customer-churn-prediction-modelling

This repository is associated with predicting the exit status of a customer from an organization or a company using independent variables present in the dataset. Hence we are building a classification model using 3 classifiers: Artificial Neural Network, Support Vector Machine and XGBoost ML algorithms and thereby comparing their accuracies. The repository also contains the RStudio code and the dataset.

customer-lifetime-value-prediction

BUSINESS PROBLEM A company wants to know the lifetime value of customers in terms of how much money they will likely bring to the company based on their first few purchase history. GOAL The goal of this project is to build a predictive model that estimates the customer lifetime value (CLV) for new customers using past purchase history of existing customers.

customerrevenueprediction

Google Analytics Customer Revenue Prediction

data-preprocessing

Making the data ready for Analysis using simple techniques

datascience

AI for Business

datascience-classificationproblem

Problem Statement : Your client is a retail banking institution. Term deposits are a major source of income for a bank. A term deposit is a cash investment held at a financial institution. Your money is invested for an agreed rate of interest over a fixed amount of time, or term. The bank has various outreach plans to sell term deposits to their customers such as email marketing, advertisements, telephonic marketing and digital marketing. Telephonic marketing campaigns still remain one of the most effective way to reach out to people. However, they require huge investment as large call centers are hired to actually execute these campaigns. Hence, it is crucial to identify the customers most likely to convert beforehand so that they can be specifically targeted via call. You are provided with the client data such as : age of the client, their job type, their marital status, etc. Along with the client data, you are also provided with the information of the call such as the duration of the call, dayand month of the call, etc. Given this information, your task is to predict if the client will subscribe to term deposit.

deeppath

Classification of Lung cancer slide images using deep-learning

deepslide

Code for the Nature Scientific Reports paper "Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks." A sliding window framework for classification of high resolution whole-slide images, often microscopy or histopathology images.

diabetesprediction

Using Pima Indians diabetes data set to predict whether a patient has diabetes or not based upon patient’s lab test result variables like Glucose, Blood Pressure, etc. using CART decision tree algorithm and K-Nearest Model achieving 76% accuracy. Python-Scikit Learn, SciPy, Pandas, MatPlotLib.

digital-marketplace

A Digital Marketplace where users can buy & sell digital goods like High Resolution Images, MP3s, Audiobooks, and eBooks. Learn how to build this project on https://codingforentrepreneurs.com/projects/digital-marketplace/

distributedml

Distributed Machine Learning for Stock Price Prediction

drivetime-sedans-used-vehicle-market

Linear regression model applied in the used vehicle market to guide the inventory selection process

ease-of-doing-business

Machine Learning based AI to determine ease of doing business in various countries

ecommerce_linear_regression

DATA DESCRIPTION: This dataset is having data of customers who buys clothes online. This file has customer email, avg. session time with stylist, Time spent on the app and website, Length of Membership. Our main objective is to predict the Yearly amount spent by the customers. ATTRIBUTES: Email: Email of the customer Address: Address of the customer Avatar: Avatar chosen by the customer Avg. Session Length: Average duration of the online session Time on App: Time spent on App Time on Website: Time spent on website Length of Membership: Time period of membership Yearly Amount Spent: Yearly amount spent by the customer

eda-python-b2b-marketing

employeeretention

Employee turn-over is a very costly problem for companies. The cost of replacing an employee if often larger than 100K USD, taking into account the time spent to interview and find a replacement, placement fees, sign-on bonuses and the loss of productivity for several months. It is only natural then that data science has started being applied to this area. Understanding why and when employees are most likely to leave can lead to actions to improve employee retention as well as planning new hiring in advance. This application of DS is sometimes called people analytics or people data science.

finance-manager

An effort to develop a software to share and split funds between friends and family. Eventually, as the software grows, this would have more and more features wherein this would predict the monthly expenses and thereby the expected savings.

finance-ml

Project related to finance. Which includes market analysis with open data from yahoo and other Internet resources. Also, the development of algorithms that would predict the purchase and sale of shares.

finance-models

Various classification models for Finance Applications. Predict if a days return will be positive or negative.

financeai

A tool that takes financial statements to create machine learning classifiers. Then it uses the classifiers to predict financial performance.

financecalc

Hybrid mobile app using ionic framework to predict and grade the financial status of a certain organization

financial-machine-learning

A curated list of practical financial machine learning tools and applications.

fingerpose

Finger pose classifier for hand landmarks detected by TensorFlow.js handpose model

finsns

We utilise SNS analysis to predict finance risks and trends

freqtrade

Free, open source crypto trading bot

future-money

app built off of the pocket money API. Predicts how much money you will have in the future. Requires additional budget data.

gekko

A bitcoin trading bot written in node - https://gekko.wizb.it/

gekko-neuralnet

Neural network strategy for Gekko

gekko-strategies

Strategies to Gekko trading bot with backtests results and some useful tools.

give-me-some-credit

This is a Kaggle Project. We have to create a string credit scoring algorithm for banks to predict the probability that somebody will experience financial distress in the next two years. Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. This competition requires participants to improve on the state of the art in credit scoring, by predicting the probability that somebody will experience financial distress in the next two years. The goal of this competition is to build a model that borrowers can use to help make the best financial decisions