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Why do we need to subtract 3 in word index in IMDb dataset from Tensorflow?
As I was working with IMDb dataset, I noticed while decoding, we need to subtract 3 in the word index to get the corresponding word. Let’s understand why.
5 Types of Analytics: From Understanding the Past to Predicting the Future
In today’s data-driven business landscape, organizations generate unprecedented volumes of information every second. The ability to transform this raw data into actionable insights has become the difference between market leaders and followers.
Top 50 Power BI Interview Questions & Answers (for Freshers)
If you’re preparing for your first data analyst role, these 50 interview questions and answers will help you get ready.
How to import data into PostgreSQL?
Working with PostgreSQL databases often requires moving data in and out of tables, and PGAdmin makes this process seamless with its intuitive interface. If you have data in CSV or text files and want to bring it into your PostgreSQL table, here’s a clear, actionable guide to help you do it effortlessly.
Fine-tune and apply content filters to a language model
Use Azure AI Foundry to fine-tune a language model, create and apply content filters.
Create a generative AI app that uses your own data
Use Azure AI Foundry to integrate custom data using RAG into a generative AI app.
Use a prompt flow to manage conversation in a chat app
Use Azure AI Foundry portal’s prompt flow to create a custom chat app that uses a user prompt and chat history as inputs, and uses a GPT model from Azure OpenAI to generate an output.
Develop a multimodal generative AI app
Use the Phi-4-multimodal-instruct generative AI model to generate responses to prompts that include text, images, and audio.
Choose and deploy a language model
Use Azure AI Foundry portal to choose, compare and deploy the models from model catalog.
Prepare for an AI development project
Use Azure AI Foundry portal to create a hub and project to build an AI solution.
What is Azure AI Foundry?
Understanding Azure AI Foundry in detail.
I saw this video on Azure AI Foundry on YouTube by John Savill.
How to create and manage Azure resources?
Discussing about different ways to create and manage Azure resources. There are many ways to interact with Azure resources and the right tool to choose depends on the requirements of a particular task.
DP-100 Preparation Notes
My notes to prepare for DP-100 exam. This includes some important concepts which may help.
15 Days of DAX Series
This is the 15 days of DAX series that I have created on LinkedIn. Learn 50+ common DAX functions in Power BI with simple examples.
AI-900 Preparation Notes
I have created this notes while I was preparing for AI-900 exam. It includes some important concepts essential for this exam.
Finally, Microsoft Power BI Certified!!!
In this post, I share my experience on how I passed the PL-300 exam and the resources that I have followed to prepare for the exam.
DP-900 Practice Questions
Some practice questions to prepare for DP-900 exam.
AZ-900 Practice Questions
Some practice questions to prepare for AZ-900 exam.
Importing Excel Data into SharePoint List using Power Automate
Automate importing data from Excel to SharePoint list using Power Automate.
My first App in Power Apps
I started learning Power Apps, and this is my first canvas app.
Storage Blob in Azure
Understanding how to create a storage blob in Azure.
Handling Duplicate Data in Azure Data Flows
In this task, we are going to remove duplicates from a csv file present in a container.
Configuring Azure Data Flow Error Handling
In this task, we are going to configure Azure Data Flow Error Handling.
Azure Data Flow Derived Columns
In this task, we are going to use derived columns transformation in Azure Data Factory to split a column into 2 columns.
Azure Data Flow Conditional Split
In this task, we are going to use conditional split in Azure Data Factory to split the data in a Persons table present in Azure SQL Database to split the data based on the age column.
Azure Data Flow Conditional Flatten
In this task, we are going to flatten the json file stored in a Container in our storage account.
In the json file, the data is an array of arrays, so we are going to flatten it to individual records.
Enable Security for Azure Data Lake
Understanding how to enable security for Azure Data Lake.
The security model in Azure Data Lake Storage supports both RBAC and ACLs. Azure role assignments are evaluated first. They take priority over any ACL assignments.
Configure a Hierarchical Namespace for Azure Data Lake
Understanding how to configure a hierarchical namespace for Azure Data Lake.
Resource Lock in Azure
Understanding how to create a resource lock in Azure.
Pricing Calculator in Azure
In this exercise, you use the Pricing calculator to estimate the cost of running a basic web application on Azure.
TCO Calculator in Azure
In this exercise, you use the Total Cost of Ownership (TCO) Calculator to compare the cost of running a sample workload in your datacenter versus on Azure.
Azure Virtual Machine
Understanding and implementing Azure VM
Sampling Distributions
Revising sampling distribution concept in statistics.
portfolio
HR Analytics Dashboard
Built a Power BI dashboard to visualize headcount, salaries, leave data, and demographics. Used DAX and Power Query for dynamic insights. Supports strategic HR decisions through clear, interactive visuals.
Student Performance Prediction
Predicted student academic outcomes using demographic and educational data. Applied EDA, preprocessing, and classification models. Helped identify at-risk students early.
Walmart Sales Analysis using SQL
Explored Walmart transaction data to uncover sales trends, top products, and customer segments. Highlighted category performance and optimization areas. Used SQL CTEs for efficient querying.
Power BI Pokedex
Created a Power BI dashboard inspired by Pokémon’s Pokedex. Integrated external data to build interactive profiles and visuals. Designed a fun, game-style user experience.
Global Superstore Sales Dashboard
Built a Power BI dashboard to analyze global sales by region, category, and time. Included dynamic filters, forecasts, and decomposition trees. Enabled trend discovery and strategic comparison.
HR Insights at Salifort Motors
Analyzed HR data to understand attrition drivers at Salifort Motors. Used EDA and ML (Random Forest, Decision Tree) for prediction. Achieved 96.2% accuracy, 93.8% AUC. Delivered actionable retention insights.
Clique Bait SQL Case Study
Used SQL to evaluate ad performance and user behavior for an online seafood store. Found a 23% lift in purchases from ad engagement. Identified top campaigns and provided optimization strategies.
Call Centre Dashboard using Excel
Designed an interactive Excel dashboard using pivot tables, charts, and slicers. Tracked key metrics like call time, volume, and amount. Improved visibility into performance trends. Strengthened my Excel visualization skills.
Danny’s Diner SQL Case Study
Analyzed customer behavior and sales for a Japanese diner using PostgreSQL. Tackled real-world questions with CTEs and subqueries. Revealed purchase patterns and visit frequency.
Furniture Pro & Co., Retailers - Power BI Case Study
Built a Power BI report analyzing sales, RFM segments, and customer behavior. Uncovered that 20% of customers drive 49% of sales. Identified high returns in Office Supplies and weak new customer acquisition.
Kindle Review Sentiment Analysis
Sentiment analysis on Amazon Kindle reviews using various NLP techniques: Implemented multiple text vectorization methods (BOW, TF-IDF, Word2Vec), built and compared different classification models, processed and analyzed the Kindle Reviews dataset.
SMS Spam Classification
A comprehensive spam detection system implementing multiple text classification approaches including Bag of Words (BOW), TF-IDF Vectorization with Multinomial Naive Bayes, Word2Vec with Random Forest Classifier
NYC Yellow Taxi Data Project using Fabric
A complete end-to-end data engineering and analytics solution implemented using Microsoft Fabric for NYC Yellow Taxi trip data (January– October 2025)
Simple RNN for IMDb Sentiment Analysis
Developed an end-to-end deep learning project using a simple RNN on the IMDb movie reviews dataset.
Next word Prediction using LSTM and GRU
Developed a deep learning model for predicting the next word in a given sequence of words, built using LSTM and GRU networks.