Highlights
Problem Definition: Client wants to predict successful all “unique Ids” from unique Id column who have high chance of getting 1 as “Target” column value.
2. Input Two Datasets: a) Training – train.csv b) Testing – test.csv
3. Train/Test Data Schema: a) 'Target' Column in the training set is Class Label. “-1” signifies missing value in the columns. b) TOP means Time on Page in seconds c) Exits means number of times unique Id has exited the page d) Binary_Var contains binary values e) Metric_Var contains continuous values f) Unique Id is the primary key/unique identifier g) Page1_Visited means whether unique id has visited the page or not
4. Metrics of Evaluation a) Confusion Matrix b) F1 Score c) Accuracy Score d) AUC_ROC_Score d) The code must be clearly commented. Visualizations are optional PS: The column names have been changed; some column values have been hashed for data confidentiality reasons. Test and Train Files are compressed and attached.
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