This project involves working with a designated dataset comprising customer details, along

with a history of previous payments and billing statements. Throughout this assignment, you will have

the opportunity to apply quantitative reasoning, critical and creative thinking, as well as communication

and problem$-$solving skills.

Problem: Customer Credit Prediction:

In this project, you will work with a dataset containing customer information along with a history of

previous payments and billing statements. Your task is to explore, analyze, clean, preprocess, and

engineer features from the data provided to you and finally develop a model to predict customer default

payments. Project Phases: $($whichever applies to your problem$)$

$1.$ Data Exploration and Analysis $($EDA$)$:

Understand the dataset's structure, dimensions, and types of data.

Explore the distribution of each feature and target variable.

Visualize relationships between features and the target variable using appropriate plots

and charts.

Identify potential patterns, trends, and outliers.

Data quality report

$2.$ Data Cleaning:

Data quality issues and mitigation plans

Identify and handle missing values appropriately $($imputation$,$ removal, etc.$).$

Address any inconsistencies or errors in the data.

Ensure data integrity and consistency.

$3.$ Feature Engineering:

Select relevant features based on domain knowledge and exploration.

Create new features that might improve predictive performance.

Convert categorical variables into numerical representations $($one$-$hot encoding, label

encoding$).$

Scale or normalize numerical features if needed.

$4.$ Problem Definition:

Define the problem you want to solve.

Clearly state your goals and objectives.

$5.$ Model Selection and Implementation:

Choose suitable predictive algorithms for your defined problem.

Split the dataset into training and testing sets.

Train, tune, and validate your chosen models.

$6.$ Evaluation and Interpretation:

Evaluate the performance of your models using appropriate evaluation metrics

$($accuracy$,$ precision, recall, MSE, R$-$squared, etc.$).$

Interpret the results and provide insights into the features that are most influential for

predictions.

$7.$ Conclusion and Recommendations:

Summarize your findings and conclusions.

Recommend actions or strategies based on your

predictions.

Discuss the potential future directions to improve the performance of your model.