librara
library(reticulate)
# Replace this with your actual Python path if it's different
use_python("C:/Users/jgpet/AppData/Local/Programs/Python/Python312/python.exe", required = TRUE)Project: Customer Insights Using COIL 2000 & Bank Marketing Data
“Unlocking Business Value through Predictive Analytics”
In today’s data-driven economy, financial and insurance companies must harness the power of analytics to understand customer behavior, enhance retention, and optimize marketing strategies. This project applies supervised machine learning and clustering techniques to two real-world datasets using Python and its rich ecosystem of libraries.
🧰 Tools & Libraries
The analysis leverages the following Python libraries:
pandas for data manipulation and preprocessing
numpy for numerical operations
matplotlib and seaborn for visualizations
scikit-learn for classification models and clustering (e.g., KMeans)
plotly for interactive visuals
imbalanced-learn for handling class imbalance (if applicable)
Bank Marketing Dataset
Source: UCI Machine Learning Repository
Description: Collected from a Portuguese banking institution, this dataset records results of a direct marketing campaign aimed at promoting term deposits. It includes features like age, job, marital status, previous contact outcomes, and a target variable indicating subscription.
🎯 Project Objectives
Predict customer responses to marketing campaigns
Identify high-value segments using clustering algorithms
Reduce customer churn through classification models
Visualize trends and feature relationships through interactive dashboards
# This code imports the os as a module to interact with the operating system
import os
# To display the current working directory to identity where Python is excecuting the scripts.
print("Current working directory:", os.getcwd())Current working directory: C:\Users\jgpet\OneDrive\Desktop\DATA ANALYST\Data Analytic Portfolio\Lets make Musicupdated\python-projectsfff
from ucimlrepo import fetch_ucirepo
# fetch dataset
bank_marketing = fetch_ucirepo(id=222)
# data (as pandas dataframes)
X = bank_marketing.data.features
y = bank_marketing.data.targets
# metadata
print(bank_marketing.metadata)
# variable information
print(bank_marketing.variables) {'uci_id': 222, 'name': 'Bank Marketing', 'repository_url': 'https://archive.ics.uci.edu/dataset/222/bank+marketing', 'data_url': 'https://archive.ics.uci.edu/static/public/222/data.csv', 'abstract': 'The data is related with 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).', 'area': 'Business', 'tasks': ['Classification'], 'characteristics': ['Multivariate'], 'num_instances': 45211, 'num_features': 16, 'feature_types': ['Categorical', 'Integer'], 'demographics': ['Age', 'Occupation', 'Marital Status', 'Education Level'], 'target_col': ['y'], 'index_col': None, 'has_missing_values': 'yes', 'missing_values_symbol': 'NaN', 'year_of_dataset_creation': 2014, 'last_updated': 'Fri Aug 18 2023', 'dataset_doi': '10.24432/C5K306', 'creators': ['S. Moro', 'P. Rita', 'P. Cortez'], 'intro_paper': {'ID': 277, 'type': 'NATIVE', 'title': 'A data-driven approach to predict the success of bank telemarketing', 'authors': 'Sérgio Moro, P. Cortez, P. Rita', 'venue': 'Decision Support Systems', 'year': 2014, 'journal': None, 'DOI': '10.1016/j.dss.2014.03.001', 'URL': 'https://www.semanticscholar.org/paper/cab86052882d126d43f72108c6cb41b295cc8a9e', 'sha': None, 'corpus': None, 'arxiv': None, 'mag': None, 'acl': None, 'pmid': None, 'pmcid': None}, 'additional_info': {'summary': "The data is related with direct marketing campaigns of a Portuguese banking institution. The marketing campaigns were based on phone calls. Often, more than one contact to the same client was required, in order to access if the product (bank term deposit) would be ('yes') or not ('no') subscribed. \n\nThere are four datasets: \n1) bank-additional-full.csv with all examples (41188) and 20 inputs, ordered by date (from May 2008 to November 2010), very close to the data analyzed in [Moro et al., 2014]\n2) bank-additional.csv with 10% of the examples (4119), randomly selected from 1), and 20 inputs.\n3) bank-full.csv with all examples and 17 inputs, ordered by date (older version of this dataset with less inputs). \n4) bank.csv with 10% of the examples and 17 inputs, randomly selected from 3 (older version of this dataset with less inputs). \nThe smallest datasets are provided to test more computationally demanding machine learning algorithms (e.g., SVM). \n\nThe classification goal is to predict if the client will subscribe (yes/no) a term deposit (variable y).", 'purpose': None, 'funded_by': None, 'instances_represent': None, 'recommended_data_splits': None, 'sensitive_data': None, 'preprocessing_description': None, 'variable_info': 'Input variables:\n # bank client data:\n 1 - age (numeric)\n 2 - job : type of job (categorical: "admin.","unknown","unemployed","management","housemaid","entrepreneur","student",\n "blue-collar","self-employed","retired","technician","services") \n 3 - marital : marital status (categorical: "married","divorced","single"; note: "divorced" means divorced or widowed)\n 4 - education (categorical: "unknown","secondary","primary","tertiary")\n 5 - default: has credit in default? (binary: "yes","no")\n 6 - balance: average yearly balance, in euros (numeric) \n 7 - housing: has housing loan? (binary: "yes","no")\n 8 - loan: has personal loan? (binary: "yes","no")\n # related with the last contact of the current campaign:\n 9 - contact: contact communication type (categorical: "unknown","telephone","cellular") \n 10 - day: last contact day of the month (numeric)\n 11 - month: last contact month of year (categorical: "jan", "feb", "mar", ..., "nov", "dec")\n 12 - duration: last contact duration, in seconds (numeric)\n # other attributes:\n 13 - campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact)\n 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)\n 15 - previous: number of contacts performed before this campaign and for this client (numeric)\n 16 - poutcome: outcome of the previous marketing campaign (categorical: "unknown","other","failure","success")\n\n Output variable (desired target):\n 17 - y - has the client subscribed a term deposit? (binary: "yes","no")\n', 'citation': None}}
name role type demographic \
0 age Feature Integer Age
1 job Feature Categorical Occupation
2 marital Feature Categorical Marital Status
3 education Feature Categorical Education Level
4 default Feature Binary None
5 balance Feature Integer None
6 housing Feature Binary None
7 loan Feature Binary None
8 contact Feature Categorical None
9 day_of_week Feature Date None
10 month Feature Date None
11 duration Feature Integer None
12 campaign Feature Integer None
13 pdays Feature Integer None
14 previous Feature Integer None
15 poutcome Feature Categorical None
16 y Target Binary None
description units missing_values
0 None None no
1 type of job (categorical: 'admin.','blue-colla... None no
2 marital status (categorical: 'divorced','marri... None no
3 (categorical: 'basic.4y','basic.6y','basic.9y'... None no
4 has credit in default? None no
5 average yearly balance euros no
6 has housing loan? None no
7 has personal loan? None no
8 contact communication type (categorical: 'cell... None yes
9 last contact day of the week None no
10 last contact month of year (categorical: 'jan'... None no
11 last contact duration, in seconds (numeric). ... None no
12 number of contacts performed during this campa... None no
13 number of days that passed by after the client... None yes
14 number of contacts performed before this campa... None no
15 outcome of the previous marketing campaign (ca... None yes
16 has the client subscribed a term deposit? None no Now that the working directory is set to the correct location we can load the files for processing.
| age;"job";"marital";"education";"default";"balance";"housing";"loan";"contact";"day";"month";"duration";"campaign";"pdays";"previous";"poutcome";"y" | |
|---|---|
| 0 | 58;"management";"married";"tertiary";"no";2143... |
| 1 | 44;"technician";"single";"secondary";"no";29;"... |
| 2 | 33;"entrepreneur";"married";"secondary";"no";2... |
| 3 | 47;"blue-collar";"married";"unknown";"no";1506... |
| 4 | 33;"unknown";"single";"unknown";"no";1;"no";"n... |
dddd
| age;"job";"marital";"education";"default";"balance";"housing";"loan";"contact";"day";"month";"duration";"campaign";"pdays";"previous";"poutcome";"y" | |
|---|---|
| 0 | 58;"management";"married";"tertiary";"no";2143... |
| 1 | 44;"technician";"single";"secondary";"no";29;"... |
| 2 | 33;"entrepreneur";"married";"secondary";"no";2... |
| 3 | 47;"blue-collar";"married";"unknown";"no";1506... |
| 4 | 33;"unknown";"single";"unknown";"no";1;"no";"n... |
| ... | ... |
| 45206 | 51;"technician";"married";"tertiary";"no";825;... |
| 45207 | 71;"retired";"divorced";"primary";"no";1729;"n... |
| 45208 | 72;"retired";"married";"secondary";"no";5715;"... |
| 45209 | 57;"blue-collar";"married";"secondary";"no";66... |
| 45210 | 37;"entrepreneur";"married";"secondary";"no";2... |
45211 rows × 1 columns