From 76d8d94b7d59e30203df8605cb432e3540927214 Mon Sep 17 00:00:00 2001 From: Ahmed Sheta Date: Sat, 3 Feb 2024 20:18:40 +0100 Subject: [PATCH] adding design documentation pdf Signed-off-by: Ahmed Sheta --- .../sprint-13/design-documentation.md | 96 ------------------ .../sprint-13/design-documentation.pdf | Bin 0 -> 199793 bytes .../design-documentation.pdf.license | 2 + 3 files changed, 2 insertions(+), 96 deletions(-) delete mode 100644 Deliverables/sprint-13/design-documentation.md create mode 100644 Deliverables/sprint-13/design-documentation.pdf create mode 100644 Deliverables/sprint-13/design-documentation.pdf.license diff --git a/Deliverables/sprint-13/design-documentation.md b/Deliverables/sprint-13/design-documentation.md deleted file mode 100644 index 0812e46..0000000 --- a/Deliverables/sprint-13/design-documentation.md +++ /dev/null @@ -1,96 +0,0 @@ - - -# Introduction - -This application is used by our industry partner SumUp to enhance information about possible leads collected at their sign-up website. This enhanced data is then -used to predict the possible value that lead could bring to SumUp using a machine learning model. The application is split into two major parts, namely the base data -collector (BDC) and the estimated value predictor (EVP). - -# Base Data Collector - -## General description - -This component is used to enhance data on possible client leads. The initial data of leads only includes the first and last name of the lead, the phone number of the -lead, the lead's email address, and the lead's company name. This data is not sufficient to predict the lead's value, hence this component is supposed to query -multiple data sources, including various APIs to enhance the provided lead data. - -## Design - -The different data sources are organised as steps in the program. Each step extends from a common parent class and implements methods to validate that it can run, perform the data collection from the particular source and perform clean up and statistics reports for itself. These steps are then collected in a pipeline object sequentially performing the steps to enhance the given data with all chosen data sources. The data sources include: - -- inspecting the possible custom domain of the email address -- retrieving multiple data from the Google Places API -- analyzing the sentiment of Google reviews using GPT -- inspecting the surrounding areas of the business using the Regional Atlas API -- searching for company-related data using the OffeneRegisterAPI -- performing sentiment analysis on reviews using GPT-4 model. - -## Data storage - -All data for this project is stored in CSV files in the client's AWS S3 storage. The files here are split into three buckets. The input data and enhanced data are stored in the events bucket, preprocessed data ready for use of ML models is stored in the features bucket and the used model and inference is stored in the model bucket. - -# Estimated Value Predictor - -This component is used to predict the possible Merchant Size a given lead might give to SumUp. The EVP includes an ML model that can perform classification to predict the Merchant Size. The model is trained on confidential historical data from SumUp and cannot be shared with the general public. It also transforms the output of the BDC into feature vectors that can be used for prediction by the ML model. The used ML model can easily be swapped out to accommodate for a quick testing and exploration phase on which model might perform best. Feature vector creation is possible through the data preprocessing that proprocesses the enriched data for the ML models. - -# Data field definitions - -This section outlines the data fields obtained for each lead. The data can be sourced from the online Lead Form or be retrieved from the internet using APIs. - -| Field Name | Type | Description | Data Source | Dependencies | Validation | Example | -| ------------------------------------------- | ------------------------------ | ----------- | ----------- | ------------ | ---------- | ----------------------- | --- | -| Last Name | | | lead data | | | Mustermann | -| First Name | | | lead data | | | Mustername | -| Company / Account | | | lead data | | | Musterfirma | -| Phone | | | lead data | | | +49123456789 | -| Email | | | lead data | | | musteremail@example.com | -| domain | string | - | - | | | @musterexample.de | -| email_valid | boolean | | | | | True/False | -| first_name_in_account | boolean | | | | | True/False | -| last_name_in_account | boolean | | | | | True/False | -| address_ver_1 | string | | | | | Musterstr.1 | -| number_formatted | string | | | | | +49123456789 | -| number_country | string | | | | | Germany | -| number_area | string | | | | | Erlangen | -| number_valid | boolean | | | | | True/False | -| number_possible | boolean | | | | | True/False | -| google_places_place_id | string | | | | | | -| google_places_business_status | string | | | | | Operational | -| google_places_formatted_address | string | | | | | Musterstr.1 | -| google_places_name | string | | | | | Mustername | -| google_places_user_ratings_total | integer | | | | | 100 | -| google_places_rating | float | | | | | 4.5 | -| google_places_price_level | float | | | | | | -| google_places_candidate_count_mail | integer | | | | | 1 | -| google_places_candidate_count_phone | integer | | | | | 1 | -| google_places_place_id_matches_phone_search | boolean | | | | | True/False | -| google_places_confidence | float | | | | | | 0.9 | -| google_places_detailed_website | string | | | | | www.musterwebsite.de | -| google_places_detailed_type | list | | | | | ["florist","store"] | -| reviews_sentiment_score | float | | | | | 0.9 | -| regional_atlas_pop_density | float | | | | | 0.9 | -| | regional_atlas_pop_development | float | | | | | 0.9 | -| regional_atlas_age_0 | float | | | | | 0.9 | -| regional_atlas_age_1 | float | | | | | 0.9 | -| regional_atlas_age_2 | float | | | | | 0.9 | -| regional_atlas_age_3 | float | | | | | 0.9 | -| regional_atlas_age_4 | float | | | | | 0.9 | -| regional_atlas_pop_avg_age | float | | | | | 0.9 | -| regional_atlas_per_service_sector | float | | | | | 0.9 | -| regional_atlas_per_trade | float | | | | | 0.9 | -| regional_atlas_employment_rate | float | | | | | 0.9 | -| regional_atlas_unemployment_rate | float | | | | | 0.9 | -| regional_atlas_per_long_term_unemployment | float | | | | | 0.9 | -| regional_atlas_investments_p_employee | float | | | | | 0.9 | -| regional_atlas_gross_salary_p_employee | float | | | | | 0.9 | -| regional_atlas_disp_income_p_inhabitant | float | | | | | 0.9 | -| regional_atlas_tot_income_p_taxpayer | float | | | | | 0.9 | -| regional_atlas_gdp_p_employee 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