A comprehensive guide and codebase for building interactive storytelling dashboards with Python, Streamlit, and Plotly. Learn how to transform static analytics into dynamic, user-driven data experiences that engage and inspire, featuring RFM segmentation, cohort analysis, and real-world insights.

🌟 Internship Program Analysis 🌟 This project explores key trends in internship opportunities across various companies and roles. Using Python (Pandas, Matplotlib, Seaborn), the dataset was cleaned, analyzed, and visualized for insights. It highlights top internship titles, locations, durations, and stipend patterns.

✨ Stock Price Prediction Using Tesla Dataset ✨ In this project, I analyzed Tesla’s historical stock data to forecast future closing prices using machine learning models like Random Forest Regressor. Through data cleaning, feature engineering, and rich visual analytics, I explored patterns in price trends, volatility, and trading volume.

🔹 Create a Interactive Dashboard Using a BI Tool 🔹 Built an interactive Netflix Movies & TV Shows dashboard using Power BI. Cleaned and transformed the dataset to ensure accurate insights. Created visuals for genres, ratings, release trends, and country-wise data. Designed KPIs and charts to highlight key patterns in Netflix content.

🌟 Transform data into interactive stories with Streamlit and Plotly, allowing users to explore insights and understand metrics dynamically.

📊 Visualize e-commerce data with this interactive dashboard, transforming raw numbers into actionable insights for better business decisions.

📊 Analyze internship listings to reveal trends, skills in demand, and opportunities, transforming raw data into actionable insights for students and organizations.