Exploring Different ML Algorithms across several Domains in Machine Learning Projects
BACKGROUND AND CONTEXT
- The Executive management of AXIS Insurance requires expert advise and recommedations from a professsional Data Analyst derived from a healthy extraction of key insights from available data in order to steer a possible product launch as well as design decision and policy formulation initiatives vis-a -vis subscription patterns as a stimulant to gain an apparent competitive edge.
Objectives
- Statistical Analysis of Business Data.
- 1.Explore the dataset and extract insights using Exploratory Data Analysis.
- 2.Prove (or disprove) that the medical claims made by the people who smoke is greater than those who don't? [Hint- Formulate a hypothesis and prove/disprove it]
- 3.Prove (or disprove) with statistical evidence that the BMI of females is different from that of males.
- 4.Is the proportion of smokers significantly different across different regions? [Hint:Create a contingency table/cross tab, Use the function :stats.chi2_contingency()]
- 5.Is the mean BMI of women with no children, one child, and two children the same? Explain your answer with statistical evidence.
BACKGROUND AND CONTEXT
- As a senior data scientist at Cars4U, you aretasked with coming up with a pricing model that can effectively predict the price of used cars and can help the business in devising profitable strategies using differential pricing. For example, if the business knows the market price, it will never sell anything below it.
Objectives
- Explore and visualize the dataset.
- Build a linear regression model to predict the prices of used cars.
- Generate a set of insights and recommendations that will help the business.
BACKGROUND AND CONTEXT
- AllLife Bank wants to focus on its credit card customer base in the next financial year. They have been advised by their marketing research team, that the penetration in the market can be improved. Based on this input, the Marketing team proposes to run personalized campaigns to target new customers as well as upsell to existing customers. Another insight from the market research was that the customers perceive the support services of the bank poorly. Based on this,the Operations team wants to upgrade the service delivery model, to ensure that customer queries are resolved faster. Head of Marketing and Head of Delivery both decide to reach out to the Data Science team for help
Objectives
- To identify different segments in the existing customer based on their spending patterns
- To identify past interaction with the bank using K-Means and Agglomerative Heirachichal Clustering Algorithms
- To profer recommendations to the bank on how best to target and market these customers along with the best customer service offering perculiar to them.
BACKGROUND AND CONTEXT
- AllLife Bank is a US bank that has a growing customer base. The majority of these customers are liability customers (depositors) with varying sizes of deposits. The number of customers who are also borrowers (asset customers) is quite small, and the bank is interested in expanding this base rapidly to bring in more loan business and in the process, earn more through the interest on loans. In particular, the management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio.You as a Data scientist at AllLife bank have to build a model that will help the marketing department to identify the potential customers who have a higher probability of purchasing the loan.
Objectives
- To predict whether a liability customer will buy a personal loan or not.
- Which variables are most significant.
- Which segment of customers should be targeted more.
BACKGROUND AND CONTEXT
- As a Data Scientist for a tourism company named "Visit with us",The Policy Maker of the company has mandated that I come up with a viable business model that seeks to expand the customer base of the company.To provoke this possibility, we will have to analyze the customers' data and information to provide recommendations to the Policy Maker and Marketing Team and also build a model to predict the potential customer who is going to purchase the newly introduced travel package.
A viable business model is a central concept that helps you to understand the existing ways of doing the business and how to change the ways for the benefit of the tourism sector.One of the ways to expand the customer base is to introduce a new offering of packages.Currently, there are 5 types of packages the company is offering - Basic, Standard, Deluxe, Super Deluxe, King. Looking at the data of the last year, we observed that 18% of the customers purchased the packages.
However, the marketing cost was quite high because customers were contacted at random without looking at the available information.The company is now planning to launch a new product i.e. Wellness Tourism Package. Wellness Tourism is defined as Travel that allows the traveler to maintain, enhance or kick-start a healthy lifestyle, and support or increase one's sense of well-being.However, this time company wants to harness the available data of existing and potential customers to make the marketing expenditure more efficient.
Objectives
- To predict which customer is more likely to purchase the newly introduced travel package.
BACKGROUND AND CONTEXT
- Thera bank recently saw a steep decline in the number of users of their credit card, credit cards are a good source of income for banks because of different kinds of fees charged by the banks like annual fees, balance transfer fees, and cash advance fees, late payment fees, foreign transaction fees, and others. Some fees are charged to every user irrespective of usage, while others are charged under specified circumstances. Customers’ leaving credit cards services would lead bank to loss, so the bank wants to analyze the data of customers and identify the customers who will leave their credit card services and reason for same – so that bank could improve upon those areas.
As a Data scientist at Thera bank, my team is charged with the responsibility of building a classification model that will help the bank improve their services so that customers do not renounce their credit cards.
Objectives
- To explore and visualize the dataset.
- Build a classification model to predict if the customer is going to churn or not
- Optimize the model using appropriate techniques
- Generate a set of insights and recommendations that will help the bank
To extract actionable insights from the data and identify areas of growth and improvement.
Major focus on the following - ● Variables that drive the sales of product ● Build customer profile (characteristics of a customer) for the different products ● Ways to capitalize based on customer characteristics