shiksha Kumari
Data Analyst | Excel, Python, Power BI | Data → Insights
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Online Retail Data Analysis (2011)
This Online Retail Data Analysis (2011) This project analyzes an Online Retail Dataset to uncover key business insights such as revenue trends, top-performing countries, and high-value customers for the year 2011. The analysis is performed using Python, with visualization libraries like Matplotlib, Seaborn, and Plotly.
Insight:
Identifies strongest international markets Top 10 Customers (Column Chart) Removed missing CustomerID Ranked customers by total revenue contribution Helps identify high-value customers for targeted marketing Country-wise Sales Distribution (Map Chart) Created a choropleth map using Plotly Visualized total quantity sold by country (excluding UK)
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E-Commerce User Behavior Analysis
This project explores an E-commerce dataset from Flipkart to understand product listings, pricing patterns, and category popularity. The goal is to perform Exploratory Data Analysis (EDA) and generate insights about which products and categories appear most frequently in the dataset. The analysis focuses on understanding product data such as product names, categories, pricing, and brand information, and visualizing trends using data visualization techniques.
Dataset Description
The dataset contains information about thousands of products listed on Flipkart. Each row represents a product listing with multiple attributes. Key attributes include: Product Name – Name of the product Product Category Tree – Category hierarchy of the product Retail Price – Original product price Discounted Price – Price after discount Brand – Brand name of the product Product Rating – Customer rating of the product Description – Product description and features Product Specifications – Detailed specifications of the product The dataset contains around 20,000 product listings, making it suitable for data analysis and visualization.
Key Insights
The dataset contains a large number of products across multiple categories. Some product categories appear significantly more often than others. Prices vary widely across different products and categories. Certain products appear multiple times in the dataset, indicating high listing frequency.
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10
This project focuses on Exploratory Data Analysis (EDA) of the Sample Superstore dataset to identify profitability patterns, loss-making products, and regional performance. The goal is to support data-driven business decisions using visualization and statistical insights.
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Customer churn is a major challenge for telecom companies. Retaining existing customers is more cost-effective than acquiring new ones. In this project, I built a Machine Learning model to predict whether a telecom customer will churn or not based on their service usage, contract details, and billing information. The goal is to help companies identify customers who are likely to leave and take preventive actions.
This project focuses on Exploratory Data Analysis (EDA) of the Sample Superstore dataset to identify profitability patterns, loss-making products, and regional performance. The goal is to support data-driven business decisions using visualization and statistical insights.
1
20