Elevate-Labs-Internship-Task-1

Data Cleaning Summary

Dataset: Mall_Customers.csv (Mall Customer Segmentation Data)

Steps Performed:

• Missing Values:
  The dataset was checked for missing values in all columns. No missing values were found in any column.

• Duplicate Rows:
  The dataset was examined for duplicate rows. No duplicates were found, so no rows were removed.

• Standardization of Text Values:
  The Gender column was standardized by converting all entries to lowercase and ensuring consistent labeling (male, female).

• Date Formats:
  No date columns were present in the dataset, so this step was not applicable.

• Column Header Renaming:
  All column headers were converted to lowercase, spaces were replaced with underscores, and special characters were removed for consistency and ease of use.

  • Original columns:
    CustomerID, Gender, Age, Annual Income (k$), Spending Score (1-100)
  • New columns:
    customerid, gender, age, annual_income_k$, spending_score_1-100

• Data Type Corrections:
  The age column was explicitly converted to integer type to ensure consistency.

• Rows After Cleaning:
  The dataset contains 200 rows after cleaning, which matches the original size since no rows were removed.

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Key Cleaning Statistics

Step	Result
Missing values handled	0 found
Duplicates removed	0 found
Columns renamed	Yes
Gender standardized	Yes
Date columns processed	Not applicable
Final row count	200

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The cleaned dataset is saved as cleaned_mall_customers.csv.

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Example of Cleaned Data

customerid	gender	age	annual_income_k$	spending_score_1-100
1	male	19	15	39
2	male	21	15	81
3	female	20	16	6

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No data was lost during cleaning. The dataset is now ready for analysis.