https://github.com/frocode/technical-task-yasa-1-llc
https://github.com/frocode/technical-task-yasa-1-llc
Last synced: 10 days ago
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- Host: GitHub
- URL: https://github.com/frocode/technical-task-yasa-1-llc
- Owner: FroCode
- Created: 2024-05-10T21:43:25.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-05-30T16:02:55.000Z (almost 2 years ago)
- Last Synced: 2024-05-31T18:04:06.089Z (almost 2 years ago)
- Language: Jupyter Notebook
- Size: 52.7 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# YASA-1 E-Commerce Data Analysis and Forecasting
## Introduction
This project is a comprehensive analytical solution developed for YASA-1 LLC, aimed at optimizing business operations and enhancing decision-making capabilities. Tasked by the Smart Business Analytics team, the project focuses on three key areas: demand forecasting, seller and product analysis, and product review sentiment analysis.
## Task 1: Demand Forecasting
### Objective
Implement a robust demand forecasting system for a short-term period (14 days) starting 7 days from the last date in the data for all product groups, including new products with minimal historical data.
### Approaches
1. **Machine Learning Forecasting**: Utilized regression models, such as Random Forest and Gradient Boosting, to predict future demand.
2. **Classical Time Series Forecasting**: Employed ARIMA models for time series forecasting.
## Task 2: Analysis of Sellers and Products
### Objective
Conduct an in-depth analysis of sellers and products on the marketplace, focusing on turnover, identifying sales leaders/outsiders in each area, and exploring the dependence of product weight on turnover and price. Additionally, provide segmentation of sellers and products with actionable business insights.
### Analysis Conducted
1. **Sellers**:
- Identified sellers with the highest and lowest turnover.
- Determined leaders and outsiders in sales within each area.
2. **Products**:
- Conducted turnover analytics to identify top-performing products.
- Analyzed the best-selling products in each category.
- Investigated the relationship between product weight, turnover, and price.
- Segmented sellers and products to derive meaningful business insights.
## Task 3: Analysis of Product Semantics
### Objective
Develop functionality to classify product review comments as positive, negative, or neutral, and analyze the correlation between text comments and numerical ratings (1-5). Identify products with the best/worst reviews, and highlight sellers who predominantly receive negative feedback. Additionally, extract and highlight price mentions in comments for competitor price analysis.
### Steps
1. **Sentiment Analysis**:
- Implemented a classifier to categorize review comments into positive, negative, or neutral.
- Analyzed the correlation between numerical ratings and text comments.
2. **Review Analytics**:
- Identified products with the best and worst reviews.
- Highlighted sellers who received mostly negative feedback.
3. **Price Extraction**:
- Extracted price mentions from review comments.
- Compared mentioned prices with actual product prices.
### Result
A visual representation of the analysis results provided in an `report1.pbix` file with accompanying code that generated the insights.
## Project Structure
- `data/`: Contains the datasets used for analysis.
- `notebooks/`: Jupyter notebooks with the analysis and visualizations.
- `README.md`: Project overview and detailed explanation.