hotel-reservations.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":6274525,"sourceType":"datasetVersion","datasetId":3607225}],"dockerImageVersionId":30761,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2024-09-10T09:41:01.089520Z","iopub.execute_input":"2024-09-10T09:41:01.090306Z","iopub.status.idle":"2024-09-10T09:41:01.488273Z","shell.execute_reply.started":"2024-09-10T09:41:01.090251Z","shell.execute_reply":"2024-09-10T09:41:01.487096Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv('/kaggle/input/hotel-bookingcvs/hotel_booking.csv')","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:15:35.460578Z","iopub.execute_input":"2024-09-10T10:15:35.461064Z","iopub.status.idle":"2024-09-10T10:15:36.262650Z","shell.execute_reply.started":"2024-09-10T10:15:35.461027Z","shell.execute_reply":"2024-09-10T10:15:36.261653Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.head()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T09:41:02.575857Z","iopub.execute_input":"2024-09-10T09:41:02.576530Z","iopub.status.idle":"2024-09-10T09:41:02.642637Z","shell.execute_reply.started":"2024-09-10T09:41:02.576480Z","shell.execute_reply":"2024-09-10T09:41:02.641455Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.tail()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T09:41:02.647275Z","iopub.execute_input":"2024-09-10T09:41:02.648929Z","iopub.status.idle":"2024-09-10T09:41:02.682687Z","shell.execute_reply.started":"2024-09-10T09:41:02.648874Z","shell.execute_reply":"2024-09-10T09:41:02.681740Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ndata.sample(10)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T09:41:02.684003Z","iopub.execute_input":"2024-09-10T09:41:02.684366Z","iopub.status.idle":"2024-09-10T09:41:02.713138Z","shell.execute_reply.started":"2024-09-10T09:41:02.684327Z","shell.execute_reply":"2024-09-10T09:41:02.711993Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.info()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T09:41:02.714463Z","iopub.execute_input":"2024-09-10T09:41:02.714794Z","iopub.status.idle":"2024-09-10T09:41:02.826720Z","shell.execute_reply.started":"2024-09-10T09:41:02.714750Z","shell.execute_reply":"2024-09-10T09:41:02.825687Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.shape","metadata":{"execution":{"iopub.status.busy":"2024-09-10T09:41:02.827932Z","iopub.execute_input":"2024-09-10T09:41:02.828285Z","iopub.status.idle":"2024-09-10T09:41:02.834532Z","shell.execute_reply.started":"2024-09-10T09:41:02.828241Z","shell.execute_reply":"2024-09-10T09:41:02.833492Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for column in data.select_dtypes(include=['category', 'object']).columns:\n    print(f\"Column: {column}\")\n    print(data[column].value_counts())\n    print(\"\\n\")","metadata":{"execution":{"iopub.status.busy":"2024-09-10T09:41:02.836147Z","iopub.execute_input":"2024-09-10T09:41:02.836520Z","iopub.status.idle":"2024-09-10T09:41:03.237863Z","shell.execute_reply.started":"2024-09-10T09:41:02.836484Z","shell.execute_reply":"2024-09-10T09:41:03.236762Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"duplicated_rows = data[data.duplicated()]","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:16:46.839270Z","iopub.execute_input":"2024-09-10T10:16:46.839746Z","iopub.status.idle":"2024-09-10T10:16:47.072055Z","shell.execute_reply.started":"2024-09-10T10:16:46.839704Z","shell.execute_reply":"2024-09-10T10:16:47.070945Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"duplicated_rows","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:16:57.634981Z","iopub.execute_input":"2024-09-10T10:16:57.636035Z","iopub.status.idle":"2024-09-10T10:16:57.650473Z","shell.execute_reply.started":"2024-09-10T10:16:57.635983Z","shell.execute_reply":"2024-09-10T10:16:57.649290Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"null_values = data.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:17:14.438218Z","iopub.execute_input":"2024-09-10T10:17:14.438661Z","iopub.status.idle":"2024-09-10T10:17:14.533913Z","shell.execute_reply.started":"2024-09-10T10:17:14.438619Z","shell.execute_reply":"2024-09-10T10:17:14.532853Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"null_values","metadata":{"execution":{"iopub.status.busy":"2024-09-10T09:41:03.596588Z","iopub.execute_input":"2024-09-10T09:41:03.597018Z","iopub.status.idle":"2024-09-10T09:41:03.606338Z","shell.execute_reply.started":"2024-09-10T09:41:03.596969Z","shell.execute_reply":"2024-09-10T09:41:03.605052Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ndata['children'].fillna(data['children'].mode()[0], inplace=True)\ndata.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:20:04.693116Z","iopub.execute_input":"2024-09-10T10:20:04.693543Z","iopub.status.idle":"2024-09-10T10:20:04.800282Z","shell.execute_reply.started":"2024-09-10T10:20:04.693506Z","shell.execute_reply":"2024-09-10T10:20:04.799264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.to_json('data.json', orient='records', lines=True)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T09:41:03.608197Z","iopub.execute_input":"2024-09-10T09:41:03.609003Z","iopub.status.idle":"2024-09-10T09:41:05.613162Z","shell.execute_reply.started":"2024-09-10T09:41:03.608928Z","shell.execute_reply":"2024-09-10T09:41:05.612073Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Impute missing values in the 'country' column with the most frequent value (mode)\ndata['country'].fillna(data['country'].mode()[0], inplace=True)\ndata.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:21:55.883280Z","iopub.execute_input":"2024-09-10T10:21:55.884200Z","iopub.status.idle":"2024-09-10T10:21:56.002964Z","shell.execute_reply.started":"2024-09-10T10:21:55.884158Z","shell.execute_reply":"2024-09-10T10:21:56.001962Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Impute missing values in the 'agent' column with \"Unknown\"\ndata['agent'].fillna('Unknown', inplace=True)\ndata.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:22:37.820791Z","iopub.execute_input":"2024-09-10T10:22:37.821322Z","iopub.status.idle":"2024-09-10T10:22:37.940626Z","shell.execute_reply.started":"2024-09-10T10:22:37.821222Z","shell.execute_reply":"2024-09-10T10:22:37.939524Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Remove the 'company' column from the data\nif 'company' in data.columns:\n    data = data.drop('company', axis=1)\nelse:\n    print(\"Column 'company' not found in DataFrame.\")\n\n# Display DataFrame information\ndata.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:23:55.505032Z","iopub.execute_input":"2024-09-10T10:23:55.505473Z","iopub.status.idle":"2024-09-10T10:23:55.642946Z","shell.execute_reply.started":"2024-09-10T10:23:55.505430Z","shell.execute_reply":"2024-09-10T10:23:55.641825Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nencoded_df = data.copy()\ntarget_column = 'is_canceled'\n\ncategorical_columns = data.select_dtypes(include=['category', 'object']).columns\ncategorical_columns = categorical_columns[categorical_columns != target_column]\n\n\nencoded_df[categorical_columns] = encoded_df[categorical_columns].astype(str)\n\n\nlabel_encoder = LabelEncoder()\n\nfor col in categorical_columns:\n    encoded_df[col] = label_encoder.fit_transform(encoded_df[col])\n\n\nencoded_df.head()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:29:07.243596Z","iopub.execute_input":"2024-09-10T10:29:07.244606Z","iopub.status.idle":"2024-09-10T10:29:09.386953Z","shell.execute_reply.started":"2024-09-10T10:29:07.244556Z","shell.execute_reply":"2024-09-10T10:29:09.385856Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"---","metadata":{}},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = encoded_df.drop(target_column, axis=1)\ny = encoded_df[target_column]","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:31:07.547451Z","iopub.execute_input":"2024-09-10T10:31:07.548091Z","iopub.status.idle":"2024-09-10T10:31:07.566076Z","shell.execute_reply.started":"2024-09-10T10:31:07.548046Z","shell.execute_reply":"2024-09-10T10:31:07.565016Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import classification_report, confusion_matrix","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:31:28.222916Z","iopub.execute_input":"2024-09-10T10:31:28.223933Z","iopub.status.idle":"2024-09-10T10:31:28.405593Z","shell.execute_reply.started":"2024-09-10T10:31:28.223884Z","shell.execute_reply":"2024-09-10T10:31:28.404561Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:31:46.334737Z","iopub.execute_input":"2024-09-10T10:31:46.335153Z","iopub.status.idle":"2024-09-10T10:31:46.389394Z","shell.execute_reply.started":"2024-09-10T10:31:46.335111Z","shell.execute_reply":"2024-09-10T10:31:46.388110Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"logistic_model = LogisticRegression(max_iter=1000)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:31:58.023008Z","iopub.execute_input":"2024-09-10T10:31:58.023450Z","iopub.status.idle":"2024-09-10T10:31:58.028655Z","shell.execute_reply.started":"2024-09-10T10:31:58.023408Z","shell.execute_reply":"2024-09-10T10:31:58.027434Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"logistic_model.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:33:14.107380Z","iopub.execute_input":"2024-09-10T10:33:14.107846Z","iopub.status.idle":"2024-09-10T10:33:29.152641Z","shell.execute_reply.started":"2024-09-10T10:33:14.107803Z","shell.execute_reply":"2024-09-10T10:33:29.151518Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred = logistic_model.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:34:16.728649Z","iopub.execute_input":"2024-09-10T10:34:16.729043Z","iopub.status.idle":"2024-09-10T10:34:16.740514Z","shell.execute_reply.started":"2024-09-10T10:34:16.729007Z","shell.execute_reply":"2024-09-10T10:34:16.739314Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(confusion_matrix(y_test, y_pred))\nprint(classification_report(y_test, y_pred))","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:34:18.863589Z","iopub.execute_input":"2024-09-10T10:34:18.864452Z","iopub.status.idle":"2024-09-10T10:34:18.924585Z","shell.execute_reply.started":"2024-09-10T10:34:18.864402Z","shell.execute_reply":"2024-09-10T10:34:18.923320Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import seaborn as sns\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import confusion_matrix\nsns.heatmap(confusion_matrix(y_test, y_pred), annot=True, fmt='d', cmap='Blues')\nplt.title('Confusion Matrix for Logistic Regression')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:37:40.702970Z","iopub.execute_input":"2024-09-10T10:37:40.703750Z","iopub.status.idle":"2024-09-10T10:37:41.319542Z","shell.execute_reply.started":"2024-09-10T10:37:40.703705Z","shell.execute_reply":"2024-09-10T10:37:41.318469Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import roc_curve, auc\ny_proba = logistic_model.predict_proba(X_test)[:, 1]\nfpr, tpr, _ = roc_curve(y_test, y_proba)\nroc_auc = auc(fpr, tpr)\nplt.plot(fpr, tpr, label=f'Logistic Regression (AUC = {roc_auc:.2f})')\nplt.plot([0, 1], [0, 1], 'k--')\nplt.title('ROC Curve for Logistic Regression')\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.legend(loc='lower right')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-09-10T10:39:29.570045Z","iopub.execute_input":"2024-09-10T10:39:29.571277Z","iopub.status.idle":"2024-09-10T10:39:29.840920Z","shell.execute_reply.started":"2024-09-10T10:39:29.571196Z","shell.execute_reply":"2024-09-10T10:39:29.839756Z"},"trusted":true},"execution_count":null,"outputs":[]}]}