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Machine Learning · Mini Project
Video Game Sales Prediction
Predicts global game sales from genre, platform and publisher
1-2 weeks build
Overview
Uses the public video game sales dataset on Kaggle to predict global sales from genre, platform, publisher and release year. Heavily skewed sales are log-transformed, high-cardinality publishers are grouped, and several regressors are compared. A Streamlit app lets you describe a hypothetical game and see its predicted sales range.
What makes it stand up
- 01EDA of sales trends by genre, platform and region
- 02Log transform to tame a heavily skewed target
- 03Publisher grouping for high-cardinality categories
- 04Linear, random forest and XGBoost regressors compared
- 05Streamlit app for what-if game launches
Module breakdown
- 01
Exploration
Sales by region, genre and year.
- 02
Preprocessing
Encoding, grouping and target transform.
- 03
Models
Three regressors with cross-validation.
- 04
Evaluation
Error metrics on original and log scales.
- 05
App
Streamlit form for a hypothetical title.
After this, you will be able to
- Explain why a log transform helps skewed targets
- Defend how you encoded hundreds of publishers
- Demo a sales estimate for a game the examiner invents
Technology stack
- Python
- Pandas
- scikit-learn
- XGBoost
- Streamlit
- Level
- Mini Project
- Domain
- Machine Learning
- Build time
- 1-2 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support