![]() campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)ġ3. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. Also, after the end of the call y is obviously known. Yet, the duration is not known before a call is performed. Important note: this attribute highly affects the output target (e.g., if duration=0 then y=’no’). duration: last contact duration, in seconds (numeric). day_of_week: last contact day of the week (categorical: ‘mon’,’tue’,’wed’,’thu’,’fri’)ġ1. month: last contact month of year (categorical: ‘jan’, ‘feb’, ‘mar’, …, ‘nov’, ‘dec’)ġ0. contact: contact communication type (categorical: ‘cellular’,’telephone’)ĩ. Related with the last contact of the current campaign: 8. loan: has personal loan? (categorical: ‘no’,’yes’,’unknown’).housing: has housing loan? (categorical: ‘no’,’yes’,’unknown’).default: has credit in default? (categorical: ‘no’,’yes’,’unknown’).education (categorical: ‘basic.4y’,’basic.6y’,’basic.9y’,’high.school’,’illiterate’,’urse’,’gree’,’unknown’).marital : marital status (categorical: ‘divorced’,’married’,’single’,’unknown’ note: ‘divorced’ means divorced or widowed).job : type of job (categorical: ‘admin.’,’blue-collar’,’entrepreneur’,’housemaid’,’management’,’retired’,’self-employed’,’services’,’student’,’technician’,’unemployed’,’unknown’).The list of features available to us are given below: There were four variants of the datasets out of which we chose “ bank-additional-full.csv” which consists of 41188 data points with 20 independent variables out of which 10 are numeric features and 10 are categorical features. The dataset was picked from UCI Machine Learning Repository which is an amazing source for publicly available datasets. About the Dataset:Īs mentioned above, the dataset consists of direct marketing campaigns data of a banking institution. So as a goal we will try to produce a similar result in our case study. The researchers in their paper have mentioned that the best result they have got was a AUC score of 0.8 and a ALIFT of 0.7. This case study is inspired by this research paper where the researchers have used a very similar dataset as the one we will be using throughout this case study for determining the success of Bank Telemarketing. The classification goal is to predict if the client will subscribe a term deposit (target variable y). We are given the data of direct marketing campaigns (phone calls) of a Portuguese banking institution. With this, let’s jump right into our Case study. Be it predicting stock prices, or in our case predicting if a customer will subscribe to a term deposit Machine learning can be an incredibly useful tool for providing better profitability. One of the industries that is being transformed the most by the recent Machine learning advances is the finance industry. And one of the ways that a organisation can improve it’s performance in the market is to capture and analyse customer data in an efficient way to improve the customer experience. Use it in an effective way and it can create a huge impact on your business, don’t leverage it and you will be left behind in this fast paced world in no time. ![]() For the rest, they are determined by banks according to market conditions.Įvery month, the Banque de France is responsible for compiling regional statistics on the bank deposits of the non-financial sector, on the basis of an agreement signed with around 300 banks within the French Banking Federation.In today’s world, data is the king. In France, the interest rates on a number of savings instruments ("A" passbook, sustainable development passbook, people's passbook, housing savings account, housing savings plan, etc.) are set by regulations. ![]() Investments in mutual fund shares/units have different characteristics: money market fund shares are close substitutes for bank deposits, while the other categories of mutual funds represent more risky investments and are not included in the monetary aggregates. ![]() They are measured using banks' financial statements and cover both deposits (with an agreed maturity of up to two years) included in the monetary aggregates and savings accounts or investments. Bank deposits are the most common form of financial investment. ![]()
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