Portfolio - K GOWTAM

PowerBI Project

Business Insights -360

Domain/Function: AtliQ Hardware with actionable insights across Finance, Sales, Marketing, and Supply Chain.

𝐏𝐫𝐨𝐛𝐥𝐞𝐦: AtliQ Hardware faced data limitations in Excel, leading to losses in Latin America. 𝐎𝐛𝐣𝐞𝐜𝐭𝐢𝐯𝐞: Craft a comprehensive Power BI dashboard for informed decision-making in Finance, Sales, Marketing, and Supply Chain.

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Profit & Loss Statement

KPIs

Project Details

Business Related Terms
🔹Gross price
🔹Pre invoice deductions
🔹Net invoice sales
🔹Post invoice deductions
🔹Net sales
🔹Manufacturing cost
🔹Freight cost
🔹Cost of Goods Sold(COGS)
🔹Gross Margin
🔹Operational Expenses
🔹Net Profit
🔹YTD - Year to Date
🔹YTG - Year to Go
🔹FY - Financial Year/ Fiscal Year

Live Interactive Dashboard

PowerBI Project

Shield Insurence

Domain/Function: Insurence

As a junior data analyst at Atliq Technologies, my goal is to conduct a comprehensive analysis of Shield Insurance, focusing on key performance indicators and trends.

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Engagement

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KPIs

Sales

Project Details

📊 Insights:
📈 March Triumph: March witnessed a historic surge – 7081 customers and a remarkable revenue of 263.84M, showcasing over 60% growth.
🌆 Delhi NCR Dominance: The region emerged as the top revenue-generating city, solidifying Shield Insurance's position as a trusted choice.
👥 Age Group Dynamics: The 31-40 age group led in both customer count and revenue, while the 65+ group displayed unique preferences.
💼 Sales Mode Mastery:
Offline agent sales mode was pivotal, contributing 55% to overall revenue. However, online channels showed promising uptrends.

Live Interactive Dashboard

PowerBI Project

Hospitality Domain

Domain/Function: Hospitality Domain

📌 Building the data modelling, to make the relationship in between tablesand structure of the data for analysis. 📌 Written measures and calculated columns using DAX to calculate keymetrics and enhance analysis more .

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KPI

Comparing to other Metrics

Project Details

My Learnings:

📈 Key business Metrics in the hotel industry, such as RevPAR, occupancy %, ADR, and realization%
📊 Understanding of hospitality terminology like DSRN, DBRN, and DURN
🔄 Establishing relationships between different data sets.
📝 Creating measures and KPIs for granular revenue analysis across different segments.
🔍 Implemented user-friendly features like a one-click filter-clearing button and dynamic data exploration through slicers.
# HOSPITALITY-DOMAIN

Live Interactive Dashboard

Python Project

AtliQ Grand Hotel Analysis

Domain/Function: Hospitality Domain

🔍 Project Overview: AtliQ Grands, with their two-decade presence in the hospitality industry, were facing stiff competition and losing market share and revenue. To tackle this, their revenue management team sought actionable insights from their historical data to regain their competitive edge.

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Engagement

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Other Tools

Jupyter Notebook

pandas

canva

Total Percentage of Booking Platforms

Average occupancy rate in each of the room categories Average occupancy rate in each of the City

Project Details

Task at Hand:
As a data analyst, I was tasked with delving into their business data spanning from May 2022 to August 2022 to uncover insights and understand the trends affecting their business.

🛠️ Tools Used:
- Jupyter Notebook
- Python (Pandas)
- Canva

❇️ Process Followed:
1️⃣ Data Import and Exploration: Understanding the dataset's structure and content.
2️⃣ Data Cleaning: Ensuring data quality and consistency by removing negative values and outliers.
3️⃣ Data Transformation: Preparing the data for analysis through merging, aggregation, value formatting, and concatenation.
4️⃣ Analysis Insights: Extracting actionable insights to drive business decisions, including creating occupancy percentage columns and answering specific questions posed by the team.
Concepts and Functions Used:
- merge(), concat(), groupby()
- min(), max(), sum(), value_counts()
- unique(), to_datetime()
- shape, describe(), info()
- mean(), median(), std()
- isnull(), fillna(), apply()

PowerBI Project

EU_Superstore

Domain/Function: Superstore

Analysis of the EU Superstore Dataset (2015-2018) across 15 European countries. Focused on sales, revenue, shipping, and customer segmentation within key product categories. Deliverable: Power BI dashboard with strategic insights and growth recommendations.

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Sales: €1,106,000 Customers: 265 YOY Sales Growth: Yearly sales increase/decrease percentage YOY Net Growth: Yearly net profit change percentage

Product Category Breakdown: Technology:38% Office Supplies: 36.4% Furniture: 25.59%

Project Details

This project focuses on analyzing the EU Superstore dataset from 2015 to 2018 to uncover key business insights. The analysis covers 15 European countries across three regions (North, South, Central) and examines sales, revenue, shipping, customer segmentation, and product performance across Consumer, Corporate, and Home Office segments. The project aims to identify trends, highlight strengths, and pinpoint growth opportunities within product categories like Furniture, Office Supplies, and Technology. The final deliverable will be a Power BI dashboard, providing strategic recommendations to enhance profitability and operational efficiency.

Project video

Live Interactive Dashboard

Python Project

Cars24 Website -Web Scraping

Domain/Function: Automobile Industry

This project scrapes used car data from Cars24 using Python. It extracts car names, prices, kilometers driven, and locations from multiple pages. The data is organized into a Pandas Data Frame and saved as a CSV file for analysis, showcasing web scraping and data manipulation skills.

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Car Name: Make, model, variant. Price: Selling price with discounts. Kilometers Driven: Distance, fuel type, transmission. Location: Availability.

Developed a Power BI dashboard from Cars24 data extracted with Beautiful Soup. Cleaned and visualized data using Power Query for insights.

Project Details

This project involves scraping used car data from the Cars24 website using Python and analyzing it in Power BI. The script utilizes libraries like requests and BeautifulSoup to extract key information such as car names, prices, kilometers driven, and locations from multiple pages of car listings. This data is then organized into a Pandas DataFrame and exported as a CSV file for further analysis.

After the data extraction, Power Query is used to clean and transform the data, ensuring accuracy and consistency for analysis. The cleaned data is then loaded into Power BI, where an interactive dashboard is built to visualize insights, such as average car prices, mileage distribution, and regional availability. This project showcases skills in web scraping, data transformation, and visualization, providing comprehensive insights into the used car market.

Hello, I am

K GOWTAM

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About

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My

Business Insights -360

Shield Insurence

Hospitality Domain

AtliQ Grand Hotel Analysis

EU_Superstore

Cars24 Website -Web Scraping

My

Awards

Contact Me

Email

Github

Linkedin

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K GOWTAM

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