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The cleaned dataset will be used for downstream analysis or reporting.\n\n### 🧰 Tools Used\n\n- Python (Pandas)\n- Jupyter Notebook / Google Colab\n- OR Excel for non-programmatic data cleaning\n\n## 📝 Cleaning Steps Performed\n\n## Task\tDescription\n\n- 🔍 Missing Values\tIdentified using .isnull() and handled by imputation or row removal.\n- ♻️ Duplicates\tRemoved using .drop_duplicates() or Excel's \"Remove Duplicates\".\n- 🧑‍💼 Standardized Text\tGender, country names, etc., were cleaned for consistency (e.g., male, Male, MALE → Male).\n- 📆 Date Format Fixes\tConverted all dates to consistent format (DD-MM-YYYY).\n- 🏷️ Column Name Cleanup\tRenamed headers to lowercase with underscores (e.g., Order Date → order_date).\n- 🔢 Data Type Corrections\tEnsured numeric fields (like age, sales) are of correct type and dates as datetime.\n\n## 🧼 Example Summary of Changes\n\n- Removed 5 duplicate rows\n- Filled 12 missing 'customer_name' values with \"Unknown\"\n- Standardized 'Gender' column to: ['Male', 'Female']\n- Converted 'order_date' to datetime format\n- Renamed columns: \"Order Date\" → \"order_date\", \"Sales Amount\" → \"sales_amount\"\n- Casted 'quantity' and 'age' columns to integer\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakshpraj%2Fdata-cleaning-and-preprocessing","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fakshpraj%2Fdata-cleaning-and-preprocessing","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fakshpraj%2Fdata-cleaning-and-preprocessing/lists"}