Bank Marketing | Who will Subscribed for deposit? 🏦

Problem Statement:

The following project focus on the analysis of a dataset of Bank Marketing which contains data or information about customers and aims to get useful insights from the data and predict if a new customer will accept a deposit offer or not. Also focus on to get important features which dominate most to define either new customer will accept the offer or not?

Contents :

• Dataset contains Total 17 columns/Features
• This Dataset contains information of 10000+ bank customers data

Bank Client Data

• 1 - age : Age of customer(numeric)
• 2 - job : Job Type (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')
• 3 - marital : marital status (categorical: 'divorced','married','single','unknown')
• 4 - education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')
• 5 - default: has credit in default? (categorical: yes/no)
• 6 - balance: bank balance(RS)
• 7 - housing: has housing loan? (categorical: yes/no)
• 8 - loan: has personal loan? (categorical: yes/no)

Info related with the last contact of the current campaign:

• 9 - contact: contact communication type (categorical: 'cellular','telephone')
• 10 - month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
• 11 - day: last contact day of the week (categorical: 'mon','tue','wed','thu','fri')
• 12 - 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.

Other Attributes:

• 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)
• 14 - pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric;-1 means client was not previously contacted)
• 15 - previous: number of contacts performed before this campaign and for this client (numeric)
• 16 - poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')

Output variable (desired target):

• 17 - deposit - has the client subscribed a term deposit? (binary: yes/no)

My Work:

• I have made this model which will predict either new customer will accept a deposit offer or not
• I have done stepwise EDA (Exploratory Data Analysis) then visualizatiion of categ and Num features to get some idea about important features or correlation
• Then I have done Feature Engineering which inclueds detecting and handling of outliers and features extraction based on my domian knowledge and visualization followed by label encoding and scaling
• I have train ML models with multiples algorithms on same data in order to Analysed & compare performance of differents models based of accuracy and complexity
• After comparing I got well accuracy with 2 algorithms which are Random Forest and Xg boost with accuracy 85% and Recall Score 88%
• Constrcut Pipeline for deployement session
• Technical tools or library used --Python,numpy,pandas,sklearn,matplotllib,xgboost
• • 👉View on Kaggle 💝
  • 👉View On Github 💝