My Works On Data Analytics

Data Analytics On Tips Dataset  In this project, I analyzed the popular “Tips” dataset from the Seaborn library. This dataset contains information about restaurant bills, tips, and customer details. The goal was to: Explore and visualize tipping patterns. Discover factors that influence tip amounts. Build a simple predictive model to estimate tips based on bill size and other factors 1. Dataset & Tools Dataset:       tips (Seaborn built-in dataset)       Tools Used: Python, Pandas, Seaborn, Matplotlib, Scikit-learn       Skills Demonstrated: Data Cleaning, EDA, Data Visualization, Linear Regression 2. Load and Explore the Dataset tips.head () total_bill tip sex smoker day time size 0 16.99 1.01 Female No Sun Dinner 2 1 10.34 1.66 Male No Sun Dinner 3 2 21.01 3.50 Male No Sun Dinner 3 3 23.68 3.31 Male No Sun Dinner 2 4 24.59 3.61 Female No Sun Dinner 4 3.Apply Lamda Function tips [ 'total_bill' ] .apply ...