This project focuses on data cleaning, exploratory analysis, and visualization of an e-commerce sales dataset. The goal is to uncover trends in payment methods, daily sales fluctuations, and other key business metrics. The EDA report can be found at: https://medium.com/@salmanajmal_4877/exploratory-data-analysis-on-pakistans-largest-e-commerce-dataset-1fad002c49e0

The Jupyter Notebook performs the following tasks:

1. Data Preprocessing

   • Handling missing values
   • Standardizing column names
   • Correcting data types

2. Exploratory Data Analysis (EDA)

   • Analyzing payment method distribution
   • Visualizing daily sales trends
   • Identifying seasonal sales spikes

3. Visualization

   • Bar charts for categorical insights (e.g., payment methods)
   • Line charts for time-series trends (e.g., daily sales)

• Cash on delivery is the most used payment method.
• Sales exhibit seasonal spikes, likely due to promotional events.

To run this project, install the following dependencies:

• Python 3.x
• Jupyter Notebook
• Pandas
• Matplotlib
• Seaborn
• NumPy

Follow these steps to execute the Jupyter Notebook:

1. Install dependencies using pip:

   pip install pandas matplotlib seaborn numpy