Raghvendra Singh Portfolio

I’m Raghvendra Singh

Business Analytics & Data Science Professional

I help businesses make data-driven decisions using analytics, dashboards and data science techniques across Ecommerce, Retail, Finance and Marketing.

I specialize in converting raw data into clear insights, measurable impact and actionable recommendations for business leaders and teams.

Profile Links

Portfolio

Below are selected projects showcasing my work in analytics, data science and business problem-solving.

1. Digital Marketing Ads Clustering for Ads24x7

2. Inferential statistics: Probability to ANOVA

3. Power BI Sales & Invetory forecasting using SARIMA, SQL, Python

4. Power BI/ Looker/ Tableu- Neerus Dashboards - Myntra payments dashboard

5. Text Analytics using NLP on political speeches analysis

6. Election Data Classification: End to end analysis

📬 Let’s Connect

📧 Email: raghavsingh0027@gmail.com
🔗 LinkedIn: https://www.linkedin.com/in/raghvendra0027

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Machine Learning vs Deep Learning : Understand the difference!

In the world of artificial intelligence (AI), terms like "Machine Learning" (ML) and "Deep Learning" (DL) are frequently used, often interchangeably. However, while both fall under the umbrella of AI, they are distinct in their methodologies, applications, and capabilities. In this post, we'll explore the key differences between machine learning and deep learning, helping you understand when and why each is used. What is Machine Learning? Machine Learning is a subset of AI focused on developing algorithms that allow computers to learn from and make predictions based on data. The core idea behind machine learning is that the system can automatically learn and improve from experience without being explicitly programmed for each task. There are three main types of machine learning: Supervised Learning : The model is trained on labeled data, which means the input data has corresponding output labels. The algorithm's goal is to learn a mapping from inputs ...

Data Analysis and Visualization with Matplotlib and Seaborn | TOP 10 code snippets for practice

Data visualization is an essential aspect of data analysis. It enables us to better understand the underlying patterns, trends, and insights within a dataset. Two of the most popular Python libraries for data visualization are Matplotlib and Seaborn . Both libraries are highly powerful, and they can be used to create a wide variety of plots to help researchers, analysts, and data scientists present data visually. In this article, we will discuss the basics of both libraries, followed by the top 10 most used code snippets for visualization. We'll also provide links to free resources and documentation to help you dive deeper into these libraries. Matplotlib and Seaborn: A Quick Overview Matplotlib Matplotlib is a low-level plotting library in Python. It allows you to create static, animated, and interactive plots. It provides a lot of flexibility but may require more code to create complex plots compared to Seaborn. Matplotlib is especially useful when you need full control ove...

Guide to Performing ETL (Extract, Transform, Load) Using SQL in Oracle and Other Databases

In the world of data engineering, ETL (Extract, Transform, Load) is a key process that allows you to efficiently extract data from various sources, transform it into a suitable format for analysis, and then load it into a target database or data warehouse. This blog will guide you through the ETL process using SQL, with code examples applicable to Oracle and other relational databases such as MySQL, PostgreSQL, and SQL Server. What is ETL? ETL stands for Extract, Transform, Load , which refers to the three key steps involved in moving data from one system to another, typically from source databases to a data warehouse. Here’s a breakdown: Extract : This step involves retrieving data from source systems such as relational databases, flat files, APIs, or cloud services. Transform : The extracted data often needs to be cleaned, formatted, aggregated, or enriched to meet the specific needs of the destination system or analytics process. Load : Finally, the transformed data is l...

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