Shubham Goyal

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Power BI is used for visualizing key insights, trends, and performance metrics, while SQL is employed to manage and query the data efficiently.

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This project leverages Excel to create customer performance reports, market comparisons, and Profit & Loss (P&L) reports by fiscal year, month, and market. It enhances sales tracking, financial evaluation, and strategic decision-making using pivot tables, advanced formulas, and data visualization for actionable insights.

This project analyzes Diwali sales data using Python, focusing on data cleaning, customer insights, and sales trends. Through EDA with Pandas, Matplotlib, and Seaborn, it uncovers top-selling products, customer demographics, and purchasing patterns, helping businesses refine sales strategies for the festive season.

This project leverages SQL to generate reports for top customers, market products, forecast accuracy, and monthly gross sales for AtliQ Hardware, a consumer electronics company. It includes stored procedures and SQL views for post and pre-invoice sales, enabling data-driven insights and improved business intelligence.

This Power BI dashboard analyzes pizza sales data to uncover trends, customer preferences, and top-selling products. It includes SQL-based data verification, interactive visualizations, and insights into sales performance, peak hours, and regional trends to support data-driven decision-making. 🚀

This Excel dashboard provides insights into sales, customer behavior, and peak business hours for a coffee shop. It analyzes revenue trends, top-selling products, order distribution, and footfall patterns to support data-driven decision-making. Built with Excel for interactive data visualization and business intelligence. 🚀

This project analyzes IPL data from the past three years to provide insights into player and team performance for Sports Basics' special edition magazine. It includes data analysis, dashboard creation in Power BI, and performance metrics to enhance fan engagement and decision-making. Built with SQL, Power BI, and Excel. 🚀

This project analyzes AtliQ Grands' historical revenue data to identify trends, optimize performance metrics, and support strategic decision-making. It includes data analysis, dashboard design in Power BI, and insight generation to help regain market position. Built with Excel and Power BI for visualization and business intelligence. 🚀

This Power BI dashboard analyzes sales performance during Diwali and Sankranti festivals. It provides insights into revenue trends, top-selling products, regional sales distribution, and customer purchasing behavior to help optimize festive season sales strategies. 🚀

This repository contains four different hypothesis testing projects, analyzing real-world data to validate assumptions and drive data-driven decisions. Each project applies statistical tests (e.g., t-tests, chi-square, ANOVA) to uncover insights and support business strategies. Built with Python, Pandas, SciPy, and Statsmodels. 🚀

This project analyzes online advertising performance using Exploratory Data Analysis, Hypothesis Testing, and Regression Analysis. It examines key metrics like click-through rates, conversion rates, and ad costs to uncover insights for optimizing ad spend and improving campaign efficiency. Built with Python, Pandas, Scikit-Learn, and Statsmodels.

This project analyzes Netflix user activity and trends using time series forecasting techniques. It includes data preprocessing, trend analysis, seasonality detection, and forecasting models like ARIMA to predict future engagement patterns. Built with Python, Pandas, and Statsmodels.

This project uses machine learning to classify messages as spam or ham based on text analysis. It includes data preprocessing, feature extraction (TF-IDF), and classification models like Logistic Regression and Naive Bayes for accurate spam detection. Built with Python and Scikit-Learn. 🚀

This repository features interactive Tableau dashboards for sales performance and healthcare analysis. It includes insights on revenue trends, regional sales, patient demographics, and hospital occupancy for data-driven decision-making. 🚀

This project leverages machine learning to predict whether a customer will recommend a product based on their review. It also applies topic modeling to uncover key themes in customer feedback. Using NLP techniques, the model processes text data, builds a classifier, and visualizes insights. Built with Python, Scikit-Learn, NLTK, and Gensim.

This project uses machine learning to classify text sentiment as positive, negative, or neutral. It includes data preprocessing, feature extraction, and models like Logistic Regression, SVM, and Random Forest. Built with Python and Scikit-Learn.

This repository contains machine learning projects covering various real-world applications. It includes data preprocessing, feature engineering, model training, and evaluation using algorithms like regression, classification, and deep learning. Built with Python, Scikit-Learn, TensorFlow, and Pandas. 🚀

This repository features SQL-based analysis on bike rentals and the NFT market. The bike rental project explores customer trends, peak rental times, and revenue insights, while the NFT case study analyzes sales trends, blockchain transactions, and market dynamics. Built using SQL queries for data cleaning, aggregation, and joins in MySQL.

📌 Credit Card Fraud Detection using Machine Learning This project focuses on detecting fraudulent credit card transactions using machine learning models like Random Forest, XGBoost, and Deep Learning. The dataset is preprocessed to handle class imbalance, and multiple models are evaluated based on ROC AUC Score and F1 Score.

This project classifies e-commerce products into predefined categories using machine learning. It includes preprocessing steps like stopword removal, punctuation cleaning, and feature extraction. Models, including LSTM, are implemented, and evaluated for better accuracy.

Config files for my GitHub profile.

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