Customer Experience Analysis

This project analyzes customer experience data to predict customer retention and generate insights through various visualizations.

Project Structure

EmailSentimentAnalysis/
├── src/
│   ├── fetchemails.py      # Email fetching functionality
│   ├── preprocess.py       # Data preprocessing
│   ├── trainmodel.py       # Model training
│   ├── predict.py          # Making predictions
│   ├── visualize.py        # Data visualization
│   └── utils.py           # Utility functions
├── data/                   # Data directory (not in git)
└── requirements.txt        # Project dependencies

Setup

1. Create a virtual environment:

python3 -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

2. Install dependencies:

pip install -r requirements.txt

3. Place your customer experience data in the data/ directory as customer_experience_data.csv

Usage

1. Train the model:

python3 src/trainmodel.py

2. Make predictions:

python3 src/predict.py --file data/customer_experience_data.csv

3. Generate visualizations:

python3 src/visualize.py

Data Format

The input CSV file should contain the following columns:

• Customer_ID
• Age
• Gender
• Location
• Num_Interactions
• Feedback_Score
• Products_Purchased
• Products_Viewed
• Time_Spent_on_Site
• Satisfaction_Score
• Retention_Status
• Gender_Encoded
• Location_Encoded
• Retention_Status_Encoded

Output

The analysis generates:

• Trained model files in data/
• Prediction results in data/predictions.csv
• Various visualizations in data/:
  • distributions.png
  • correlation_matrix.png
  • segment_analysis.png
  • demographic_analysis.png
  • interaction_analysis.png
  • time_analysis.png
  • product_engagement.png

License

MIT License