The purpose of this project is to analyze the sales of amazon in order to gain insights about the popularity of different types of products along with the states majorly contributing in the sales. This project involves the use of libraries like numpy, pandas, matplotlib and seaborn

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Exploratory Data Analysis (EDA) on Amazon Sales Dataset using Python | Data Cleaning, Visualization, and Insights

Analysis of Amazon product dataset using Python and Colab EDA and visualization on Amazon review data Amazon dataset analysis using Pandas, Matplotlib, and Machine Learning Data analysis project using Amazon sales/review dataset

Exploratory Data Analysis (EDA) on Amazon Sales Dataset — data cleaning, visualization, and outlier handling using Python and Pandas

Performed EDA on Amazon Sales 2025 dataset using Python (Pandas, Matplotlib, Seaborn). Analyzed sales trends, customer behavior, and payment methods. Generated visual insights including monthly sales, top categories, and cumulative revenue trends.

# Amazon Sales EDA CodeAlpha Internship Task 2 # Overview Exploratory Data Analysis performed on Amazon Sales Dataset as part of CodeAlpha Data Analytics Internship. # Tools Used - Python, Pandas, NumPy, Matplotlib, Seaborn # What's Covered Data Cleaning EDA & Visualizations Hypothesis Testing Anomaly Detection

This project performs exploratory data analysis (EDA) on a real-world Amazon sales dataset to extract meaningful business insights using powerful Python libraries — Pandas, NumPy, Matplotlib, and Seaborn.

This project focuses on performing Exploratory Data Analysis (EDA) on Amazon's Diwali sales dataset. The dataset contains information on sales during the Diwali festival. The goal is to clean, analyze, and visualize key sales trends using Python's powerful libraries, including Pandas, NumPy, Matplotlib, and Seaborn.

A full end-to-end Python EDA project on an Amazon sales dataset — uncovering revenue trends, category performance, regional insights, discount impact, customer behaviour, and variable relationships using pandas, matplotlib, and seaborn.

Performed exploratory data analysis (EDA) on Amazon product sales data using Python libraries like Pandas, NumPy, and Seaborn. Cleaned and processed large datasets, identified sales trends, and created insightful visualizations. Provided actionable insights through data-driven reporting to improve sales strategies and performance.

This project focuses on analyzing Amazon sales data using Python. The main goal is to clean the dataset, perform exploratory data analysis (EDA), and extract meaningful insights related to product pricing, discounts, and customer ratings.

This project is a complete end-to-end **Exploratory Data Analysis (EDA)** on an Amazon Sales dataset containing **100,000 orders**. The goal of this project is to clean raw data, engineer meaningful features, analyze sales performance, and extract **business-driven insights** using Python.

I analyzed an Amazon dataset using Python, leveraging Pandas and NumPy for data cleaning and manipulation. Using Matplotlib and Seaborn for EDA, I uncovered trends in customer behavior, sales, and product performance. The insights provided guidance on inventory optimization and marketing strategies, enhancing data analysis.

A data analysis project using Python, focused on exploring and understanding sales data from Amazon. The project involves cleaning the dataset, performing exploratory data analysis (EDA), and visualizing key trends and insights using popular data manipulation and visualization libraries such as pandas, numpy, matplotlib, and seaborn.

Exploratory Data Analysis (EDA) of Amazon Product Sales dataset (42,675 rows, 17 columns) from Kaggle using Python, Pandas, and Plotly. The project focuses on visualizing product ratings, discounts, and pricing patterns through bar charts and scatter plots to uncover actionable insights on product performance and category-wise pricing trends.