https://github.com/sultanazhari/customer-habit-analysis-model

Megaline company wants to develop a model that can analyze consumer behavior and recommend one of Megaline's two new plans: Smart or Ultra. In this classification task, we need to develop a model that is able to choose the right package
https://github.com/sultanazhari/customer-habit-analysis-model

accuracy-score decision-tree-classifier logistic-regression matplotlib-pyplot numpy pandas python3 random-forest-classifier seaborn train-test-using-sklearn

Last synced: about 1 month ago
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Megaline company wants to develop a model that can analyze consumer behavior and recommend one of Megaline's two new plans: Smart or Ultra. In this classification task, we need to develop a model that is able to choose the right package

• Host: GitHub
• URL: https://github.com/sultanazhari/customer-habit-analysis-model
• Owner: sultanazhari
• License: mit
• Created: 2024-07-01T01:28:45.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-07-01T01:33:58.000Z (6 months ago)
• Last Synced: 2024-07-05T13:51:25.150Z (6 months ago)
• Topics: accuracy-score, decision-tree-classifier, logistic-regression, matplotlib-pyplot, numpy, pandas, python3, random-forest-classifier, seaborn, train-test-using-sklearn
• Language: Jupyter Notebook
• Homepage:
• Size: 122 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Customer-habit-analysis-model

# Project description

Mobile operator Megaline was dissatisfied that most of their customers were still on the old plan. The company wanted to develop a model that could analyze consumer behavior and recommend one of Megaline's two new plans: Smart or Ultra.

You have access to behavioral data of customers who have switched to the new plan (from the Statistical Data Analysis course project). In this classification task, you need to develop a model that is able to select the right plan. Now that you have completed the data pre-processing step, you can move on to the model building stage.

Develop a model with the highest possible accuracy. In this project, the threshold for accuracy is 0.75. Don't forget to check the accuracy of your model using a test dataset.

# Data description

Each observation in our dataset contains monthly behavioral information about a single user. The information includes: 

сalls - number of calls


minutes - total call duration in minutes


messages - number of text messages


mb_used - internet usage traffic in MBs


is_ultimate - plan for the current month (Ultimate - 1, Surf - 0)