{"id":17842091,"url":"https://github.com/yangboz/lotteryprediction","last_synced_at":"2025-04-05T17:09:46.185Z","repository":{"id":54469484,"uuid":"1898385","full_name":"yangboz/LotteryPrediction","owner":"yangboz","description":":full_moon_with_face: Lottery prediction besides of following \"law of proability\",\"Probability: Independent Events\", there are still \"Saying \"a Tail is due\", or \"just one more go, my luck is due to change\" is called The Gambler's Fallacy\" existed.","archived":false,"fork":false,"pushed_at":"2024-03-29T09:18:56.000Z","size":19874,"stargazers_count":281,"open_issues_count":0,"forks_count":126,"subscribers_count":46,"default_branch":"master","last_synced_at":"2025-03-29T16:08:46.901Z","etag":null,"topics":["exploratory-data-analysis","keras","mine-data","predictive-analytics","scatter-plot","series-analysis","tensoflow","time-series-analysis"],"latest_commit_sha":null,"homepage":"http://yangboz.github.io/LotteryPrediction","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/yangboz.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE.md","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2011-06-15T04:22:30.000Z","updated_at":"2025-03-11T18:41:07.000Z","dependencies_parsed_at":"2023-01-29T23:31:03.294Z","dependency_job_id":"3aa9795d-ec16-4cea-9fbb-1b6da0977c40","html_url":"https://github.com/yangboz/LotteryPrediction","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yangboz%2FLotteryPrediction","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yangboz%2FLotteryPrediction/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yangboz%2FLotteryPrediction/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/yangboz%2FLotteryPrediction/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/yangboz","download_url":"https://codeload.github.com/yangboz/LotteryPrediction/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247369953,"owners_count":20927928,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["exploratory-data-analysis","keras","mine-data","predictive-analytics","scatter-plot","series-analysis","tensoflow","time-series-analysis"],"created_at":"2024-10-27T21:09:51.348Z","updated_at":"2025-04-05T17:09:46.156Z","avatar_url":"https://github.com/yangboz.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Preface\r\n\r\nPredicting is making claims about something that will happen, often based on information from past and from current state.\r\n![Screenshot of \"Prediction\"](http://www.obitko.com/tutorials/neural-network-prediction/images/prediction.gif)\r\n\r\nEveryone solves the problem of prediction every day with various degrees of success. For example weather, harvest, energy consumption, movements of forex (foreign exchange) currency pairs or of shares of stocks, earthquakes, and a lot of other stuff needs to be predicted.\r\n...\r\n\r\nnow I am taking the course of Wharton's Business Analytics: From Data to Insights program! \r\n\r\n\r\nWeek 2: Module Introduction and Instructions\r\nBy the end of Week 2 - Descriptive Analytics: Describing and Forecasting Future Events, you should be able to:\r\n\r\nUse historical data to estimate forecasts for future events using trends and seasonality\r\nCalculate the descriptive sample statistics for demand distributions\r\nDiscuss drawbacks of using Moving Averages Forecasting\r\nKey Activities for Week 2\r\nVideos 1-29\r\nPractice Quiz 1: Newsvendor Concepts\r\nPractice Quiz 2: Moving Averages\r\nPractice Quiz 3: Trends and Seasonality\r\nWeek 2: Knowledge Check\r\nAssignment 2: iD Fresh Food Case Study\r\n\r\n**Cotler Pricing Sheet**\r\n\r\n*Effective Date: 1204/2023\r\n\r\n| **Package**                     | **Features**                                                      | **Pricing**                    |\r\n|----------------------------------|--------------------------------------------------------------------|--------------------------------|\r\n| **Open Source**                  | - Basic analytics functionality                                   | **Free**                       |\r\n|                                  | - GPTs free trail:   [GPTs:https://chat.openai.com/gpts/editor/g-OtkLCltUZ]                        |\r\n|                                  | - Limited customization                                           |                                 |\r\n|----------------------------------|--------------------------------------------------------------------|--------------------------------|\r\n| **Low-Cost, Low-Accuracy**       | - Enhanced prediction capabilities                                 | **$9.99/month**                |\r\n|                                  | - Email support   zheng532@126.com or WeChat ID zhenglw532\r\n|                                  | - Limited precision                                                |\r\n|                                  | - Suitable for small-scale projects                                |                                |\r\n|----------------------------------|--------------------------------------------------------------------|--------------------------------|\r\n| **Mid-High Cost, SOTA Accuracy** | - State-of-the-art prediction accuracy                             | **$49.99/month**               |\r\n|                                  | - Priority email and chat support                                  |                                |\r\n|                                  | - High precision and customization options                         |                                |\r\n|                                  | - Suitable for medium to large-scale projects                      |                                |\r\n|----------------------------------|--------------------------------------------------------------------|--------------------------------|\r\n| **Enterprise Custom Solutions**  | - Tailored solutions for specific business needs                   | **Contact Us for a Quote**     |\r\n|                                  | - Dedicated account manager and premium support                    | mailto zheng532@126.com        |\r\n|                                  | - Advanced machine learning models                                 |                                |\r\n|                                  | - Scalable infrastructure for high-demand applications             |                                |\r\n\r\n**Notes:**\r\n- All prices are listed on a per-month basis.\r\n- Custom enterprise solutions are available upon request; please contact our sales team for detailed discussions.\r\n- Prices are subject to change; please refer to our website or contact our sales team for the most up-to-date information.\r\n\r\nFor inquiries or to subscribe to a plan, please contact our sales team zheng532@126.com.\r\n\r\n## Train the model: \r\n\r\nUse the training data to train the model, adjusting the model's parameters as needed to improve its accuracy.\r\n\r\n## the model predict: \r\n\r\nUse the testing data to evaluate the model's performance and fine-tune it as needed.\r\nDeploy the model: Deploy the trained model in a production environment, where it can be used to analyze real-time lottery data and make predictions about future draws.\r\n\r\n\r\nThis is just one possible approach to building an AI transformer architecture model for time-series lottery data analytics.\r\n\r\n\r\nThere may be other approaches that could also be effective, depending on the specific requirements and constraints of the project.\r\n\r\n## data Visualize examples:\r\n\r\n### using Flash\r\n\r\n![Screenshot of \"LotteryPrediction\"](https://raw.githubusercontent.com/yangboz/LotteryPrediction/master/dataV/Flex/src/assets/screenshots/lp.jpg)\r\n![Screenshot of \"LotteryPrediction\"](https://raw.githubusercontent.com/yangboz/LotteryPrediction/master/dataV/Flex/src/assets/screenshots/lp_time_slice.jpg)\r\n![Screenshot of \"LotteryPrediction\"](https://raw.githubusercontent.com/yangboz/LotteryPrediction/master/dataV/Flex/src/assets/screenshots/lp_time_slice_compare.jpg)\r\n\r\n## data visualization\r\n\r\n  ## using fbProphet:\r\n  \r\n![fbProphet](https://raw.githubusercontent.com/yangboz/LotteryPrediction/master/dataV/python/fbProphet.jpg)\r\n![darts](https://raw.githubusercontent.com/yangboz/LotteryPrediction/master/dataV/python/darts.jpg)\r\n\r\n  ## todos:\r\n  \r\nstreamlit: https://docs.streamlit.io/en/stable/api.html#display-data\r\n\r\nplotly:https://plotly.com/python/time-series/\r\n\r\n# Live Demos\r\n\r\nhttps://yangboz.github.io/labs/lp/LotteryPrediction_AmCharts_R.swf \r\nhttps://yangboz.github.io/labs/lp/LotteryPrediction_AmCharts_RCX.swf\r\nhttps://yangboz.github.io/labs/lp/LotteryPrediction_FlexCharts.swf\r\n\r\n\r\n## notes\r\n\r\nbesides of following  \"law of proability\",\"Probability: Independent Events\", there are still \"Saying \"a Tail is due\", or \"just one more go, my luck is due to change\" is called The Gambler's Fallacy\" existed.\r\n\r\nhere we are not garantee to help with you to  win lottery prize. if you got lucky from here. please donate here, we also donate to charities.\r\n\r\n## Please donate to ETH: 0xa45542927c06591a224c28ca3596a3bD56C499fb \r\n\r\n[howto install and use it?]https://github.com/yangboz/LotteryPrediction/wiki#how-can-i-install-and-use-it\r\n\r\n\r\n\r\nfirst of first, we can not grantee 100% of prediction accuracy to your get rich dream.\r\n## custom company service mailto: z@smartkit.club, with your sample history lottery-data, and must have plain text of game-rule's introduction.\r\n\r\n## Refs:\r\n\r\nhttp://deeplearning4j.org/usingrnns.html\r\n\r\nhttp://www.scriptol.com/programming/list-algorithms.php\r\n\r\nhttp://www.ipedr.com/vol25/54-ICEME2011-N20032.pdf\r\n\r\nhttp://www.brightpointinc.com/flexdemos/chartslicer/chartslicersample.html\r\n\r\nhttp://stats.stackexchange.com/questions/68662/using-deep-learning-for-time-series-prediction\r\n\r\n\r\n[Python logutils](https://code.google.com/p/logutils/)\r\n\r\n[Python data analysis_pandas](http://pandas.pydata.org/)\r\n\r\n[Python data minning_orange](http://orange.biolab.si/)\r\n\r\n[Python data-mining and pattern recognition packages](http://www.researchpipeline.com/wordpress/2011/02/15/python-data-mining-packages/)\r\n\r\n[Python Machine Learning Packages](http://web.media.mit.edu/~stefie10/technical/pythonml.html)\r\n\r\n[Conference on 100 YEARS OF ALAN TURING AND 20 YEARS OF SLAIS](http://ailab.ijs.si/dunja/TuringSLAIS-2012/)\r\n\r\n[USA Draft Lottery 1970](http://lib.stat.cmu.edu/DASL/Stories/DraftLottery.html )\r\n\r\n[Python Scikit-Learn](http://scikit-learn.org/)\r\n\r\n[Python Multivarite Pattern Analysis](http://www.pymvpa.org/)\r\n\r\n[BigML](https://bigml.com)\r\n\r\n[Patsy](http://patsy.readthedocs.org)\r\n\r\n[StatModel](http://statsmodels.sourceforge.net/)\r\n\r\n[Neural Lotto — Lottery Drawing Predicting Method](http://www.neural-lotto.net/index.php/en/)\r\n\r\n[Random.org](http://www.random.org/clients/http/)\r\n\r\n[Predictive Analytics Guide](http://www.predictiveanalyticsworld.com/predictive_analytics.php)\r\n \r\n[TensorFlow Tutorial for Time Series Prediction:] (https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series)\r\n\r\n# Roadmap:\r\n\r\n## landing page: \r\n\r\n## PoCs \r\n\r\nhttps://github.com/yangboz/LotteryPrediction/tree/master/pocs\r\n\r\n\r\n## API public service:\r\n\r\n## Phase I.Graphics: Looking at Data; \r\n\r\n1.A single variable:Shape and Distribution; ( Dot/Jitter plots,Histograms and Kernel Density Estimates,Cumulative Distribution Function,Rank-Order...)\r\n\r\n2.Two variables:Establishing Relationships; ( Scatter plots,Conquering Noise,Logarithmic Plots,Banking...)\r\n\r\n3.Time as a variable: Time-Series Analysis; (Smoothing,Correlation,Filters,Convolutions..)\r\n\r\n4.More than two variables;Graphical Multivariate Analysis;(False-color Plots,Multi plots...)\r\n\r\n5.Intermezzo:A Data Analysis Session;(Session,gnuplot..)\r\n\r\n6...\r\n\r\n## Phase II.Analytics: Modeling Data;\r\n\r\n1.Guesstimation and the back of envelope;\r\n\r\n2.Models from scaling arguments;\r\n\r\n3.Arguments from probability models;\r\n\r\n4...\r\n\r\n## Phase III.Computation: Mining Data;\r\n\r\n1.Simulations;\r\n\r\n2.Find clusters;\r\n\r\n3.Seeing the forest for the decision trees;\r\n\r\n4....\r\n\r\n## Phase IV.Applications: Using Data;\r\n\r\n1.Reporting, BI (Business Intelligence),Dashboard;\r\n\r\n2.Financial calculations and modeling;\r\n\r\n3.Predictive analytics;\r\n\r\n4....\r\n\r\n=======\r\n# Draft plan\r\n\r\nPhase I.Graphics: Looking at Data; \r\n\r\n1.A single variable:Shape and Distribution; ( Dot/Jitter plots,Histograms and Kernel Density Estimates,Cumulative Distribution Function,Rank-Order...)\r\n\r\n2.Two variables:Establishing Relationships; ( Scatter plots,Conquering Noise,Logarithmic Plots,Banking...)\r\n\r\n3.Time as a variable: Time-Series Analysis; (Smoothing,Correlation,Filters,Convolutions..)\r\n\r\n4.More than two variables;Graphical Multivariate Analysis;(False-color Plots,Multi plots...)\r\n\r\n5.Intermezzo:A Data Analysis Session;(Session,gnuplot..)\r\n\r\n6...\r\n\r\nPhase II.Analytics: Modeling Data;\r\n\r\n1.Guesstimation and the back of envelope;\r\n\r\n2.Models from scaling arguments;\r\n\r\n3.Arguments from probability models;\r\n\r\n4...\r\n\r\nPhase III.Computation: Mining Data;\r\n\r\n1.Simulations;\r\n\r\n2.Find clusters;\r\n\r\n3.Seeing the forest for the decision trees;\r\n\r\n4....\r\n\r\nPhase IV.Applications: Using Data;\r\n\r\n1.Reporting, BI (Business Intelligence),Dashboard;\r\n\r\n2.Financial calculations and modeling;\r\n\r\n3.Predictive analytics;\r\n\r\n4....\r\n\r\n# TODO:\r\n\r\n fbprophet model finetune: https://facebook.github.io/prophet/docs/quick_start.html\r\n \r\n \r\n ## ChatGPT \r\n \r\n according to  ChatGPT's advice:\r\n \r\n Writing a lottery prediction program can be a challenging task as it involves analyzing past lottery results, identifying patterns, and using statistical techniques to make predictions about future draws.\r\n\r\nHere are some steps you can follow to write a lottery prediction program:\r\n\r\nCollect data: Gather a large dataset of past lottery results, including the numbers drawn and the date of the draw.\r\n\r\n\r\nPreprocess data: Clean and organize the data to remove any errors or inconsistencies.\r\n\r\n\r\nAnalyze data: Use statistical techniques such as frequency analysis, clustering, and regression to identify patterns and trends in the data.\r\n\r\n\r\nBuild a model: Use the insights gained from the data analysis to build a predictive model that can make predictions about future lottery draws.\r\n\r\n\r\nTest the model: Use the model to make predictions on a separate dataset of past lottery results and evaluate its performance.\r\n\r\nFine-tune the model: If necessary, make adjustments to the model based on the results of the testing phase to improve its accuracy.\r\n\r\n\r\nImplement the program: Write the code for the lottery prediction program, including any necessary user interfaces and input/output mechanisms.\r\n\r\n\r\nIt is important to note that the accuracy of a lottery prediction program \r\n\r\nwill depends on the quality of the data, \r\n\r\nthe complexity of the model,\r\n\r\n\r\nand the skill of the developer. \r\n\r\n\r\nThere is no guarantee that a lottery prediction program will be successful,\r\n\r\n\r\n\r\nand using such a program for financial gain may not be legal in some jurisdictions.\r\n \r\n \r\n \r\n \r\n ## References\r\n \r\nTensorFlow Tutorial for Time Series Prediction: https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series\r\n\r\nTime Series Forecasting made easy with Darts \r\n\r\nhttps://unit8co.github.io/darts/#:~:text=darts%20is%20a%20Python%20library,%2C%20similar%20to%20scikit%2Dlearn.\r\n\r\n\r\n\r\nETSformer: Exponential Smoothing Transformers for Time-Series Forecasting \r\n\r\nhttps://blog.salesforceairesearch.com/etsformer-time-series-forecasting/\r\n\r\n\r\n## papers\r\n\r\nhttps://www.datascience.us/predicting-success-in-lottery-with-deep-learning/\r\n\r\n## free version support and trail\r\n\r\nhttps://github.com/yangboz/LotteryPrediction/wiki\r\n\r\n## Commercial support and training\r\n\r\nCommercial support and training is available , please mailto zheng532@126.com or WeChat ID zhenglw532\r\n  with your historic-data and plain english description and budget plan 10$~50$ PER CASE .\r\n\r\n\r\n##\r\n\r\n##  verify WIP\r\n\r\nTransformer way: https://github.com/yangboz/Informer2020?tab=readme-ov-file\r\n\r\nLLM way:  https://github.com/KimMeen/Time-LLM/tree/main/scripts\r\n\r\n\r\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyangboz%2Flotteryprediction","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fyangboz%2Flotteryprediction","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fyangboz%2Flotteryprediction/lists"}