diff --git "a/models/model_ventilation.txt" "b/models/model_ventilation.txt"
new file mode 100644--- /dev/null
+++ "b/models/model_ventilation.txt"
@@ -0,0 +1,19204 @@
+tree
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+is_linear=0
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+
+
+Tree=997
+num_leaves=25
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+split_feature=18 62 41 22 28 28 34 28 9 2 22 65 35 29 2 29 55 27 27 44 29 50 34 62
+split_gain=0.112628 0.866465 0.21357 0.047683 0.0266043 0.0252503 0.0103367 0.0378776 0.0580598 0.0270228 0.0267541 0.0237829 0.0204264 0.0193865 0.0290145 0.0179432 0.00808754 0.00719104 0.0968656 0.786556 0.0210235 0.0196904 0.0327158 0.0188789
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=2 -2 3 4 5 6 7 10 -9 -8 12 -10 13 15 -15 17 -7 18 20 -20 -1 22 -19 -23
+right_child=1 -3 -4 -5 -6 16 9 8 11 -11 -12 -13 -14 14 -16 -17 -18 21 19 -21 -22 23 -24 -25
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+internal_weight=0 45 180518 180497 180476 180449 180409 180358 66 51 180292 44 180266 180229 58 180171 40 180146 179046 138 178908 1100 241 859
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+is_linear=0
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+
+
+Tree=998
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+
+
+Tree=999
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+split_gain=0.219786 0.172787 0.270454 0.0267767 0.0204791 0.026575 0.052072 0.0487465 0.0470571 0.037568 0.0121238 0.0105068 0.346343 0.0194991 0.0234507 0.0330532 0.01744 0.0144136 0.018544 0.0136613 0.0134285 0.0158025 0.0105389 0.00806766 0.0300512 0.0203522 0.00587204 0.0146938 0.00355103 0.00259582
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+decision_type=2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
+left_child=1 2 3 4 5 6 11 9 -9 -7 -5 12 23 14 15 19 -16 -15 -19 -13 21 -20 -21 24 25 26 27 -1 -22 -24
+right_child=-2 -3 -4 10 -6 7 -8 8 -10 -11 -12 13 -14 17 16 -17 -18 18 20 22 28 -23 29 -25 -26 -27 -28 -29 -30 -31
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+leaf_weight=179803 23 24 21 21 21 20 20 28 28 20 21 21 35 31 35 24 20 24 21 35 22 20 25 24 27 27 22 128 21 79
+leaf_count=179803 23 24 21 21 21 20 20 28 28 20 21 21 35 31 35 24 20 24 21 35 22 20 25 24 27 27 22 128 21 79
+internal_value=0 -3.78959e-08 8.47594e-08 -5.88362e-08 4.97188e-09 -3.44447e-08 6.17718e-08 -0.000181154 -0.0003886 0.000108631 -0.000274925 2.5811e-13 -1.20248e-07 5.73522e-05 0.000117029 5.78518e-05 0.000314401 -4.50604e-05 1.41221e-05 0.000114517 -6.1895e-05 -0.000203031 7.51244e-05 9.02458e-08 6.34613e-08 1.18135e-07 7.3107e-08 5.11905e-08 7.20666e-05 1.98079e-05
+internal_weight=0 180668 180644 180623 180581 180560 180464 96 56 40 42 180444 180066 378 239 184 55 139 108 160 84 41 139 180031 180007 179980 179953 179931 43 104
+internal_count=180691 180668 180644 180623 180581 180560 180464 96 56 40 42 180444 180066 378 239 184 55 139 108 160 84 41 139 180031 180007 179980 179953 179931 43 104
+is_linear=0
+shrinkage=0.01089
+
+
+end of trees
+
+feature_importances:
+exact_1=18416
+exact_3=6303
+std_1=3096
+min_1=2519
+max_1=2161
+exact_6=1912
+std_3=1851
+min_3=1546
+min_6=1498
+std_12=1496
+std_6=1408
+mean_1=1348
+min_12=1289
+min_24=1212
+max_3=1188
+mean_96=1074
+median_3=1066
+day=1050
+median_24=1027
+median_192=980
+mean_24=945
+mean_12=930
+mean_192=914
+mean_48=911
+median_96=903
+min_48=893
+median_6=885
+max_24=883
+median_48=859
+std_96=859
+day_sin=858
+mean_6=856
+hour=830
+std_24=821
+std_48=771
+max_6=763
+hour_cos=742
+median_12=705
+min_96=704
+max_12=694
+std_192=679
+min_192=664
+day_cos=654
+mean_3=652
+max_96=609
+max_48=607
+max_192=561
+month=551
+month_cos=516
+hour_sin=515
+median_1=452
+exact_12=433
+day_of_week=403
+exact_24=381
+day_of_week_sin=224
+month_sin=193
+day_of_week_cos=164
+season=138
+type=133
+season_sin=106
+is_working_hours=46
+part_of_day=39
+exact_96=39
+is_weekend=33
+exact_48=19
+season_cos=18
+
+parameters:
+[boosting: gbdt]
+[objective: regression]
+[metric: l2]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 1000]
+[learning_rate: 0.01089]
+[num_leaves: 96]
+[num_threads: 0]
+[seed: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 14]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.108207]
+[bagging_fraction: 0.938336]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 2]
+[bagging_seed: 3]
+[feature_fraction: 0.675395]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 0]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0.00254259]
+[lambda_l2: 0.0212147]
+[linear_lambda: 0]
+[min_gain_to_split: 0]
+[drop_rate: 0.1]
+[max_drop: 50]
+[skip_drop: 0.5]
+[xgboost_dart_mode: 0]
+[uniform_drop: 0]
+[drop_seed: 4]
+[top_rate: 0.2]
+[other_rate: 0.1]
+[min_data_per_group: 100]
+[max_cat_threshold: 32]
+[cat_l2: 10]
+[cat_smooth: 10]
+[max_cat_to_onehot: 4]
+[top_k: 20]
+[monotone_constraints: ]
+[monotone_constraints_method: basic]
+[monotone_penalty: 0]
+[feature_contri: ]
+[forcedsplits_filename: ]
+[refit_decay_rate: 0.9]
+[cegb_tradeoff: 1]
+[cegb_penalty_split: 0]
+[cegb_penalty_feature_lazy: ]
+[cegb_penalty_feature_coupled: ]
+[path_smooth: 0]
+[interaction_constraints: ]
+[verbosity: -1]
+[saved_feature_importance_type: 0]
+[use_quantized_grad: 0]
+[num_grad_quant_bins: 4]
+[quant_train_renew_leaf: 0]
+[stochastic_rounding: 1]
+[linear_tree: 0]
+[max_bin: 255]
+[max_bin_by_feature: ]
+[min_data_in_bin: 3]
+[bin_construct_sample_cnt: 200000]
+[data_random_seed: 1]
+[is_enable_sparse: 1]
+[enable_bundle: 1]
+[use_missing: 1]
+[zero_as_missing: 0]
+[feature_pre_filter: 1]
+[pre_partition: 0]
+[two_round: 0]
+[header: 0]
+[label_column: ]
+[weight_column: ]
+[group_column: ]
+[ignore_column: ]
+[categorical_feature: ]
+[forcedbins_filename: ]
+[precise_float_parser: 0]
+[parser_config_file: ]
+[objective_seed: 5]
+[num_class: 1]
+[is_unbalance: 0]
+[scale_pos_weight: 1]
+[sigmoid: 1]
+[boost_from_average: 1]
+[reg_sqrt: 0]
+[alpha: 0.9]
+[fair_c: 1]
+[poisson_max_delta_step: 0.7]
+[tweedie_variance_power: 1.5]
+[lambdarank_truncation_level: 30]
+[lambdarank_norm: 1]
+[label_gain: ]
+[lambdarank_position_bias_regularization: 0]
+[eval_at: ]
+[multi_error_top_k: 1]
+[auc_mu_weights: ]
+[num_machines: 1]
+[local_listen_port: 12400]
+[time_out: 120]
+[machine_list_filename: ]
+[machines: ]
+[gpu_platform_id: -1]
+[gpu_device_id: -1]
+[gpu_use_dp: 0]
+[num_gpu: 1]
+
+end of parameters
+
+pandas_categorical:[]