Online education companies must understand the reasons why their users churn to increase customer loyalty and reduce customer churn. In this project, we will analyze the cancellation reasons of churned users and segment the users' feedback. Additionally, we will perform sentiment analysis for original answers and place these answers in the closest category.
Dataset Description
The data set we created for this project includes the subscription types and cancellation reasons of churned users. The data set is polluted with standard answers, unique answers and some meaningless statements.
UserID: Unique ID of the user
SubscriptionType: Subscription type (Basic, Standard, Premium)
ChurnReason: User's reason for cancellation (Choice from standard answers or original answer)
ChurnReasonCategory: Cancellation reason category (To be determined by data cleaning and sentiment analysis)
ChurnReasonSentiment: Result of sentiment analysis of original answers (Positive, Negative, Neutral)
Project Steps
Data Loading and First Look: We will load the dataset and take a general look. We will examine the structure of the data and identify missing or incorrect data.
Cleaning of Missing and Erroneous Data: We will fill in the missing values and correct the incorrect data. In this step, we will perform the necessary cleaning operations to ensure the accuracy and integrity of the data.
Categorizing Churn Reasons: We will determine the standard answers and place the original answers in the closest category with sentiment analysis.
Data Analysis and Visualization: We will analyze the categorized churn causes and visualize the results.
Determination of Deficiencies: According to the analysis results, we will identify the shortcomings of the company and offer strategic improvement suggestions.
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