Sanidhya Shukla

Walmart Business Case Study

September 1, 2025 – October 1, 2025

🚀 Walmart Data Analytics Case Study | Confidence Interval & Central Limit Theorem I recently completed a comprehensive data analytics project on Walmart, exploring how statistical concepts like Confidence Intervals and the Central Limit Theorem (CLT) can help uncover key retail insights and guide better business decisions. 🏪 Project Overview Walmart operates on razor-thin margins, making analytics essential for optimizing sales and operations. This study aimed to analyze weekly sales data across multiple stores, identify patterns, and apply statistical inference to estimate future trends. 🎯 Objectives Analyze sales distribution and regional performance Apply Confidence Intervals to estimate sales parameters Demonstrate CLT through simulations for reliable sampling Derive business-driven insights from statistical findings 🧮 Tech Stack & Methods Used Python (Pandas, NumPy, Matplotlib, Seaborn, SciPy) for data cleaning, visualization, and inferential statistics. Conducted Exploratory Data Analysis (EDA) — detecting outliers, seasonal trends, and correlations. Simulated Central Limit Theorem to validate normality in sample means and estimated 95% confidence intervals for weekly sales forecasting. 🔍 Key Insights Seasonal peaks in weekly sales during holidays highlight strong cyclic demand. Certain regions consistently outperform others, showing location-driven sales potential. Confidence Intervals provided reliable sales range predictions, helping in data-backed forecasting. 💡 Business Impact This analysis empowers Walmart to improve inventory planning, store-level operations, and forecast accuracy by leveraging statistics beyond averages — turning data into actionable insights. 📚 Tech Stack: Python | Pandas | NumPy | Seaborn | SciPy | Jupyter Notebook 💬 Open to discussions and feedback from fellow data enthusiasts! #DataAnalytics #WalmartCaseStudy #Python #Statistics #EDA #ConfidenceInterval #CLT #DataScience #BusinessAnalytics #RetailInsights

AEROFIT TRADEMILL ANALYSIS

August 1, 2025 – September 1, 2025

🚀 Aerofit Treadmill Case Study: Leveraging Python & Statistics for Business Insights under Akash Rajpuria, Ritwik Malla as my instructors I recently worked on an Aerofit Treadmill Sales Analysis Project, where the objective was to help the company understand customer buying patterns and optimize marketing strategies using EDA (Exploratory Data Analysis) + Statistics in Python. 🏬 Business Context Aerofit manufactures three types of treadmills: KP281 – Budget-friendly, basic functionality KP481 – Mid-range, balanced features KP781 – Premium, advanced features The company wanted to answer: 1️⃣ Which customer segments prefer which model? 2️⃣ What demographic & behavioral factors influence treadmill purchases? 3️⃣ How can statistical insights guide marketing campaigns? 🔎 EDA & Statistical Insights Using Pandas, NumPy, Matplotlib, Seaborn, and SciPy, I performed data cleaning, visualization, and hypothesis testing. ✔️ Customer Demographics Majority of KP281 buyers → Young, budget-conscious customers. KP481 → Balanced preference among middle-aged customers. KP781 → Higher income groups, professionals, and fitness enthusiasts. ✔️ Income & Product Choice Clear upward trend: Higher-income groups consistently prefer KP781. Income distribution analysis confirmed strong positive correlation with treadmill price. ✔️ Age Distribution KP281 dominated among 20–30 years. KP481 had popularity in 30–45 years. KP781 saw strong adoption in 40+ age groups with higher spending power. ✔️ ANOVA & Chi-Square Tests Significant relationship found between Income Level & Product Preference. Age also showed statistically significant influence on treadmill model choice. ⚙️ hashtag#Approach & hashtag#Methodology 1️⃣ Data Preprocessing – Cleaned missing values, structured categorical variables. 2️⃣ Exploratory Data Analysis (EDA) – Used histograms, bar charts, and boxplots to study sales trends. 3️⃣ Statistical Testing – ANOVA & Chi-Square to validate hypotheses.

Blinkit Sales Analysis

August 1, 2025 – August 1, 2025

🛒 Blinkit Sales Analysis – EDA Project This project presents a comprehensive Exploratory Data Analysis (EDA) on Blinkit's sales dataset using Python. It includes data cleaning, KPI extraction, visualization, and actionable business insights aimed at optimizing sales and outlet performance. 📌 Objectives Visualize sales performance across different categories. Derive insights and strategic recommendations for improving sales. Blinkit_Sales_Analysis_Report.docx – Detailed report with EDA explanation, charts, and insights. blinkit_data.csv – Original dataset used for analysis. BlinkintDA.pdf – Annotated notebook/PDF with code and outputs. 🔸 Total Sales: $1,201,681 🔸 Average Sales: $141 🔸 Number of Items Sold: 8,523 🔸 Average Customer Rating: 4.0 📈 Visualizations Created Pie Chart: Sales by Fat Content Bar Chart: Sales by Item Type Stacked Column Chart: Sales by Fat Content across Outlet Locations Line Plot: Sales by Outlet Establishment Year Donut Chart: Sales by Outlet Size Funnel Chart: Sales by Outlet 📌 Key Insights Low Fat items perform better in sales compared to Regular ones. Fruits and Vegetables dominate in sales volume. Tier 3 locations have a strong customer base. Medium-sized outlets yield higher sales. Newly established outlets (post-2015) are more profitable. 💡 Recommendations Invest more in health-conscious product lines. Focus on expanding in Tier 3 regions with medium-sized outlets. Promote and leverage positive customer ratings to drive engagement. Optimize inventory for top-selling categories like Fruits, Household, and Health. 🚀 Future Improvements Integration with time-series fore Product recommendation models 📌 This project is part of my journey into Data Analytics. Feel free to explore the report, give feedback, or connect on https://lnkd.in/ 🏷️ thanks to instructors under guidance of @Akash Rajpuria,@Amit Singh i made this here is collab link https://lnkd.in/dGEpvWtk #EDA #Python #BlinkitAnalysis #SalesAnalytics #DataAnalytics

Netflix Business Case

August 1, 2025 – August 1, 2025

🚀 Netflix Case Study: Deep Exploratory Data Analysis (EDA) & Strategic Insights Under the valuable guidance of @Akash Rajpuriya and Aniruddha (Ani) Mukherjee ,Amit Singh I recently completed a comprehensive Exploratory Data Analysis (EDA) on Netflix’s dataset. This project not only strengthened my data storytelling skills but also highlighted how data-driven insights can guide business strategies in the highly competitive OTT space. 📊 Key Objectives Understand Netflix’s content distribution across years, countries, and genres Analyze the evolution of movies vs TV shows Explore ratings patterns and audience preferences Provide business recommendations to strengthen Netflix’s global positioning 🔍 Deep EDA Insights 1️⃣ Content Growth Over Time Netflix saw a sharp rise in content post-2015, aligning with its aggressive international expansion. The peak was around 2018–2020, after which growth plateaued—indicating a shift from volume to quality-driven strategy. 2️⃣ Movies vs TV Shows Movies account for nearly 70% of total content, while TV shows are growing steadily. This indicates an opportunity to invest further in episodic content to increase binge-watching retention. 3️⃣ Geographic Distribution 🌍 The U.S. dominates content production, but countries like India, South Korea, and the U.K. are rapidly contributing. Local-language originals are proving to be key growth drivers in regional markets. 4️⃣ Genre Trends Drama, Comedy, and Documentaries top the charts globally. Thrillers and Crime content have shown consistent demand—suggesting viewers seek engagement-driven narratives. 5️⃣ Ratings Analysis A significant portion of content is rated TV-MA (Mature Audience), reflecting Netflix’s appeal to adult viewers. However, the family/children segment is underrepresented, signaling untapped market potential. 💡 Business Insights & Recommendations ✅ Invest in Regional Originals Expand aggressively in India, South Korea, Africa, and Latin America by producing culturally

Fit Bit Analysis

July 1, 2025 – July 1, 2025

📊 Fitbit Data Analysis with NumPy – My End-to-End Exploration! 🧠💪 Over the past few days, I dove deep into a Fitbit dataset using Python and NumPy, focusing on user behavior, activity trends, and health metrics. Here’s what I uncovered using pure NumPy—no Pandas, no external libraries—just clean vectorized logic! 🔍 Key Analyses Performed: ✅ Data Preprocessing: step_count = data[:, 1].astype(int) mood = data[:, 2] calories = data[:, 3].astype(int) sleep = data[:, 4].astype(int) activity = data[:, 5] ✅ Insights Extracted: 📌 1. Active Days Average Step Count np.mean(step_count[activity == 'Active']) 📌 2. Days with >5000 Steps & >150 Calories np.sum((step_count > 5000) & (calories > 150)) 📌 3. % of Days in 'Sad' Mood sad_percentage = np.sum(mood == 'Sad') / len(mood) * 100 📌 4. Max Steps on <6 Hours Sleep np.max(step_count[sleep < 6]) 📌 5. Avg Calories per Hour of Sleep on Inactive Days inactive_mask = activity == 'Inactive' avg_cph = np.mean(calories[inactive_mask] / sleep[inactive_mask]) 📌 6. Mood with Highest Avg Step Count unique_moods = np.unique(mood) mood_avg = {m: np.mean(step_count[mood == m]) for m in unique_moods} max_mood = max(mood_avg, key=mood_avg.get) 📌 7. Correlation between Steps & Calories np.corrcoef(step_count, calories)[0, 1] 📌 8. Longest 'Inactive' Streak with Below-Average Calories mean_cal = np.mean(calories) condition = (activity == 'Inactive') & (calories < mean_cal) # loop to calculate max streak... 📌 9. Highest Calories/Sleep Ratio Day max_ratio_index = np.argmax(calories / sleep) date[max_ratio_index] 📌 10. 3-Day Max Calories Burned Window window_sum = [sum(calories[i:i+3]) for i in range(len(calories)-2)] start_date = date[np.argmax(window_sum)] 📈 Key Findings: Highest activity mood: 🥇 Happy Most frequent sleep hour: 🛏️ 5 hours Days with high output but low rest → need for balance! Weak correlation between sleep and calories (r ≈ 0.02)

A Coffee Chain Case Study ☕

May 1, 2025 – May 1, 2025

🚀 Uncovering the Why Behind the Losses at CCD – A Coffee Chain Case Study ☕ Over the past few days, I worked on a business case study centered on CCD (Coffee Chain), which had been struggling with financial losses. Leveraging Excel, I dove deep into their operational and financial data to identify key insights and potential root causes. 🔍 What I did: Analyzed sales, profit margins, and cost of goods sold across various stores and product lines. Investigated regional performance trends and marketing spend efficiency. Compared actual vs. target metrics for sales, COGS, and profit. 📊 Tools Used: Microsoft Excel for data cleaning, pivot analysis, and visualization. 📈 Key Insights: Several stores in larger markets underperformed despite higher marketing expenses. Certain product categories had high COGS and low profit margins—opportunities for optimization. Clear gaps between actual and target values highlighted areas needing strategic focus. #DataAnalysis #Excel #CaseStudy #RetailAnalytics #BusinessStrategy #CoffeeChain #ScalerProject #DataDriven #Profitability 💡 Key Analytical Questions & Insights 1. Which stores are underperforming? Compare actual profit vs. target profit by store and region. Identify stores consistently falling below target, especially in large markets (which should ideally perform better). 📌 Insight: Some large-market stores (e.g., in urban/high-traffic areas) are posting below-average profits despite higher marketing spend. 2. Are certain product lines or types dragging overall profitability? Analyze COGS vs. Sales and Profit margins by Product Type and Product. Evaluate whether expensive-to-produce products are yielding poor returns. 📌 Insight: A few high-COGS products with low margins are contributing disproportionately to overall losses. 3. Is the marketing spend effective? Plot Marketing Expenses vs. Sales/Profit per store and product type. Check for positive correlation or diminishing returns.

Data Analysis for Leading Brazilian Retailer

April 1, 2025 – April 1, 2025

Data Analysis for Leading Brazilian Retailer Description: Conducted comprehensive data analysis for a top Americas retailer, with a focus on their Brazil operations. Leveraged SQL to extract valuable insights, optimizing business strategies. Achievements: Explored dataset, revealing data types, customer distribution, and trends. Identified e-commerce trends, including seasonal peaks and buying habits. Analyzed orders, visualizing regional distribution and economic impact. Investigated sales dynamics, delivery times, and payment methods. Generated insights for strategic improvements. Impact: Data-driven decisions enhanced marketing, supply chain, and customer service, elevating satisfaction and efficiency for improved business performance. Mast a post from above text paragraph in detailed and analytical way for posting in linkedin Sure! Here’s a polished and detailed LinkedIn post you can use to showcase your project in a professional and analytical tone: Data-Driven Success in Brazilian Retail | Project Spotlight Thrilled to share a recent data analysis project I led for one of the leading retailers in the Americas, focusing specifically on their operations in Brazil. Scope & Tools: Using SQL as the primary tool, I conducted a deep dive into their transactional and operational data to uncover key insights that would shape smarter business strategies. Key Insights Uncovered: Data Exploration: Began with a thorough exploration of the dataset—mapping data types, customer distribution, and key trends across time and regions. E-Commerce Behavior: Identified significant seasonal buying patterns and customer behavior trends, helping the team plan better promotional and inventory strategies. Order Analysis: Visualized regional order distribution and correlated it with economic indicators to understand market penetration and potential. Sales Dynamics: Analyzed sales volumes, delivery times, and payment preferences, offering clear direction

Data Analysis for Leading Brazilian Retailer

April 1, 2025 – April 1, 2025

Data Analysis for Leading Brazilian Retailer Description: Conducted comprehensive data analysis for a top Americas retailer, with a focus on their Brazil operations. Leveraged SQL to extract valuable insights, optimizing business strategies. Achievements: Explored dataset, revealing data types, customer distribution, and trends. Identified e-commerce trends, including seasonal peaks and buying habits. Analyzed orders, visualizing regional distribution and economic impact. Investigated sales dynamics, delivery times, and payment methods. Generated insights for strategic improvements. Impact: Data-driven decisions enhanced marketing, supply chain, and customer service, elevating satisfaction and efficiency for improved business performance. Mast a post from above text paragraph in detailed and analytical way for posting in linkedin Sure! Here’s a polished and detailed LinkedIn post you can use to showcase your project in a professional and analytical tone: Data-Driven Success in Brazilian Retail | Project Spotlight Thrilled to share a recent data analysis project I led for one of the leading retailers in the Americas, focusing specifically on their operations in Brazil. Scope & Tools: Using SQL as the primary tool, I conducted a deep dive into their transactional and operational data to uncover key insights that would shape smarter business strategies. Key Insights Uncovered: Data Exploration: Began with a thorough exploration of the dataset—mapping data types, customer distribution, and key trends across time and regions. E-Commerce Behavior: Identified significant seasonal buying patterns and customer behavior trends, helping the team plan better promotional and inventory strategies. Order Analysis: Visualized regional order distribution and correlated it with economic indicators to understand market penetration and potential. Sales Dynamics: Analyzed sales volumes, delivery times, and payment preferences, offering clear direction

Target SQL Business Intelligence Case Study

March 1, 2025 – April 1, 2025

Tools Used: SQL (BigQuery), Excel, Data Visualization Tools (e.g., Tableau), Business Intelligence Frameworks Analyzed large-scale Brazilian e-commerce data to uncover customer behavior, regional trends, delivery logistics, payment patterns, and market opportunities. 💡 Key Responsibilities & Achievements: Data Engineering: Cleaned and explored multi-table datasets involving customers, orders, payments, order items, and delivery timelines. Advanced SQL Analysis: Extracted KPIs such as monthly orders, delivery times, payment trends. Identified top-performing states and those with fastest deliveries using time and cost metrics. Trend & Seasonality Modeling: Discovered month-over-month and seasonal demand patterns to optimize inventory and marketing efforts. Logistics Optimization: Measured delivery time against estimates; suggested warehousing strategies for top-performing regions. Economic Impact Analysis: Tracked YoY payment value growth (18% from 2017 to 2018) and calculated average freight and order values per region. Customer Segmentation: Mapped customer distribution by city/state, revealing areas for expansion and market penetration. 📊 Detailed Case Study Breakdown for Portfolio or Interview Presentation 1. Data Exploration & Cleaning Imported all tables including customers, orders, order_items, payments, and performed schema validation. Verified data types, nulls, and structure using BigQuery’s INFORMATION_SCHEMA. 2. Customer & Geographic Insights Unique cities/states with orders: Found customer reach across Brazil—valuable for assessing geographic penetration. City & State Counts: Aided in understanding market concentration vs. potential for regional expansion. 3. Temporal & Seasonal Trends Monthly & Yearly Order Volume: Extracted order trends (2016–2018), showing steady growth and peak seasons (e.g., holidays). Time-of-Day Purchase Behavior: Determined peak activity during afternoon and night, suggesting optimal ad timing.

big Mart data sales prediction

April 1, 2023 – May 1, 2023

using diffrent models bigmart will try to understand the properties of products and stores which play a key role on increasing sales

STFE

September 1, 2021 – July 1, 2022

SMALL TURNINE FAN ENGINE IN THIS IM WORKING WITH THE SAME CONCEPT THAT IS BEING USED AS THAT IF ENGINE FUEL CONTROL SYSTEM (EFCS) STEPPER MOTOR FUEL CONTROLLING ENCODER PANEL BALDOR MOTOR CONTROLLING

TD EFCS

July 1, 2021 – July 1, 2022

IN this project is all about show the ST DFC project is all about the engine fuel control system and also in this project it is basically it has two parts one is that there is centrifugal pump and another it is there is gear pump with the help of that we monitor different different think liKE MPOP BPOP MBP UPSTRERAM,DOWNSTREAM,DELTAP,TESTING,MAKING REPORTS AND GIVING TO SCIENTISTS OF DRDO GTRE project is going on CV Raman Nagar bengaluru IN FRONT OG BAGHMANE TECH PARK

Key Strengths

Cultural & Operational Fit

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Cultural & Operational Fit