13 Oct Telecom Customer Churn Prediction Assessment
Telecom Customer Churn Prediction Assessment
Customer Churn is a burning problem for Telecom companies. In this project, we simulate one such case of customer churn where we work on a data of postpaid customers with a contract. The data has information about the customer usage behavior, contract details and the payment details. The data also indicates which were the customers who canceled their service. Based on this past data, we need to build a model which can predict whether a customer will cancel their service in the future or not.
You are expected to do the following :
- EDA (16 Marks)
- How does the data looks like, Univariate and bivariate analysis. Plots and charts which illustrate the relationships between variables (4 Marks)
- Look out for outliers and missing values (4 Marks)
- Check for multicollinearity & treat it (4 Marks)
- Summarize the insights you get from EDA (4 Marks)
- Build Models and compare them to get to the best one (39 Marks)
- Logistic Regression (8 Marks)
- KNN (8 Marks)
- Naive Bayes (8 Marks) (is it applicable here? comment and if it is not applicable, how can you build an NB model in this case?)
- Model Comparison using Model Performance metrics & Interpretation (15 Marks)
- Actionable Insights (5 marks)
- Interpretation & Recommendations from the best model
Please note the following:
- Your submission should be a Word Document with a word limit of 3000 words. Appendices are not counted in the word limit.
- Also, share the R code & Interpretation.
- You must give the sources of data presented. Do not refer to blogs; Wikipedia etc.
- Any assignment found copied/ plagiarized with candidate(s) will not be graded and marked as zero.
- Please ensure timely submission as post deadline assignment will not be accepted.
Rubric
Project 4 Rubric (1)Project 4 Rubric (1)CriteriaRatingsPtsThis criterion is linked to a Learning Outcome1.1 EDA – Basic data summary, Univariate, Bivariate analysis, graphs4.0 pts
This criterion is linked to a Learning Outcome1.2 EDA – Check for Outliers and missing values and check the summary of the dataset4.0 pts
This criterion is linked to a Learning Outcome1.3 EDA – Check for Multicollinearity – Plot the graph based on Multicollinearity & treat it.4.0 pts
This criterion is linked to a Learning Outcome1.4 EDA – Summarize the insights you get from EDA4.0 pts
This criterion is linked to a Learning Outcome2.1 Applying Logistic Regression4.0 pts
This criterion is linked to a Learning Outcome2.2 Interpret Logistic Regression4.0 pts
This criterion is linked to a Learning Outcome2.3 Applying KNN Model4.0 pts
This criterion is linked to a Learning Outcome2.4 Interpret KNN Model4.0 pts
This criterion is linked to a Learning Outcome2.5 – Applying Naive Bayes Model4.0 pts
This criterion is linked to a Learning Outcome2.6 Interpret Naive Bayes Model4.0 pts
This criterion is linked to a Learning Outcome2.7 Confusion matrix interpretation for all models5.0 pts
This criterion is linked to a Learning Outcome2.8 Interpretation of other Model Performance Measures for logistic <KS, AUC, GINI>5.0 pts
This criterion is linked to a Learning Outcome2.9 Remarks on Model validation exercise <Which model performed the best>5.0 pts
This criterion is linked to a Learning Outcome3. Actionable Insights and Recommendations5.0 pts
Total Points: 60.0Attachments area
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