README for App User Segmentation Notebook

Overview

This Jupyter notebook (_appUserSegmentation.ipynb) is designed to analyze user behavior data from an application, segment users based on their engagement, and provide insights into various user behavior metrics. The notebook employs techniques such as K-means clustering, correlation analysis, and anomaly detection to extract meaningful patterns from the data.

Structure of the Notebook

1. Data Import and Initial Exploration:

   • The notebook begins by importing the user behavior dataset (userbehaviour.csv) and performing preliminary data exploration. This includes checking for null values, reviewing column information, and calculating descriptive statistics.

2. User Behavior Analysis:

   • This section analyzes key user metrics such as screen time and spending. The notebook calculates the highest, lowest, and average values for these metrics and explores their relationships with user ratings and other variables.

3. User Segmentation:

   • Using K-means clustering, the notebook segments app users into different groups based on their behavior. The segments are then visualized to understand the distribution of users across different clusters.

4. Correlation Analysis:

   • This section investigates the correlations between various metrics, such as screen time, amount spent, and user ratings, providing insights into how these variables are related.

5. Anomaly Detection:

   • The notebook includes an anomaly detection step using the Isolation Forest technique to identify unusual patterns in user search queries.

6. Summary:

   • The final section of the notebook provides a summary of the analysis, including key observations and insights derived from the data.

How to Use

1. Prerequisites:

   • Ensure you have the necessary Python libraries installed: pandas, numpy, seaborn, matplotlib, scikit-learn.

2. Running the Notebook:

   • Open the notebook in Jupyter Notebook or Jupyter Lab.
   • Run the cells sequentially to perform the analysis.

3. Customizing the Analysis:

   • You can modify the number of clusters in the K-means algorithm or adjust the features used in the anomaly detection step to suit your specific analysis needs.

4. Interpreting Results:

   • The visualizations and outputs generated by the notebook will help you interpret the segmentation and behavior patterns of app users. The summary section provides a concise interpretation of the findings.