LotteryBuyPredictionWebApp - Lottery Purchase Prediction Model

Overview

Lottery Purchase Prediction Model

Objective and Goal

  • Predict the lottery type that the user in the session will buy, using the discrete features from the user face image and user's historical purchase data.

  • Recommend lottery types to users and improve the order conversion rate, in order to increase sales revenue.

Data Source

  • The feature from the user face image in the Session (from Baidu Face Recognition API):

    Beauty, Expression, Emotion, Face ID (optional, only for old users)

  • Other features from Session:

    Session Time (in 24 hours)

  • Use the session face id to get user id, and retrieve the historical order data of this user:

    City, Lottery Type, New/Old Users, Lottery Station Type (supermarket, restaurant), Total Purchase Days, Frequently Purchase Lottery Type

    If this is a new user and there is no user id, the feature from historical order data will be replaced by mean or mode.

Data Cleaning and Selected Features

Transform the continuous variables to one-hot encoding variables, and check whether they are strongly correlated with the dependent variable. There are 18 features in total after variable selection:

Feature Source
Beauty session face
Laugh session face
Neutral Emotion session face
Positive Emotion session face
Session Time session
Yichang user attribute
Enshi user attribute
Wuhan user attribute
Ten Times Good Luck historical order
Qilecai historical order
Shilitaohua historical order
Other Lottery Type historical order
Total Purchase Days historical order
New User historical order
Clubhouse historical order
Chess Room historical order
Supermarket historical order
Restaurant historical order

Model Structure

Concatenate all the feaures, and input to a 3-layers MLP in PyTorch. Then perform a multiclass classification task and predict the lottery type the user will buy in the session (Two-color Ball, Ten Times Good Luck, Welfare Lottery 3D, Other Lottery Type).

Prediction result using historical data

Accuracy metrics using the data from 07/2021:

Type Accuracy
Average Accuracy 0.913
No Buy 0.833
Ten Times Good Luck 0.814
Two-color Ball 0.961
Welfare Lottery 3D 0.822
Other Lottery Type 0.908

Model Call Method

python3 app.py \
    --port=8827 \
    --debug=False \
    --host='127.0.0.1' \
    --appname='buy_prediction' \
    --threaded=True
You might also like...
Doge-Prediction - Coding Club prediction ig

Doge-Prediction Coding Club prediction ig Basically: Create an application that

In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.
In this project we investigate the performance of the SetCon model on realistic video footage. Therefore, we implemented the model in PyTorch and tested the model on two example videos.

Contrastive Learning of Object Representations Supervisor: Prof. Dr. Gemma Roig Institutions: Goethe University CVAI - Computational Vision & Artifici

Step by Step on how to create an vision recognition model using LOBE.ai, export the model and run the model in an Azure Function
Step by Step on how to create an vision recognition model using LOBE.ai, export the model and run the model in an Azure Function

Step by Step on how to create an vision recognition model using LOBE.ai, export the model and run the model in an Azure Function

[ECCVW2020] Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DiMP)
[ECCVW2020] Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DiMP)

Feel free to visit my homepage Robust Long-Term Object Tracking via Improved Discriminative Model Prediction (RLT-DIMP) [ECCVW2020 paper] Presentation

TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Prediction.

TalkNet 2 [WIP] TalkNet 2: Non-Autoregressive Depth-Wise Separable Convolutional Model for Speech Synthesis with Explicit Pitch and Duration Predictio

Implementation of FitVid video prediction model in JAX/Flax.
Implementation of FitVid video prediction model in JAX/Flax.

FitVid Video Prediction Model Implementation of FitVid video prediction model in JAX/Flax. If you find this code useful, please cite it in your paper:

A modified version of DeepMind's Alphafold2 to divide CPU part (MSA and template searching) and GPU part (prediction model)

ParallelFold Author: Bozitao Zhong This is a modified version of DeepMind's Alphafold2 to divide CPU part (MSA and template searching) and GPU part (p

 How to Become More Salient? Surfacing Representation Biases of the Saliency Prediction Model
How to Become More Salient? Surfacing Representation Biases of the Saliency Prediction Model

How to Become More Salient? Surfacing Representation Biases of the Saliency Prediction Model

Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.
Sign Language is detected in realtime using video sequences. Our approach involves MediaPipe Holistic for keypoints extraction and LSTM Model for prediction.

RealTime Sign Language Detection using Action Recognition Approach Real-Time Sign Language is commonly predicted using models whose architecture consi

Using Streamlit to host a multi-page tool with model specs and classification metrics, while also accepting user input values for prediction.

Predicitng_viability Using Streamlit to host a multi-page tool with model specs and classification metrics, while also accepting user input values for

A linear regression model for house price prediction

Linear_Regression_Model A linear regression model for house price prediction. This code is using these packages, so please make sure your have install

A logistic regression model for health insurance purchasing prediction

Logistic_Regression_Model A logistic regression model for health insurance purchasing prediction This code is using these packages, so please make sur

nfelo: a power ranking, prediction, and betting model for the NFL

nfelo nfelo is a power ranking, prediction, and betting model for the NFL. Nfelo take's 538's Elo framework and further adapts it for the NFL, hence t

Bianace Prediction Pytorch Model

Bianace Prediction Pytorch Model Main Results ETHUSDT from 2021-01-01 00:00:00 t

A hybrid framework (neural mass model + ML) for SC-to-FC prediction

The current workflow simulates brain functional connectivity (FC) from structural connectivity (SC) with a neural mass model. Gradient descent is applied to optimize the parameters in the neural mass model.

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction

Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction Requirements The code has been tested running under Python 3.7.4, with the foll

Model search is a framework that implements AutoML algorithms for model architecture search at scale
Model search is a framework that implements AutoML algorithms for model architecture search at scale

Model search (MS) is a framework that implements AutoML algorithms for model architecture search at scale. It aims to help researchers speed up their exploration process for finding the right model architecture for their classification problems (i.e., DNNs with different types of layers).

Capture all information throughout your model's development in a reproducible way and tie results directly to the model code!
Capture all information throughout your model's development in a reproducible way and tie results directly to the model code!

Rubicon Purpose Rubicon is a data science tool that captures and stores model training and execution information, like parameters and outcomes, in a r

Model summary in PyTorch similar to `model.summary()` in Keras

Keras style model.summary() in PyTorch Keras has a neat API to view the visualization of the model which is very helpful while debugging your network.

Owner
Wanxuan Zhang
MS in Analytics at University of Chicago
Wanxuan Zhang
BestBuy Script Designed to purchase any item when it becomes available.

prerequisites: Selnium; undetected-chromedriver. This Script is designed to order an Item provided a link from BestBuy.com only.

Bransen Smith 0 Jan 12, 2022
Best Buy Bot used to add products to cart for purchase.

To Install the Best Buy Bot These instructions are for Mac users only. Clone this Repo to your machine. BestBuyBot Open in VScode. Is Python installed

Robert Estrella 1 Dec 11, 2021
Software for quick purchase of mystery boxes on Binance.

english | русский язык Software for quick purchase of mystery boxes on Binance. Purpose Installation & setup Motivation Specification Disclaimer Purpo

Ellis 5 Mar 8, 2022
Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly

Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly Code for this paper Ultra-Data-Efficient GAN Tra

VITA 77 Oct 5, 2022
Pytorch implementation of our paper under review — Lottery Jackpots Exist in Pre-trained Models

Lottery Jackpots Exist in Pre-trained Models (Paper Link) Requirements Python >= 3.7.4 Pytorch >= 1.6.1 Torchvision >= 0.4.1 Reproduce the Experiment

Yuxin Zhang 27 Jun 28, 2022
Efficient Lottery Ticket Finding: Less Data is More

The lottery ticket hypothesis (LTH) reveals the existence of winning tickets (sparse but critical subnetworks) for dense networks, that can be trained in isolation from random initialization to match the latter’s accuracies.

VITA 20 Sep 4, 2022
[CVPR 2021] "The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models" Tianlong Chen, Jonathan Frankle, Shiyu Chang, Sijia Liu, Yang Zhang, Michael Carbin, Zhangyang Wang

The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models Codes for this paper The Lottery Tickets Hypo

VITA 59 Dec 28, 2022
PyTorch implementation of the paper The Lottery Ticket Hypothesis for Object Recognition

LTH-ObjectRecognition The Lottery Ticket Hypothesis for Object Recognition Sharath Girish*, Shishira R Maiya*, Kamal Gupta, Hao Chen, Larry Davis, Abh

null 16 Feb 6, 2022
Lottery by Ethereum Blockchain

Lottery by Ethereum Blockchain Set your web3 provider url in .env PROVIDER=https://mainnet.infura.io/v3/<YOUR-INFURA-TOKEN> Create your source file .

John Torres 3 Dec 23, 2021
Price-Prediction-For-a-Dream-Home - A machine learning based linear regression trained model for house price prediction.

Price-Prediction-For-a-Dream-Home ROADMAP TO THIS LINEAR REGRESSION BASED HOUSE PRICE PREDICTION PREDICTION MODEL Import all the dependencies of the p

DIKSHA DESWAL 1 Dec 29, 2021