https://github.com/jimmymugendi/bulding-a-decision-tree-to-predict-customer-churn

This repo desribes bulding a decision tree to predict customer churn in a given organisation
https://github.com/jimmymugendi/bulding-a-decision-tree-to-predict-customer-churn

accuracy-score decision-tree-classifier matplotlib-pyplot numpy pandas-dataframe scikit-learn

Last synced: about 7 hours ago
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This repo desribes bulding a decision tree to predict customer churn in a given organisation

• Host: GitHub
• URL: https://github.com/jimmymugendi/bulding-a-decision-tree-to-predict-customer-churn
• Owner: Jimmymugendi
• Created: 2024-07-26T07:45:19.000Z (4 months ago)
• Default Branch: main
• Last Pushed: 2024-07-26T08:47:59.000Z (4 months ago)
• Last Synced: 2024-07-26T09:32:46.959Z (4 months ago)
• Topics: accuracy-score, decision-tree-classifier, matplotlib-pyplot, numpy, pandas-dataframe, scikit-learn
• Language: Jupyter Notebook
• Homepage:
• Size: 141 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
## Dataset Description

We will use a synthetic dataset for this exercise. The dataset contains the following columns:

`CustomerID`: A unique identifier for each customer.

`Age`: The age of the customer.

`MonthlyCharge`: The monthly bill amount for the customer.

`CustomerServiceCalls`: The number of times the customer contacted   customer service.

        

Churn: This is our target variable, indicating whether the customer churned (Yes) or not (No).

### Step-by-Step Instructions

`Setup the Environment`:

Import necessary libraries: Pandas for data manipulation, Scikit-learn for machine learning, and Matplotlib for visualization.

`Create the Dataset`:

Use Python to create a synthetic dataset. We'll make a small dataset for simplicity.

`Data Preparation`:

Split the data into features (X) and the target variable (y).

Further split the dataset into training and testing sets.

`Build the Decision Tree Model`:

Use Scikit-learn to create a DecisionTreeClassifier.

Train the model on the training data.

`Evaluate the Model`:

Make predictions on the test set.

Calculate the accuracy of the model.

`Visualize the Decision Tree`:

Use Matplotlib to visualize how the decision tree makes decisions.

`Discuss the Results`:

Interpret the decision tree.

Discuss how it can be used by the company to reduce customer churn.