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DESCRIPTION","metadata":{}},{"cell_type":"markdown","source":"# Goal of the Competition  \n\n\n> This competition dataset contains three-dimensional accelerometer data from the lower back of subjects who experienced episodes of gait freezing - a common disability symptom among people with Parkinson's disease. Freezing of gait (FOG) negatively impacts walking ability and impedes mobility and independence.\n\n\n\n> Our goal is to detect the onset and cessation of each freezing episode, as well as the occurrence of three types of gait freezing events within these series: \"Start hesitation\", \"Turn\", and \"Walking\".  \n","metadata":{}},{"cell_type":"markdown","source":"> In this part, we will be working towards achieving our client's goal of detecting the onset and cessation of freezing episodes during gait, as well as identifying three different types of freezing events: \"Start hesitation\", \"Turn\", and \"Walking\". Additionally, based on the data obtained, we will develop algorithms to predict the potential onset of freezing episodes.","metadata":{}},{"cell_type":"markdown","source":"> We will mainly use the events.csv file Metadata for each FoG event in all data series. The time of the events corresponds to the labels in the data series.","metadata":{}},{"cell_type":"markdown","source":"> Description of the values:\n\n\n|   Name:   |   Type:   |     Meaning:    |\n|-----------|-----------|-----------------|\n|  **Id**   |   num  | The data series the event occured in|\n| **Init**  |   num  |Time (s) the event began|\n| **Completion**  |  num  | Time (s) the event ended | \n| **Type** |  cat  | Whether StartHesitation, Turn, or Walking|\n| **Kinetic** |  num - bin  | Whether the event was kinetic (1) and involved movement, or akinetic (0) and static|                           \n \n NB! **Data is in units of m/s^2 for tdcsfog/ and g for defog/ and notype / Данные представлены в единицах м/с^2 для tdcsfog/ и g для defog/ и notype** 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Exploratory Data Analysis","metadata":{}},{"cell_type":"markdown","source":"## Imports  \n\n*******************************","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport os\nimport warnings\nimport optuna\nfrom joblib import load\nimport joblib\n\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler, OneHotEncoder\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import accuracy_score, confusion_matrix, classification_report\nfrom sklearn.model_selection import train_test_split, cross_val_score\n\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.107010Z","iopub.execute_input":"2023-05-09T17:45:23.107433Z","iopub.status.idle":"2023-05-09T17:45:23.114885Z","shell.execute_reply.started":"2023-05-09T17:45:23.107399Z","shell.execute_reply":"2023-05-09T17:45:23.113580Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Settings  \n\n**************************","metadata":{}},{"cell_type":"code","source":"# Pandas defaults\npd.options.display.max_colwidth = 100\npd.options.display.max_rows = 500\npd.options.display.max_columns = 100\npd.options.display.float_format = '{:.2f}'.format\npd.options.display.colheader_justify = 'left'","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.116993Z","iopub.execute_input":"2023-05-09T17:45:23.117354Z","iopub.status.idle":"2023-05-09T17:45:23.131802Z","shell.execute_reply.started":"2023-05-09T17:45:23.117325Z","shell.execute_reply":"2023-05-09T17:45:23.130851Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# others\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.133475Z","iopub.execute_input":"2023-05-09T17:45:23.134106Z","iopub.status.idle":"2023-05-09T17:45:23.145701Z","shell.execute_reply.started":"2023-05-09T17:45:23.134074Z","shell.execute_reply":"2023-05-09T17:45:23.144715Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Constants  \n\n*****************************************","metadata":{}},{"cell_type":"code","source":"#PATH_REMOTE = ''                    # remote path to data\n\nCR = '\\n'                                     # new line\nRANDOM_STATE = RANDOM_SEED = RS = 66          # random_state\nTEST_FRAC = 0.1                               # delayed sampling fraction\n\nN_TRIALS = 10                                 # number of tries for fitting of hyperparameters\nN_CV = 4                                      # number of folds during cross-validation\nMAX_ITER = 1000                               # max number of iterations for LinearRegression\nDEGREE_POLYNOMIAL = 5                         # degree for polynomial expansion","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.147939Z","iopub.execute_input":"2023-05-09T17:45:23.148297Z","iopub.status.idle":"2023-05-09T17:45:23.159159Z","shell.execute_reply.started":"2023-05-09T17:45:23.148258Z","shell.execute_reply":"2023-05-09T17:45:23.158332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Data preparation  \n\n***************************************************************************","metadata":{}},{"cell_type":"markdown","source":"## Read and Check data\n","metadata":{}},{"cell_type":"code","source":"# Daily, Defog, and Tdcsfog data:\n# daily_df = pd.read_csv('C:/Users/Admin/Desktop/DS studies/Data/Parkinsons/Data/daily_metadata.csv')\n# defog_df = pd.read_csv('C:/Users/Admin/Desktop/DS studies/Data/Parkinsons/Data/defog_metadata.csv')\n# tdcsfog_df = pd.read_csv('C:/Users/Admin/Desktop/DS studies/Data/Parkinsons/Data/tdcsfog_metadata.csv')\n# #/kaggle/input/copy-train-metadata/tdcsfog_metadata.csv\n# #\n# # Other three dataframes:\nevents_df = pd.read_csv('/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/events.csv')\n# subjects_df = pd.read_csv('C:/Users/Admin/Desktop/DS studies/Data/Parkinsons/Data/subjects.csv')\n# tasks_df = pd.read_csv('C:/Users/Admin/Desktop/DS studies/Data/Parkinsons/Data/tasks.csv')","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.651313Z","iopub.execute_input":"2023-05-09T17:45:23.652054Z","iopub.status.idle":"2023-05-09T17:45:23.673390Z","shell.execute_reply.started":"2023-05-09T17:45:23.651997Z","shell.execute_reply":"2023-05-09T17:45:23.672332Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = events_df\n\ndf.head()","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.675710Z","iopub.execute_input":"2023-05-09T17:45:23.676052Z","iopub.status.idle":"2023-05-09T17:45:23.689684Z","shell.execute_reply.started":"2023-05-09T17:45:23.676015Z","shell.execute_reply":"2023-05-09T17:45:23.688340Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.info()","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.691477Z","iopub.execute_input":"2023-05-09T17:45:23.692121Z","iopub.status.idle":"2023-05-09T17:45:23.710955Z","shell.execute_reply.started":"2023-05-09T17:45:23.692073Z","shell.execute_reply":"2023-05-09T17:45:23.709438Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def preprocess_data(df):\n    \"\"\"\n    A function for data preprocessing that takes a DataFrame as input and returns \n    a DataFrame after applying necessary transformations\n    \"\"\"\n    # Removing duplicates\n    df.drop_duplicates(inplace=True)\n\n    # Converting categorical features\n    df['Type'] = df['Type'].astype('category')\n    df['StartHesitation'] = np.where(df['Type'] == 'StartHesitation', 1, 0)\n    df['Turn'] = np.where(df['Type'] == 'Turn', 1, 0)\n    df['Walking'] = np.where(df['Type'] == 'Walking', 1, 0)\n\n    # Creating a feature called Duration\n    df['Duration'] = df['Completion'] - df['Init']\n\n    # Defining the value of the target feature\n    df['Target'] = np.where(df['Type'] == 'StartHesitation', df['StartHesitation'], np.where(df['Type'] == 'Turn', df['Turn'], df['Walking']))\n\n    return df\n","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.712662Z","iopub.execute_input":"2023-05-09T17:45:23.713075Z","iopub.status.idle":"2023-05-09T17:45:23.720061Z","shell.execute_reply.started":"2023-05-09T17:45:23.713045Z","shell.execute_reply":"2023-05-09T17:45:23.719055Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def get_X_y(df):\n    \"\"\"\n    A function to split a DataFrame into a feature matrix X and a target variable vector y\n    \"\"\"\n    X = df.drop(['StartHesitation', 'Turn', 'Walking', 'Target'], axis=1)\n    y = df['Target']\n    return X, y","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.722821Z","iopub.execute_input":"2023-05-09T17:45:23.723169Z","iopub.status.idle":"2023-05-09T17:45:23.736496Z","shell.execute_reply.started":"2023-05-09T17:45:23.723138Z","shell.execute_reply":"2023-05-09T17:45:23.735027Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def evaluate_model(X, y, model):\n    \"\"\"\n    A function for evaluating the model's performance using cross-validation\n    \"\"\"\n    scores = cross_val_score(model, X, y, cv=5, scoring='accuracy')\n    return np.mean(scores), np.std(scores)","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.739384Z","iopub.execute_input":"2023-05-09T17:45:23.739753Z","iopub.status.idle":"2023-05-09T17:45:23.752546Z","shell.execute_reply.started":"2023-05-09T17:45:23.739723Z","shell.execute_reply":"2023-05-09T17:45:23.751571Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ndef evaluate_submission(y_true, y_pred):\n    \"\"\"\n   A function for evaluating the quality of predictions on test data\n    \"\"\"\n    acc = accuracy_score(y_true, y_pred)\n    cm = confusion_matrix(y_true, y_pred)\n    return acc, cm\n\n# Data preprocessing\n\ndf = preprocess_data(df)","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.754267Z","iopub.execute_input":"2023-05-09T17:45:23.754687Z","iopub.status.idle":"2023-05-09T17:45:23.777507Z","shell.execute_reply.started":"2023-05-09T17:45:23.754647Z","shell.execute_reply":"2023-05-09T17:45:23.776124Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.778672Z","iopub.execute_input":"2023-05-09T17:45:23.779460Z","iopub.status.idle":"2023-05-09T17:45:23.799619Z","shell.execute_reply.started":"2023-05-09T17:45:23.779420Z","shell.execute_reply":"2023-05-09T17:45:23.798305Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"The original DataFrame contains 3712 records and 5 features. In this case, categorical feature conversion, duplicate removal, creation of a new feature called Duration, and determination of the target feature called Target are performed\n\nThe preprocess_data function performs data preprocessing, including duplicate removal, categorical feature conversion, creation of the Duration feature, and determination of the target feature\n\nThe get_X_y function splits the DataFrame into a feature matrix X and a target variable vector y\n\nThe evaluate_model function evaluates the model's performance using cross-validation\n\nThe evaluate_submission function evaluates the quality of predictions on test data","metadata":{}},{"cell_type":"markdown","source":"![MT%20long.jpg](attachment:MT%20long.jpg)","metadata":{},"attachments":{"MT%20long.jpg":{"image/jpeg":"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Splitting the data into training and test sets\n\nX_train, X_test, y_train, y_test = train_test_split(df.drop('Target', axis=1), df['Target'], test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.801537Z","iopub.execute_input":"2023-05-09T17:45:23.801987Z","iopub.status.idle":"2023-05-09T17:45:23.812815Z","shell.execute_reply.started":"2023-05-09T17:45:23.801945Z","shell.execute_reply":"2023-05-09T17:45:23.811732Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Creating a list of numerical and categorical features\n\nnum_features = ['Init', 'Completion', 'Duration', 'StartHesitation','Turn', 'Walking', 'Kinetic']\ncat_features = ['Id', 'Type']","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.814029Z","iopub.execute_input":"2023-05-09T17:45:23.814342Z","iopub.status.idle":"2023-05-09T17:45:23.824318Z","shell.execute_reply.started":"2023-05-09T17:45:23.814314Z","shell.execute_reply":"2023-05-09T17:45:23.823288Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Creating transformers for numerical and categorical features\n\nnum_transformer = Pipeline(steps=[\n('imputer', SimpleImputer(strategy='median')),\n('scaler', StandardScaler())\n])\n\ncat_transformer = Pipeline(steps=[\n('imputer', SimpleImputer\n(strategy='most_frequent')),\n('onehot', OneHotEncoder(handle_unknown='ignore'))\n])","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.826081Z","iopub.execute_input":"2023-05-09T17:45:23.826834Z","iopub.status.idle":"2023-05-09T17:45:23.837549Z","shell.execute_reply.started":"2023-05-09T17:45:23.826792Z","shell.execute_reply":"2023-05-09T17:45:23.836706Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Combining transformers using ColumnTransformer\n\npreprocessor = ColumnTransformer(\ntransformers=[\n('num', num_transformer, num_features),\n('cat', cat_transformer, cat_features)\n])","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.838850Z","iopub.execute_input":"2023-05-09T17:45:23.839946Z","iopub.status.idle":"2023-05-09T17:45:23.848931Z","shell.execute_reply.started":"2023-05-09T17:45:23.839914Z","shell.execute_reply":"2023-05-09T17:45:23.847852Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Combining data preprocessing and model into a single pipeline\n\npipeline = Pipeline(steps=[('preprocessor', preprocessor),\n('classifier', RandomForestClassifier())])","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.850212Z","iopub.execute_input":"2023-05-09T17:45:23.851244Z","iopub.status.idle":"2023-05-09T17:45:23.861822Z","shell.execute_reply.started":"2023-05-09T17:45:23.851194Z","shell.execute_reply":"2023-05-09T17:45:23.860686Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Training the model\n\npipeline.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:23.865740Z","iopub.execute_input":"2023-05-09T17:45:23.866096Z","iopub.status.idle":"2023-05-09T17:45:24.369961Z","shell.execute_reply.started":"2023-05-09T17:45:23.866066Z","shell.execute_reply":"2023-05-09T17:45:24.368889Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Making predictions\n\npredictions = pipeline.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:24.371278Z","iopub.execute_input":"2023-05-09T17:45:24.371794Z","iopub.status.idle":"2023-05-09T17:45:24.409344Z","shell.execute_reply.started":"2023-05-09T17:45:24.371763Z","shell.execute_reply":"2023-05-09T17:45:24.408340Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Concatenating all predicted data into one DataFrame\n\nall_predictions = pd.DataFrame({'Id': X_test['Id'], 'Predictions': predictions})","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:24.410590Z","iopub.execute_input":"2023-05-09T17:45:24.411116Z","iopub.status.idle":"2023-05-09T17:45:24.416514Z","shell.execute_reply.started":"2023-05-09T17:45:24.411085Z","shell.execute_reply":"2023-05-09T17:45:24.415196Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Printing the accuracy of the model on the test set\n\nprint(f\"Accuracy score on test set: {accuracy_score(y_test, predictions)}\")","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:24.418046Z","iopub.execute_input":"2023-05-09T17:45:24.418341Z","iopub.status.idle":"2023-05-09T17:45:24.432345Z","shell.execute_reply.started":"2023-05-09T17:45:24.418315Z","shell.execute_reply":"2023-05-09T17:45:24.430968Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # Load the submission dataset\n\n# submission = pd.read_csv('submission.csv')\n\n# Load the sample submission dataset\n\nsample_submission = pd.read_csv('/kaggle/input/tlvmc-parkinsons-freezing-gait-prediction/sample_submission.csv')","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:24.433977Z","iopub.execute_input":"2023-05-09T17:45:24.434423Z","iopub.status.idle":"2023-05-09T17:45:24.623667Z","shell.execute_reply.started":"2023-05-09T17:45:24.434381Z","shell.execute_reply":"2023-05-09T17:45:24.622577Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Creating a DataFrame with the desired number of rows\n\n# submission_one = pd.DataFrame({'Id': [f'003f117e14_{i}'  for i in range(0, 286370)],\n#                               'StartHesitation': 0,\n#                               'Turn': 0,\n#                               'Walking': 0})\n\n# Adding predicted values to the corresponding columns\n\nsubmission_one = pd.DataFrame({'Id': ['003f117e14_' + str(i) for i in range(286370)],\n                               'StartHesitation': 0,\n                               'Turn': 0,\n                               'Walking': 0})\n\nfor i in range(len(predictions)):\n    id_str = sample_submission['Id'][i]\n    submission_one.loc[submission_one['Id'] == id_str, ['StartHesitation', 'Turn', 'Walking']] = predictions[i]\n# for i in range(1, len(predictions)+1):\n#     id_str = f'003f117e14_{i}' # rows id\n#     if id_str in submission_one['Id'].values:\n#         submission_one.loc[submission_one['Id']==id_str, ['StartHesitation', 'Turn', 'Walking']] = predictions[i-1]\n\n# for i in range(len(predictions)):\n#     id_str = f'003f117e14_{i-1}' # rows id\n#     if id_str in submission['Id'].values:\n#         submission_one.loc[submission_one['Id']==id_str, ['StartHesitation', 'Turn', 'Walking']] = predictions[i]\n        \n# Saving the DataFrame to a CSV file\n# submission_one.to_csv('submission_one.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:24.625214Z","iopub.execute_input":"2023-05-09T17:45:24.625552Z","iopub.status.idle":"2023-05-09T17:45:38.925913Z","shell.execute_reply.started":"2023-05-09T17:45:24.625525Z","shell.execute_reply":"2023-05-09T17:45:38.924538Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission_one.info()","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:38.927253Z","iopub.execute_input":"2023-05-09T17:45:38.927611Z","iopub.status.idle":"2023-05-09T17:45:38.984779Z","shell.execute_reply.started":"2023-05-09T17:45:38.927578Z","shell.execute_reply":"2023-05-09T17:45:38.983550Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_submission.info()","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:38.986443Z","iopub.execute_input":"2023-05-09T17:45:38.986779Z","iopub.status.idle":"2023-05-09T17:45:39.042648Z","shell.execute_reply.started":"2023-05-09T17:45:38.986750Z","shell.execute_reply":"2023-05-09T17:45:39.041531Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Merge the two datasets on the 'Id' column\n\n# Merge the two datasets on the 'Id' column using the 'left' join type\n\n# submission = pd.concat([sample_submission.set_index('Id'), submission_one.set_index('Id')], axis=1, join='outer')\n# submission.reset_index(inplace=True)\n\nsubmission = pd.merge(submission_one[['Id']], sample_submission, on='Id', how='left')\nsubmission.fillna(0, inplace=True)\nsubmission.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:39.044158Z","iopub.execute_input":"2023-05-09T17:45:39.044503Z","iopub.status.idle":"2023-05-09T17:45:40.197201Z","shell.execute_reply.started":"2023-05-09T17:45:39.044474Z","shell.execute_reply":"2023-05-09T17:45:40.196293Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.apply(lambda x: x.unique())","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:49:15.374213Z","iopub.execute_input":"2023-05-09T17:49:15.374966Z","iopub.status.idle":"2023-05-09T17:49:15.451303Z","shell.execute_reply.started":"2023-05-09T17:49:15.374927Z","shell.execute_reply":"2023-05-09T17:49:15.449836Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission['Walking'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:51:37.410088Z","iopub.execute_input":"2023-05-09T17:51:37.410531Z","iopub.status.idle":"2023-05-09T17:51:37.421201Z","shell.execute_reply.started":"2023-05-09T17:51:37.410463Z","shell.execute_reply":"2023-05-09T17:51:37.420012Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission = pd.read_csv('submission.csv')\n\nprint(submission.columns)\nprint(submission.shape)\nprint(submission)","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:51:47.512097Z","iopub.execute_input":"2023-05-09T17:51:47.512523Z","iopub.status.idle":"2023-05-09T17:51:47.737202Z","shell.execute_reply.started":"2023-05-09T17:51:47.512494Z","shell.execute_reply":"2023-05-09T17:51:47.735843Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# # Save the merged dataset to a CSV file\n\n# submission.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:40.394414Z","iopub.execute_input":"2023-05-09T17:45:40.395473Z","iopub.status.idle":"2023-05-09T17:45:40.400748Z","shell.execute_reply.started":"2023-05-09T17:45:40.395428Z","shell.execute_reply":"2023-05-09T17:45:40.399576Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Saving the model\n\njoblib.dump(pipeline, 'model.joblib')\n\n#Loading the model\n\nloaded_pipeline = joblib.load('model.joblib')","metadata":{"execution":{"iopub.status.busy":"2023-05-09T17:45:40.402181Z","iopub.execute_input":"2023-05-09T17:45:40.402556Z","iopub.status.idle":"2023-05-09T17:45:40.513724Z","shell.execute_reply.started":"2023-05-09T17:45:40.402527Z","shell.execute_reply":"2023-05-09T17:45:40.512735Z"},"trusted":true},"execution_count":null,"outputs":[]}]}