predict-total-sales

In many businesses, identifying which customers will make a purchase (and when), is a critical exercise. This is true for both brick-and-mortar outlets and online stores. The data provided in this challenge is website traffic data acquired from an online retailer. The data provides information on customer's website site visit behavior. Customers may visit the store multiple times, on multiple days, with or without making a purchase. Your goal is to predict how much sales revenue can be expected from each customer. The variable revenue lists the amount of money that a customer spends on a given visit. Your goal is to predict how much money a customer will spend, in total, across all visits to the site, during the allotted one-year time frame (August 2016 to August 2017). More specifically, you will need to predict a transformation of the aggregrate customer-level sales value based on the natural log. That is, if a customer has multiple revenue transactions, then you should compute the sum of all the revenue generated across all of the transactions

predicting-catalog-demand

In this project, i analyzed a business problem in the mail-order catalog business. You're tasked with predicting how much money your company can expect to earn from sending out a catalog to new customers. This task involved building the model and applying the results in order to provide a recommendation to management.

predicting-icu-patient-clinical-deterioration---report

For this project, I used publicly available Electronic Health Records (EHRs) datasets. The MIT Media Lab for Computational Physiology has developed MIMIC-IIIv1.4 dataset based on 46,520 patients who stayed in critical care units of the Beth Israel Deaconess Medical Center of Boston between 2001 and 2012. MIMIC-IIIv1.4 dataset is freely available to researchers across the world. A formal request should be made directly to www.mimic.physionet.org, to gain access to the data. There is a required course on human research ‘Data or Specimens Only Research’ prior to data access request. I have secured one here -www.citiprogram.org/verify/?kb6607b78-5821-4de5-8cad-daf929f7fbbf-33486907. We built flexible and better performing model using the same 17 variables used in the SAPS II severity prediction model. The question ‘Can we improve the prediction performance of widely used severity scores using a more flexible model?’ is the central question of our project. I used the exact 17 variables used to develop the SAPS II severity prediction algorithm. These are 13 physiological variables, three underlying (chronic) disease variables and one admission variable. The physiological variables includes demographic (age), vital (Glasgow Comma Scale, systolic blood pressure, Oxygenation, Renal, White blood cells count, serum bicarbonate level, blood sodium level, blood potassium level, and blood bilirubin level). The three underlying disease variables includes Acquired Immunodeficiency Syndrome (AIDS), metastatic cancer, and hematologic malignancy. Finally, whether admission was scheduled surgical or unscheduled surgical was included in the model. The dataset has 26 relational tables including patient’s hospital admission, callout information when patient was ready for discharge, caregiver information, electronic charted events including vital signs and any additional information relevant to patient care, patient demographic data, list of services the patient was admitted or transferred under, ICU stay types, diagnoses types, laboratory measurments, microbiology tests and sensitivity, prescription data and billing information. Although I have full access to the MIMIC-IIIv1.4 datasets, I can not share any part of the data publicly. If you are interested to learn more about the data, there is a MIMIC III Demo dataset based on 100 patients https://mimic.physionet.org/gettingstarted/demo/. If you are interested to requesting access to the data - https://mimic.physionet.org/gettingstarted/access/. Linked repositories: Exploratory-Data-Analysis-Clinical-Deterioration, Data-Wrangling-MIMICIII-Database, Clinical-Deterioration-Prediction-Model--Inferential-Statistics, Clinical-Deterioration-Prediction-Model--Ensemble-Algorithms-, Clinical-Deterioration-Prediction-Model--Logistic-Regression, Clinical-Deterioration-Prediction-Model---KNN © 2020 GitHub, Inc.

predicting-revenue

A project from the MSiA course, Predictive Analytics I, that predicting future revenue for a retail company based on customers' transaction data

predictive_analysis_in_hospitality_sector

Predictive Analysis for the Hospitality Sector - Revenue and Customer Prediction

profit-calculator-for-startups-flask-website

This website can predict the profit of venture capitalists can expect if he is going to put his own money in the startup given he or she has access to company's R&D spent , Administration spent , marketing spent and state where that startup is located.

project-employee-attrition-prediction-

Business Problem: The main problem that exists across all businesses irrespective of geography or type of industry is employee attrition. Some of the problems faced are : Expensive in terms of both money and time to train new employees Loss of experienced employees Impact on productivity Impact on profit We are using classification model to predict the employees who will tend to leave the company.

projectaiai

AiAi.care project is teaching computers to "see" chest X-rays and interpret them how a human Radiologist would. We are using 700,000 Chest X-Rays + Deep Learning to build an FDA 💊 approved, open-source screening tool for Tuberculosis and Lung Cancer. After an MRMC clinical trial, AiAi CAD will be distributed for free to emerging nations, charitable hospitals, and organizations like WHO 🌏 We will also release our pretrained models and weights as Medical Imagenet.

prophet

Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.

purchasing-intention-prediction

The project trains an Artificial Neural Network which can predict whether a visitor will generate revenue for the company or not.

pyfolio

Portfolio and risk analytics in Python

python-business-analytics

Python solutions to solve practical business problems.

quantitative_finance

A Data Science project using Python in order to predict whether an equity in the IT sector has a positive growth or a negative growth with the use of technical indicators concluding with a comparative analysis of performance of various machine learning models

r2

R2 Bitcoin Arbitrager is an automatic arbitrage trading system powered by Node.js + TypeScript.

reaction

Reaction is an API-first, headless commerce platform built using Node.js, React, GraphQL. Deployed via Docker and Kubernetes.

real-time-person-removal

Removing people from complex backgrounds in real time using TensorFlow.js in the web browser

restaurant-data-analysis

Restaurant tablets allow customers to browse the menu, place orders, play games, and pay their bill right from their seat. When the devices are installed in a restaurant (“go-live date” in the workbook) they influence customer behavior and subsequently the restaurant’s bottom line. The first tab of the workbook contains daily totals for net sales, number of POS checks, and labor costs for 14 restaurants over a 15-month period. The second tab contains additional information about each restaurant including the state the restaurant is located in, the franchise group it belongs to, as well as the “go-live” date for when the device was installed. Restaurants with an “n/a” in the go-live column never had the devices installed and may be thought of as a control group. We analyze the impact of the introduction of the device using A/B Testing. Additionally, restaurant makes money from selling games and is interested in being able to accurately predict our revenue. Using the dataset, we create a predictive model of Game Revenue based on variables

revenue

Predicting Revenue with TensorFlow insurance data example

revenue-forecasting

Implementing a production level forecasting model for finance to predict future trends in revenue

revenue-prediction

[kaggle competition] Restaurant Revenue Prediction

revenue_prediction

revenue prediction model

revenuepredict

risk-profile-prediction

CS 221 Project. Risk profile prediction using neural networks: A study of multilateral-finance projects.

rl-market-maker

sales-forecasting-using-time-series-analysis

Business Case of Deere & Co. Deere and copmany forecast higher sales of machinery in the next financial year as the world’s largest tractor manufacturer downplayed the impact of the U.S.-China trade war on soybean prices. Deere also forecast its equipment sales will rise by about 30 percent in the current fiscal year. The company expects farmers’ net returns per acre in 2019 will rise as much as 20 percent to the highest level in about five years, Chief Finance Officer Rajesh Kalathur said on the call. Now with this challenging demand, we need data science team to help them Deere is a tractor and farm equipment manufacturing company, was established in 1838. The company has shown a consistent growth in its revenue from tractor sales since its inception. However, over the years the company has struggled to keep it’s inventory and production cost down because of variability in sales and tractor demand. The management at PowerHorse is under enormous pressure from the shareholders and board to reduce the production cost. Additionally, they are also interested in understanding the impact of their marketing and farmer connect efforts towards overall sales. In the same effort, they have hired you as a data science and predictive analytics consultant. Can you help them in optimizing and solving their business Problem

sales-revenue-prediction

Sales revenue prediction

santander

This repository aims at predict if customers will make a specific transaction irrespective of the amount of money transacted

santander-customer-transaction-prediction

In this challenge, I have tried to identify which customers will make a specific transaction in the future, irrespective of the amount of money transacted. The data provided for this competition has the same structure as the real data santander has available to solve this problem.

sensback

Ergonomic back detection using machine learning.

servicebot

Open-source subscription management & billing automation system