End to end EDA on this bank telemarketing campaign data set to infer knowledge on where bank has to put more effort to improve it's positive response rate.
Attribute details.xlsx contains Attribute details
Bank+Marketing+Campaign_EDA-blank+notebook (1).ipynb contains Python code and analysis.
bank_marketing_updated_v1.csv contains the bank marketing data
The bank provides financial services/products such as savings accounts, current accounts, debit cards, etc. to its customers. In order to increase its overall revenue, the bank conducts various marketing campaigns for its financial products such as credit cards, term deposits, loans, etc. These campaigns are intended for the bank’s existing customers. However, the marketing campaigns need to be cost-efficient so that the bank not only increases their overall revenues but also the total profit.
A bank conducted a telemarketing campaign for one of its financial products called ‘Term Deposits’ to help foster long-term relationships with existing customers. The dataset contains information about all the customers who were contacted during a particular year to open term deposit accounts with the bank. Analyse the given dataset to find patterns and provide inferences/solutions for the future marketing campaigns using EDA.
Term deposits, also called fixed deposits, are the cash investments made for a specific time period ranging from 1 month to 5 years for predetermined fixed interest rates. The fixed interest rates offered for term deposits are higher than the regular interest rates for savings accounts. The customers receive the total amount (investment plus the interest) at the end of the maturity period. Also, the money can only be withdrawn at the end of the maturity period. Withdrawing money before that will result in penalty charges, and the customer will not receive any interest returns.
Subject Matter Expert : Mirza Rahim Baig
Analytics Lead, Flipkart
Subject Matter Expert : Anand S
CEO, Gramener
For data analysis, the following libraries are used
Pandas: It is a library to deal with dataframes in Python. Pandas is an acronym derived from panel data. It is solely used for data analysis purposes in Python.
NumPy: This library is used for performing numerical operations on a dataset.
Matplotlib: Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.
Seaborn: Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
There are multiple types of data types available in the data set. some of them are numerical type and some of categorical type.
Following are the some of the types of variables:
Numeric data type: banking dataset: salary, balance, duration and age.
Categorical data type: banking dataset: education, job, marital, poutcome and month etc.
Ordinal data type: banking dataset: Age group.
Time and date type
Coordinates type of data: latitude and longitude type.
Checklist for fixing rows:
Delete summary rows: Total and Subtotal rows
Delete incorrect rows: Header row and footer row
Delete extra rows: Column number, indicators, Blank rows, Page No.
Checklist for fixing columns:
Merge columns for creating unique identifiers, if needed, for example, merge the columns State and City into the column Full address.
Split columns to get more data: Split the Address column to get State and City columns to analyse each separately.
Add column names: Add column names if missing.
Rename columns consistently: Abbreviations, encoded columns.
Delete columns: Delete unnecessary columns.
Align misaligned columns: The data set may have shifted columns, which you need to align correctly.
Set values as missing values: Identify values that indicate missing data, for example, treat blank strings, "NA", "XX", "999", etc., as missing.
Adding is good, exaggerating is bad: You should try to get information from reliable external sources as much as possible, but if you can’t, then it is better to
retain missing values rather than exaggerating the existing rows/columns.
Delete rows and columns: Rows can be deleted if the number of missing values is insignificant, as this would not impact the overall analysis results. Columns can be
removed if the missing values are quite significant in number.
Fill partial missing values using business judgement: Such values include missing time zone, century, etc. These values can be identified easily.
Types of missing values:
MCAR: It stands for Missing completely at random (the reason behind the missing value is not dependent on any other feature).
MAR: It stands for Missing at random (the reason behind the missing value may be associated with some other features).
MNAR: It stands for Missing not at random (there is a specific reason behind the missing value).
Major approaches to the treat outliers:
Imputation,Deletion of outliers,Binning of values,Cap the outlier
Checklist for data standardization exercises:
Standardise units: Ensure all observations under one variable are expressed in a common and consistent unit, e.g., convert lbs to kg, miles/hr to km/hr, etc.
Scale values if required: Make sure all the observations under one variable have a common scale.
Standardise precision for better presentation of data, e.g., change 4.5312341 kg to 4.53 kg.
Remove extra characters such as common prefixes/suffixes, leading/trailing/multiple spaces, etc. These are irrelevant to analysis.
Standardise case: String variables may take various casing styles, e.g., UPPERCASE, lowercase, Title Case, Sentence case, etc.
Standardise format: It is important to standardise the format of other elements such as date, name, etce.g., change 23/10/16 to 2016/10/23, “Modi, Narendra” to “Narendra Modi", etc.
Unordered data do not have the notion of high-low, more-less etc. Example:
Type of loan taken by a person = home, personal, auto etc.
Organisation of a person = Sales, marketing, HR etc.
Job category of persone.
Marital status of any one.
There are three ways to analyse the numeric- numeric data types simultaneously.
Scatter plot: describes the pattern that how one variable is varying with other variable.
Correlation matrix: to describe the linearity of two numeric variables.
Pair plot: group of scatter plots of all numeric variables in the data frame.