Portfolio Project:-

End-To-End E-COMMERCE DATA_ANALYST PROJECT

Analyzed a large dataset using Pandas for data cleaning and null value removal. Developed a dimensional model and pipeline for processing, with visualizations created in Matplotlib, Seaborn, and a dashboard in Power BI. Tools: Pandas, NumPy, Matplotlib, Seaborn, Power BI.

Technologies used:

Python,matplotlib,numpy,pandas, Jupyter Notebooks,SQL,POWER_BI.

Project Workflow:

Data Collection:-

Download raw eCommerce data from Kaggle, ensuring it contains customer details, sales transactions, and product performance information.

Data Storage:-

Load the raw data into a SQL database to organize and manage it efficiently.

Data Loading:-

Use Jupyter Notebooks to query the SQL database for data analysis. Tools like SQLAlchemy or pymysql can be used to connect SQL with Python.

Data Cleaning and Transformation:-

Clean and preprocess the data using Pandas,Handle missing values and outliers,Normalize and transform data into analysis-ready formats,Add new features.

Exploratory Data Analysis (EDA):-

Analyze the data in Jupyter Notebooks using,Matplotlib and Seaborn for visualizing sales trends, customer segmentation, and product performance. Statistical summaries to uncover patterns.

Data Insights and Visualization:-

Summarize insights and create visual reports using Power BI:

Architecture Map:-

DATASETS LINK:-

https://www.kaggle.com/datasets/olistbr/brazilian-ecommerce/

E-commerce Data Model

E-commerce Dimension_Model

IMAGE_OF_DASHBOARD

DATA ANALYSIS WITH PYTHON:-

1.Importing Datasets

2.Cleaning the Data

3.Data frame manipulation

4.Summarizing the Data

5.Building data pipeline

DATA_VISUALIZATION