diff --git a/machine_learning/k_means_clust.py b/machine_learning/k_means_clust.py
index 7c8142aab878..d93c5addf2ee 100644
--- a/machine_learning/k_means_clust.py
+++ b/machine_learning/k_means_clust.py
@@ -11,10 +11,10 @@
   - initial_centroids , initial centroid values generated by utility function(mentioned
     in usage).
   - maxiter , maximum number of iterations to process.
-  - heterogeneity , empty list that will be filled with hetrogeneity values if passed
+  - heterogeneity , empty list that will be filled with heterogeneity values if passed
     to kmeans func.
 Usage:
-  1. define 'k' value, 'X' features array and 'hetrogeneity' empty list
+  1. define 'k' value, 'X' features array and 'heterogeneity' empty list
   2. create initial_centroids,
         initial_centroids = get_initial_centroids(
             X,
@@ -31,8 +31,8 @@
             record_heterogeneity=heterogeneity,
             verbose=True # whether to print logs in console or not.(default=False)
             )
-  4. Plot the loss function, hetrogeneity values for every iteration saved in
-     hetrogeneity list.
+  4. Plot the loss function and heterogeneity values for every iteration saved in
+     heterogeneity list.
         plot_heterogeneity(
             heterogeneity,
             k
@@ -198,13 +198,10 @@ def report_generator(
     df: pd.DataFrame, clustering_variables: np.ndarray, fill_missing_report=None
 ) -> pd.DataFrame:
     """
-    Function generates easy-erading clustering report. It takes 2 arguments as an input:
-        DataFrame - dataframe with predicted cluester column;
-        FillMissingReport - dictionary of rules how we are going to fill missing
-        values of for final report generate (not included in modeling);
-    in order to run the function following libraries must be imported:
-        import pandas as pd
-        import numpy as np
+    Generates a clustering report. This function takes 2 arguments as input:
+        df - dataframe with predicted cluster column
+        fill_missing_report - dictionary of rules on how we are going to fill in missing
+        values for final generated report (not included in modelling);
     >>> data = pd.DataFrame()
     >>> data['numbers'] = [1, 2, 3]
     >>> data['col1'] = [0.5, 2.5, 4.5]
@@ -306,10 +303,10 @@ def report_generator(
     a.columns = report.columns  # rename columns to match report
     report = report.drop(
         report[report.Type == "count"].index
-    )  # drop count values except cluster size
+    )  # drop count values except for cluster size
     report = pd.concat(
         [report, a, clustersize, clusterproportion], axis=0
-    )  # concat report with clustert size and nan values
+    )  # concat report with cluster size and nan values
     report["Mark"] = report["Features"].isin(clustering_variables)
     cols = report.columns.tolist()
     cols = cols[0:2] + cols[-1:] + cols[2:-1]