API Query & Call Booking Control

21.0 GBP

21.0 GBP peopleperhour Technology & Programming Overseas
297 days ago

Description

I need some code to run on a server; it can be Ruby, Python, PHP ... we will rent the server from Ionos or similar service provider.
The code will have 3 components; 1. Pull data from a booking system with API calls: probably 24hrs of data at a time.2. Process the data to determine if there is a booking in the next 5mins3. When there is a booking, send an API Call to the CourtAdmin system to wake up the system in one of two states; - Squash/racketball booking only - Interactive Games booking
I will supply the API Documentation for the inital booking system and the interactiveCOURT admin system
I work at the Squash Club where both systems are installed in the UK.

关注公众号,不定期副业成功案例分享
Follow WeChat

Success story sharing

Want to stay one step ahead of the latest teleworks?

Subscribe Now

Similar Teleworks

I want help in explaining my data science projects in interview in a detailed manner . I am data science professional looking for a job switch of 3 years. Projects: 1) CONSUMER COMPLAINT CLASSIFICATION (NLP) Building an API and training model to classify future complaints based on its content for a banking firm. The dataset is of 2 million rows, 5+ years historical data. Skills/Technology: Python, SVM classification, Random Forest ,Flask, Glove ,Word2Vec 2) PREDICTIVE MAINTENANCE Built an end to end machine learning model for a heavy industry firm which records different features like power,temperature etc for machines and predicting whether failure will happen or not. Skill/Technology: Python, FastAPI, Digital Ocean.,Streamlit,Docker 3) RECOMMENDATION ENGINE FOR AUTOMATED TRADING PLATFORM Developed custom Technical Indicators Functions to analyse historical price data and market trends, triggering buying signals when specific conditions were met. Empowered traders clients with actionable insights by providing timely buy signals aligned with market trends. Skill/Technology: TA-Lib, TensorFlow, NumPy, Pandas, sklearn, statsmodels 4) INSURANCE POLICY CROSS SELLING A classification/ranking project aimed to detect health insurance customers most likely to buy a new type of insurance from the company - car insurance. To solve this problem a machine learning model was built to classify the customers by their probability of buying the insurance. The Heroku platform was used to deploy the ML model, which will respond to requests via API. Skill/Technology: XGBoost,LightGBM, NumPy, Flask,Heroku 5) CUSTOMER CHURN PREDICTION FOR A MALAYSIAN BANK Developed a model to analyse customer data and predict churn to boost customer retention.Employed statistical techniques on customer data using Pandas, Seaborn, and Sklearn. Reduced customer churn rate by 7%, leading to increased revenue, lower marketing costs, and enhanced customer loyalty.
30.0 GBP Technology & Programming peopleperhour Overseas
6 hour ago