diff --git a/.env b/.env
new file mode 100644
index 0000000..7e1b7c9
--- /dev/null
+++ b/.env
@@ -0,0 +1 @@
+PYTHONPATH=lab
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index bc4386f..aecac62 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,3 +1,18 @@
 Pipfile
 
-.vscode/
\ No newline at end of file
+.vscode/
+.ipynb_checkpoints
+__pycache__
+.idea/
+*.bundle
+*.csv
+*.joblib
+*.kvmodel
+*.npy
+*.pt
+*.png
+*.tgz
+.mypy_cache
+.ropeproject
+.coverage
+*.log
\ No newline at end of file
diff --git a/Pipfile b/Pipfile
new file mode 100644
index 0000000..042e9bc
--- /dev/null
+++ b/Pipfile
@@ -0,0 +1,19 @@
+[[source]]
+url = "https://pypi.python.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+pandas = "==1.4.3"
+matplotlib = "==3.5.2"
+numpy = "==1.23.1"
+scikit-learn = "==1.1.1"
+
+[dev-packages]
+ipykernel = "==6.15.1"
+seaborn = "==0.11.2"
+pytest = "==7.1.2"
+pylint = "==2.14.5"
+
+[requires]
+python_version = "3.9"
diff --git a/Pipfile.lock b/Pipfile.lock
new file mode 100644
index 0000000..6a62348
--- /dev/null
+++ b/Pipfile.lock
@@ -0,0 +1,1198 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "ce9e0ca9fda505f55511104f6d5f3c7acf3e276626a7e50d8cb9dd5abdc892d7"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.9"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.python.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {
+        "cycler": {
+            "hashes": [
+                "sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3",
+                "sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f"
+            ],
+            "markers": "python_version >= '3.6'",
+            "version": "==0.11.0"
+        },
+        "fonttools": {
+            "hashes": [
+                "sha256:9a1c52488045cd6c6491fd07711a380f932466e317cb8e016fc4e99dc7eac2f0",
+                "sha256:d73f25b283cd8033367451122aa868a23de0734757a01984e4b30b18b9050c72"
+            ],
+            "markers": "python_version >= '3.7'",
+            "version": "==4.34.4"
+        },
+        "joblib": {
+            "hashes": [
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+                "sha256:e2f83e18fe2f4c9e7db597e988f72712c0c3676d337d8b101f6758107c42425b",
+                "sha256:e3fb1677c720409d5f671e39bac6c9e0e422584e5f518bfd50aa4cbbea02433f",
+                "sha256:ee2b1b1769f6707a8a445162ea16dddf74285c3964f605877a20e38545c3c462",
+                "sha256:ee6acae74a2b91865910eef5e7de37dc6895ad96fa23603d1d27ea69df545015",
+                "sha256:ef3f72c9666bba2bab70d2a8b79f2c6d2c1a42a7f7e2b0ec83bb2f9e383950af"
+            ],
+            "markers": "python_version >= '2.7' and python_version not in '3.0, 3.1, 3.2, 3.3, 3.4'",
+            "version": "==1.14.1"
+        }
+    }
+}
diff --git a/lab/processes/config.py b/lab/processes/config.py
new file mode 100644
index 0000000..b2af415
--- /dev/null
+++ b/lab/processes/config.py
@@ -0,0 +1,126 @@
+"""
+This file contain the class that encapsulate config for preprocess
+"""
+
+import numpy as np
+
+
+class DataRawColumns:
+    """
+    This class has all names of columns for the raw dataframe
+    """
+
+    ID = "id"
+    BATHROOMS = "bathrooms"
+    BATHROOMS_TEXT = "bathrooms_text"
+    NEIGHBOURHOOD_GROUP_CLEANSED = "neighbourhood_group_cleansed"
+    PROPERTY_TYPE = "property_type"
+    ROOM_TYPE = "room_type"
+    LATITUDE = "latitude"
+    LONGITUDE = "longitude"
+    ACCOMMODATES = "accommodates"
+    BEDROOMS = "bedrooms"
+    BEDS = "beds"
+    AMENITIES = "amenities"
+    PRICE = "price"
+
+    SUBSET_TRAINING = [
+        ID,
+        BATHROOMS,
+        NEIGHBOURHOOD_GROUP_CLEANSED,
+        PROPERTY_TYPE,
+        ROOM_TYPE,
+        LATITUDE,
+        LONGITUDE,
+        ACCOMMODATES,
+        BEDROOMS,
+        BEDS,
+        AMENITIES,
+        PRICE,
+    ]
+
+
+class DataPreprocessColumns:
+    """
+    This class has all names of columns for the preprocess dataframe
+    """
+
+    ID = "id"
+    NEIGHBOURHOOD = "neighbourhood"
+    PROPERTY_TYPE = "property_type"
+    ROOM_TYPE = "room_type"
+    LATITUDE = "latitude"
+    LONGITUDE = "longitude"
+    ACCOMMODATES = "accommodates"
+    BATHROOMS = "bathrooms"
+    BEDROOMS = "bedrooms"
+    BEDS = "beds"
+    PRICE = "price"
+    CATEGORY = "category"
+    TV = "TV"
+    INTERNET = "Internet"
+    AIR_CONDITIONING = "Air_conditioning"
+    KITCHEN = "Kitchen"
+    HEATING = "Heating"
+    WIFI = "Wifi"
+    ELEVATOR = "Elevator"
+    BREAKFAST = "Breakfast"
+
+
+class ConfigPreprocess:
+    """
+    This class encapsulate the config for preprocess
+    """
+
+    # Paths
+    RAW_FILE = "data/raw/listings.csv"
+    PREPROCESS_FILE = "data/processed/new_processed_listings.csv"
+
+    # Preprocess config
+    MIN_PRICE = 10
+    BINS_PRICE = [10, 90, 180, 400, np.inf]
+    LABELS_PRICE = [0, 1, 2, 3]
+    MAPING_COLUMNS = {
+        DataPreprocessColumns.ROOM_TYPE: {
+            "Shared room": 1,
+            "Private room": 2,
+            "Entire home/apt": 3,
+            "Hotel room": 4,
+        },
+        DataPreprocessColumns.NEIGHBOURHOOD: {
+            "Bronx": 1,
+            "Queens": 2,
+            "Staten Island": 3,
+            "Brooklyn": 4,
+            "Manhattan": 5,
+        },
+    }
+
+
+class ConfigTrain:
+    """
+    This class encapsulate the config for train process
+    """
+
+    # Features info
+    FEATURE_NAMES = [
+        DataPreprocessColumns.NEIGHBOURHOOD,
+        DataPreprocessColumns.ROOM_TYPE,
+        DataPreprocessColumns.ACCOMMODATES,
+        DataPreprocessColumns.BATHROOMS,
+        DataPreprocessColumns.BEDROOMS,
+    ]
+    FEATURE_CATEGORY = DataPreprocessColumns.CATEGORY
+
+    # Split parameters
+    TEST_SIZE = 0.15
+    RANDOM_STATE_SPLIT = 1
+
+    # Train parameters
+    N_ESTIMATORS = 500
+    RANDOM_STATE_TRAIN = 0
+    CLASS_WEIGHT = "balanced"
+    N_JOBS = 4
+
+    # Paths
+    FOLDER_PATH = "models/"
diff --git a/lab/processes/preprocess/main.py b/lab/processes/preprocess/main.py
new file mode 100644
index 0000000..0d9e569
--- /dev/null
+++ b/lab/processes/preprocess/main.py
@@ -0,0 +1,28 @@
+"""
+This file contains the code for launch the preprocess
+"""
+import logging
+
+import pandas as pd
+
+from processes.config import ConfigPreprocess
+from processes.preprocess.preprocess import preprocess
+
+logging.basicConfig(
+    format="%(asctime)s,%(msecs)d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s",
+    datefmt="%Y-%m-%d:%H:%M:%S",
+    level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+if __name__ == "__main__":
+
+    # Load dataset
+    logger.info("Preprocessing %s", ConfigPreprocess.RAW_FILE)
+    df_raw = pd.read_csv(ConfigPreprocess.RAW_FILE)
+
+    # Preprocess dataset
+    df_result = preprocess(df=df_raw)
+
+    # Save the preprocess dataframe
+    df_result.to_csv(ConfigPreprocess.PREPROCESS_FILE)
diff --git a/lab/processes/preprocess/preprocess.py b/lab/processes/preprocess/preprocess.py
new file mode 100644
index 0000000..b5fd14f
--- /dev/null
+++ b/lab/processes/preprocess/preprocess.py
@@ -0,0 +1,180 @@
+"""
+This file contains all functions for preprocessing the dataset
+"""
+import logging
+
+import numpy as np
+import pandas as pd
+
+from processes.config import ConfigPreprocess, DataPreprocessColumns, DataRawColumns
+
+logger = logging.getLogger(__name__)
+
+
+def prepare_bathrooms_column(text: str) -> float:
+    """
+    Extract number of bathtrooms from text
+
+    Args:
+        text (str): _description_
+
+    Returns:
+        float: _description_
+    """
+
+    try:
+        return float(text.split(" ")[0]) if isinstance(text, str) else np.NaN
+    except ValueError:
+        return np.NaN
+
+
+def rename_columns(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Rename the column in the dataframe
+
+    Args:
+        df (pd.DataFrame): preprocess dataframe
+
+    Returns:
+        pd.DataFrame: the dataframe updated
+    """
+
+    return df.rename(columns={DataRawColumns.NEIGHBOURHOOD_GROUP_CLEANSED: DataPreprocessColumns.NEIGHBOURHOOD})
+
+
+def preprocess_nan(df: pd.DataFrame) -> None:
+    """
+    This function deal with nan values in the dataframe
+
+    Args:
+        df (pd.DataFrame): preprocess dataframe
+
+    Returns:
+        pd.DataFrame: the dataframe updated
+    """
+    return df.dropna(axis=0)
+
+
+def preprocess_categorical_column(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Prepare the categorical column
+
+    Args:
+        df (pd.DataFrame): preprocess dataframe
+
+    Returns:
+        pd.DataFrame: the dataframe updated
+    """
+    # Convert price to value
+    df[DataPreprocessColumns.PRICE] = df[DataPreprocessColumns.PRICE].str.extract(r"(\d+).")
+    df[DataPreprocessColumns.PRICE] = df[DataPreprocessColumns.PRICE].astype(int)
+
+    # Remove values below configured value
+    df = df[df[DataPreprocessColumns.PRICE] >= ConfigPreprocess.MIN_PRICE].copy()
+
+    # Categorize values
+    df[DataPreprocessColumns.CATEGORY] = pd.cut(
+        df[DataPreprocessColumns.PRICE], bins=ConfigPreprocess.BINS_PRICE, labels=ConfigPreprocess.LABELS_PRICE
+    )
+
+    return df
+
+
+def create_new_column(df: pd.DataFrame, column_search: str, new_column_name: str) -> pd.DataFrame:
+    """
+    Create a new column if the text contains a specific text
+
+    Args:
+        df (pd.DataFrame): dataframe for search and create new column
+        column_search (str): column where search the text
+        new_column_name (str): new column name and text to search in original column
+
+    Returns:
+        pd.DataFrame: the dataframe updated
+    """
+    df[new_column_name] = df[column_search].str.contains(new_column_name)
+    df[new_column_name] = df[new_column_name].astype(int)
+    return df
+
+
+def preprocess_amenities_column(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Create new columns in from amenities column
+
+    Args:
+        df (pd.DataFrame): preprocess dataframe
+
+    Returns:
+        pd.DataFrame: the dataframe updated
+    """
+    columns_to_add = [
+        DataPreprocessColumns.TV,
+        DataPreprocessColumns.INTERNET,
+        DataPreprocessColumns.AIR_CONDITIONING,
+        DataPreprocessColumns.KITCHEN,
+        DataPreprocessColumns.HEATING,
+        DataPreprocessColumns.WIFI,
+        DataPreprocessColumns.ELEVATOR,
+        DataPreprocessColumns.BREAKFAST,
+    ]
+
+    for new_column in columns_to_add:
+        df = create_new_column(df=df, column_search=DataRawColumns.AMENITIES, new_column_name=new_column)
+
+    return df.drop(DataRawColumns.AMENITIES, axis=1)
+
+
+def preprocess_mapping_columns(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Convert in categorical with map some columns
+
+    Args:
+        df (pd.DataFrame): dataframe to transform
+
+    Returns:
+        pd.DataFrame: dataframe updated
+    """
+    for column, mapping in ConfigPreprocess.MAPING_COLUMNS.items():
+        df[column] = df[column].map(mapping)
+
+    return df
+
+
+def preprocess(df: pd.DataFrame) -> pd.DataFrame:
+    """
+    Preprocess the original dataframe
+
+    Args:
+        df (pd.DataFrame): dataframe to preprocess
+
+    Returns:
+        pd.DataFrame: the dataframe updated
+    """
+
+    # Create a copy of df
+    df_preprocess = df.copy()
+
+    # Create bathrooms column from bathrooms text
+    df_preprocess[DataRawColumns.BATHROOMS] = df_preprocess[DataRawColumns.BATHROOMS_TEXT].apply(
+        prepare_bathrooms_column
+    )
+
+    # Get columns of interest
+    df_preprocess = df_preprocess[DataRawColumns.SUBSET_TRAINING]
+
+    # Rename columns
+    df_preprocess = rename_columns(df_preprocess)
+
+    # Prepare categorical column
+    df_preprocess = preprocess_categorical_column(df_preprocess)
+
+    # Prepare new columns
+    df_preprocess = preprocess_amenities_column(df_preprocess)
+
+    # Prepare mapping columns
+    df_preprocess = preprocess_mapping_columns(df_preprocess)
+
+    # Deal with nan values
+    df_preprocess = preprocess_nan(df_preprocess)
+
+    return df_preprocess
diff --git a/lab/processes/preprocess/preprocess_test.py b/lab/processes/preprocess/preprocess_test.py
new file mode 100644
index 0000000..64f6c42
--- /dev/null
+++ b/lab/processes/preprocess/preprocess_test.py
@@ -0,0 +1,134 @@
+"""
+This file contains all test for preprocess.py
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+
+from processes.preprocess.preprocess import (
+    create_new_column,
+    prepare_bathrooms_column,
+    preprocess_categorical_column,
+    preprocess_mapping_columns,
+    preprocess_nan,
+    rename_columns,
+)
+
+
+@pytest.mark.parametrize(
+    "text_input, expected",
+    [("3 bathrooms", 3), ("bathrooms", np.NaN), ("no bathrooms", np.NaN), ("", np.NaN), (np.NaN, np.NaN)],
+)
+def test_prepare_bathrooms_column(text_input, expected):
+    # GIVEN an input text and result expected
+    # WHEN executed
+    result = prepare_bathrooms_column(text=text_input)
+
+    # THEN the result has been like expected
+    if np.isnan(expected):
+        assert np.isnan(result)
+    else:
+        assert result == expected
+
+
+def test_rename_columns_with_column_name():
+    # GIVEN a dataframe with column to rename
+    data_raw = [["one"], ["two"], ["three"]]
+    df_test = pd.DataFrame(data_raw, columns=["neighbourhood_group_cleansed"])
+    expected_columns = ["neighbourhood"]
+
+    # WHEN executed rename columns
+    df_test = rename_columns(df=df_test)
+
+    # THEN the columns name has to be like expected
+    assert expected_columns == df_test.columns
+
+
+def test_rename_columns_without_column_name():
+    # GIVEN a dataframe without column to rename
+    data_raw = [["one"], ["two"], ["three"]]
+    df_test = pd.DataFrame(data_raw, columns=["other_column"])
+    expected_columns = ["other_column"]
+
+    # WHEN executed rename columns
+    df_test = rename_columns(df=df_test)
+
+    # THEN the columns name has to be like expected
+    assert expected_columns == df_test.columns
+
+
+def test_preprocess_nan_values():
+    # GIVEN a dataframe without column to rename
+    data_raw = [["one", np.NaN], ["two", "a"], ["three", "b"], [np.NaN, "b"]]
+    df_test = pd.DataFrame(data_raw, columns=["column_a", "column_b"])
+    expected_size = 2
+    expected_column_a = ["two", "three"]
+    expected_column_b = ["a", "b"]
+
+    # WHEN executed preprocess nan
+    df_test = preprocess_nan(df=df_test)
+
+    # THEN the dataframe have new len
+    assert expected_size == len(df_test), "Wrong size of dataframe"
+    # AND the values in column a are like expected
+    assert expected_column_a == list(df_test["column_a"].values), "Error in column a"
+    # AND the values in column b are like expected
+    assert expected_column_b == list(df_test["column_b"].values), "Error in column b"
+
+
+def test_preprocess_categorical_column():
+    # GIVEN a dataframe with information about price
+    data_raw = [["$150.00"], ["$8.00"], ["$500.00"], ["$200.00"], ["$30.00"], ["$80.00"]]
+    df_test = pd.DataFrame(data_raw, columns=["price"])
+    expected_len = 5
+    expected_count = [2, 1, 1, 1]
+
+    # WHEN preprocess the categorical column
+    df_test = preprocess_categorical_column(df=df_test)
+
+    # THEN the dataframe have a new len
+    assert expected_len == len(df_test), "Wrong size of dataframe"
+    # AND must have expected counts
+    for i, value in enumerate(df_test["category"].value_counts()):
+        assert value == expected_count[i], "Wrong count for category " + str(i)
+
+
+def test_create_new_column():
+    # GIVEN a dataframe with a simple column
+    data_raw = [["the one"], ["the search_value two"], ["the three"], ["four search_value"], ["five"]]
+    df_test = pd.DataFrame(data_raw, columns=["description"])
+    expected_new_column = [0, 1, 0, 1, 0]
+
+    # WHEN execute create_new_column
+    df_test = create_new_column(df_test, "description", "search_value")
+
+    # THEN the dataframe have two columns
+    assert len(df_test.columns) == 2, "Wrong number of columns"
+    assert list(df_test.columns) == ["description", "search_value"], "Wrong name of columns"
+    assert list(df_test["search_value"].values) == expected_new_column, "Wrong values in columns"
+
+
+@pytest.mark.parametrize(
+    "neighborhood, room, expected_neighborhood, expected_room",
+    [
+        ("Shared room", "Queens", 1, 2),
+        ("Private room", "Bronx", 2, 1),
+        ("Hotel room", "Brooklyn", 4, 4),
+        ("Shared room", "Bronx", 1, 1),
+        ("Hotel room", "Manhattan", 4, 5),
+        ("Entire home/apt", "Staten Island", 3, 3),
+    ],
+)
+def test_preprocess_mapping_columns(neighborhood, room, expected_neighborhood, expected_room):
+    # GIVEN  dataframe with columns to map
+    data_raw = [[neighborhood, room]]
+    df_test = pd.DataFrame(data_raw, columns=["neighbourhood", "room_type"])
+
+    # WHEN executed it
+    df_test = preprocess_mapping_columns(df_test)
+
+    # THEN the results must be like expected
+    assert len(df_test) == 1, "Wrong size of dataframe"
+    assert df_test.loc[0, "neighbourhood"] == expected_neighborhood, "Wrong category for neighborhood"
+    assert df_test.loc[0, "room_type"] == expected_room, "Wrong category for room type"
diff --git a/lab/processes/preprocess/solution.md b/lab/processes/preprocess/solution.md
new file mode 100644
index 0000000..66bc9df
--- /dev/null
+++ b/lab/processes/preprocess/solution.md
@@ -0,0 +1,5 @@
+## Comentar al DS
+- Si sacamos la categoría de la columna price, ¿no sería mejor eliminarla del subset de columnas para el entrenamiento?
+
+## Decisiones:
+- Se crea una clase config para encapsular la configuración y no usar string mágicos
\ No newline at end of file
diff --git a/lab/processes/train/main.py b/lab/processes/train/main.py
new file mode 100644
index 0000000..4f4cf1c
--- /dev/null
+++ b/lab/processes/train/main.py
@@ -0,0 +1,25 @@
+"""
+This file contains the code for launch the train step
+"""
+import logging
+
+import pandas as pd
+
+from processes.config import ConfigPreprocess
+from processes.train.train import train
+
+logging.basicConfig(
+    format="%(asctime)s,%(msecs)d %(levelname)-8s [%(filename)s:%(lineno)d] %(message)s",
+    datefmt="%Y-%m-%d:%H:%M:%S",
+    level=logging.INFO,
+)
+logger = logging.getLogger(__name__)
+
+if __name__ == "__main__":
+
+    # Load dataset
+    logger.info("Training with %s", ConfigPreprocess.PREPROCESS_FILE)
+    df_preprocess = pd.read_csv(ConfigPreprocess.PREPROCESS_FILE)
+
+    # Train model
+    train(df=df_preprocess)
diff --git a/lab/processes/train/solution.md b/lab/processes/train/solution.md
new file mode 100644
index 0000000..22f9f18
--- /dev/null
+++ b/lab/processes/train/solution.md
@@ -0,0 +1,7 @@
+## Comentar al DS
+- ¿Podriamos categorizar property_type?
+
+## Decisiones:
+
+## Por hacer:
+- Usar algún framework para hacer tracking de los resultados de los entrenamientos
diff --git a/lab/processes/train/train.py b/lab/processes/train/train.py
new file mode 100644
index 0000000..8e28a70
--- /dev/null
+++ b/lab/processes/train/train.py
@@ -0,0 +1,59 @@
+"""
+This file have functions for the train of the model
+"""
+import pickle
+from datetime import datetime
+
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, roc_auc_score
+from sklearn.model_selection import train_test_split
+
+from processes.config import ConfigTrain
+from processes.train.train_result import TrainResult
+
+
+def train(df: pd.DataFrame):
+    """This function train and save results
+
+    Args:
+        df (pd.DataFrame): preprocessed dataframe
+    """
+
+    X, y = df[ConfigTrain.FEATURE_NAMES], df[ConfigTrain.FEATURE_CATEGORY]
+
+    # Division train test
+    X_train, X_test, y_train, y_test = train_test_split(
+        X, y, test_size=ConfigTrain.TEST_SIZE, random_state=ConfigTrain.RANDOM_STATE_SPLIT
+    )
+
+    # Create and train model
+    clf = RandomForestClassifier(
+        n_estimators=ConfigTrain.N_ESTIMATORS,
+        random_state=ConfigTrain.RANDOM_STATE_TRAIN,
+        class_weight=ConfigTrain.CLASS_WEIGHT,
+        n_jobs=ConfigTrain.N_JOBS,
+    )
+    clf.fit(X_train, y_train)
+
+    # Create result model
+    y_pred = clf.predict(X_test)
+    y_prob = clf.predict_proba(X_test)
+
+    train_result = TrainResult(
+        accuracy=accuracy_score(y_test, y_pred),
+        roc_auc_score=roc_auc_score(y_test, y_prob, multi_class="ovr"),
+        importances=dict(zip(ConfigTrain.FEATURE_NAMES, clf.feature_importances_)),
+        conf_m=confusion_matrix(y_test, y_pred),
+        report=classification_report(y_test, y_pred, output_dict=True),
+        parameters={
+            "n_estimators": ConfigTrain.N_ESTIMATORS,
+            "class_weight": ConfigTrain.CLASS_WEIGHT,
+            "test_split": ConfigTrain.TEST_SIZE,
+        },
+    )
+
+    # Save model and result
+    model_name = ConfigTrain.FOLDER_PATH + "random_forest_classifier_" + str(train_result.get_date()) + ".pkl"
+    pickle.dump((clf, train_result), open(model_name, "wb"))
diff --git a/lab/processes/train/train_result.py b/lab/processes/train/train_result.py
new file mode 100644
index 0000000..be47b4e
--- /dev/null
+++ b/lab/processes/train/train_result.py
@@ -0,0 +1,46 @@
+"""
+This file contain the class definition for a train result
+"""
+from datetime import datetime
+
+
+class TrainResult:
+    """
+    Class that encapsulate the information for train result
+    """
+
+    def __init__(
+        self, accuracy: float, roc_auc_score: float, importances: dict, conf_m: list, report: dict, parameters: dict
+    ) -> None:
+        self.__date = datetime.now()
+        self.__accuracy = accuracy
+        self.__roc_auc_score = roc_auc_score
+        self.__importances = importances
+        self.__conf_m = conf_m
+        self.__report = report
+        self.__parameters = parameters
+
+    def get_date(self) -> datetime:
+        """
+        Return the datetime of result creation
+
+        Returns:
+            datetime: datetime of result creation
+        """
+        return self.__date
+
+    def get_dict(self) -> dict:
+        """
+        Convert the train result object in a dictionary
+
+        Returns:
+            dict: transformation
+        """
+        return {
+            "accuracy": self.__accuracy,
+            "roc_auc_score": self.__roc_auc_score,
+            "feature importances": self.__importances,
+            "confusion matrix": self.__conf_m,
+            "report": self.__report,
+            "model parameters": self.__parameters,
+        }
diff --git a/lab/processes/train/traing_test.py b/lab/processes/train/traing_test.py
new file mode 100644
index 0000000..e69de29
diff --git a/models/random_forest_classifier_2022-08-03 13:03:22.161225.pkl b/models/random_forest_classifier_2022-08-03 13:03:22.161225.pkl
new file mode 100644
index 0000000..0e4d15a
Binary files /dev/null and b/models/random_forest_classifier_2022-08-03 13:03:22.161225.pkl differ
diff --git a/setup.cfg b/setup.cfg
new file mode 100644
index 0000000..1da26e2
--- /dev/null
+++ b/setup.cfg
@@ -0,0 +1,14 @@
+[flake8]
+max-line-length = 120
+exclude =
+  .git
+  __pycache__
+  .venv
+  .pytest_cache
+
+[isort]
+profile = black
+line_length = 120
+
+[pycodestyle]
+max-line-length = 120