constcoder={name:'Komal',skills:['html', 'css', 'Bootstrap', 'React', 'postgrace', 'MySql', 'MongoDB', 'Docker', 'AWS'],hardWorker:true,quickLearner:true,problemSolver:true,hireable:function() {return(this.hardWorker&&this.problemSolver&&this.skills.length>=5);};};ABOUT ME
Who I am?
My name is Komal SAID. Data Analyst with expertise in building machine learning solutions and interactive dashboards. Leveraged Python (Pandas, NumPy, Scikit-learn) to develop predictive models for e-commerce analytics, achieving 95% model accuracy. Designed comprehensive Power BI dashboards featuring drill-down capabilities and real-time data refresh. Proficient in SQL for complex data extraction and manipulation, with proven ability to translate technical findings into business recommendations.
Skills
PROJECTS
Sales Forecasting Model
constproject={name:'Sales Forecasting Model',tools: ['Python', 'Pandas', 'NumPy', 'Matplotlib', 'Scikit-learn', 'Statsmodels', 'Prophet', 'Power BI', 'Flask/Django REST', 'Jupyter Notebook],myRole:Data Analyst / ML Developer,Description: I built a machine learning model to forecast next week's product sales for a retail store (similar to D-Mart). The dataset contained large historical sales records including product, category, store, and transaction details. I performed data cleaning, feature engineering, and exploratory data analysis using Python and Pandas. Then, I trained models such as ARIMA, Prophet, and Random Forest Regressor, comparing their accuracy. I also developed RESTful APIs to serve the predictions and integrated them with a Power BI dynamic dashboard, enabling real-time visualization of sales trends and inventory insights. The final solution helped predict sales trends, optimize stock management, and reduce overstock/understock issues.,};Nepika-AI Skin Care App
constproject={name:'Nepika-AI Skin Care App',tools: ['Python', 'TensorFlow', 'OpenCV', 'Scikit-learn', 'Pandas', 'NumPy', 'Matplotlib', 'Node.js', 'Express', 'MongoDB', 'JWT],myRole:Backend Developer / AI Engineer,Description: I developed the complete backend and AI model for Nepika-AI, a skin care analysis and recommendation app. The system detects and analyzes skin conditions using computer vision and deep learning models trained on dermatological datasets. Based on the detected issues (such as acne, dark spots, or dryness), the app provides personalized product recommendations. The backend is built with Node.js and Express, with APIs connecting the AI model to the frontend. Data preprocessing, model training, and evaluation were done using Python, TensorFlow, and OpenCV. The app supports real-time image analysis and delivers accurate skincare insights to users.,};Skin Condition Detection Model
constproject={name:'Skin Condition Detection Model',tools: ['Python', 'TensorFlow', 'Keras', 'OpenCV', 'NumPy', 'Pandas', 'Matplotlib', 'Scikit-learn],myRole:AI/ML Developer,Description: I built a Convolutional Neural Network (CNN) based deep learning model to detect and analyze multiple skin conditions from facial images. The model classifies issues such as acne, pigmentation, dark spots, and wrinkles, and provides a percentage-based analysis for each condition. Data preprocessing and augmentation techniques were applied to improve model accuracy. The system was trained on large annotated dermatology datasets and optimized for performance. This model can be integrated into skincare applications to help users understand their skin condition and track improvements over time.,};