{
  "core_params": {
    "target_variable": "revenue",
    "prediction_type": "regression",
    "crossval": {
      "samprate": 0.8
    },
    "input": {
      "datasetProjectKey": "DATAIKU_TSHIRTS",
      "dataset": "crm_and_web_history_enriched"
    }
  },
  "modeling_params": {
    "random_forest_regression": {
      "enabled": true,
      "n_estimators": [
        10,
        25,
        50
      ],
      "n_jobs": 2,
      "max_tree_depth": 0,
      "min_samples_leaf": 3
    },
    "ridge_regression": {
      "enabled": false
    },
    "lasso_regression": {
      "enabled": false
    },
    "leastsquare_regression": {
      "enabled": false
    },
    "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"
      },
      "gender": {
        "category_handling": "DUMMIFY",
        "missing_handling": "IMPUTE",
        "missing_impute_with": "MODE",
        "name": "gender",
        "rescaling": false,
        "role": "INPUT",
        "type": "CATEGORY"
      },
      "age": {
        "missing_handling": "IMPUTE",
        "missing_impute_with": "MEDIAN",
        "name": "age",
        "rescaling": false,
        "role": "INPUT",
        "type": "NUMERIC"
      },
      "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"
      },
      "price_first_item_purchased": {
        "missing_handling": "IMPUTE",
        "missing_impute_with": "MEDIAN",
        "name": "price_first_item_purchased",
        "rescaling": false,
        "role": "INPUT",
        "type": "NUMERIC"
      }
    },
    "reduce": {
      "enable": false,
      "disable": true,
      "kept_variance": 0.95
    },
    "deduplication": {
      "enabled": false,
      "deduplicate_on": "pages_visited"
    }
  },
  "type": "prediction",
  "metadata": {
    "name": "Audit CRM by predicting revenue on all data",
    "description": "### Explanation \n\nUsing all known historical data on customers, we can quite accurately predict revenue with the information that the company gathered.\n\n#### Audit the quality of Dataiku Tshirts CRM policy\n\n- What are the key informations the company need to gather about customers ? Preditive analytics helps understanding the how much gathered informations worth the effort.\n- Business people can learn a lot about the __key facts impacting revenue__ by analyzing the best model variables importance \u003d\u003e look at the __variables importance__ tab in the best [result](/projects/DATAIKU_TSHIRTS/modelbenches/prediction/SE6GF9Pi/results/) of this bench to get an idea.",
    "tags": [
      "Machine Learning",
      "Business intelligence",
      "Regression"
    ]
  },
  "sample_guess_stats": {
    "nrows": 29799,
    "features": {
      "pages_visited": {
        "cardinality": 14,
        "data_type": "float64",
        "sample_values": "7.0; 9.0; 6.0; 14.0; 3.0",
        "role": "INPUT",
        "variable": "pages_visited",
        "missings": 0,
        "type": "NUMERIC"
      },
      "revenue": {
        "cardinality": 463,
        "data_type": "float64",
        "sample_values": "153.0; 225.0; 159.0; 154.0; 167.0",
        "role": "INPUT",
        "variable": "revenue",
        "missings": 0,
        "type": "NUMERIC"
      },
      "gender": {
        "cardinality": 2,
        "data_type": "object",
        "sample_values": "m; f",
        "role": "INPUT",
        "variable": "gender",
        "missings": 0,
        "type": "CATEGORY"
      },
      "age": {
        "cardinality": 80,
        "data_type": "float64",
        "sample_values": "43.0; 31.0; 55.0; nan; 32.0",
        "role": "INPUT",
        "variable": "age",
        "missings": 4182,
        "type": "NUMERIC"
      },
      "campain": {
        "cardinality": 2,
        "data_type": "object",
        "sample_values": "true; false",
        "role": "INPUT",
        "variable": "campain",
        "missings": 0,
        "type": "CATEGORY"
      },
      "ip_country": {
        "cardinality": 130,
        "data_type": "object",
        "sample_values": "united states; south africa; china; spain; germany",
        "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"
      },
      "price_first_item_purchased": {
        "cardinality": 10,
        "data_type": "float64",
        "sample_values": "22.0; 44.0; 28.0; 42.0; 10.0",
        "role": "INPUT",
        "variable": "price_first_item_purchased",
        "missings": 0,
        "type": "NUMERIC"
      }
    }
  }
}