Data cleaning is one of the most crucial steps in any data science or analytics project. In this challenge, I worked on a real-world dataset from Kaggle with over 100,000 rows, performing various Pandas operations to clean, preprocess, and prepare it for further analysis.

📂 Dataset Details For this challenge, I selected the E-commerce Sales Dataset from Kaggle containing around 120,000 rows and 12 columns.

It includes data such as:

🧾 Order ID 👤 Customer Name 🛒 Product & Quantity 💰 Sales & Discount 🌍 Region 📅 Order Date Before Cleaning:

Rows → 120,000 Columns → 12 File format → .csv

⚙️ Tools & Environment Python 3 Google Colab Libraries: Pandas, NumPy, Matplotlib