Data Mining Project - Clustering - Report Writing - Business Assignment Help

Assignment Task:

Data Mining Project instructions:

• You have to submit 2 files : 

  1. Business Report: In this, you need to submit all the answers to all the questions in a sequential manner. It should include the detailed explanation of the approach used, insights, inferences, all outputs of codes like graphs, tables etc. Your report should not be filled with codes. You will be evaluated based on the business report.

  2. Jupyter Notebook file: This is a must and will be used for reference while evaluating

Problem 1: Clustering

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

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

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

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

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

Data Dictionary for Market Segmentation:

1. spending: Amount spent by the customer per month (in 1000s)

2. advance_payments: Amount paid by the customer in advance by cash (in 100s)

3. probability_of_full_payment: Probability of payment done in full by the customer to the bank

4. current_balance: Balance amount left in the account to make purchases (in 1000s)

5. credit_limit: Limit of the amount in credit card (10000s)

6. min_payment_amt : minimum paid by the customer while making payments for purchases made monthly (in 100s)

7. max_spent_in_single_shopping: Maximum amount spent in one purchase (in 1000s)

Problem 2: CART-RF-ANN

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, write 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 write an inference which model is best/optimized.
2.5 Inference: Basis on these predictions, what are the business insights and recommendations

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