Internship project under CodeSentinel, featuring Python programming tasks, problem-solving exercises, and mini projects for practical learning.

🔹 Clean & Explore with Missing and Categorical Data 🔹Python script to clean and preprocess Titanic dataset using Pandas & NumPy. Handles missing values and encodes categorical data. Produces a clean, numeric dataset ready for analysis and machine learning models.

🔹 Visualize Key Insights From Data 🔹 Created multiple plots (bar, pie, histogram, scatter, heatmap, boxplot) using Pandas, NumPy, Matplotlib & Seaborn to analyze sales and profit trends. Delivered clear business insights through visual storytelling and exploratory analysis.

🔹 Analyze Sales or Survey Data Using Grouping 🔹 Analyzed customer behavior by applying grouping and aggregation on gender, location, product category, and subscription status. Generated business-friendly summaries highlighting spending patterns, demographics, and loyalty insights. Built reusable scripts for efficient data summarization.