{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.14"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":81933,"databundleVersionId":9643020,"sourceType":"competition"},{"sourceId":10178857,"sourceType":"datasetVersion","datasetId":6287320}],"dockerImageVersionId":30804,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false},"papermill":{"default_parameters":{},"duration":243.736434,"end_time":"2024-12-16T14:33:46.661366","environment_variables":{},"exception":null,"input_path":"__notebook__.ipynb","output_path":"__notebook__.ipynb","parameters":{},"start_time":"2024-12-16T14:29:42.924932","version":"2.6.0"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"#optimize treshold","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:34:20.491393Z","iopub.execute_input":"2024-12-22T10:34:20.491725Z","iopub.status.idle":"2024-12-22T10:34:20.534542Z","shell.execute_reply.started":"2024-12-22T10:34:20.491691Z","shell.execute_reply":"2024-12-22T10:34:20.533460Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"!pip -q install /kaggle/input/pytorch-tabnet-4-1-0-py3-none-any-whl/pytorch_tabnet-4.1.0-py3-none-any.whl","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:34:20.535826Z","iopub.execute_input":"2024-12-22T10:34:20.536150Z","iopub.status.idle":"2024-12-22T10:35:06.399762Z","shell.execute_reply.started":"2024-12-22T10:34:20.536119Z","shell.execute_reply":"2024-12-22T10:35:06.398420Z"},"papermill":{"duration":44.049452,"end_time":"2024-12-16T14:30:29.838763","exception":false,"start_time":"2024-12-16T14:29:45.789311","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport os\nimport re\nfrom sklearn.base import clone, BaseEstimator, RegressorMixin\nfrom sklearn.metrics import cohen_kappa_score, accuracy_score, mean_squared_error\nfrom sklearn.model_selection import StratifiedKFold, train_test_split\nfrom sklearn.decomposition import PCA\nfrom sklearn.datasets import make_classification\nfrom scipy.optimize import minimize\nfrom concurrent.futures import ThreadPoolExecutor\nfrom tqdm import tqdm\n\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nimport matplotlib.pyplot as plt\nfrom keras.models import Model\nfrom keras.layers import Input, Dense\nfrom keras.optimizers import Adam\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom pytorch_tabnet.tab_model import TabNetRegressor\nfrom pytorch_tabnet.callbacks import Callback\n\nfrom colorama import Fore, Style\nfrom IPython.display import clear_output\nimport warnings\nfrom lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\nfrom catboost import CatBoostRegressor\nfrom sklearn.ensemble import VotingRegressor, RandomForestRegressor, GradientBoostingRegressor\nfrom sklearn.impute import SimpleImputer, KNNImputer\nfrom sklearn.pipeline import Pipeline\nwarnings.filterwarnings('ignore')\npd.options.display.max_columns = None\nfrom sklearn.compose import ColumnTransformer\n\n","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:35:06.403522Z","iopub.execute_input":"2024-12-22T10:35:06.403971Z","iopub.status.idle":"2024-12-22T10:35:30.508711Z","shell.execute_reply.started":"2024-12-22T10:35:06.403932Z","shell.execute_reply":"2024-12-22T10:35:30.507470Z"},"papermill":{"duration":21.633436,"end_time":"2024-12-16T14:30:51.477189","exception":false,"start_time":"2024-12-16T14:30:29.843753","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"SEED = 42\nn_splits = 5","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:35:30.513535Z","iopub.execute_input":"2024-12-22T10:35:30.513881Z","iopub.status.idle":"2024-12-22T10:35:30.519080Z","shell.execute_reply.started":"2024-12-22T10:35:30.513848Z","shell.execute_reply":"2024-12-22T10:35:30.517663Z"},"papermill":{"duration":0.01213,"end_time":"2024-12-16T14:30:51.494128","exception":false,"start_time":"2024-12-16T14:30:51.481998","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# 1. Processes a file containing time-series data\n\ndef process_file(filename, dirname):\n    df = pd.read_parquet(os.path.join(dirname, filename, 'part-0.parquet'))\n    df.drop('step', axis=1, inplace=True)\n    return df.describe().values.reshape(-1), filename.split('=')[1]\n\n# 2. Aggregates statistics from multiple files into a single DataFrame\n\ndef load_time_series(dirname) -> pd.DataFrame:\n    ids = os.listdir(dirname)\n    with ThreadPoolExecutor() as executor:\n        results = list(tqdm(executor.map(lambda fname: process_file(fname, dirname), ids), total=len(ids)))\n\n    stats, indexes = zip(*results)\n    df = pd.DataFrame(stats, columns=[f\"stat_{i}\" for i in range(len(stats[0]))])\n    df['id'] = indexes\n    return df\n","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:35:30.520597Z","iopub.execute_input":"2024-12-22T10:35:30.521034Z","iopub.status.idle":"2024-12-22T10:35:30.536227Z","shell.execute_reply.started":"2024-12-22T10:35:30.520990Z","shell.execute_reply":"2024-12-22T10:35:30.534957Z"},"papermill":{"duration":0.013837,"end_time":"2024-12-16T14:30:51.531394","exception":false,"start_time":"2024-12-16T14:30:51.517557","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# read csv files\ntrain = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/train.csv')\ntest = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')\nsample = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/sample_submission.csv')","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:35:30.537662Z","iopub.execute_input":"2024-12-22T10:35:30.538017Z","iopub.status.idle":"2024-12-22T10:35:30.643821Z","shell.execute_reply.started":"2024-12-22T10:35:30.537985Z","shell.execute_reply":"2024-12-22T10:35:30.642808Z"},"papermill":{"duration":0.093494,"end_time":"2024-12-16T14:30:51.629578","exception":false,"start_time":"2024-12-16T14:30:51.536084","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_train.parquet\")\ntest_ts = load_time_series(\"/kaggle/input/child-mind-institute-problematic-internet-use/series_test.parquet\")","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:35:30.645271Z","iopub.execute_input":"2024-12-22T10:35:30.645734Z","iopub.status.idle":"2024-12-22T10:37:05.553608Z","shell.execute_reply.started":"2024-12-22T10:35:30.645688Z","shell.execute_reply":"2024-12-22T10:37:05.551225Z"},"papermill":{"duration":82.238334,"end_time":"2024-12-16T14:32:13.872576","exception":false,"start_time":"2024-12-16T14:30:51.634242","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train = train.dropna(subset='sii')\ntrain = train.reset_index(drop=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.556360Z","iopub.execute_input":"2024-12-22T10:37:05.556910Z","iopub.status.idle":"2024-12-22T10:37:05.579108Z","shell.execute_reply.started":"2024-12-22T10:37:05.556852Z","shell.execute_reply":"2024-12-22T10:37:05.577631Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"y = train['sii']","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:37:05.584868Z","iopub.execute_input":"2024-12-22T10:37:05.585269Z","iopub.status.idle":"2024-12-22T10:37:05.592723Z","shell.execute_reply.started":"2024-12-22T10:37:05.585236Z","shell.execute_reply":"2024-12-22T10:37:05.591363Z"},"papermill":{"duration":0.029319,"end_time":"2024-12-16T14:32:13.925478","exception":false,"start_time":"2024-12-16T14:32:13.896159","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train['sii'].isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.594256Z","iopub.execute_input":"2024-12-22T10:37:05.594711Z","iopub.status.idle":"2024-12-22T10:37:05.615366Z","shell.execute_reply.started":"2024-12-22T10:37:05.594661Z","shell.execute_reply":"2024-12-22T10:37:05.614071Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"y.isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.617693Z","iopub.execute_input":"2024-12-22T10:37:05.618483Z","iopub.status.idle":"2024-12-22T10:37:05.634338Z","shell.execute_reply.started":"2024-12-22T10:37:05.618442Z","shell.execute_reply":"2024-12-22T10:37:05.632545Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"#time_series_cols = train_ts.columns.tolist()\n#time_series_cols.remove(\"id\")\n\ntrain = pd.merge(train, train_ts, how=\"left\", on='id')\ntest = pd.merge(test, test_ts, how=\"left\", on='id')\n\ntrain = train.drop('id', axis=1)\ntest = test.drop('id', axis=1)  ","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.635983Z","iopub.execute_input":"2024-12-22T10:37:05.636385Z","iopub.status.idle":"2024-12-22T10:37:05.692356Z","shell.execute_reply.started":"2024-12-22T10:37:05.636337Z","shell.execute_reply":"2024-12-22T10:37:05.691109Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Get the intersection of columns in train and df_test\ncommon_columns = train.columns.intersection(test.columns)\n\ntest = test[common_columns]\ntrain = train[common_columns]","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.693954Z","iopub.execute_input":"2024-12-22T10:37:05.694473Z","iopub.status.idle":"2024-12-22T10:37:05.705041Z","shell.execute_reply.started":"2024-12-22T10:37:05.694425Z","shell.execute_reply":"2024-12-22T10:37:05.703435Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Dividing data on categorical and numerical (without target column)\n\ncat_columns = train.select_dtypes(include=['object', 'category']).columns\nnum_columns = train.select_dtypes(exclude=['object', 'category']).columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.706838Z","iopub.execute_input":"2024-12-22T10:37:05.707299Z","iopub.status.idle":"2024-12-22T10:37:05.730250Z","shell.execute_reply.started":"2024-12-22T10:37:05.707250Z","shell.execute_reply":"2024-12-22T10:37:05.729115Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"num_columns","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.731760Z","iopub.execute_input":"2024-12-22T10:37:05.732270Z","iopub.status.idle":"2024-12-22T10:37:05.745272Z","shell.execute_reply.started":"2024-12-22T10:37:05.732220Z","shell.execute_reply":"2024-12-22T10:37:05.744002Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Define transformers for numerical and categorical columns\nnum_transformer = Pipeline(steps=[\n    ('imputer', SimpleImputer(strategy='mean')),\n    ('scaler', StandardScaler())\n])\n\ncat_transformer = Pipeline(steps=[\n    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),\n    ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output = False))\n])\n\n# Combine transformers using ColumnTransformer\npreprocessor = ColumnTransformer(\n    transformers=[\n        ('num', num_transformer, num_columns),\n        ('cat', cat_transformer, cat_columns)\n    ],remainder = 'drop')\n\n# Create a pipeline with the preprocessor\npipeline = Pipeline(steps=[\n    ('preprocessor', preprocessor)])\n\n# Apply the pipeline \n\ntrain = pd.DataFrame(pipeline.fit_transform(train))\ntest = pd.DataFrame(pipeline.transform(test))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.746712Z","iopub.execute_input":"2024-12-22T10:37:05.747133Z","iopub.status.idle":"2024-12-22T10:37:05.837932Z","shell.execute_reply.started":"2024-12-22T10:37:05.747089Z","shell.execute_reply":"2024-12-22T10:37:05.836502Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train['sii'] = y","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.839689Z","iopub.execute_input":"2024-12-22T10:37:05.840270Z","iopub.status.idle":"2024-12-22T10:37:05.848289Z","shell.execute_reply.started":"2024-12-22T10:37:05.840201Z","shell.execute_reply":"2024-12-22T10:37:05.846851Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train['sii'].isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.850878Z","iopub.execute_input":"2024-12-22T10:37:05.851953Z","iopub.status.idle":"2024-12-22T10:37:05.870395Z","shell.execute_reply.started":"2024-12-22T10:37:05.851895Z","shell.execute_reply":"2024-12-22T10:37:05.869235Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"y.isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-22T10:37:05.871835Z","iopub.execute_input":"2024-12-22T10:37:05.872755Z","iopub.status.idle":"2024-12-22T10:37:05.886542Z","shell.execute_reply.started":"2024-12-22T10:37:05.872701Z","shell.execute_reply":"2024-12-22T10:37:05.885245Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\n\ndef quadratic_weighted_kappa(y_true, y_pred):\n    return cohen_kappa_score(y_true, y_pred, weights='quadratic')\n\ndef threshold_Rounder(oof_non_rounded, thresholds):\n    return np.where(oof_non_rounded < thresholds[0], 0,\n                    np.where(oof_non_rounded < thresholds[1], 1,\n                             np.where(oof_non_rounded < thresholds[2], 2, 3)))\n\ndef evaluate_predictions(thresholds, y_true, oof_non_rounded):\n    rounded_p = threshold_Rounder(oof_non_rounded, thresholds)\n    return -quadratic_weighted_kappa(y_true, rounded_p)\n\ndef TrainML(model_class, test_data):\n    X = train.drop(['sii'], axis=1)\n    y = train['sii']\n\n    SKF = StratifiedKFold(n_splits=n_splits, shuffle=True, random_state=SEED)\n    \n    train_S = []\n    test_S = []\n    \n    oof_non_rounded = np.zeros(len(y), dtype=float) \n    oof_rounded = np.zeros(len(y), dtype=int) \n    test_preds = np.zeros((len(test_data), n_splits))\n\n    for fold, (train_idx, test_idx) in enumerate(tqdm(SKF.split(X, y), desc=\"Training Folds\", total=n_splits)):\n        X_train, X_val = X.iloc[train_idx], X.iloc[test_idx]\n        y_train, y_val = y.iloc[train_idx], y.iloc[test_idx]\n\n        model = clone(model_class)\n        model.fit(X_train, y_train)\n\n        y_train_pred = model.predict(X_train)\n        y_val_pred = model.predict(X_val)\n\n        oof_non_rounded[test_idx] = y_val_pred\n        y_val_pred_rounded = y_val_pred.round(0).astype(int)\n        oof_rounded[test_idx] = y_val_pred_rounded\n\n        train_kappa = quadratic_weighted_kappa(y_train, y_train_pred.round(0).astype(int))\n        val_kappa = quadratic_weighted_kappa(y_val, y_val_pred_rounded)\n\n        train_S.append(train_kappa)\n        test_S.append(val_kappa)\n        \n        test_preds[:, fold] = model.predict(test_data)\n        \n        print(f\"Fold {fold+1} - Train QWK: {train_kappa:.4f}, Validation QWK: {val_kappa:.4f}\")\n        clear_output(wait=True)\n\n    print(f\"Mean Train QWK --> {np.mean(train_S):.4f}\")\n    print(f\"Mean Validation QWK ---> {np.mean(test_S):.4f}\")\n\n    KappaOPtimizer = minimize(evaluate_predictions,\n                              x0=[0.6264773 , 0.89171596, 1.64], args=(y, oof_non_rounded), \n                              method='Nelder-Mead')\n    assert KappaOPtimizer.success, \"Optimization did not converge.\"\n    thresholds = KappaOPtimizer.x\n    \n    oof_tuned = threshold_Rounder(oof_non_rounded, thresholds)\n    tKappa = quadratic_weighted_kappa(y, oof_tuned)\n\n    print(f\"----> || Optimized QWK SCORE :: {Fore.CYAN}{Style.BRIGHT} {tKappa:.3f}{Style.RESET_ALL}\")\n\n    fold_weights = [1.25, 1.0, 1.0, 1.0, 1.0]\n    tpm = test_preds.dot(fold_weights) / np.sum(fold_weights)\n    tpTuned = threshold_Rounder(tpm, thresholds)\n    \n    submission = pd.DataFrame({\n        'id': sample['id'],\n        'sii': tpTuned\n    })\n\n    return submission\n\n# [0.6264773 , 0.89171596, 1.64]\n# [0.5, 1.49, 2.5]\n","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:37:05.888822Z","iopub.execute_input":"2024-12-22T10:37:05.889209Z","iopub.status.idle":"2024-12-22T10:37:05.915122Z","shell.execute_reply.started":"2024-12-22T10:37:05.889166Z","shell.execute_reply":"2024-12-22T10:37:05.913740Z"},"papermill":{"duration":0.080505,"end_time":"2024-12-16T14:32:14.347643","exception":false,"start_time":"2024-12-16T14:32:14.267138","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\nmodel = XGBRegressor(\n    learning_rate=0.05,\n    max_depth=6,\n    n_estimators=200,\n    subsample=0.8,\n    colsample_bytree = 0.8,\n    reg_alpha=1,\n    reg_lambda=5,\n    random_state=SEED\n)\n\n# we get out of fold predictions for further exploration\nsubmission = TrainML(model, test)\nsubmission","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:37:05.916607Z","iopub.execute_input":"2024-12-22T10:37:05.917509Z","iopub.status.idle":"2024-12-22T10:37:18.878457Z","shell.execute_reply.started":"2024-12-22T10:37:05.917442Z","shell.execute_reply":"2024-12-22T10:37:18.877011Z"},"papermill":{"duration":26.5997,"end_time":"2024-12-16T14:32:40.970640","exception":false,"start_time":"2024-12-16T14:32:14.370940","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.base import BaseEstimator, RegressorMixin\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.model_selection import train_test_split\nfrom pytorch_tabnet.callbacks import Callback\nimport os\nimport torch\nfrom pytorch_tabnet.callbacks import Callback\n\nfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier\n# Model parameters for LightGBM\nParams = {\n    'learning_rate': 0.045,\n    'max_depth': 12,\n    'num_leaves': 478,\n    'min_data_in_leaf': 13,\n    'feature_fraction': 0.893,\n    'bagging_fraction': 0.784,\n    'bagging_freq': 4,\n    'lambda_l1': 10,  # Increased from 6.59\n    'lambda_l2': 0.01  # Increased from 2.68e-06\n}\n\n\n# XGBoost parameters\nXGB_Params = {\n    'learning_rate': 0.05,\n    'max_depth': 6,\n    'n_estimators': 200,\n    'subsample': 0.8,\n    'colsample_bytree': 0.8,\n    'reg_alpha': 1,  # Increased from 0.1\n    'reg_lambda': 5,  # Increased from 1\n    'random_state': SEED\n}\n\n\nCatBoost_Params = {\n    'learning_rate': 0.05,\n    'depth': 6,\n    'iterations': 200,\n    'random_seed': SEED,\n   # 'cat_features': cat_c,\n    'verbose': 0,\n    'l2_leaf_reg': 10  # Increase this value\n}\n\n\n\n# Create model instances\nLight = LGBMRegressor(**Params, random_state=SEED, verbose=-1, n_estimators=300)\nXGB_Model = XGBRegressor(**XGB_Params)\nCatBoost_Model = CatBoostRegressor(**CatBoost_Params)\n#TabNet_Model = TabNetWrapper(**TabNet_Params)\n\n# Combine models using Voting Regressor\nvoting_model = VotingRegressor(estimators=[\n    ('lightgbm', Light),\n    ('xgboost', XGB_Model),\n    ('catboost', CatBoost_Model)\n    ])\n#,weights=[5.0,4.0,4.0]\n# Train the ensemble model\nSubmission2 = TrainML(voting_model, test)\n\n# Save submission\n#Submission2.to_csv('submission.csv', index=False)\nSubmission2","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:37:18.879912Z","iopub.execute_input":"2024-12-22T10:37:18.880254Z","iopub.status.idle":"2024-12-22T10:37:53.718114Z","shell.execute_reply.started":"2024-12-22T10:37:18.880223Z","shell.execute_reply":"2024-12-22T10:37:53.716954Z"},"papermill":{"duration":62.565547,"end_time":"2024-12-16T14:33:43.613959","exception":false,"start_time":"2024-12-16T14:32:41.048412","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Save submission\nSubmission2.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-12-22T10:37:53.719526Z","iopub.execute_input":"2024-12-22T10:37:53.719896Z","iopub.status.idle":"2024-12-22T10:37:53.729136Z","shell.execute_reply.started":"2024-12-22T10:37:53.719862Z","shell.execute_reply":"2024-12-22T10:37:53.727861Z"},"papermill":{"duration":0.035554,"end_time":"2024-12-16T14:33:43.673327","exception":false,"start_time":"2024-12-16T14:33:43.637773","status":"completed"},"tags":[],"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"---\n**Author:** Beata Faron  \n[LinkedIn](https://www.linkedin.com/in/beata-faron-24764832/) • [Kaggle](https://www.kaggle.com/beatafaron)\n\n*Data Scientist with a background in business, design, and machine learning. Focused on time series forecasting and real-world applications.*\n","metadata":{}},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}