End-to-end data analysis pipeline for Apple iPhone pricing and customer sentiment on Flipkart marketplace. Problem Statement Analyze pricing strategies, discount patterns, and customer satisfaction metrics across iPhone product lines to identify market opportunities and consumer preferences. Tech Stack Python 3.9 Pandas 1.4.4 Jupyter Notebook Dataset Overview 62 iPhone products scraped from Flipkart with complete data (no missing values). Key Metrics:
Price range: ₹39,900 - ₹149,900 11 features including pricing, ratings, reviews, and specifications 4.5-4.7 star average ratings 100K+ customer reviews across products
Analysis Pipeline
- Data Exploration pythondf = pd.read_csv("apple_products.csv") df.count() # Validate completeness df['Mrp'].max() # 149900 df['Mrp'].min() # 39900
- Feature Engineering Extracted model names from product titles using string slicing: pythondf['Model Name'] = df['Product Name'].str[6:15]
- Price Segmentation
Premium (≥₹100K): 22 products - iPhone 11 Pro Max, iPhone 12 Pro series Mid-range (₹50-100K): 31 products - iPhone 11, iPhone 12 variants Budget (<₹50K): 9 products - iPhone SE, iPhone 8 series
- Customer Insights
iPhone 11 Pro Max: Highest ratings (4.7★) iPhone SE: Most reviewed (95K+ ratings) Zero discount on new Pro Max models Up to 29% discount on older Pro models
Key Findings
Premium positioning: Pro Max models command ₹117K-149K with minimal discounting Volume drivers: SE models dominate review counts despite lower pricing Rating consistency: Tight 4.5-4.7 range across all price segments Discount strategy: Aggressive discounts (14-29%) on previous-gen Pro models
Business Applications
Pricing optimization: Identify sweet spots in each segment Inventory planning: High-review products indicate strong demand Marketing strategy: Leverage high-rated models for promotions Competitive analysis: Benchmark against market positioning
Quick Start bashgit clone pip install pandas numpy jupyter notebook Open Apple_Products_Analysis.ipynb and run all cells. Files ├── apple_products.csv # Raw data ├── Apple_Products_Analysis.ipynb # Analysis notebook └── README.md Next Steps
Time-series pricing analysis Review text sentiment mining Predictive pricing models Competitor brand comparison