Problem Statement

The retail marketing department of a bank is planning to develop campaigns that more

effectively target potential loan customers. The goal is to increase the conversion rate with

minimal budget expenditure. To support this, the department seeks to build a predictive model

to identify depositors with a high probability of purchasing a consumer loan. This model will

enhance campaign efficiency by focusing efforts on customers most likely to convert, thereby

increasing the success ratio and reducing campaign costs.

The dataset contains information on customers' financial profiles, including their

demographics, financial products, and services usage. This data can be used for various

analytical purposes, such as understanding customer behaviour, financial product usage, and

creditworthiness.

Dataset:Customer$\_$Financial$\_$Info.csv can be downloaded from drive

$1.$ Import Libraries$/$Dataset

a$.$ Download the dataset.

b$.$ Import the required libraries.

$2.$ Data Visualization and Exploration $[1$M$]$

a$.$ Print $2$ rows for sanity check to identify all the features present in the dataset and if

the target matches with them.

b$.$ Provide appropriate data visualizations to get an insight about the dataset.

c$.$ Do the correlational analysis on the dataset. Provide a visualization for the same.

Will this correlational analysis have effect on feature selection that you will perform

in the next step? Justify your answer. Answer without justification will not be

awarded marks.

$3.$ Data Pre$-$processing and cleaning $[2$M$]$

a$.$ Do the appropriate pre$-$processing of the data like identifying NULL or Missing

Values if any, handling of outliers if present in the dataset, skewed data etc. Mention

the pre$-$processing steps performed in the markdown cell.

b$.$ Apply appropriate feature engineering techniques. Apply the feature transformation

techniques like Standardization, Normalization, etc. You are free to apply the

appropriate transformations depending upon the structure and the complexity of

your dataset. Provide proper justification. Techniques used without justification

will not be awarded marks. Explore a few techniques for identifying feature

importance for your feature engineering task.

$4.$ Model Building $[5$M$]$

a$.$ Split the dataset into training and test sets. Answer without justification will not

be awarded marks. $[1$M$]$

i$.$ Train $=80\%$ Test $=20\%$

ii$.$ Also, try to split the dataset with different ratios of your choice.

b$.$ Build model using Logistic model and decision tree $[4$ M$]$

i$.$ Tune hyperparameters $($e$.$g$.,$ number of trees, maximum depth$)$ using crossvalidation. Justify your answer.

$5.$ Performance Evaluation $[2$M$]$

a$.$ Compare the performance of the Logistic Regression and Decision Tree models

using appropriate evaluation metrics.

b$.$ Provide insights into which model performs better and why. Answer without

justification will not be awarded marks.

Instructions for Assignment Evaluation

$1.$ Please follow the naming convention as $\_.$ipynb.

Eg $$ for group $1$ with problem statement $1,$ your notebooks should be named as $$

Group$1\_$Problem$\_$Statement$\_1.$ipynb.

$2.$ Inside each jupyter notebook, you are required to mention your name, Group detailsand the

Assignment dataset you will be working on$.$

$3.$ Organize your code in separate sections for each task. Add comments to make the code

readable.

$4.$ Deep Learning Models are strictly not allowed. You are encouraged to learn classical Machine

learning techniques and experience their behavior.

$5.$ Notebooks without output shall not be considered for evaluation.

$6.$ Prepare a jupyter notebook to build, train and evaluate a Machine Learning model on the given

dataset. Please read the instructions carefully.

$7.$ Each group consists of up to $5$ members. All members of the group will work on thesame

problem statement.

$8.$ Each group should upload assignment on LMS in respective locations under ASSIGNMENT

Tab. Assignment submitted via means other than through LMS will not be graded.

$9.$ The executed ipynb file with clearsubdivision of the codes and brief description of the purpose

of respective code needs to be uploaded on LMS$.$ All the executed tablesor graphs and results

should be present in the ipynb file.

$10.$Only two files should be uploaded in canvas without zipping them. One is ipynb fileand other

one html output of the ipynb file. No other files should be uploaded.$---$please follow this