Decode With Heena

  {Click on image for broad clear view} 🧠 Project Overview This Power BI dashboard was created to visually communicate various health tips and preventive measures . The goal was to present health-related information in a clean, interactive format — perfect for awareness campaigns or clinics. It’s a creative project that blends data visualization with public health education . 🎯 Purpose To build a dashboard that: Helps users quickly understand daily health practices Encourages a preventive lifestyle Presents data in a visually appealing way 🛠 Tools Used Power BI Desktop Excel Sheets with health info as data source Custom visuals, icons, filters Data cards, pie charts, bar graphs 📊 Dashboard Highlights 🧴 Hygiene Tips: Importance of hand washing, mask-wearing 🥦 Nutrition Basics: Healthy food groups & balanced meals 🧘‍♀️ Mental Wellness: Stress reduction, sleep habits 🧪 Preventive Health: Regular checkups and fitness tips ...

🔍 Project Background Savr is a fictional retail brand created for a mock business dashboard challenge. The goal was to visualize sales, customer trends, and performance metrics using Power BI — just like you'd do in a real business setting. 🎯 Business Objective To help the marketing and operations team at Savr: Track sales performance by region and product Identify best- and worst-performing categories Understand customer behavior and returns 🛠 Tools Used Power BI – for building dynamic dashboards Excel – for handling raw data DAX – to create calculated metrics Power Query – for data transformation 📈 Dashboard Features Here’s what the final dashboard includes: Sales Overview: Shows total sales, quantity sold, returns, and profit across all regions. Top Products & Categories: Highlights high-revenue items and low performers. Customer Segment Analysis: Breaks down metrics by customer types (e.g., Loyal, New). Dynamic Filter...

 🔍 Project Overview ForecastFlow is a simple and interactive time series forecasting app that I built using Flask  and Facebook Prophet . It allows users to upload their own CSV files and generate future predictions with just a few clicks after uploading data  —no coding more analysis based ! This was a self-driven project to explore time series forecasting and learn how to deploy ML models with a user-friendly interface. 🎯 What Problem It Solves Forecasting sales, demand, stock levels, or customer trends is critical in business. But many non-technical users struggle with coding-heavy tools. ForecastFlow simplifies that process — anyone can generate insights from their own data. 🛠️ Tools and Technologies Used Python  Pandas & NumPy – Data preprocessing Facebook Prophet – Time series modeling Flask  – App deployment and UI Matplotlib & Plotly – Visualizing forecast results ⚙️ Features of ForecastFlow 📁 Upload CSV files...

🛍️ Project Overview During my final year, I created a multi-platform e-commerce product recommendation system called ShopeE-Mate . This tool helps users find the best deals across Amazon, Flipkart, and JioMart — all in one place. 💡 The Problem Users waste a lot of time switching between apps and websites just to find the best price for the same product. I wanted to solve that using automation and ML/AI. 🛠️ Tools and Technologies Used 🔎 Web Scraping: RPA UiPath, Selenium 📊 Data Processing: Pandas, NumPy , Google colab 📦 Machine Learning: Scikit-learn, Tokenization(NLP) 🧠 Recommendation Models: Content-based, Collaborative, Hybrid  🌐 Web Framework: Flask  🛢 Database system : MySql XAMPP server ⚙️ How It Works Step 1: Product data is scraped in real-time using automation tools Step 2: Data is cleaned and structured using Pandas Step 3: ML models rank the products based on user preference, rating, and discount Step 4: Flask displays...

  {Click on image for broad clear view} 🔍 Project Overview This project focuses on predicting sales for Big Mart stores across India using historical data. I built a machine learning model that analyzes factors like product price, store type, location, and promotions to forecast future sales with greater accuracy. 🎯 Business Problem Big Mart needed a reliable way to forecast sales to: Avoid overstocking and stockouts Optimize inventory and staffing Plan promotions more effectively Using machine learning helps make smarter, data-driven decisions. 🛠 Tools & Techniques Used Python + Google Colab Libraries: Pandas, Seaborn, Matplotlib, XGBoost, Scikit-learn Model Used: XGBoost Regressor Data Source: Kaggle – Big Mart Sales Dataset ⚙️ Key Steps I Followed Data cleaning and handling missing values Feature encoding (Label Encoding for categories) Exploratory Data Analysis using Seaborn Model training with XGBoost Evaluation usi...