AI Councel Lab

Project Overview In this project, I built an end-to-end Business Analytics & Data Science solution using SQL, Power BI, and Python to: Analyze historical sales, profit, discounts, and units sold Build an Executive Summary Dashboard for leadership Forecast next 3 months of Sales & Units Sold Support inventory planning and business decision-making This project simulates a real-world eCommerce / Retail analytics use case , combining ETL, BI reporting, and predictive modeling in a single workflow. Business Objective Primary Goals Provide leadership with a single-source executive dashboard Identify sales, profit, and regional performance trends Predict future demand (Sales & Units Sold) for: Inventory planning Revenue forecasting Procurement & supply-chain readiness Key Questions Answered How are sales and profits trending over time? Which regions and segments drive the most value? What will be the expected sales & unit demand for the next 3 months? Architecture ...

Project Overview In this project, I performed text analytics and natural language processing (NLP) on three historic U.S. Presidential inaugural speeches to understand their linguistic structure, vocabulary usage, and dominant themes . Speeches Analyzed Franklin D. Roosevelt – 1941 John F. Kennedy – 1961 Richard Nixon – 1973 The goal was not political analysis, but language analysis using Python and NLP libraries. Git Link Problem Definition The objectives of this analysis were: Compute text statistics for each speech: Number of characters Number of words Number of sentences Average word length Perform text preprocessing : Lowercasing Removing punctuation, numbers, and special characters Stopword removal Stemming Identify the most frequently used words across all three speeches Visualize dominant themes using a Word Cloud Data Source The speeches were sourced from the NLTK Inaugural Corpus , which contains official U.S. presidential inaugural addresses dating back to 1789. from nlt...

Problem Definition The objective of this project is to predict voter preference (Labour vs Conservative) using demographic, economic perception, political leadership ratings, and political awareness variables. This is a binary classification problem , where the target variable is: vote_Labour (1 = Labour, 0 = Conservative) The analysis aims to: Understand data structure and distributions Identify relationships between predictors and voting behavior Build and compare multiple classification models Select the best model based on performance metric Git Link Dataset Overview Rows: 1,525 voters Columns: 9 features + 1 target Data Types: Numerical: Age, economic conditions, leader ratings, political knowledge Categorical: Vote, Gender Missing Values: None Duplicates: 8 (not materially impactful) Target Variable Distribution Labour voters: ~70% Conservative voters: ~30% ➡️ Dataset is moderately imbalanced , which makes recall and AUC important evaluation metrics in addition to accuracy...

Created dashboards on Looker, Power BI & Tableau  Power BI Dashboard:  https://drive.google.com/file/d/1izIJOq0mk-Irg1uRboD_3H9IoX44yZex/view?usp=drive_link Tableu Dashboard:  https://public.tableau.com/app/profile/raghvendra.singh4020/vizzes Looker Dashboard: https://lookerstudio.google.com/reporting/c7ed19af-48c8-4e13-9cdc-859047999a16/page/FKgRB

🔍 Project Overview  This project demonstrates the application of inferential statistics to solve multiple real-world problems across sports analytics, manufacturing quality control, marketing operations and healthcare . The objective was to move beyond descriptive statistics and apply probability theory, hypothesis testing, and ANOVA techniques to draw meaningful conclusions and support data-driven decision-making. Download Complete Report from Git Open on Git 🎯 Key Objectives Apply probability concepts to real datasets Use normal distribution and Z-tests for quality analysis Perform hypothesis testing (Z-test, T-test) Analyze multi-factor effects using One-Way & Two-Way ANOVA Translate statistical results into business insights and recommendations 🧠 Problem 1: Sports Injury Probability Analysis Business Question Can player position help explain the likelihood of foot injuries in a football team? Approach Used conditional probability and join...

The ads24x7 is a Digital Marketing company which has now got seed funding of $10 Million. They are expanding their wings in Marketing Analytics. They collected data from their Marketing Intelligence team and now wants you (their newly appointed data analyst) to segment type of ads based on the features provided. Use Clustering procedure to segment ads into homogeneous groups. View Python Code View Pdf Report 🔍 Project Objective This project focuses on applying unsupervised machine learning and dimensionality reduction techniques to solve two real-world analytical problems: Segment digital advertisements based on performance metrics to optimize marketing strategy. Reduce high-dimensional census data using PCA to extract meaningful population insights efficiently. The project demonstrates strong skills in EDA, clustering, PCA, business interpretation, and actionable recommendations . 🧠 Part 1: Digital Marketing Ads Clustering (Business Analytics + ML) 📌 Problem Statement A digital ...