Customer Churn Analysis with Python and Tableau

Excited to share my latest Data Analytics Project: Customer Shopping Behavior Analysis 📊 In this project, I worked on: ✔️ Data cleaning & EDA using Python ✔️ SQL queries for business insights ✔️ Interactive Power BI dashboard ✔️ End-to-end analytics workflow Key insights: 🔹 Identified high-value customer segments 🔹 Discovered top-performing product categories 🔹 Analyzed purchasing trends This project helped me strengthen my skills in Python, SQL, and Power BI. Looking forward to feedback and opportunities in Data Analytics 🚀 #DataAnalytics #Python #SQL #PowerBI #DataScience #Projects #OpenToWork@

🚀 My first ever dashboard build on Tableu I’m excited to share my first Sales Analytics Dashboard built using Python and Tableau. This project helped me move from just learning tools to actually applying them to analyze real business data. 📊 What the dashboard shows: * Monthly sales trends over time * Sales performance across different countries * Product category insights (Electronics, Sports, Beauty, Clothing, Faniurture, Groceries.) * Customer segmentation (Consumer, Corporate, Home Office) 💡 Key insights: * Sales are relatively stable with fluctuations that may indicate seasonal trends * Certain countries consistently outperform others, highlighting key markets * Electronics and Sports are among the top-performing categories * The Home Office segment generates the highest sales 🛠 Tools used: * Python → Data cleaning & preparation * Tableau → Data visualization & dashboard design 🎯 What I learned: * Turning raw data into meaningful insights * Building dashboards that support decision-making * Communicating data clearly through visualization This is just the beginning—I’m currently working on more advanced projects more exciting projects🥹 #DataAnalytics #Tableau #SQL #Python #OpenToWork #DataScience #Analytics #Dashboard

From raw customer data to meaningful business insights! I’m excited to share my latest Customer Behavior Data Analytics Project, where I followed the complete analytics workflow from data cleaning to dashboard storytelling. Tools Used: Python(Pandas) | SQL (MySQL) | Power BI Here’s what I worked on: - Loaded and explored the dataset using Python; - Performed EDA and handled missing values; - Used SQL / MySQL to answer real business questions; - Designed an interactive Power BI dashboard for decision-making; What made this project exciting was not just creating visuals, but understanding the story behind customer purchases: - Which product categories perform best; - Which customers spend the most; - How buying behavior changes over time; - Trends that can improve marketing decisions; This project improved my practical skills in Python, SQL, and Power BI, while also helping me think more like a business-focused data analyst. Thanks to Amlan Mohanty for providing this amazing Data Analytics project Github Link:https://lnkd.in/db3K6hGy I’d love to hear your feedback and suggestions! #DataAnalytics #Python #SQL #MySQL #PowerBI #EDA #DashboardDesign #BusinessIntelligence #CustomerAnalytics #DataAnalyst #OpenToWork

Which customers generates the most revenue? Which customers are at risk of leaving? To answer this questions, I built an end-to-end Customer Analytics Project using Python, SQL and Tableau.🚀 In this project, I performed: • Data Cleaning and EDA • Data Analysis using MySQL • RFM Segmentation and Customer Lifetime Value (CLV) • Customer Retention Analysis • Built an interactive dashboard in Tableau This project helped me understand how data analysis can be used to identify high-value customers, retention rate, customer behavior and improve retention using data. You can check out the full project here: 👇 Github Link : https://lnkd.in/d7Q3kk96 #DataAnalytics #SQL #Python #Tableau #CustomerAnalytics #OpenToWork #PortfolioProject #Learning

🚀 Data Analytics Project: Customer Behaviour Analysis (Python | SQL | Power BI) I recently built an end-to-end data analytics project to understand how customer behaviour impacts business revenue and decision-making. 🔍 Key Insights: 📊 Young adults contribute the highest share of revenue, making them a key target segment. 🚚 Customers using express shipping tend to have higher average spending. 💳 A large portion of loyal customers are not subscribed—highlighting a strong opportunity for conversion. 🛍️ Certain product categories rely heavily on discounts to drive sales volume. 📈 Customer purchasing patterns vary significantly across categories and demographics. 💡 Key Business Recommendations: • Target high-value segments (young adults) with personalized marketing • Promote subscription plans to loyal customers to improve retention   • Optimise shipping strategies to maximize revenue • Reduce dependency on discounts by improving product positioning  ⚙️ What I did: ✔ Cleaned and transformed raw data using Python (Pandas)   ✔ Performed SQL analysis in PostgreSQL to extract business insights   ✔ Built an interactive Power BI dashboard with dynamic filters and KPIs  🔗 GitHub Project: https://lnkd.in/gQ276Tp4 This project helped me strengthen my skills in data analysis, SQL, and dashboarding. #DataAnalytics #DataScience #Python #SQL #PostgreSQL #PowerBI #BusinessAnalytics #DataVisualization #DataAnalyst #AnalyticsProject #Dashboard #KPI #Insights #EDA #FeatureEngineering #DataCleaning #DataPreprocessing #BusinessIntelligence #DataDriven #Tech #Learning #PortfolioProject #EndToEndProject #DataProjects #AnalyticsLife

I spent today building a end-to-end data analytics project - and I found something quite interesting. The Superstore dataset looks like a healthy business on the surface. $2.3M in sales. Growing every year. But when I dug into the numbers: 📉 Orders with discounts above 20% are collectively LOSING $135K 🪑 The Tables sub-category lost $17,725 despite $206K in sales 🗺️ Texas lost $25,729 — despite being a high-sales state 👥 111 customers haven't purchased in over 500 days The data tells a completely different story than the top-line numbers. Here's what I built to find it: → Full exploratory data analysis in Python (pandas, matplotlib, seaborn) → K-Means clustering to segment 793 customers by behavior → Interactive Tableau dashboard to visualize every finding 🔗 Live dashboard: https://lnkd.in/gHiezwBR 💻 GitHub: https://lnkd.in/gUAraeEj #DataAnalytics #Python #Tableau #MachineLearning #DataScience #OpenToWork

Most beginner data analysts focus on tools. SQL. Tableau. Python. But businesses don’t care about tools. They care about answers that make or save money. While working on a dataset recently, I realized something interesting: 👉 The biggest problem isn’t always what everyone tracks. For example: • Companies obsess over customer churn • But often ignore failed payments, drop-offs, or hidden leaks And those can quietly cost more than churn itself. That’s when it clicked for me: Data analysis isn’t about dashboards. It’s about finding what’s being missed. I’m now focusing my projects on: • Revenue leaks • Customer behavior • Decision-making insights Not just visuals. If you’re learning data analytics, try this shift: ➡️ Don’t just ask “what can I show?” ➡️ Ask “what is this data hiding?” #DataAnalytics #SQL #Tableau #BusinessIntelligence #OpenToWork

📊 Customer Behavior Dashboard | Power BI + Python + SQL Project Excited to share my latest Customer Behavior Dashboard, where I combined Python, SQL, and Power BI to analyze customer purchasing behavior and generate actionable business insights. This project demonstrates how raw data can be cleaned, analyzed, and transformed into interactive dashboards to support data-driven decision-making. 🔍 Key Insights from the Dashboard: • 👥 3.9K Customers analyzed • 💰 $59.76 Average Purchase Amount • ⭐ 3.75 Average Review Rating • 🛍️ Clothing category generated the highest revenue • 📈 Young Adults contributed the highest sales among age groups • 📦 Interactive filters for Subscription Status, Gender, Category, and Shipping Type 🛠️ Tools & Technologies Used: • 🐍 Python (Data Cleaning, EDA using Pandas & Matplotlib) • 🗄️ SQL (Data querying & business insights extraction) • 📊 Power BI (Dashboard development & visualization) • ⚡ DAX (KPIs & calculated measures) • 🔄 Power Query (Data transformation) 📌 Key Skills Demonstrated: • Data Cleaning & Preprocessing • SQL-based Data Analysis • Exploratory Data Analysis (EDA) • Dashboard Design & Data Storytelling • Business Insights Generation 🔗 GitHub Repository: https://lnkd.in/gNaTHYba I’m actively building end-to-end data analytics projects to strengthen my portfolio. #PowerBI #Python #SQL #DataAnalytics #DataAnalyst #BusinesAnalysis #DataVisualization #learningjourney #PowerQuery #AnalyticsPortfolio #DataScience

🚀 Sales Forecasting & Business Performance Dashboard (Python + Tableau) we recently built an end-to-end data analytics project focused on analyzing historical sales and predicting future trends to support better business decision-making. (Pradyuamn Borade , Pankaj Mane , Yash Hire ) 🔍 What I did: • Cleaned and processed raw sales data using Python (Pandas) • Performed time-series forecasting using ARIMA (statsmodels) • Generated a 6-month sales forecast based on historical patterns • Created a structured dataset combining actual + predicted values • Built an interactive Tableau dashboard with KPIs and filters 📊 Dashboard Highlights: • Total Sales KPI & Profit Margin analysis • Monthly Sales Trend with Forecast visualization • Region-wise and Category-wise performance breakdown • Interactive filters (Region & Category) for dynamic analysis 💡 Key Insights: • Sales show a consistent upward trend with seasonal fluctuations • Peak performance observed around late 2017 • Forecast suggests stable growth (~72K–75K monthly) • Technology category contributes the highest revenue • Business can optimize inventory planning based on demand trends 🧠 Tech Stack: Python (Pandas, Statsmodels) | Tableau | Excel 📌 Key Learning: Bridging Python-based forecasting with Tableau visualization helped me understand how real-world data pipelines support business insights and decision-making. #DataAnalytics #Tableau #Python #Forecasting #BusinessIntelligence #Projects #LearningJourney

I am currently learning Data Analytics and one thing I had to figure out on my own was : where do I even begin? So if you are just starting out like me, here is the roadmap I am following in 2026. ✔ Step 1 - Excel: The best starting point. Formulas, Pivot Tables and data cleaning. Builds your foundation before anything else. ✔ Step 2 - SQL: Learning to pull and query data from databases. Every analyst role asks for this. ✔ Step 3 - Data Visualisation: Power BI or Tableau. Because analysing data is only half the job; presenting it clearly is the other half. ✔ Step 4 - Python (Basics): Pandas and NumPy for handling data. You don't need to be a developer, just comfortable with the basics. ✔ Step 5 - Statistics: Mean, median, correlation, distributions. Tools make more sense once you understand the numbers behind them. ✔ Step 6 - Real Projects: Working on actual datasets to build a portfolio. This is what makes your profile stand out. ✔ Step 7 - Communication: Being able to explain your findings to someone non-technical. Often the most underrated skill. Still on this journey myself, but sharing it as I go. 🚀 If you are on the same path, let's connect and grow together! #DataAnalytics #DataAnalyst #LearningInPublic #CareerGrowth #SQL #Excel #PowerBI #Python #2026Goals