Have fun. Maybe win something along the way. Maybe not.
There is no guarantee that you will win anything and this code is a personal learning experiment. You may lose a lot of real money using this script to play the lottery. There is no warranty and the code is provided AS-IS. Play at your own risk. If this works and you win, consider yourself extremely lucky.
I strongly recommend using pipenv. Once you've created your virtual environment, just do pipenv sync.
If you prefer to do it the long way, your best bet is to look at the import statements. As a convenience, I will try to keep this list updated. You will need to install the following.
colorloglz4numpypandasscipyscikit-learn
Go to the main directory and run python lottery.py --gamedir <GAMEDIRECTORY>.
The argument GAMEDIRECTORY is a case-sensitive value of the game that you want to run. For example:
python lottery.py --gamedir NJ_Pick6. Please note that not all games are fully working yet.
accuracy_allowance: The model accuracy must be above this, in decimal. (.05 = 5%)
ball_game_range_low: This is the lowest number of the main game
ball_game_range_high: This is the highest number of the main game
mode_allowance: Percentage (in decimal) for how far from the mode you can be. 0.05 (5%) from the mode of the sums.
mean_allowance: Percentage (in decimal) for how far from the mean you can be. 0.05 (5%) from the mean of the sums.
model_save_path: Define the path to save models. You probably shouldn't change this.
game_balls: Number of balls. Index 0 is the date. Take the max range and subtract 1 for the number of balls in play.
game_balls_extra_low: Low range of the Powerball, Mega ball, or another ball that can repeat from the main game.
game_balls_extra_high: High range of the Powerball, Mega ball, or another ball that can repeat from the main game.
test_size: Percentage of testing data. 20% is recommended.
train_size: Percentage of training data. 80% is recommended.
timeframe_in_days: Limits the number of days it looks back. e.g. if the game rules change. Defaults to 60 years.
- Focus right now is on NJ Pick 6 and Cash 5 because they have straight numbers, nothing duplicated.
- Testing various ways.
- Good luck.
- Validate that the sum of the predicted numbers is within x% of the mode of the most common sum of winning numbers.
- This would make more sense if I graphed it out. Maybe, again, one day.
Error: InconsistentVersionWarning: Trying to unpickle estimator DecisionTreeRegressor from version x.y.z.post1 when using version a.b.c. This might lead to breaking code or invalid results. Use at your own risk.
Solution: Delete the *.joblib files in the models directory and rerun. The files will be regenerated.