Bank-Classifying-Term-Deposit-Subscriptions

The classification goal is to predict whether the client will subscribe (1/0) to a term deposit (variable y).

Data set Link:

https://www.kaggle.com/gowthamchowdry/bank-classifying-term-deposit-subscriptions

Approach:

1. Data import
2. Exploratory Data Analysis
3. Feature Enineering
4. Features selection: Used correlation, Anova, Chi2, Features_importance
5. Build the model and predict on test data
6. Error plot

Usage

• Just run jupyter notebook in terminal and it will run in your browser.

  Install Jupyter here i've you haven't.

Modules needed:

- matplotlib
- import statsmodels.api as sm
- Pandas
- numpy
- Scikit-Learn &
- seaborn

Model Evaluation

The Model accuracy is 0.9075018208302986 precision recall f1-score support

       0       0.96      0.94      0.95     10981
       1       0.57      0.67      0.62      1376

accuracy                           0.91     12357

EDA

• What proportion( percent ) of potential clients accepted to suscribe to term deposits vs. refused to suscribe to term deposits are the questions to be answered.
• Is there a significant difference between clients who subscribe to term deposits and those who are denied deposits?

Summary:

• 52.6 percent said they would not subscribe to term deposits, while 47.4 percent said they would. Our labels are dispersed rather evenly.
• May was the month with the most marketing activity (25.3 percent), while December had the least marketing activity (0.985 percent ).
• During the month of May, there is a significant difference between rejected and accepted term deposit subscriptions. (See the Plot of Distribution)
• May had the highest amount of activity, but the lowest ratio (negative), indicating that there were more rejected term deposit subscription requests than accepted requests.
• Despite the fact that the largest ratios were in March, September, October, and December, marketing activity (requested offers from the marketing department) was substantially lower.
• The majority of the bank's potential clientele are between the ages of 30 and 35.
• Clients in their twenties and thirties: Approximately 60% of potential clients in this age group subscribed to term deposit suscriptions.
• 30s - 50s: Term deposit accounts were subscribed to by around 40% of potential clients in this age group.
• Term deposits for those in their 60s and older are around 76 percent subscribed!
• The population segments with the youngest and oldest members were the most likely to open a term deposit account. -Management, blue-collar employees, and technicians received the most term deposit subscription proposals from the marketing department.
• The marketing department made the fewest offers to students, entrepreneurs, and housemaids.
• 74.7 percent of students signed up for term deposits (Which was expected since the youngest segment of the population is most likely to be a student)
• 66.3 percent of pensioners were inclined to subscribe to term deposits (This was also expected since the oldest segment of the population is most likely to be retirees).