kal1kidan/mobile-banking-reviews-Challenge-week2

This repository contains my implementation of Week 2 Challenge, focused on analyzing customer satisfaction for Ethiopian mobile banking apps using real Google Play Store reviews. # 📌 Mobile Banking Review Analysis – Week 2 Challenge This repository contains all work completed for **10 Academy – AI Mastery Program, Week 2 Challenge**, focusing on **mobile banking reviews**. The project covers data scraping, cleaning, sentiment analysis, thematic extraction, and reporting. --- ## 📁 Project Structure ``` mobile-banking-reviews-Challenge-week2/ │ ├── Scripts/ # Python scripts for Task-1 & Task-2 │ ├── task1_scraper.py # Web scraping logic │ ├── task1_cleaner.py # Cleaning & preprocessing │ └── Sentiment_demo.py # VADER sentiment + thematic extraction │ ├── notebooks/ │ ├── task1_scraping_cleaning.ipynb # Task-1 exploratory notebook │ ├── task2_analysis.ipynb # Task-2 analysis & visualizations │ └── data/ │ ├── raw/ │ │ └── reviews_raw.csv # Original scraped reviews │ ├── interim/ │ └── processed/ │ └── reviews_clean.csv # Cleaned dataset │ ├── reports/ │ └── Interim_Report.pdf # 4-page early analysis report │ ├── README.md # Project documentation └── requirements.txt # Python dependencies ``` --- ## 🚀 Task 1 — Data Scraping & Cleaning **Objective:** Collect mobile banking reviews from Google Play Store and preprocess them for analysis. **Key Steps:** * Automated scraping using **BeautifulSoup** / Play Store API wrapper * Extracted: * Reviewer name * Rating * Review text * Review date * Cleaned text by removing: * Emojis * HTML artifacts * URLs * Stopwords * Extra whitespace **Outputs:** * `notebooks/data/raw/reviews_raw.csv` – Raw scraped reviews * `notebooks/data/processed/reviews_clean.csv` – Cleaned dataset **Deliverables:** ✔ `task1_scraper.py` ✔ `task1_cleaner.py` ✔ `task1_scraping_cleaning.ipynb` --- ## 🔍 Task 2 — Sentiment & Thematic Analysis **Sentiment Analysis:** * Implemented using **VADER Sentiment Analyzer** * Output column …