Preface

Predicting is making claims about something that will happen, often based on information from past and from current state.

Everyone 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. ...

now I am taking the course of Wharton's Business Analytics: From Data to Insights program!

Week 2: Module Introduction and Instructions By the end of Week 2 - Descriptive Analytics: Describing and Forecasting Future Events, you should be able to:

Use historical data to estimate forecasts for future events using trends and seasonality Calculate the descriptive sample statistics for demand distributions Discuss drawbacks of using Moving Averages Forecasting Key Activities for Week 2 Videos 1-29 Practice Quiz 1: Newsvendor Concepts Practice Quiz 2: Moving Averages Practice Quiz 3: Trends and Seasonality Week 2: Knowledge Check Assignment 2: iD Fresh Food Case Study

Cotler Pricing Sheet

*Effective Date: 1204/2023

| Package | Features | Pricing | |----------------------------------|--------------------------------------------------------------------|--------------------------------| | Open Source | - Basic analytics functionality | Free | | | - GPTs free trail: [GPTs:https://chat.openai.com/gpts/editor/g-OtkLCltUZ] | | | - Limited customization | | |----------------------------------|--------------------------------------------------------------------|--------------------------------| | Low-Cost, Low-Accuracy | - Enhanced prediction capabilities | $9.99/month | | | - Email support zheng532@126.com or WeChat ID zhenglw532 | | - Limited precision | | | - Suitable for small-scale projects | | |----------------------------------|--------------------------------------------------------------------|--------------------------------| | Mid-High Cost, SOTA Accuracy | - State-of-the-art prediction accuracy | $49.99/month | | | - Priority email and chat support | | | | - High precision and customization options | | | | - Suitable for medium to large-scale projects | | |----------------------------------|--------------------------------------------------------------------|--------------------------------| | Enterprise Custom Solutions | - Tailored solutions for specific business needs | Contact Us for a Quote | | | - Dedicated account manager and premium support | mailto zheng532@126.com | | | - Advanced machine learning models | | | | - Scalable infrastructure for high-demand applications | |

Notes:

• All prices are listed on a per-month basis.
• Custom enterprise solutions are available upon request; please contact our sales team for detailed discussions.
• Prices are subject to change; please refer to our website or contact our sales team for the most up-to-date information.

For inquiries or to subscribe to a plan, please contact our sales team zheng532@126.com.

Train the model:

Use the training data to train the model, adjusting the model's parameters as needed to improve its accuracy.

the model predict:

Use the testing data to evaluate the model's performance and fine-tune it as needed. Deploy 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.

This is just one possible approach to building an AI transformer architecture model for time-series lottery data analytics.

There may be other approaches that could also be effective, depending on the specific requirements and constraints of the project.

data Visualize examples:

using Flash

data visualization

using fbProphet:

todos:

streamlit: https://docs.streamlit.io/en/stable/api.html#display-data

plotly:https://plotly.com/python/time-series/

Live Demos

https://yangboz.github.io/labs/lp/LotteryPrediction_AmCharts_R.swf https://yangboz.github.io/labs/lp/LotteryPrediction_AmCharts_RCX.swf https://yangboz.github.io/labs/lp/LotteryPrediction_FlexCharts.swf

notes

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.

here 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.

Please donate to ETH: 0xa45542927c06591a224c28ca3596a3bD56C499fb

[howto install and use it?]https://github.com/yangboz/LotteryPrediction/wiki#how-can-i-install-and-use-it

first of first, we can not grantee 100% of prediction accuracy to your get rich dream.

custom company service mailto: z@smartkit.club, with your sample history lottery-data, and must have plain text of game-rule's introduction.

Refs:

http://deeplearning4j.org/usingrnns.html

http://www.scriptol.com/programming/list-algorithms.php

http://www.ipedr.com/vol25/54-ICEME2011-N20032.pdf

http://www.brightpointinc.com/flexdemos/chartslicer/chartslicersample.html

http://stats.stackexchange.com/questions/68662/using-deep-learning-for-time-series-prediction

Python logutils

Python data analysis_pandas

Python data minning_orange

Python data-mining and pattern recognition packages

Python Machine Learning Packages

Conference on 100 YEARS OF ALAN TURING AND 20 YEARS OF SLAIS

USA Draft Lottery 1970

Python Scikit-Learn

Python Multivarite Pattern Analysis

BigML

Patsy

StatModel

Neural Lotto — Lottery Drawing Predicting Method

Random.org

Predictive Analytics Guide

[TensorFlow Tutorial for Time Series Prediction:] (https://github.com/tgjeon/TensorFlow-Tutorials-for-Time-Series)

Roadmap:

landing page:

PoCs

https://github.com/yangboz/LotteryPrediction/tree/master/pocs

API public service:

Phase I.Graphics: Looking at Data;

1.A single variable:Shape and Distribution; ( Dot/Jitter plots,Histograms and Kernel Density Estimates,Cumulative Distribution Function,Rank-Order...)

2.Two variables:Establishing Relationships; ( Scatter plots,Conquering Noise,Logarithmic Plots,Banking...)

3.Time as a variable: Time-Series Analysis; (Smoothing,Correlation,Filters,Convolutions..)

4.More than two variables;Graphical Multivariate Analysis;(False-color Plots,Multi plots...)

5.Intermezzo:A Data Analysis Session;(Session,gnuplot..)

6...

Phase II.Analytics: Modeling Data;

1.Guesstimation and the back of envelope;

2.Models from scaling arguments;

3.Arguments from probability models;

4...

Phase III.Computation: Mining Data;

1.Simulations;

2.Find clusters;

3.Seeing the forest for the decision trees;

4....

Phase IV.Applications: Using Data;

1.Reporting, BI (Business Intelligence),Dashboard;

2.Financial calculations and modeling;

3.Predictive analytics;

4....

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Draft plan

Phase I.Graphics: Looking at Data;

1.A single variable:Shape and Distribution; ( Dot/Jitter plots,Histograms and Kernel Density Estimates,Cumulative Distribution Function,Rank-Order...)

2.Two variables