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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Using NLP for Text Analytics with HTML Links, Stop Words, and Sentiment Analysis in Python

In the world of data science, text analytics plays a crucial role in deriving insights from large volumes of unstructured text data. Whether you're analyzing customer feedback, social media posts, or web articles, natural language processing (NLP) can help you extract meaningful information. One interesting challenge in text analysis involves handling HTML content, extracting meaningful text, and performing sentiment analysis based on predefined positive and negative word lists. In this blog post, we will dive into how to use Python and NLP techniques to analyze text data from HTML links, filter out stop words, and calculate various metrics such as positive/negative ratings, article length, and average sentence length. Prerequisites To follow along with the examples in this article, you need to have the following Python packages installed: requests (to fetch HTML content) beautifulsoup4 (for parsing HTML) nltk (for natural language processing tasks) re (for regular exp...

NumPy and Pandas for Data Science: A Comprehensive Guide

In the world of Data Science , working with large datasets, performing data manipulation, and analyzing numerical information is a fundamental task. To make these tasks easier and more efficient, Python has two powerful libraries: NumPy and Pandas . These libraries are widely used for data manipulation, analysis, and visualization and are crucial tools for any data scientist. Let’s take a deep dive into both NumPy and Pandas , exploring their functionality and how they empower data scientists to work smarter and faster. 1. What is NumPy? NumPy (Numerical Python) is an open-source library used for numerical computing in Python. It provides support for working with large, multi-dimensional arrays and matrices, and offers a wide range of mathematical functions to operate on these arrays. Key Features of NumPy: Efficient Array Operations: NumPy arrays, or ndarrays , are far more efficient in terms of memory and computational speed compared to Python’s native lists. Vectorizati...

Understanding Neural Network Models for Regression: ANN, RNN, and CNN

In the world of machine learning, neural networks play a crucial role in solving complex problems. They have shown remarkable performance in various domains, from image classification to natural language processing. However, one of the fundamental tasks that neural networks can perform is regression —predicting continuous values based on input features. In this blog post, we'll explore three types of neural network models— Artificial Neural Networks (ANN) , Recurrent Neural Networks (RNN) , and Convolutional Neural Networks (CNN) —and discuss how they can be used for regression tasks. Additionally, we'll walk through code examples and explain how to train these models for regression problems. What is Regression? Regression is a type of supervised learning where the model is trained to predict continuous values. Common examples of regression tasks include predicting house prices, stock market trends, or temperature forecasting. The primary goal is to find the best-fit line (...

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