📝 Repository Details for "Data Cleaning Script" Title: Automated Data Cleaning & Preprocessing Pipeline Description: A Python-based utility using the Pandas library to transform raw, "messy" data into a clean, analysis-ready format.

🚀 Key Features Missing Value Handling: Automatically detects and fills null values using statistical methods (mean/median) or drops incomplete records. Duplicate Removal: Identifies and clears redundant data entries to ensure dataset integrity. Standardization: Automatically converts column names to a consistent naming convention (lowercase_with_underscores). Data Type Correction: Ensures numerical, categorical, and datetime fields are correctly formatted. Outlier Detection: Highlights or removes anomalous data points that could skew analysis. Export Pipeline: Generates a structured, clean .csv file ready for visualization or Machine Learning. 🛠️ Tech Stack Language: Python 3.x Libraries: Pandas, NumPy IDE: VS Code / Jupyter Notebook 📊 Practical Use Case Perfect for cleaning raw sales data, survey responses, or web-scraped datasets before performing Exploratory Data Analysis (EDA).