diff --git "a/models/modelo_cluster_1.txt" "b/models/modelo_cluster_1.txt"
new file mode 100644--- /dev/null
+++ "b/models/modelo_cluster_1.txt"
@@ -0,0 +1,20517 @@
+tree
+version=v4
+num_class=1
+num_tree_per_iteration=1
+label_index=0
+max_feature_idx=27
+objective=regression
+feature_names=precio_total_lag_1 precio_total_lag_2 precio_total_lag_3 precio_total_lag_4 precio_total_lag_5 precio_total_lag_6 precio_total_lag_7 precio_total_lag_8 precio_total_lag_9 precio_total_lag_10 precio_total_lag_11 precio_total_lag_12 precio_total_lag_13 precio_total_lag_14 precio_total_lag_15 precio_total_lag_16 precio_total_lag_17 precio_total_lag_18 precio_total_lag_19 precio_total_lag_20 precio_total_lag_21 precio_total_lag_22 precio_total_lag_23 precio_total_lag_24 mes marca_id_encoded año cluster_id
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+precio_total_lag_1=2551
+precio_total_lag_2=1810
+precio_total_lag_3=1232
+marca_id_encoded=1185
+precio_total_lag_5=1071
+precio_total_lag_6=981
+precio_total_lag_4=883
+año=784
+precio_total_lag_11=734
+mes=680
+precio_total_lag_8=450
+precio_total_lag_7=385
+precio_total_lag_13=374
+precio_total_lag_9=367
+precio_total_lag_12=265
+precio_total_lag_10=236
+precio_total_lag_14=218
+precio_total_lag_19=196
+precio_total_lag_20=187
+precio_total_lag_15=164
+precio_total_lag_18=113
+precio_total_lag_17=63
+precio_total_lag_21=62
+precio_total_lag_16=60
+precio_total_lag_23=4
+precio_total_lag_22=3
+
+parameters:
+[boosting: gbdt]
+[objective: regression]
+[metric: rmse]
+[tree_learner: serial]
+[device_type: cpu]
+[data_sample_strategy: bagging]
+[data: ]
+[valid: ]
+[num_iterations: 1000]
+[learning_rate: 0.01]
+[num_leaves: 64]
+[num_threads: 0]
+[seed: 0]
+[deterministic: 0]
+[force_col_wise: 0]
+[force_row_wise: 0]
+[histogram_pool_size: -1]
+[max_depth: 7]
+[min_data_in_leaf: 20]
+[min_sum_hessian_in_leaf: 0.001]
+[bagging_fraction: 0.8]
+[pos_bagging_fraction: 1]
+[neg_bagging_fraction: 1]
+[bagging_freq: 5]
+[bagging_seed: 3]
+[feature_fraction: 0.8]
+[feature_fraction_bynode: 1]
+[feature_fraction_seed: 2]
+[extra_trees: 0]
+[extra_seed: 6]
+[early_stopping_round: 0]
+[early_stopping_min_delta: 0]
+[first_metric_only: 0]
+[max_delta_step: 0]
+[lambda_l1: 0]
+[lambda_l2: 0]
+[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: 24,25,27]
+[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:[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [3, 4, 5, 9, 10, 12, 13, 14, 15, 16, 19, 21, 23, 25, 28, 29, 31, 37, 39, 40, 41, 42, 44, 49, 50, 52, 53, 57, 58, 62, 63, 64, 66, 68, 69, 72, 74, 75, 77, 78, 79, 83, 85, 89, 91, 93, 96, 98, 99, 101, 103, 105, 109, 110, 115, 118, 119, 121, 123, 124, 128, 129, 131, 134, 136, 138, 139, 140, 142, 144, 146, 152, 153, 154, 160, 161, 162, 164, 165, 168, 171, 173, 187, 191, 208, 230, 247, 253, 268, 273, 277, 278], [1]]