Projects

 

1. ShopeE-Mate – {Multi-Platform E-commerce Recommendation /Comparison system}. 

  • Created a multi-platform E-commerce product recommendation system named ShopeE-Mate that suggests the best deals from Amazon, Flipkart, and JioMart in one place. 
  • Used UiPath to automate web scraping of product data (name, price, ratings, discount) from all three platforms.
  •  Applied machine learning for product recommendations, automated data collection, and built a user-friendly web app interface.
  • Tech Used: Python, Selenium, UiPath, Scikit-learn, Flask, MySql, Google colab, Machine Learning.

🔗To know more[ShopeE-Mate Blog ]  GitHub Link: [ShopeE-Mate Project]

2. BigMart Sales Prediction.

  • Predicted sales using regression models after cleaning and analyzing BigMart store data.
  • Performed feature engineering and EDA.
  • Tech Used: Python, Pandas, Scikit-learn, Regression Models, EDA
🔗To know more[Big-Mart Sale pred blog]

 3. Health Tips Dashboard (Data Analysis).

  • Built a dynamic dashboard for marketing and sales analytics. 
  • This dashboard contains slicers, KPIs, region-wise heatmaps, and custom visuals for HealthTips. 
  • Tech Used : Power BI, DAX Power Query, KPI Cards, Data Cleaning, Interactive Visuals, Slicers & Filters, Forecasting ,Interactive Dashboard Design, Adv. Excel, GitHub. 

🔗To know more[HealthTip Power Bi dashboard]  GitHub Link: [Power BI Health Tips]

4. Savr Dashboard – Smart Spending Overview For 2023-2024 yr (Data Analysis).

  • This Power BI dashboard provides a comprehensive visualization of user spending habits using Savr, a smart budgeting and expense management application. It covers all key financial.  
  • Tech Used : Power BI Dashboard Design, DAX (Data Analysis Expressions), Data Modeling & Relationships, KPI Tracking & Goal Analysis, User and Category-wise Insights, Time Series Visualization, Slicers & Filters for Interactivity, Pie/Bar/Donut Chart Customization, Cross-visual Filtering, Financial Data Analysis

🔗To know more [Savr Finance Dasboard blog]   Github Link: [Savr Power BI Dashboard]


5. ForecastFlow – Sales Forecasting with Time Series (Machine learning ).

  • Built forecasting models to predict future sales using ARIMA, Prophet, and LSTM.
  • ForecastFlow, a simple and interactive app that allows users to upload time-series datasets (like sales, temperature, or traffic) and get future predictions using machine learning — all through a clean, no-code interface built with Flask.
  • Tech Used: Python, Pycharm, Flask , ARIMA, Prophet, LSTM.
🔗To know more [Forecast Flow blog]   GitHub Link : [ForecastFlow Project]












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How i built ShopeE-Mate

How I Built ShopeE-Mate (An E-commerce Product Recommendation /Comparison Using Python and Machine Learning)

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🛍️ 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...
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Sales Forecasting for Big Mart Using XGBoost Regression — A Machine Learning Approach

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  {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...
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Welcome to Decode With Heena

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I'm Heena Shaikh , a Data Science graduate with a passion for turning raw data into meaningful insights. Through this blog, I share my real-world projects, virtual internship experiences, and tutorials in data analytics, machine learning, and business reporting. Whether you're a recruiter, a fellow learner, or someone just curious about data — I'm glad you're here! 👉 Feel free to explore my About Me and Get my Resume , check out my Projects , check out my Blog Posts , or Contact Me anytime.
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