Introduction
Welcome to our latest blog post, where we're we're excited to present a new sample project requirement! In this blog, we'll delve into the intricacies of "Predictive Analysis for Savings Account Openings." This project entails leveraging machine learning techniques to predict clients' likelihood of opening a savings account based on diverse demographic, socio-economic, and campaign-related factors. We'll explore the dataset provided, outline our objective of developing an accurate predictive model, and discuss our solution approach. Additionally, we'll showcase some output, including model performance evaluation and key insights gleaned from the analysis.
Project Requirements :
Problem Statement:
The task at hand involves predicting whether a client will open a savings account based on various demographic, socio-economic, and campaign-related factors. Given the dataset provided, our objective is to develop a predictive model that can accurately classify clients as potential savers or non-savers. This entails employing machine learning techniques to analyze the dataset and create a model capable of discerning patterns indicative of future savings account openings.
Objective:
The primary objective of this project is to develop a predictive model that accurately determines whether a client will open a savings account.
The Background
Our task is to predict whether someone will open a savings account. You will find more details about the dataset in the data section.
Grading and Evaluation
This project has two parts: 1) Building the best model you can and provide your best model outcome as submission and 2) Reporting your thinking through an executive summary explaining data set, preprocessing, model tryout and model performance evaluation with code line as attached (listed below).
Model Evaluation
Evaluate models using Area under ROC (AUC).
Submission Format for Models
Submission files must be .csv files. They have to contain two columns: Id and outcome. Every customer in the given dataset should be under the Id column. There is an example in the data section.
The file should contain a header and have the following format:
Executive Summary Evaluation
Your executive summary report must be a maximum of 5 PowerPoint slides. It should include the following topics:
Data understanding (visualization, etc.)
Data preparation (variable treatment, feature creation)
Modeling (what model did you use)
Evaluation methodology (how did you evaluate your model)
Managerial implications
Our task is to predict whether someone will open a savings account on a local bank's marketing campaign. You will find more details about the dataset in the data section.
Evaluation
This project has two parts: 1) Building the best model you can and testing it in our kernel, and 2) Reporting your thinking through an executive summary..
Dataset Description
You will get two datasets. train.csv is for training your model, and test.csv contains the information to predict. The submission has to be strictly in the format indicated in the sample_submission.csv.
Files
train.csv - the training set
test.csv - the test set
sample_submission.csv - a sample submission file in the correct format
Columns
Client information
id - client id (numeric)
age - age of client (numeric)
job - type of job (categorical: "admin.","artisan","entrepreneur", "housemaid", "management", "retired", "self-employed", "services", "student", "technician", "unemployed", "unknown")
civil - marital status of client (categorical: "divorced", "married", "single","unknown"; note: "divorced" means divorced or widowed)
education - education of client (categorical: "4K", "6K", "K9", "K12", "illiterate", "apprenticeship", "university", "unknown")
credit - has credit in default? (categorical: "no","yes","unknown")
hloan - has housing loan? (categorical: "no","yes","unknown")
ploan - has personal loan? (categorical: "no","yes","unknown")
Campaign details
ctype - contact communication type (categorical: "cellular","telephone")
month - last contact month of year (categorical: "jan", "feb", "mar", …, "nov", "dec")
day - last contact day of the week (categorical: "mon","tue","wed","thu","fri")
ccontact - current number of contacts performed during this campaign and for this client (numeric, includes last contact)
lcdays - number of days that passed by since client was last contacted by a previous campaign (numeric; 999 means client was not previously contacted)
pcontact - number of contacts performed before this campaign and for this client (numeric)
presult - outcome previous marketing campaigns (categorical: "failure","nonexistent","success")
Socioeconomic indicators
employment - employment variation rate - quarterly indicator (numeric)
cprice - consumer price index - monthly indicator (numeric)
cconf - consumer confidence index - monthly indicator (numeric)
euri3 - euribor 3 month rate - daily indicator (numeric)
employees - number of employees - quarterly indicator (numeric)
Outcome variable (target)
outcome - has the client opened a saving account? (binary: 1 = "yes", 0 = "no")
Solution Approach
In this part, we'll talk about how we tackled the challenges in the "Predictive Analysis for Savings Account Openings" project. We'll discuss the steps we took and the strategies we used in this project.
Dataset Used
Utilized a dataset consisting of client information, campaign details, and socioeconomic indicators.
Data sourced from local bank's marketing campaign, comprising two sets: training and test.
Basic Data Information
Explored key features such as age, job type, marital status, education level, etc.
Analyzed data distribution and identified potential predictors of savings account openings.
Data Processing Techniques
Conducted data preprocessing to handle missing values and encode categorical variables.
Utilized LabelEncoder for categorical encoding and StandardScaler for numerical feature scaling.
Feature Selection
Selected relevant features for model training based on their potential impact on predicting savings account openings.
Engineered new features like 'last_contact_date' and 'avg_contacts_per_day' to enhance model performance.
Testing and Training
Split the dataset into training and testing sets using train_test_split function.
Employed StratifiedKFold for robust cross-validation during model training.
Algorithms Used
Explored a variety of machine learning algorithms including Logistic Regression, Decision Trees, Random Forests, XGBoost, K-Nearest Neighbors, Naive Bayes, and LightGBM.
Evaluation Used
Evaluated model performance using Area under ROC (AUC) metric to assess predictive accuracy.
Plotted ROC curves to visualize performance comparison among different algorithms.
Output :
At CodersArts, we're passionate about transforming banking with our latest project, Predictive Analysis for Savings Account Openings. Our team specializes in utilizing cutting-edge machine learning techniques to analyze client data and forecast their likelihood of opening a savings account. By leveraging our expertise in predictive analytics, we aim to revolutionize how banks identify potential savers, ultimately driving growth and enhancing customer satisfaction.
From conceptualization to execution, CodersArts guides you through every phase of the project journey. We meticulously explore the dataset, identifying key demographic, socio-economic, and campaign-related factors that influence savings account openings. Our team then develops and fine-tunes predictive models to accurately classify clients as potential savers or non-savers. Through rigorous testing and evaluation, we ensure the reliability and effectiveness of our predictive algorithms, empowering banks to make informed decisions and optimize their marketing strategies.
But our dedication doesn't end there. CodersArts is committed to delivering actionable insights that fuel strategic decision-making in the banking sector. By employing advanced machine learning algorithms and robust evaluation methodologies, we uncover valuable insights into client behavior and preferences. These insights enable banks to tailor their marketing campaigns, personalize customer experiences, and drive higher conversion rates. With CodersArts as your partner, navigating the complexities of predictive analysis for savings account openings is both seamless and rewarding.
If you require any assistance with the project discussed in this blog, or if you find yourself in need of similar support for other projects, please don't hesitate to reach out to us. Our team can be contacted at any time via email at contact@codersarts.com.