Welcome to Decode With Heena

-

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.

Hi, I’m Heena Shaikh — a Data enthusiast with a passion for analytics, storytelling, and machine learning. I enjoy exploring data to uncover patterns, solve problems, and make meaningful decisions. With skills in Python, MySQL, R, Data Visualization , Machine Learning and automation tools, I'm constantly learning and building towards a career in data science.

Comments

Post a Comment

How i built ShopeE-Mate

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

-
Image
🛍️ 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...
Read more

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

-
Image
  {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...
Read more