{
  "core_params": {
    "target_variable": "revenue",
    "prediction_type": "regression",
    "crossval": {
      "samprate": 0.8
    },
    "input": {
      "datasetProjectKey": "DATAIKU_TSHIRTS",
      "dataset": "web_history_enriched"
    }
  },
  "modeling_params": {
    "random_forest_regression": {
      "enabled": true,
      "n_estimators": [
        100
      ],
      "n_jobs": 6,
      "max_tree_depth": 0,
      "min_samples_leaf": 3
    },
    "ridge_regression": {
      "enabled": true
    },
    "lasso_regression": {
      "enabled": true
    },
    "leastsquare_regression": {
      "enabled": true
    },
    "random_forest_classification": {
      "enabled": false,
      "n_jobs": 1,
      "max_tree_depth": 8,
      "min_samples_leaf": 3
    },
    "logistic_regression": {
      "enabled": false,
      "l1": false,
      "l2": false,
      "C": [
        1.0
      ]
    },
    "svc_classifier": {
      "enabled": false,
      "C": [
        1.0
      ],
      "gamma": [
        0.0
      ],
      "linear": false,
      "poly": false,
      "rbf": false,
      "sigmoid": false,
      "coef0": 0.0,
      "tol": 0.001,
      "max_iter": -1
    },
    "sgd_classifier": {
      "enabled": false,
      "log": false,
      "modified_huber": false,
      "l2": false,
      "l1": false,
      "elasticnet": false,
      "alpha": 0.0,
      "l1_ratio": 0.15,
      "n_jobs": 1
    }
  },
  "preprocessing_params": {
    "per_feature": {
      "pages_visited": {
        "missing_handling": "IMPUTE",
        "missing_impute_with": "MEDIAN",
        "name": "pages_visited",
        "rescaling": false,
        "role": "INPUT",
        "type": "NUMERIC"
      },
      "revenue": {
        "name": "revenue",
        "rescaling": false,
        "role": "TARGET",
        "type": "NUMERIC"
      },
      "ip": {
        "missing_handling": "IMPUTE",
        "missing_impute_with": "MODE",
        "name": "ip",
        "rescaling": false,
        "role": "REJECT",
        "role_reason": "CATEGORY_CARDINALITY",
        "type": "CATEGORY"
      },
      "campain": {
        "category_handling": "DUMMIFY",
        "missing_handling": "IMPUTE",
        "missing_impute_with": "MODE",
        "name": "campain",
        "rescaling": false,
        "role": "INPUT",
        "type": "CATEGORY"
      },
      "ip_country": {
        "category_handling": "DUMMIFY",
        "missing_handling": "IMPUTE",
        "missing_impute_with": "MODE",
        "name": "ip_country",
        "rescaling": false,
        "role": "INPUT",
        "role_reason": "CATEGORY_CARDINALITY",
        "type": "CATEGORY"
      },
      "customer_id": {
        "missing_handling": "IMPUTE",
        "missing_impute_with": "MODE",
        "name": "customer_id",
        "rescaling": false,
        "role": "REJECT",
        "role_reason": "CATEGORY_CARDINALITY",
        "type": "CATEGORY"
      }
    },
    "reduce": {
      "enable": false,
      "disable": true,
      "kept_variance": 0.95
    },
    "deduplication": {
      "enabled": false,
      "deduplicate_on": "pages_visited"
    }
  },
  "type": "prediction",
  "metadata": {
    "name": "Predict revenue using historical navigational data on the web site",
    "description": "### Explanation \n\nUsing only navigational data gathered during the first purchase of customers, we try to predict the monthly revenue he will generate.\n\n### Stragegy\n\n1. We construct in this bench the [best predictive model](/projects/DATAIKU_TSHIRTS/modelbenches/prediction/OrPlcuMm/results/) based on historical data that we have previously processed. \n2. Once this model built, we will [update it frequently](/projects/DATAIKU_TSHIRTS/flow/) with the most recent data to get prediction that corresponds to latest customers trends.\n3. This updated model will then be used to [predict the monthly revenue on the new customers](/projects/DATAIKU_TSHIRTS/datasets/predicted_revenue/explore/).",
    "tags": [
      "Machine Learning",
      "Regression",
      "Revenue Prediction",
      "Best model"
    ]
  },
  "sample_guess_stats": {
    "nrows": 29799,
    "features": {
      "pages_visited": {
        "cardinality": 14,
        "data_type": "float64",
        "sample_values": "2.0; 6.0; 5.0; 4.0; 3.0",
        "role": "INPUT",
        "variable": "pages_visited",
        "missings": 0,
        "type": "NUMERIC"
      },
      "revenue": {
        "cardinality": 459,
        "data_type": "float64",
        "sample_values": "149.0; 241.0; 233.0; 231.0; 185.0",
        "role": "INPUT",
        "variable": "revenue",
        "missings": 0,
        "type": "NUMERIC"
      },
      "ip": {
        "cardinality": 29799,
        "data_type": "object",
        "sample_values": "137.136.24.131; 138.210.203.174; 180.146.226.87; 96.35.80.144; 131.188.76.70",
        "role": "INPUT",
        "variable": "ip",
        "missings": 0,
        "type": "CATEGORY"
      },
      "campain": {
        "cardinality": 2,
        "data_type": "object",
        "sample_values": "false; true",
        "role": "INPUT",
        "variable": "campain",
        "missings": 0,
        "type": "CATEGORY"
      },
      "ip_country": {
        "cardinality": 131,
        "data_type": "object",
        "sample_values": "united states; japan; germany; brazil; france",
        "role": "INPUT",
        "variable": "ip_country",
        "missings": 0,
        "type": "CATEGORY"
      },
      "customer_id": {
        "cardinality": 27347,
        "data_type": "object",
        "sample_values": "a4dc1548af; e8f6f7853c; 5802162c20; 67dc195843; 7c52e0eab6",
        "role": "INPUT",
        "variable": "customer_id",
        "missings": 0,
        "type": "CATEGORY"
      }
    }
  }
}