Week_2 Customer Experience Analytics for Fintech Apps

Task-1 Data collection and preprocessing

Description

• A Real-World Data Engineering Challenge: Scraping, Analyzing, and Visualizing Google Play Store Reviews

Setup

1. Clone the repo: git clone https://github.com/nanecha/Bank-Reviews-Analysis>
2. Install dependencies: pip install -r requirements.txt
3. Run analysis: `python scripts/run_pipeline.py 4.google play scraping

• Collecting individual bank Reviews**
• Normalize Date**
• data cleaning -sentimental anlasysis
• visualization

Task_2 Bank Reviews Sentiment and Thematic Analysis

Overview

This project analyzes customer reviews from three Ethiopian banking apps (CBE, Dashen, BOA) to extract sentiment and key themes using NLP techniques.

Features

• Data Collection: Scrapes reviews from Google Play Store
• Sentiment Analysis: Uses TextBlob and VADER to classify reviews
• Thematic Analysis: Identifies common themes using TF-IDF and rule-based clustering
• Visualization: Generates sentiment distributions and word clouds

Dependenceis

• See requirement.text

Workflow

1. Data Collection:

python banks = ['com.combanketh.mobilebanking', 'com.dashen.dashensuperapp', 'com.boa.boaMobileBanking'] reviews = fetch_reviews(banks)

2. Preprocessing:

• Cleans text (lowercase, remove punctuation)
• Tokenization and lemmatization
• Handles missing values
• Analysis:
  • Sentiment classification (Positive/Negative/Neutral)
  • TF-IDF keyword extraction
  • Theme clustering (Account Access, Transactions, UI/UX etc.)
• Visualization:
  • Sentiment distribution charts
• Word clouds for positive reviews

Outputs

• CSV files with cleaned data and sentiment scores
• Visual reports:
• Sentiment by bank and rating
• Top keywords in positive/negative reviews
• Thematic analysis results

Task-3 stored cleaned in oracle database

Task-4 Insights and Recommendations

• Derive insights from sentiment and themes, visualize results, and recommend app improvements
• Visualization: