| Dataset Characteristics | Multivariate |
|---|---|
| Subject Area | Business |
| Associated Tasks | Classification |
| Feature Type | Categorical, Integer |
| Number of Instances | 41199 |
| Number of Features | 20 |
- age (numeric)
- job: Type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')
- marital: Marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)
- education: Education level (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')
- default: Has credit in default? (categorical: 'no','yes','unknown')
- housing: Has housing loan? (categorical: 'no','yes','unknown')
- loan: Has personal loan? (categorical: 'no','yes','unknown')
- contact: Contact communication type (categorical: 'cellular','telephone')
- month: Last contact month of the year (categorical: 'jan', 'feb', 'mar', …, 'nov', 'dec')
- day_of_week: Last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
- duration: Last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.
- campaign: Number of contacts performed during this campaign and for this client (numeric, includes last contact)
- pdays: Number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means the client was not previously contacted)
- previous: Number of contacts performed before this campaign and for this client (numeric)
- poutcome: Outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')
- emp.var.rate: Employment variation rate - quarterly indicator (numeric)
- cons.price.idx: Consumer price index - monthly indicator (numeric)
- cons.conf.idx: Consumer confidence index - monthly indicator (numeric)
- euribor3m: Euribor 3-month rate - daily indicator (numeric)
- nr.employed: Number of employees - quarterly indicator (numeric)
- y: Has the client subscribed to a term deposit? (binary: 'yes','no')
- Exploratory Data Analysis
- Data Preparation for Model Training
- Model Training using Logistic Regression
- Model Performance Comparison
The primary classification goal is to predict if the client will subscribe (yes/no) to a term deposit (variable y).
- Import the necessary libraries
- Read the data as a data frame
- Perform basic EDA
- Shape of the data
- Data type of each attribute
- Checking the presence of missing values
- 5-point summary of numerical attributes
- Checking the presence of outliers
- Prepare the data to train a model
- Check if data types are appropriate
- Handle missing values
- Train the model with logistic regression module
- Note and comment on performances along different metrics
The data is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict if the client will subscribe a term deposit (variable y).
The dataset comprises direct marketing campaigns conducted by a Portuguese banking institution, based on phone calls. The goal is to predict whether the client will subscribe to a term deposit (variable y). The marketing campaigns span from May 2008 to November 2010.
This dataset is publicly available for research and was analyzed in [Moro et al., 2014]. Please cite the following if you use this database: [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014