ananyo-sen-b2530066/ml-sales-prediction

How ananyo-sen-b2530066/ml-sales-prediction works

ML Sales Prediction is a Python analysis project for exploring video-game sales data and estimating global sales. Users can run EDA or either of two standalone regression scripts. Each regression workflow loads and prepares the dataset, encodes features, trains several regressors, evaluates validation/test predictions, and visualizes results. The enhanced workflow additionally uses a log-transformed target and adds regional-sales features. Both scripts are self-contained; the available evidence does not show one calling the other. Regression details are based on the provided excerpts, so unsampled implementation specifics are not inferred.

Main entry points: game_regression_original.py Original pipeline · game_regression_all.py Enhanced pipeline · game_data_eda.py EDA analysis · vgsales.csv Sales dataset

Components 16

Data exploration

EDA analysis game_data_eda.py

Sales prediction

Original pipeline game_regression_original.py
Clean and engineer game_regression_original.py
Split and encode game_regression_original.py
Regressor models game_regression_original.py
Evaluate predictions game_regression_original.py
Enhanced pipeline game_regression_all.py
Sales feature engineering game_regression_all.py
Split and encode game_regression_all.py
Regressor models game_regression_all.py
Log-scale evaluation game_regression_all.py

Analysis outputs

EDA charts game_data_eda.py
Model visualizations game_regression_original.py
Model visualizations game_regression_all.py

Other components

Analyst
Sales dataset vgsales.csv

Connections 17

Analyst → EDA analysis: runs README.md
Analyst → Original pipeline: runs README.md
Analyst → Enhanced pipeline: runs README.md
EDA analysis → Sales dataset: reads game_data_eda.py
EDA analysis → EDA charts: plots game_data_eda.py
Original pipeline → Sales dataset: reads game_regression_original.py
Original pipeline → Clean and engineer: prepares game_regression_original.py
Original pipeline → Split and encode: splits and encodes game_regression_original.py
Original pipeline → Regressor models: trains game_regression_original.py
Original pipeline → Evaluate predictions: evaluates game_regression_original.py
Original pipeline → Model visualizations: plots results game_regression_original.py
Enhanced pipeline → Sales dataset: reads game_regression_all.py
Enhanced pipeline → Sales feature engineering: engineers features game_regression_all.py
Enhanced pipeline → Split and encode: splits and encodes game_regression_all.py
Enhanced pipeline → Regressor models: trains game_regression_all.py
Enhanced pipeline → Log-scale evaluation: evaluates game_regression_all.py
Enhanced pipeline → Model visualizations: plots results game_regression_all.py

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