/market_share_predictor

Primary LanguageJupyter Notebook

market_share_predictor

Summary of work

To keep track of the work please review the notebooks in the following order:

1. Dats_Analysis.ipynb :

Analysing data and deciding which unnecessary features to drop

2. Train_Model.ipynb :

Preprocessing the train data and evaluate different models (using 70% of train data for training and the rest 30% for testing the model) and compared their r2 score and mae to choose the best. as a result, the ensemble model RandomForestRegressor is the best model for us.

result table :

Model R2 Score MAE CV R2-Score
RandomForestRegressor 0.9006 0.9263 0.8946 (+/-) 0.0015

3. Test_Prediction.ipynb :

Preprocessing train and test data, training the RandomForestRegressor model on train data and then predict the test data.

the final prediction is saved in test_prediction.csv file.

Requirments :

  • sklearn
  • pandas
  • numpy
  • matplotlib