https://github.com/sksubhadeep/cutomer_call_list_data_cleaning_using_python
https://github.com/sksubhadeep/cutomer_call_list_data_cleaning_using_python
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/sksubhadeep/cutomer_call_list_data_cleaning_using_python
- Owner: sksubhadeep
- Created: 2023-09-01T15:30:19.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-09-04T08:04:23.000Z (over 1 year ago)
- Last Synced: 2024-12-30T06:14:40.564Z (5 months ago)
- Language: Jupyter Notebook
- Size: 14.6 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Data_Cleaning_using_Python
A journey through the transformation of messy data into actionable insights was embarked upon through the data cleaning process in python, where:
1️⃣ **Duplicate Rows Eliminated**: Duplicate rows from the Customer Call List DataFrame were eliminated, ensuring data accuracy.
2️⃣ **Bid Farewell to 'Not_Useful_Column'**: The 'Not_Useful_Column' that was cluttering the dataset was bid farewell to,thus streamlining the data.
3️⃣ **'Last_Name' Given a Polish**: The 'Last_Name' column was cleaned, making it more consistent and user-friendly.
4️⃣ **Phone Numbers Reformatted**: Phone numbers were reformatted into a consistent and correct format by the Python script.
5️⃣ **Address Splitting Undertaken**: The address was split into 'Street_Address,' 'State,' and 'Zipcode' columns for improved data structure.
6️⃣ **Boolean Values Standardized**: Boolean columns were standardized , making them easier to work with for analysis.
7️⃣ **Customer Data Filtered**: Data for customers who either didn't provide phone numbers or indicated that they didn't want to be contacted was removed, respecting their preferences.
8️⃣ **Index Reset**: The DataFrame's index was reset, resulting in a cleaner and more organized presentation of the data.
9️⃣ **Data Preserved**: Finally, the cleaned data was saved to a directory, ensuring it's readily available for analysis and future use.
Data cleaning isn't just about tidying up data; it's about ensuring the quality and integrity of our information, which in turn empowers us to make data-driven decisions confidently.