mekbib-codes/customer-experience-analytics

Analysis of customer satisfaction with mobile banking apps by collecting and processing user reviews from the Google Play Store for three Ethiopian banks: Commercial Bank of Ethiopia (CBE), Bank of Abyssinia (BOA) and Dashen Bank. # customer-experience-analytics Analysis of customer satisfaction with mobile banking apps by collecting and processing user reviews from the Google Play Store for three Ethiopian banks: Commercial Bank of Ethiopia (CBE), Bank of Abyssinia (BOA), and Dashen Bank. ## Project Overview This project implements a full data pipeline for customer experience analytics, including: - Automated scraping of Google Play Store reviews for selected banking apps. - Data cleaning, preprocessing, and aggregation using modular Python scripts and Jupyter notebooks. - Sentiment analysis and thematic keyword extraction to identify satisfaction drivers and pain points. - Visualization of sentiment trends and thematic mapping of user feedback. - Storage of processed data in an Oracle database for further analysis and reporting. ## Directory Structure - `scripts/` – Core data processing, analysis, and utility scripts. - `notebooks/` – Jupyter notebooks for data cleaning, analysis, and visualization. - `data/` – Input and output datasets. - `tests/` – Automated tests for scripts and utilities. - `config/` – Configuration files (e.g., database credentials). - `main.py` – Project entry point for preparing and loading data into the Oracle database. --- ## Script: `main.py` **Purpose:** Prepares the final DataFrame by merging sentiment and keyword analysis results, maps bank names to IDs, and loads the processed data into the Oracle database. **Key Steps:** 1. **Load Data:** - Loads sentiment analysis results and keyword extraction results from CSV files. - Merges the two DataFrames on the review ID. - Cleans and renames columns for consistency. 2. **Database Connection:** - Loads Oracle credentials from a `.env` file. - Establishes a connection using the `OracleDBHandler` utility. 3. **Bank Table Population:** - Inserts unique bank names into the `local_banks` table, skipping duplicates. - Fetches the mapping of bank names to their corresponding IDs. 4. **Data Preparation:** - Maps …