Posts

End-to-End Emotion Detection: Data Processing, Modeling & Real-Time Deployment

-
Image
🎭 Building a Robust Real-Time Emotion Detection System Using Ensemble Learning   🔗 GitHub Repository: https://github.com/KToppo/Emotion-Detection-ML Human emotion recognition has emerged as a powerful tool in modern AI applications—ranging from digital well-being solutions to marketing analytics and interactive systems. In this project, I built a Real-Time Emotion Detection System that uses a camera feed or an image URL to classify a person’s facial expression into one of several emotion categories. The complete project — including code, models, pipelines, and demo — is available on GitHub: 👉 https://github.com/KToppo/Emotion-Detection-ML This blog documents the entire journey — from data preprocessing to final deployment — and highlights the experiments, improvements, and insights gained along the way. 📂 Project Structure Here is the complete directory structure: ├── models/ │ ├── labels_1.pkl │ ├── labels_2.pkl │ ├── labels_3.pkl │ ├── M1SMOTE_boost.png │ ├── M1...
Post a Comment
Read more

📚 Building a Book Recommendation System with Streamlit & Collaborative Filtering

-
Image
Recommendation systems are used in many of the apps we use every day – from Netflix suggesting movies to Amazon recommending products . As a book lover and data science enthusiast , I wanted to create a system that helps readers find new books based on what they like . In this post , I’ll walk you through how I built a Book Recommendation System using Python , Streamlit , and Collaborative Filtering .   📌 Project Overview The goal was to create a simple yet effective web app that: * Shows the Top 50 most popular books based on average ratings.   * Recommends similar books to a user - selected title using collaborative filtering.   * Provides a clean , interactive user interface built with Streamlit.   📁 Dataset I used the Book Recommendation Dataset from Kaggle, which includes : * Books.csv – Book details like title , author , and image URL * Users.csv – User demographic data * Ratings .csv – Book rat...
Post a Comment
Read more

Lessons from the Leaders: How Veeba, Jumbo King, and Royal Enfield Built Sustainable Businesses Through Customer Focus and Strategic Growth

-
The Cornerstones of Success: Simplicity, Innovation, and Customer Centricity In the dynamic world of business, the path to enduring success is rarely linear. However, certain fundamental principles consistently guide organizations towards profitability and longevity. Based on the inspiring journeys of Veeba, Jumbo King, and Royal Enfield, we can identify three core elements: Simplicity, Innovation, and Customer Centricity . 1. Simplicity as the Foundation: Veeba: The brand's inception focused on a limited product line, prioritizing quality and safety above all else. The founder's unwavering commitment to serving only products he would feed his own children instilled a strong foundation of trust and quality. Jumbo King: The initial focus was laser-sharp: to provide fresh, fast, and consistently high-quality Vada Pavs. This simplicity allowed them to perfect their core offering before expanding. Royal Enfield: Siddhartha Lal recognized the need for a focused approach. He stre...
Post a Comment
Read more

Unlock the Full Potential of Jupyter Notebooks in VS Code | A Step-by-Step Guide to Using Jupyter Notebooks in VS Code

-
Image
Leveraging Jupyter Notebooks with VS Code: A Comprehensive Guide Visual Studio Code, a popular and versatile code editor, offers seamless integration with Jupyter Notebooks, providing data scientists and developers with a powerful environment for interactive data analysis. This guide will walk you through the steps of setting up and using Jupyter Notebooks within VS Code, taking full advantage of its autocompletion features. Prerequisites: VS Code: Ensure you have the latest version installed. Python: Python should be installed on your system. Jupyter Notebook: Jupyter Notebook needs to be installed. Step-by-Step Guide: Open Your Workspace: Launch VS Code and navigate to the folder where your Jupyter Notebook files are saved. This can be done using the File Explorer within VS Code. Install Jupyter Notebook Extension: If not already installed, search for the "Jupyter" extension in the VS Code extensions marketplace and install it. This extension provides essential feat...
Post a Comment
Location: 89C8+C7 Danwar, Uttar Pradesh, India
Read more

Mastering Portfolio Analysis with Python: Calculate Risk and Return | Can Python Predict Your Next Million-Dollar Investment?

-
Predicting Your Portfolio's Future: Unveiling the Code Hey financial wizards and data enthusiasts! Today, we're diving into the world of Python and portfolio prediction. We'll be dissecting a code script that helps you estimate the future risk and return of your investment choices. But first, a disclaimer: Predicting the market with absolute certainty is a fool's errand. This script is a tool to inform your decisions, not a magic crystal ball. Now, let's crack the code! The script utilizes the nselib library to access historical stock data from the National Stock Exchange of India (NSE). Here's a breakdown of the key functions: format_data : This function takes a list of stock symbols and retrieves their closing prices for the past year. It then cleans and formats the data into a Pandas DataFrame for easy analysis. Expected_risk : This function calculates the expected risk of your portfolio based on the weights assigned to each stock and their historical co...
Post a Comment
Location: 4, Kamraj Ln, Meena Bagh, Krishna Manon Lane Area, New Delhi, Delhi 110011, India
Read more