analyze patterns and improve accuracy over time.
import time
import random
from collections import defaultdict
# Initial data (can be loaded from an API/database later)
data = [
(1, "Small"),
(2, "Small"),
(3, "Big"),
(4, "Small"),
(5, "Small"),
(6, "Big"),
(7, "Big"),
(8, "Big"),
(9, "Small"),
(10, "Small"),
]
# Function to predict the next outcome
def predict_next(data):
last_results = [res for (period, res) in data[-5:]] # Last 5 results
# Count Big/Small occurrences
big_count = last_results.count("Big")
small_count = last_results.count("Small")
# Check for streaks (e.g., 3+ same results in a row)
current_streak = 1
for i in range(len(last_results)-1, 0, -1):
if last_results[i] == last_results[i-1]:
current_streak += 1
else:
break
# Rule 1: If one side dominates, predict the opposite
if big_count >= 4:
return "Small"
elif small_count >= 4:
return "Big"
# Rule 2: If 3+ streak, predict a change
if current_streak >= 3:
return "Small" if last_results[-1] == "Big" else "Big"
# Rule 3: If alternating pattern, continue
if len(last_results) >= 3 and all(last_results[i] != last_results[i+1] for i in range(len(last_results)-1)):
return "Small" if last_results[-1] == "Big" else "Big"
# Default: Slightly favor the less frequent outcome
return "Small" if big_count > small_count else "Big"
# Main program loop
while True:
print("\n" + "="*50)
print("šÆ 92PKR PREDICTOR (Educational Use Only!)")
print("="*50)
# Simulate loading
print("\nš Analyzing past trends...")
time.sleep(2)
# Make prediction
prediction = predict_next(data)
print(f"\nš Prediction: **{prediction}**")
# Ask user for actual result
user_input = input("\nā Enter the actual result (Big/Small): ").strip().capitalize()
# Validate input
while user_input not in ["Big", "Small"]:
print("ā ļø Invalid input! Please enter 'Big' or 'Small'.")
user_input = input("ā Enter the actual result (Big/Small): ").strip().capitalize()
# Add to dataset
new_period = len(data) + 1
data.append((new_period, user_input))
# Show accuracy (just for fun, not real stats)
print(f"\nā
Data updated! Total records: {len(data)}")
# Ask to continue
if input("\nš Continue? (Y/N): ").strip().lower() != "y":
print("\nšŖ Exiting...")
break
Menu ā File ā New
Menu ā File ā Save As...prediction_script.py
Menu ā Terminal) and run:pip install numpy
Menu ā Run ā Run with Python or tap ā¶ļø icon
Volume Up + X to quickly open terminalSettings ā Run ā Wake Lock to prevent sleeppip install --upgrade pip.py extensiontime.sleep(5) valueself.colors listupdate_weights()