Welcome to my GitHub! I'm a passionate Data Analyst who loves transforming raw data into meaningful insights using modern analytics tools and techniques.

• 🎓 BCA Graduate
• 📈 Skilled in SQL, Excel, Power BI, Python, Pandas, NumPy
• 📚 Completed certifications in:
  • Google Data Analytics
  • Microsoft Excel in Udemy
  • MySQL in Cisco
• 🧠 Experience in building dashboards, reports, and data models
• 🔎 Interested in real-world analytics projects using MySQL, Python, and Power BI

• Python
• Pandas
• NumPy
• Machine learning

• SQL (MySQL)
• Excel (VLOOKUP, Pivot Tables, Data Cleaning)

• Power BI
• Tableau
• Matplotlib / Seaborn (basic)

• Problem Solving
• Communication
• Data Interpretation
• Analytical thinking
• Data Manipulation

Skills: SQL | Excel | Power BI | Python

• Completed a Sales Insights project using Excel, MySQL, and Power BI with Atliq Technologies data.
• Performed data retrieval, cleaning, and modeling to prepare accurate datasets.
• Built Power BI dashboards to visualize key sales metrics and trends.
• Showcased end-to-end data handling and business insights generation.

Skills: SQL | Python (Pandas) | Power BI**

• Used MySQL to extract Amazon product orders, reviews, and sales data.
• Cleaned and analyzed data using Python (Pandas) to identify trends and top-performing products.
• Built Power BI dashboards to visualize ratings, sales, and return patterns.
• Provided insights to improve product listings and enhance customer experience.

Skills: Machine learning | Python | Pandas | Numpy | Matplotlib | Seaborn | Scikit-learn | Classification models**

• Built a machine learning model to predict chronic kidney disease using a real-world healthcare dataset from Kaggle.
• Cleaned and preprocessed data by handling missing values, encoding categorical variables, and converting text-based numerical features.
• Performed exploratory data analysis and correlation analysis using heatmaps to identify key predictive features.
• Implemented and evaluated multiple classification models including Random Forest, Naive Bayes, KNN, Decision Tree, and SVM.
• Achieved highest performance with Random Forest, reaching 98% accuracy and 100% recall.

Skills: Machine learning | Python | Regression analysis | Model evaluation | Pandas | Numpy | Matplotlib | Seaborn | Scikit-learn | Scipy |**

• Developed an end-to-end machine learning regression model to predict house prices using real-world housing data.
• Performed data cleaning, preprocessing, and exploratory data analysis using Pandas, NumPy, Matplotlib, and Seaborn.
• Implemented and compared regression models including Linear Regression, Random Forest, Decision Tree, and KNN using Scikit-learn and SciPy.
• Evaluated model performance using Mean Squared Error (MSE), Mean Absolute Error (MAE), and R-Squared (R²) metrics.
• Identified Linear Regression as the best-performing model based on R² score and error metrics.

Skills: Tableau, Data Visualization, Dashboard Design, KPI Analysis, Time-Series Analysis, Geographic Analysis, Business Insights

• Built an end-to-end interactive Tableau dashboard to analyze British Airways customer reviews across multiple service metrics, including cabin staff service, seat comfort, food, entertainment, and value for money.
• Designed dynamic KPIs and parameter-driven metric selection to allow flexible analysis by date range, traveller type, seat class, aircraft group, and region.
• Performed time-series analysis to identify trends in customer satisfaction over multiple years and detect service consistency and performance dips.
• Conducted geographic and aircraft-level analysis to compare customer ratings across countries and aircraft models, highlighting operational strengths and improvement areas.
• Delivered business-ready insights through intuitive visual design and storytelling, demonstrating strong data visualization, analytical thinking, and Tableau expertise.

• Built an end-to-end Sales Analytics Dashboard in Excel analyzing 260+ transactions across 12 months, leveraging 15+ functions (SUMIF, INDEX/MATCH, SUMPRODUCT, AVERAGEIF, COUNTIFS) to deliver real-time KPIs on revenue, profit, and sales rep performance across 5 regions and 4 product categories.
• Designed a multi-sheet data model with 1,200+ dynamic formulas covering aggregate analysis, monthly trend tracking, and interactive order lookup — ensuring zero formula errors and full auto-recalculation upon data changes.
• Created an executive-level visual dashboard featuring 4 charts (trend line, bar, pie) and KPI scorecards for Total Revenue, Profit, Order Volume, and Margin — enabling data-driven decisions on top performers, regional gaps, and category profitability.

• 💼 LinkedIn: https://www.linkedin.com/in/arunsanthosh07
• 📧 Email: arunsanthosh7000@gmail.com
• 🌐 Portfolio: Coming soon

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