Other posts in this series: Bank Marketing - Part 1 - Making the call - An Exploratory Data Analysis Bank Marketing - Part 2 - Who will say yes? - Building Classification Models In Part 1, we found that the subscription rate changed a lot between 2008 and 2010, as interest rates fell and the bank changed its campaigns. In Part 2, we built models on a random sample of the calls and tested them on the other calls from the same period. They did well. But when we trained them on the earlier calls and tested them on the later calls, they did much worse.
Other posts in this series: Bank Marketing - Part 1 - Making the call - An Exploratory Data Analysis Bank Marketing - Part 3 - Keeping up with the times - Sliding and Expanding Window Models In Part 1, we explored the Bank Marketing dataset: 41,188 calls in which a Portuguese bank asked its clients to open a term deposit. Only about 1 in 9 clients said yes.
In this post, we build models that predict whether a client will subscribe. A model is a formula that a computer learns from examples. A classification model does not just answer yes or no: it gives each client a probability of saying yes. A bank can use these probabilities to call the most promising clients first.
Other posts in this series: Bank Marketing - Part 2 - Who will say yes? - Building Classification Models Bank Marketing - Part 3 - Keeping up with the times - Sliding and Expanding Window Models A Portuguese bank ran a series of telemarketing campaigns between May 2008 and November 2010. Its staff called clients and asked them to open a term deposit, a savings account that pays a fixed interest rate for a fixed period. The Bank Marketing dataset (Moro, Cortez and Rita, 2014) records 41,188 of these calls: who the client was, how and when they were called, the state of the economy at the time, and whether the client said yes.
In my previous post, we’ve seen how kids improve year-by-year in swimming. Certainly kids get stronger and faster as they grow. But how much impact does practice and coaching have? As I mentioned in my previous post, coaches do not keep a record of attendance during practice. There are daily hour long practice sessions during the summers. But even though it isn’t known how regular the kids are in attending practice, the number of meets that a kid participates in during the season, can be a good proxy. Afterall, it’s highly unlikely that a kid participates in meets while mostly skipping practice.
This past summer, my daughter joined a swim team. Her team is among several teams that are part of an area league. All summer long, developmental and competitive meets are organized where teams compete on a one-to-one basis. All kids in a team swim in developmental meets. For competitive meets, top 3 or 4 kids are chosen by age groups. Meets are organized in community pools that are either 25 meters or 25 yards in length. The team schedules are packed with practices and events which are intense but thoroughly enjoyable.
Recently I listened to a podcast featuring Victor Haghani of Elm Partners, who described a fascinating coin-flipping experiment. The experiment was designed to be played for 30 minutes by participants in groups of 2-15 in university classrooms or office conference rooms, without consulting each other or the internet or other resources. To conduct the experiment, a custom web app was built for placing bets on a simulated coin with a 60% chance of coming up heads. Participants used their personal laptops or work computers to play. They were offered a stake of \(\$25\) to begin, with a promise of a check worth the final balance (subject to a caveat) in their game account. No out-of-pocket betting or any gimmicks. But they had to agree to remain in the room for 30 minutes.
Amazon is led by its charismatic founder and CEO, Jeff Bezos, who is widely hailed as one of the top visionaries of this era. At the end of the fiscal year in March, Amazon publishes a letter to shareholders written by Bezos, in which he summarises his thoughts, business and management philosophy. While reading these letters, among other things that strikes is Bezos’ clarity of thought, expressed in a concise and easy to comprehend way. It is a very rare quality possessed by very few business leaders. Few other names that come to mind are Warren Buffett, Charlie Munger, Elon Musk and late Steve Jobs.
Everyone using a smartphone or a mobile device has used an onscreen smart keyboard that tries to predict the next set of words that the user might want to type. Typically, upto 3 words are predicted, which are displayed in a row at the top of the keyboard. Given that typing on a glass pane without tactile feedback, could be very frustrating at times, the smart keyboard goes a long way in alleviating these issues.
Who’re the best batsmen in cricket today? Is there a way to define a set of objective criteria that provides an unbiased conclusion? In my last post, I selected a list of top batsmen in the history of cricket, by simply filtering all batsmen who have had a career batting average of 50 or more in any format. Prior to that I eliminated newcomers, specialist bowlers and unsuccessful players by another set of filters.
In a previous post, I used data from Statsguru and looked at a brief history of cricket with respect to debut years and career spans of players. In this post, I use detailed player statistics from the same dataset to select top batsmen who have played this game.
Distribution of matches played by all players Let’s take a look at the summary statistics of number of matches played by all players in each format of the game.