🎯 Aspiring Data Scientist | 📊 SQL, Python, Power BI, Excel, Machine Learning| 💡 Passionate about turning data into actionable insights

Dynamic and detail-oriented Data Analyst with expertise in SQL, Python, Power BI, and exploratory data analysis (EDA). Proficient in crafting end-to-end analytics projects, focusing on clean, organized, and well-documented solutions for creating impactful dashboards and reports. Skilled at leveraging data insights and rapidly learning new technologies to drive informed decision-making.

My goal: To deliver data-driven solutions that empower businesses!

• Databases: MySQL
• Python: pandas, numpy, seaborn, matplotlib
• Visualization Tools: Power BI, Excel, Matplotlib, Seaborn
• Web Scrapping: BeautifulSoup, requests, selenium
• Machine Learning: scikit-learn, statsmodels
• Other Tools: Jupyter Notebook, Streamlit, Excel

• Tech Stack: Python (Selenium, BeautifulSoup, Pandas, NumPy, Scikit-learn), MySQL, Power BI
• Description: Scraped and analyzed 10,000 IMDb movies (2014-2024), built a machine learning model to predict missing genres, stored data in MySQL, and developed an interactive Power BI dashboard for insights.
• Highlights:
  • Automated web scraping using Selenium & BeautifulSoup
  • Data cleaning & ML-based genre prediction using RandomForest
  • SQL queries for movie insights (top-rated, longest duration, trends)
  • Interactive Power BI dashboard with filters, charts, and KPIs
• IMDb Movies Analysis
• Demo

• Tech Stack: SQL, Python, Power BI
• Description: Insights into customer preferences and behavior through SQL-based data analysis and interactive Power BI dashboards.
• Highlights:
  • Clean, normalized datasets
  • SQL scripts for transformations
  • Screenshots of Power BI dashboards
• E-commerce Analytics
  • Demo

• Tech Stack: Python, Jupyter, Streamlit,
• Description: Built and deployed a Streamlit-based customer churn prediction app using ML models.
• Highlights:
  • Preprocessed and Exploratory Data Analysis
  • Hyperparameter tuning and evaluation on multiple models
  • Interactive Streamlit app
• Customer Churn Prediction App
  • Demo

• Tech Stack: Python, SQL, Power BI
• Description: Analyzed HR data to gain insights into employee performance, attrition, and workforce trends.
• Highlights:
  • SQL queries for workforce analytics
  • Data-driven insights on employee engagement
  • Power BI dashboards for visualization
• HR Analytics
  • Demo

• Tech Stack: Python, XGB Regressor, Streamlit

• Description: Predict weight changes based on lifestyle factors like caloric intake, sleep quality, and stress levels.

• Highlights:

  • Interactive Streamlit app
  • Hyperparameter tuning and evaluation
  • Dataset insights via visualizations

• Weight Change Prediction App

  • Demo

• Analyzed Colorado motor vehicle sales data using Python (Pandas, Matplotlib, Seaborn) to identify trends, seasonality, and county-level performance. Developed ARIMA/SARIMA models for accurate sales forecasting, uncovering seasonal patterns and key economic factors. Provided actionable insights to guide business strategy and policy recommendations.
• Colorado Motor Vehicle Sales Data Analysis Project

• Tech Stack: Python, SQL, Jupyter Notebook
• Description: Uncovered customer behavior and sales trends with SQL queries and Python analysis.
• Highlights:
  • SQL scripts for querying datasets
  • Python-based visualizations (matplotlib, seaborn)
  • Interactive findings in Jupyter
• Target E-Commerce Data Analysis Project

• Tech Stack: Python
• Description: A Python-based quiz game inspired by 'Kon Banega Crorepati,' featuring rewards, penalties, and strategic gameplay.
• Highlights:
  • General knowledge question bank
  • Engaging game logic with hints
  • Interactive terminal-based user interface
• KBC (Kon Banega Crorepati) Python Game

: [email protected]

Thanks for visiting my profile! Feel free to reach out if you have any questions or just want to discuss data analytics. 🚀