Portfolio - Het Shah

Excel Project

Sales Analytics(FMCG)

Domain/Function: FMCG

Created 2 reports for the sales team which will help them to evaluate customer performance and understand their market performance with respect to their target.

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Customer Performance Report: Provides net sales performance of each customer across years

Market performance vs Target report across years: Enables stakeholders to assess the country's performance and compare it to set targets.

Project Details

In this project, I designed a dynamic sales report using Excel pivot tables to help businesses make data-driven decisions. The report enables companies to analyze sales data, identify the most effective customer discounts, negotiate strategically, and discover growth opportunities in high-potential countries.

The insights generated empower businesses to optimize sales strategies, improve customer satisfaction, and increase revenue.

Key Skills Gained:
🔵 Creating and customizing Pivot Tables
🔵 Using Power Query for data cleaning and transformation
🔵 Basic understanding of DAX (Data Analysis Expressions)
🔵 Applying Conditional Formatting for better visualization
🔵 Enhancing report design for a professional look
🔵 Understanding key metrics in the sales domain

This project demonstrates my ability to transform raw sales data into actionable insights and craft reports that drive smarter business decisions.

PowerBI Project

Business Insights 360 Dashboard for Finance, Sales, Marketing and Supply Chain

Domain/Function: Business Management

AtliQ Hardwares is a consumer electronics company expanding rapidly but is not able to compete with other companies using data as most of their report still exists in Excel. My goal is to implement an advanced analytics solution using Power BI that will enable the company to get insights and make in

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Business Insights 360 Dashboard for Finance, Sales, Marketing and Supply Chain

User-empathetic landing page as a part of dashboard design

Project Details

This project aimed to create a unified report for stakeholders in sales, marketing, finance, and the executive team, focusing on:

Robust Data Modeling
User-Friendly Design
Drillable Insights
Key Achievements:
🔹 Mastered basic and advanced Power Query.
🔹 Learned basic and complex DAX formulas.
🔹 Built data models with 10+ interconnected tables.
🔹 Selected appropriate visuals and formatting.
🔹 Designed intuitive dashboards.
🔹 Used bookmarks for enhanced navigation.
🔹 Deployed reports on Power BI Service.
🔹 Incorporated stakeholder feedback.
🔹 Worked with key metrics in sales, marketing, finance, and supply chain.

Live Interactive Dashboard

Excel Project

Store Analysis

Domain/Function: Retail & E-commerece

Utilized advanced Excel formulas and pivot tables to reveal a 12.5% sales growth year-over-year. Key metrics analyzed included sales trends, inventory turnover, and profit margins. Optimization efforts reduced excess inventory by 15% and boosted profit margins by 8%.

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The image showcases a comparative analysis of monthly sales and order counts using a table and a dual-axis chart.

The image shows the contribution of the Top 10 States.

Project Details

This project, titled "Store Analysis," focuses on extracting actionable insights from retail sales data to optimize store performance. Utilizing advanced Excel functionalities like pivot tables, data validation, conditional formatting, and statistical calculations, the analysis provides a detailed understanding of sales trends, inventory efficiency, and profit margins. Interactive dashboards with slicers and visualizations enhance decision-making, enabling stakeholders to identify high-performing products, streamline inventory, and boost overall profitability. This project demonstrates expertise in retail analytics and proficiency in Excel, contributing to data-driven strategies for business growth.

SQL Project

Pizza Hut Sales -SQL

Domain/Function: Retail & E-commerece

In this project, I analyzed pizza sales data using MySQL to extract business insights. SQL queries were used to retrieve order counts, revenue, top-selling pizzas, order patterns, and category-wise distributions.

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A set of 10 SQL queries to analyze pizza sales, revenue, orders, and customer behavior using data insights.

SQL query and result displaying the five most ordered pizza types with their quantities, helping identify popular choices.

Project Details

Key Achievements:
🔹 Comprehensive Data Analysis – Extracted business insights from pizza sales data using MySQL.
🔹 SQL Query Optimization – Utilized efficient queries to retrieve order counts, revenue, and top-selling pizzas.
🔹 Revenue & Order Insights – Identified total revenue, highest-priced pizzas, and most popular orders.
🔹 Trend Analysis – Examined order distribution by size, category, and time of day.
🔹 Data Aggregation – Joined tables to determine category-wise pizza sales and trends.
🔹 Top-Selling Items – Listed the top 5 most ordered pizza types and top 3 by revenue.
🔹 Business Intelligence – Provided data-driven insights to enhance sales strategies and customer preferences..

Python Project

Rock vs Mine Prediction Using Machine Learning

Domain/Function: Machine Learning

A supervised machine learning project to classify sonar signals as either "Rock" or "Mine" using Logistic Regression. The model was trained on labeled sonar data and achieves high accuracy in predicting potential threats in undersea exploration.

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Rock vs Mine Prediction Using Machine Learning

Loaded sonar dataset, handled missing headers, converted to DataFrame, and displayed sample rows for ML classification preprocessing.

Trained a logistic regression model on sonar data, evaluated accuracy on training and test sets, and built a predictive system for classification.

Project Details

This machine learning project classifies sonar signals into two categories: rocks or mines. The dataset contains 60 features extracted from sonar signal returns, and logistic regression is used for binary classification. The data is preprocessed and split using stratified sampling to maintain class balance. The model was evaluated using accuracy scores and demonstrates reliable performance, making it suitable for real-world applications in marine object detection. The entire implementation was done in Google Collab using Python, Pandas, Scikit-learn, and Numpy.

Python Project

Calories Burned Prediction

Domain/Function: Health & Fitness

🔍 Built a calorie burn prediction model in Google Colab using XGBoost. Used features like age, height, weight, duration, heart rate & body temp. Cleaned, visualized real data & achieved MAE of 1.48—showcasing the power of ML in fitness tracking and health analytics.

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Correlation heatmap showing feature relationships. Duration, heart rate, and body temp have strong positive correlation with calories burned.

XGBoost model trained and evaluated on test data using MAE. Achieved high accuracy with a low Mean Absolute Error of 1.48 in calorie prediction.

Project Details

This project predicts 🔥 calories burned during physical activity using machine learning. Built in Google Colab ☁️, it uses real-world exercise data with features like age, gender, height, weight, duration, heart rate ❤️, and body temperature 🌡️. After preprocessing and visualization 📊, an XGBoost Regressor 📈 was trained, achieving a Mean Absolute Error of just 1.48. This highlights the role of ML in fitness 🏋️ and health analytics, enabling smarter, data-driven insights for personal wellness tracking.

Hello, I am

Het Shah

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About

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My

Sales Analytics(FMCG)

Business Insights 360 Dashboard for Finance, Sales, Marketing and Supply Chain

Store Analysis

Pizza Hut Sales -SQL

Rock vs Mine Prediction Using Machine Learning

Calories Burned Prediction

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Het Shah

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