Welcome to our project, "Visa for Lisa." In this endeavor, we embark on a mission to empower Galaxy Bank's marketing strategy, with the ultimate aim of significantly increasing the number of deposit customers who accept loan offers. The project revolves around leveraging data-driven insights and sophisticated machine learning techniques to refine marketing campaigns and optimize customer conversion rates. Through extensive data analysis, model development, and strategic decision-making, we intend to revolutionize how Galaxy Bank targets potential loan customers while retaining them as loyal depositors.
The primary objective of our project is to create a predictive model that identifies deposit customers most likely to accept loan offers. This model enables the bank to focus its marketing efforts on a subset of customers who are more inclined to convert, leading to a more efficient allocation of resources and a boost in overall conversion rates. We aim to align our approach with the broader mission of Galaxy Bank: increasing loan acceptance rates and driving the growth of its personal loan customer base while nurturing and expanding its deposit customer relationships.
Through this presentation, we will unveil the comprehensive journey we have undertaken, spanning five major stages:
Data Collection and Cleaning: We started by collecting data and ensuring its quality and relevance.
Data Exploration: We delved deep into the dataset, gaining valuable insights into customer attributes and behaviors.
Data Visualization: Our data visualization efforts encompassed exploring correlations, distribution by age, and other key visualizations to inform our strategies.
Machine Learning: We employed various machine learning models to predict loan acceptance and identified the most effective one for our goal.
Communication: With our results in hand, we prepared an extensive presentation for Galaxy Bank's marketing team, providing them with the necessary tools and insights to enhance their marketing strategy and increase conversion rates. We have also crafted a blog post to document our journey, share our findings, and provide a more detailed account of our methods and results.
Our project sets out to create a model that combines machine learning and marketing insights to bring out the best in Galaxy Bank's marketing strategy. We believe that by accurately predicting which deposit customers are most likely to accept loan offers, we can help the bank increase its loan conversion rates with precision and confidence.
Stay tuned to discover how we achieved our mission, what the data tells us, and how Galaxy Bank can embark on an exciting journey toward enhancing its marketing potential.
3. INTRODUCTION
"In this project, our primary goal is to enhance Galaxy Bank's marketing strategy
and, in turn, elevate the number of deposit customers who accept loan offers. Our
mission revolves around five key objectives:
Boost Conversion Rates: We aim to maximize the effectiveness of the bank's
marketing campaigns, increasing the rate at which potential customers, especially
depositors, accept loan offers.
Data-Driven Insights: Through data-driven insights, we seek to identify potential loan
customers and craft marketing strategies that precisely target them.
Predictive Modeling: Leveraging predictive modeling techniques, such as machine
learning, we'll forecast and comprehend customer behavior, allowing the bank to make
well-informed, data-backed decisions.
Optimize Marketing: Our efforts are directed at optimizing marketing campaigns and
strategies, effectively encouraging customers to embrace loan offers while
maintaining their status as depositors.
Strategic Decision-Making: We will make strategic decisions grounded in data
analysis and predictive modeling, ensuring our marketing initiatives align seamlessly
with Galaxy Bank's objectives.
Join us on this journey to revolutionize marketing, boost loan acceptance rates,
and refine our approach to targeting potential loan customers.Together, we'll
explore the power of data-driven marketing and strategic decision-making."
4. DATA COLLECTION AND CLEANING
After collecting the dataset, the data underwent a cleaning process to
ensure its accuracy and reliability.The result is the clean dataset you see
above
5.
6. DATA EXPLORATION &
VISUALIZATIONS
Project's Correlation Heat Map
•Data Insights: The correlation heat map
offers insights into how different customer
attributes in our dataset are related.
•Influence Identification: It helps us identify
which factors have a significant impact on
the likelihood of customers accepting loan
offers.
•Targeted Marketing: Enables us to make
informed decisions on how to tailor
marketing efforts and boost conversion
rates.
•Strategy Optimization: Utilizing
correlations to improve our marketing
strategy, ultimately leading to better
customer engagement.
7. DATA EXPLORATION &
VISUALIZATIONS Education Level and Loan Acceptance
Based on the visualization here, one can see
that
Undergraduate level: has a lower acceptance
rates, influenced by factors like income and
financial needs.
Graduate level : has a moderate acceptance
rates.
Advanced & professional level : has a higher
acceptance , indicating strong financial
stability often due to higher earning
potential
8. DATA EXPLORATION &
VISUALIZATIONS
Loan Acceptance by Age Groups
Here we can understand our dataset by looking at
loan acceptance by Age group.
Young: High acceptance rates, often due to various
factors.
Middle Age: Moderate acceptance rates, reflecting
established financial stability and needs.
Old: Moderate acceptance rates, influenced by post-
retirement financial needs.
9. Histogram
Our Histograms provide a visual representation of the distribution of
numerical data, allowing us to grasp data patterns.
Each histogram represents a specific attribute, such as income, family size, or
education, offering insights into the data's characteristics.
Analyzing histograms helps us in understanding customer demographics, a
vital aspect of marketing strategy optimization.
Age Group Distribution Pie Chart:
•Age Segmentation:The pie chart segments customers into age groups,
providing an overview of the distribution.
•Customer Profiling: It aids in profiling customers by age, allowing for
targeted marketing strategies to specific age brackets.
•Tailored Marketing: Understanding the age distribution helps customize
marketing efforts, increasing the likelihood of loan acceptance among
different age groups.
These visuals play a crucial role in gaining insights and optimizing marketing
strategies for Galaxy Bank.
10. MACHINE LEARNING
Our project commenced
with a comprehensive
lineup of models.
These models included
Logistic Regression,
DecisionTrees, Random
Forest, SupportVector
Machines (SVM), K-
Nearest Neighbors, and
Multilayer Perceptron. It
was like assembling a
diverse palette of colors,
each having unique
potential.
Before venturing into the
exciting world of modeling,
we laid the foundation
through rigorous data
testing and careful data
splitting.This stage was
crucial to ensure that our
models had a pristine
canvas on which to work
their magic.
Throughout this project we
use 4 major metrics;
Precision, Recall, and the
F1-Score and accuracy.
Each of these metrics
played a pivotal role in
evaluating the models'
performance. Precision
helped us identify the
proportion of true positive
predictions, while Recall
emphasized the
importance of capturing as
many of these positive
cases as possible.The F1-
Score was the harmonious
blend of Precision and
Recall, allowing us to
balance both metrics.
11. MACHINE LEARNING
In this project, accuracy served as a crucial metric to measure the overall performance of the models. It
provided an insight into how often the model's predictions were correct. While it was an essential measure,
especially for balanced datasets, it was not the sole deciding factor.
After going through each model to discern their unique abilities.Among the ensemble, one model
emerged as the star - the Random Forest. Its capability to excel in recall, which is crucial for
predicting potential loan customers, set it apart.
The Random Forest model was selected as the champion. Its exceptional performance in recall made
it an ideal choice for the project's core objective - enhancing conversion rates.
In machine learning, accuracy often takes
the spotlight, but in this project, we shifted
our focus to prioritize recall.Why? Because
recall allowed us to capture more potential
loan customers. It was the game-changer.
Recall was the key to maximizing our
conversion rates. By emphasizing recall, we
could identify more customers likely to
accept loan offers, thus enhancing the
effectiveness of our marketing campaigns.
12. HYPER-PARAMETERTUNING
Hyperparameter tuning in this project helps optimize the
Random Forest model for better performance in terms of
precision, recall, and F1-Score, aligning it with the project's
goal of improving conversion rates for loan acceptance at
Galaxy Bank.
By using F1-Score, we're emphasizing the
importance of both precision and recall for
our objective.
Cross-validation ensures our model works
well on unseen data.
Our best model emerges with the optimal
settings.
This "recipe" ensures our model is fine-tuned
for superior performance, aligning perfectly
with our project goal of enhancing conversion
rates at Galaxy Bank. 🚀
13. MODEL VALIDATION
Our Model validation process guarantees that our model is not just a
one-time star performer but a dependable asset, aligning perfectly
with our mission to enhance conversion rates at Galaxy Bank. 🌟
Accuracy: 0.94
Precision: 0.82
Recall: 0.60
F1-Score: 0.69
#Random Forest: It's chosen because it's a robust and
accurate model that handles complex data well.
#Recall Optimization: Prioritizing recall helps capture potential loan customers, increase
conversion rates, and aligns with Galaxy bank's strategic marketing goals. It balances effectively
identifying true positives while being mindful of false positives
Logistic
Regression:
Accuracy = 0.93
DecisionTree:
Accuracy = 0.94
Random Forest:
Accuracy = 0.95
SVM: Accuracy =
0.90
K-Nearest
Neighbors:
Accuracy = 0.92
Multilayer
Perceptron:
Accuracy = 0.94
14. Prioritizing Recall Over Accuracy.
Due to the goal of our project , we are prioritizing Recall over Accuracy.
Recall over Accuracy in this project would help in
Boosting Conversion Rates:We're focused on increasing customer acceptance of loan
offers – essential for our bank's marketing success.
Capture Missed Opportunities: High recall ensures we identify most customers likely
to accept loans, reducing missed opportunities.
Balancing Precision and Recall:A few false positives are acceptable, but missing real
acceptances isn't.
Imbalanced Data: Our dataset skews towards non-acceptance. Recall reflects our
model's performance better in such cases.
Data-Driven Success: Prioritizing recall aligns with our goal of boosting conversion
rates. It's our key to targeted marketing success."
15. This project was not just about data and models; it was deeply
aligned with Galaxy Bank's mission.We embarked on this
journey to enhance conversion rates, allowing the bank to
effectively target potential loan customers.
We are not merely presenting outcomes; we are calling upon
everyone to join us in redefining banking and revolutionizing
marketing.Together, we pave the way for more effective
strategies, better customer targeting, and ultimately, a
brighter future.
This comprehensive presentation takes you through our
journey, highlighting the importance of recall in enhancing
marketing conversion rates and underscoring our alignment
with Galaxy Bank's mission.
16. RECOMMENDATIONS
Data-Driven Strategy: 📈Our journey was guided by data insights. For Galaxy
Bank's future, data is the compass to navigate the competitive finance
landscape.
Recall Optimization: 🔄 Prioritizing recall fuels enhanced customer targeting.
We've honed the art of reaching potential loan customers with precision.
Random Forest Magic: 🌲 Random Forest's recall-focused model proved
invaluable. Its ability to grasp intricate patterns boosted our conversion rates.
Continuous Monitoring: 📡Our strategy isn't static. Continuous model
monitoring keeps it agile and adaptive to evolving customer behavior.
MissionAccomplished: 🚀With enhanced conversion rates, Galaxy Bank is
poised for growth. Our data-driven strategy paves the way for a brighter
financial future.
17. RECOMMENDATIONS
Data is the cornerstone of our strategy for Galaxy Bank's
future.
Recall optimization helps us target potential loan customers
more effectively.
Random Forest's precision in capturing customer behavior
was the key to our success.
With higher conversion rates, Galaxy Bank's future is primed
for success.
Our mission to enhance Galaxy Bank's marketing efforts has
achieved results. We're excited about the journey ahead! 🌠
