End-to-End Data Analytics Project

This repository contains an end-to-end data analytics project that utilizes Python and SQL to process and analyze data. The goal is to demonstrate the complete lifecycle of a data analytics workflow, from dataset acquisition to answering key business questions.

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Project Overview

Steps:

1. Dataset Acquisition

   • Use the Kaggle API to programmatically download the dataset.

2. Data Processing and Cleaning

   • Use Pandas for cleaning and transforming the dataset to ensure data quality and consistency.

3. Data Storage

   • Load the cleaned data into SQL Server for efficient querying and analysis.

4. Data Analysis

   • Write SQL queries to answer interesting and business-critical questions based on the dataset.

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Dataset

The dataset contains information about sales transactions and includes the following fields:

• product_id: Unique identifier for each product.
• sale_price: The price at which a product was sold.
• category and sub_category: Hierarchical categorization of the products.
• region: Geographic region where the order was placed.
• order_date: The date the order was made.

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Questions Answered

The analysis answers some insightful questions, such as:

• Find top 10 highest reveue generating products!
• Find top 5 highest selling products in each region!
• Find month over month growth comparison for 2022 and 2023 sales eg : jan 2022 vs jan 2023!
• For each category which month had highest sales!
• which sub category had highest growth by profit in 2023 compare to 2022

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Technologies Used

• Python: For data processing and integration.
  • Libraries: pandas, numpy, sqlalchemy, os
• SQL Server: For data storage and analysis.
• Kaggle API: For dataset acquisition.

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How to Run

Prerequisites:

• Install Python (3.x recommended).
• Set up SQL Server and configure a database.