E-commerce Customer Churn Dataset

Context

This dataset belongs to a leading online E-commerce company. The company wants to identify customers who are likely to churn, so they can proactively approach these customers with promotional offers.

Content

The dataset contains various features related to customer behavior and characteristics, which can be used to predict customer churn.

Features

1. Tenure: Tenure of a customer in the company (numeric)
2. WarehouseToHome: Distance between the warehouse to the customer's home (numeric)
3. NumberOfDeviceRegistered: Total number of devices registered to a particular customer (numeric)
4. PreferedOrderCat: Preferred order category of a customer in the last month (categorical)
5. SatisfactionScore: Satisfactory score of a customer on service (numeric)
6. MaritalStatus: Marital status of a customer (categorical)
7. NumberOfAddress: Total number of addresses added for a particular customer (numeric)
8. Complaint: Whether any complaint has been raised in the last month (binary)
9. DaySinceLastOrder: Days since last order by customer (numeric)
10. CashbackAmount: Average cashback in last month (numeric)
11. Churn: Churn flag (target variable, binary)

Task

The main task is to predict customer churn based on the given features. This is a binary classification problem where the target variable is 'Churn'.

Potential Applications

1. Customer Retention: Identify at-risk customers and take proactive measures to retain them.
2. Targeted Marketing: Design specific marketing campaigns for customers likely to churn.
3. Service Improvement: Analyze features contributing to churn and improve those aspects of the service.

Acknowledgements

This dataset is provided for educational purposes. While it represents a real-world scenario, the data itself may be simulated or anonymized.

Dataset

This dataset was created by PalashFendarkar

Contents

The customer churn dataset is a collection of customer data that focuses on predicting customer churn, which refers to the tendency of customers to stop using a company's products or services. The dataset contains various features that describe each customer, such as their credit score, country, gender, age, tenure, balance, number of products, credit card status, active membership, estimated salary, and churn status. The churn status indicates whether a customer has churned or not. The dataset is used to analyze and understand factors that contribute to customer churn and to build predictive models to identify customers at risk of churning. The goal is to develop strategies and interventions to reduce churn and improve customer retention

Dataset

This dataset was created by Panda-monium

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Dataset

This dataset was created by Hectar

Released under Other (specified in description)

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A Tour & Travels Company Wants To Predict Whether A Customer Will Churn Or Not Based On Indicators Given Below. Help Build Predictive Models And Save The Company's Money. Perform Fascinating EDAs. The Data Was Used For Practice Purposes And Also During A Mini Hackathon, Its Completely Free To Use

Dataset

This dataset was created by Amal Joseph3377

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Dataset

This dataset was created by Rubel Mia

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Dataset

This dataset was created by Al Amin

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It contains the following files:

This dataset is for ABC Multistate bank with following columns:

1. customer_id, unused variable.
2. credit_score, used as input.
3. country, used as input.
4. gender, used as input.
5. age, used as input.
6. tenure, used as input.
7. balance, used as input.
8. products_number, used as input.
9. credit_card, used as input.
10. active_member, used as input.
11. estimated_salary, used as input.
12. churn, used as the target. 1 if the client has left the bank during some period or 0 if he/she has not.

Aim is to Predict the Customer Churn for ABC Bank.

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If you found the dataset useful, your upvote will help others discover it. Thanks for your support!

This dataset simulates customer behavior for a fictional telecommunications company. It contains demographic information, account details, services subscribed to, and whether the customer ultimately churned (stopped using the service) or not. The data is synthetically generated but designed to reflect realistic patterns often found in telecom churn scenarios.

Purpose:

The primary goal of this dataset is to provide a clean and straightforward resource for beginners learning about:

• Exploratory Data Analysis (EDA): Understanding customer characteristics and identifying potential drivers of churn through visualization and statistical summaries.
• Data Preprocessing: Handling categorical features (like converting text to numbers) and scaling numerical features.
• Classification Modeling: Building and evaluating simple machine learning models (like Logistic Regression or Decision Trees) to predict customer churn.

Features:

The dataset includes the following columns:

• CustomerID: Unique identifier for each customer.
• Age: Customer's age in years.
• Gender: Customer's gender (Male/Female).
• Location: General location of the customer (e.g., New York, Los Angeles).
• SubscriptionDurationMonths: How many months the customer has been subscribed.
• MonthlyCharges: The amount the customer is charged each month.
• TotalCharges: The total amount the customer has been charged over their subscription period.
• ContractType: The type of contract the customer has (Month-to-month, One year, Two year).
• PaymentMethod: How the customer pays their bill (e.g., Electronic check, Credit card).
• OnlineSecurity: Whether the customer has online security service (Yes, No, No internet service).
• TechSupport: Whether the customer has tech support service (Yes, No, No internet service).
• StreamingTV: Whether the customer has TV streaming service (Yes, No, No internet service).
• StreamingMovies: Whether the customer has movie streaming service (Yes, No, No internet service).
• Churn: (Target Variable) Whether the customer churned (1 = Yes, 0 = No).

Data Quality:

This dataset is intentionally clean with no missing values, making it easy for beginners to focus on analysis and modeling concepts without complex data cleaning steps.

Inspiration:

Understanding customer churn is crucial for many businesses. This dataset provides a sandbox environment to practice the fundamental techniques used in churn analysis and prediction.

Dataset

This dataset was created by Md Maaz

Released under Other (specified in description)

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Explore the 'Bank Churn (test)' dataset, a comprehensive collection designed for evaluating predictive models and analyzing customer attrition in the banking sector. This test dataset, derived from real-world scenarios, offers a robust platform to assess the effectiveness of machine learning algorithms in predicting and understanding bank churn dynamics.

• Main objective: Find out customers who will churn and who will not.
• Methodology: It is a classification problem. We will use decision tree and random forest to predict the outcome.
• Steps Involved
• Read the data
• Check for data types https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F1ffb600d8a4b4b36bc25e957524a3524%2FPicture1.png?generation=1688638600831386&alt=media" alt="">

1. Change character vector to factor vector as this is as classification problem
2. Drop the variable which is not significant for the analysis. We drop "customerID".
3. Check for missing values. None are found.
4. Split the data into train and test so we can use the train data for building the model and use test data for prediction. We split this into 80-20 ratio (train/test) using the sample function.
5. Install and run libraries (rpart, rpart.plot, rattle, RColorBrewer, caret)
6. Run decision tree using rpart function. The dependent variable is Churn and 19 other independent variables

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F8d3442e6c82d8026c6a448e4780ab38c%2FPicture2.png?generation=1688638685268853&alt=media" alt=""> 9. Plot the decision tree

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Average customer churn is 27%. The churn can take place if the tenure is more than >=7.5 and there is no internet service

1. Tuning the model
2. Define the search grid using the expand.grid function
3. Set up the control parameters through 5 fold cross validation
4. When we print the model we get the best CP = 0.01 and an accuracy of 79.00%

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1. Predict the model
2. Find out the variables which are most and least significant. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F61beb4224e9351cfc772147c43800502%2FPicture5.png?generation=1688639468638950&alt=media" alt="">

Significant variables are Internet Service, Tenure and the least significant are Streaming Movies, Tech Support.

USE RANDOM FOREST

1. Run library(randomForest). Here we are using the default ntree (500) and mtry (p/3) where p is the number of independent variables. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fc27fe7e83f0b53b7e067371b69c7f4a7%2FPicture6.png?generation=1688640478682685&alt=media" alt="">

   Through confusion matrix, accuracy is coming 79.27%. The accuracy is marginally higher than that of decision tree i.e 79.00%. The error rate is pretty low when predicting "No" and much higher when predicting "Yes".

2. Plot the model showing which variables reduce the gini impunity the most and least. Total charges and tenure reduce the gini impunity the most while phone service has the least impact.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fec25fc3ba74ab9cef1a81188209512b1%2FPicture7.png?generation=1688640726235724&alt=media" alt="">

1. Predict the model and create a new data frame showing the actuals vs predicted values

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F50aa40e5dd676c8285020fd2fe627bf1%2FPicture8.png?generation=1688640896763066&alt=media" alt="">

1. Plot the model so as to find out where the OOB (out of bag ) error stops decreasing or becoming constant. As we can see that the error stops decreasing between 100 to 200 trees. So we decide to take ntree = 200 when we tune the model.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F87211e1b218c595911fbe6ea2806e27a%2FPicture9.png?generation=1688641103367564&alt=media" alt="">

Tune the model mtry=2 has the lowest OOB error rate

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https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fc7045eba4ee298c58f1bd0230c24c00d%2FPicture11.png?generation=1688641605829830&alt=media" alt="">

Use random forest with mtry = 2 and ntree = 200

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F01541eff1f9c6303591aa50dd707b5f5%2FPicture12.png?generation=1688641634979403&alt=media" alt="">

Through confusion matrix, accuracy is coming 79.71%. The accuracy is marginally higher than that of default (when ntree was 500 and mtry was 4) i.e 79.27% and of decision tree i.e 79.00%. The error rate is pretty low when predicting "No" and m...

Dataset

This dataset was created by Rashid60

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Dataset

This dataset was created by Halime Doğan

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This dataset contains synthetic data simulating customer behavior for a Netflix-like video streaming service. It includes 5,000 records with 14 carefully engineered features designed for churn prediction modeling, business insights, and customer segmentation.

The dataset is ideal for:

Machine learning classification tasks (churn vs. non-churn)

Exploratory data analysis (EDA)

Customer behavior modeling in OTT platforms

Dataset

This dataset was created by Cyril Bedu Addo

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Dataset

This dataset was created by Karam ALhanatleh

Released under Apache 2.0

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Dataset

This dataset was created by Rohit Salla

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