Skillset

 

🛠️ Tools & Technologies I Use

📊 Power BI

I use Power BI to create interactive dashboards with filters, KPIs, drill-down visuals, and slicers — helping stakeholders make data-driven decisions.

🐍 Python

I use Python for data preprocessing, analysis, and building machine learning models using Pandas, NumPy, Scikit-learn, and other key libraries.

🧠 Machine Learning

I’ve implemented algorithms like XGBoost, Linear Regression, and Prophet for tasks such as forecasting, classification, and recommendation systems.

🗃️ SQL

I write SQL queries to extract, manipulate, and analyze structured data from relational databases to answer real business questions.

🌐 Flask

I use Flask to build Python-powered web apps where I can integrate machine learning models and deploy tools for end users.

💻 Jupyter Notebook

Jupyter is my go-to environment for exploratory data analysis, prototyping ML models, and documenting insights with visual outputs.

📈 R Programming

I use R for statistical analysis and data visualization, especially when working with large datasets or when in academic/statistical settings.

🤖 UiPath

I automate repetitive workflows like web scraping and data collection using UiPath bots, making processes faster and error-free.

📁 Excel

I confidently use Excel for data entry, pivot tables, charts, and formula-based reporting when quick insights are needed.

📚 Data Visualization

I use tools / libraries like Power BI, Tableau , Seaborn, Matplotlib, and Plotly to present complex data in clear, beautiful, and impactful visuals.



💫 Soft Skills That Strengthen My Work

🔍 Attention to Detail

I focus on data accuracy, spotting trends, and catching small issues that could affect the bigger picture.

🧠 Critical Thinking

I enjoy breaking down complex problems and approaching them logically to find efficient, data-backed solutions.

🗣️ Communication

Whether it's explaining a dashboard or summarizing insights, I can translate data into clear, simple language for both technical and non-technical audiences.

🤝 Team Collaboration

I work well in cross-functional teams and believe that combining diverse perspectives leads to stronger outcomes.

⏳ Time Management

I’m organized and able to manage multiple projects or tasks by prioritizing deadlines and maintaining quality.

📚 Curiosity & Learning

I love learning new tools, exploring use cases, and staying updated with trends in data, AI, and technology.

🎯 Problem Solving

I approach data challenges with creativity and logic — whether it’s cleaning messy datasets or building smarter models.


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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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