Machine Learning is a latest buzzword floating around. It desrves to, as it is one of the most interesting subfield of Computer Science.
What does Machine Learning really means?
Machine Learning is an application of artificial intelligence(AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed.
Machine Learning focuses on the development of computer programs that can access data and use it to learn for themsleves.
The process of learning begins with data, such as, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide. The primary aim is to allow the computers learn automatically without human intervention or assistance and adjust actions accordingly.
This repository is about different Machine Learning algorithm approaches as per the industry practices.
- Telecom Churn Prediction
- Credit Fraud Detection
- Spam Detection
- Customer Segmentation using RFM Analysis
- A churn with respect to the Telecom industry, is defined as the percentage of subscribers moving from a specific service to a service provider to another in a period of time.
- Research shows that the companies have an avergae churn of 1.9 to 2 percent month on month and annualized churn ranging from 10 to 60 percent.
- An effort to build a model which helps in reducing the churn rate for a telecom company.
- Link for the Jupyter notebook
- Credit card fraud is a wide-ranging term for theft and fraud committed using or involving a payment card, such aas credit or debit card, as fraudulent source of funds in a transaction.
- The purpose may be to obtain goods without paying, or to obtain unautorized funds from an account.
- Credit card fraud is also and adjunt to identity theft.
- Building a robust model so that credit card companies are able to recognize the fraudulent card transactions so that customets are not charged for items that they did not purchase.
- Link for the Jupyter notebook
- This is a beginner's example of document classification task which involves classifying an email as spam or not spam mail.
- Spam box in your Gmail account is the best example of this.
- Link for the Jupyter notebook
- Businesses that lack the monetary aspect, like viewership, readership, or surfing-oriented products, could use Engagement parameters instead of Monetary factors.
- The Engagement parameter could be defined as a composite value based on metrics such as bounce rate, visit duration, number of pages visted, time spent per page etc.
- RFM stand for Recency, Frequency and Monetary.
- Link for the Jupyter notebook