Data Cleaning — SegmentIQ

1

Remove Duplicates

Exact duplicate rows can inflate cluster sizes artificially. We use drop_duplicates() to ensure each customer record is unique.

          Python
           before = len(df)  # Record count before

           df = df.drop_duplicates()  # Remove duplicate rows

           removed = before - len(df)  # Count removed

           print(f"Removed {removed} duplicates")

Result: 0 duplicate rows removed.

2

Drop CustomerID

CustomerID is just an identifier — it carries no business information and would confuse the clustering algorithm if included.

          Python
           df = df.drop(columns=['CustomerID'])

           print(df.columns.tolist())
        

Dropped: CustomerID

3

Rename Columns

Renaming columns to Python-friendly names (no spaces, no special characters) makes code cleaner and avoids indexing issues.

          Python
           df = df.rename(columns={

               'Annual Income (k$)'     : 'Annual_Income',

               'Spending Score (1-100)' : 'Spending_Score'

           })

Annual Income (k$) → Annual_Income Spending Score (1-100) → Spending_Score

4

Encode Gender

Machine learning algorithms require numeric inputs. We encode Gender as a binary column: Male=1, Female=0. This is standard label encoding for binary categoricals.

          Python
           df['Gender_Encoded'] = df['Gender'].map({'Male': 1, 'Female': 0})

           print(df['Gender_Encoded'].value_counts())
        

Encoding: Male = 1 Female = 0

5

Verify Final State

After all cleaning steps, we verify: no remaining nulls, correct dtypes, and correct shape.

          Python
           print(df.shape)           # Confirm row/column count

           print(df.isnull().sum())  # Verify no nulls

           print(df.dtypes)          # Verify types

Gender 0 nulls

Age 0 nulls

Annual_Income 0 nulls

Spending_Score 0 nulls

Gender_Encoded 0 nulls

Gender	Age	Annual_Income	Spending_Score	Gender_Encoded
Male	19	15	39	1
Male	21	15	81	1
Female	20	16	6	0
Female	23	16	77	0
Female	31	17	40	0
Female	22	17	76	0
Female	35	18	6	0
Female	23	18	94	0
Male	64	19	3	1
Female	30	19	72	0

🧹 Data Cleaning