i am just starting with machine learning. I am trying to deal with simple examples.
i have a *.csv file with columns containing text, numbers and dates. My goal, for learning purposes, is to classify the rows of this file in unsupervised mode using for example KMeans.

I don’t know how to combine all transformers to use it with KMeans.
Maybe someone can help me with different suggestions.

`

import numpy as np
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.pipeline import make_pipeline
from sklearn.base import BaseEstimator, TransformerMixin

class DateTransformer(BaseEstimator, TransformerMixin):
    def fit(self, X, y=None):
        return self

    def transform(self, X):
       X_date_encoded = np.zeros_like(X)
       valid_dates = ~np.isnat(X).all(axis=1)
       if np.any(valid_dates):
          X_date_encoded[valid_dates] = (X[valid_dates] - X[valid_dates].min()) / (
                    X[valid_dates].max() - X[valid_dates].min())
       return X_date_encoded


def classify_excel_rows(file_path, n_clusters=3):
    """
    Classify the rows of an Excel sheet using unsupervised learning (KMeans).

    Args:
    - file_path: The path to the Excel file.
    - n_clusters: The number of clusters to form.

    Returns:
    - A DataFrame with an additional column 'Cluster' indicating the cluster each row belongs to.
   """

   # Read the Excel file
   data = pd.read_csv(file_path, sep=';', skiprows=4)

   data['Value'] = data['Value'].str.replace(',', '.').astype(float)
   data['Value'] = pd.to_numeric(data['Value'], errors='coerce')

   date_format = "%d.%m.%Y"
   data['Date_1'] = pd.to_datetime(data['Date_1'], format=date_format, errors='coerce')
   data['Date_2'] = pd.to_datetime(data['Date_2'], format=date_format, errors='coerce')

   # Identify different data types
   text_columns = data.select_dtypes(include=['object']).columns.tolist()
   numeric_columns = data.select_dtypes(include=['float64']).columns
   date_columns = data.select_dtypes(include=['datetime']).columns

   # Preprocess the data
   numeric_transformer = make_pipeline(StandardScaler())
   text_transformer = make_pipeline(TfidfVectorizer())
   date_transformer = make_pipeline(DateTransformer())

   **X = ???? # how to combine all transformers to use it with KMeans?**

   [...]

   # Perform K-Means clustering
   kmeans = KMeans(n_clusters=n_clusters, random_state=42)
   labels = kmeans.fit_predict(X)

`

I have tried with the example code above. I don’t know how to use KMeans with this type of data for unsupervised learning/classification. I expect to cluster the rows of my *.csv file according to the column values.