Reference no: EM132858315

A leading bank wants to develop a customer segmentation to give promotional offers to its customers. They collected a sample that summarizes the activities of users during the past few months. You are given the task to identify the segments based on credit card usage.

Read the data and do exploratory data analysis. Describe the data briefly.

Do you think scaling is necessary for clustering in this case? Justify

Apply hierarchical clustering to scaled data. Identify the number of optimum clusters using Dendrogram and briefly describe them

Apply K-Means clustering on scaled data and determine optimum clusters. Apply elbow curve and silhouette score.

Describe cluster profiles for the clusters defined. Recommend different promotional strategies for different clusters.

An Insurance firm providing tour insurance is facing higher claim frequency. The management decides to collect data from the past few years. You are assigned the task to make a model which predicts the claim status and provide recommendations to management. Use CART, RF & ANN and compare the models' performances in train and test sets.

2.1 Data Ingestion: Read the dataset. Do the descriptive statistics and do null value condition check, also provide an inference on it.

2.2 Data Split: Split the data into test and train, build classification model CART, Random Forest, Artificial Neural Network

2.3 Performance Metrics: Check the performance of Predictions on Train and Test sets using Accuracy, Confusion Matrix, Plot ROC curve and get ROC_AUC score for each model

2.4 Final Model: Compare all the model and also provide an inference which model is best/optimized.

2.5 Inference: Based on the whole Analysis, what are the business insights and recommendations