Bank Term Deposit Subscription Predictor

Columns

• age: The age of the customer (Numerical) [4-6]. Mean age is 41.2 years [8].
• job: The occupation or employment status of the customer (Categorical) [4-6]. The most common occupations are management and blue-collar, each accounting for 21% of the records [8].
• marital: The marital status of the customer (Categorical) [4-6]. The majority of customers are married (62%) [9].
• education: The education level attained by the customer (Categorical) [4, 5, 10]. Secondary education is the most common at 51%, followed by tertiary at 30% [9].
• default: Indicates whether the customer has credit in default (Categorical, Boolean) [4, 5, 10]. 98% of customers do not have credit in default [9].
• balance: The balance in the customer's account (Numerical) [4, 10, 11]. The mean balance is approximately 1.42k [12].
• housing: Indicates whether the customer has a housing loan (Categorical, Boolean) [4, 10, 11]. 57% of customers have a housing loan [13].
• loan: Indicates whether the customer has a personal loan (Categorical, Boolean) [13]. 85% of customers do not have a personal loan [13].
• contact: The type of communication used to contact customers, such as phone or cellular (Categorical) [4, 10, 11]. Cellular is the most common contact method (64%) [13].
• day: The day of the month when the last contact with customers was made (Numerical) [2, 10, 14]. The mean day is 15.9 [14].
• month: The month of the last contact with customers (Categorical) [14]. May (31%) and July (16%) are the most common months for contact [14].
• duration: The duration in seconds of the last contact with customers during the campaign (Numerical) [2, 10, 14]. The mean duration is 264 seconds [15].
• campaign: The number of contacts performed during this campaign for each customer (Numerical) [2, 15]. The mean number of contacts per campaign is 2.79 [16].
• pdays: The number of days passed since the customer was previously contacted from a previous campaign (Numerical) [2, 7, 16]. Many records show -1, indicating no previous contact [17].
• previous: The number of contacts performed before this campaign for each customer (Numerical) [17]. The mean number of previous contacts is 0.54 [17].
• poutcome: The outcome from the previous marketing campaign (Categorical) [2, 7, 17]. The outcome is unknown for 82% of contacts, with failure at 11% [18].
• y: The target variable, indicating whether the customer subscribed to a term deposit (Categorical, Boolean) [18]. 12% of customers subscribed to a term deposit [18].

Distribution

Usage

• Predictive Modelling: Building models to forecast whether a customer will subscribe to a term deposit [11].
• Customer Segmentation: Grouping customers based on characteristics and behaviour to tailor marketing strategies [21].
• Campaign Optimisation: Analysing the effectiveness of communication types, contact frequency, and previous campaign outcomes to improve future marketing efforts [21].

Coverage

License

Who Can Use It

• Businesses in similar domains aiming to understand their target audience and optimise marketing [3].
• Machine learning practitioners for building and evaluating predictive models [2, 3].
• Data scientists and analysts for customer segmentation and behavioural insights [21].
• Marketing strategists looking to enhance campaign effectiveness and return on investment [21].

Dataset Name Suggestions

• Bank Term Deposit Subscription Predictor
• Customer Marketing Campaign Success
• Portuguese Bank Deposit Campaign Outcomes
• Direct Marketing Customer Behaviour

Attributes