This project analyzes sales data to identify patterns, trends, and best-selling products. The insights gained from the analysis are aimed at supporting business decision-making and enhancing sales strategies.
- Calculation of total sales.
- Monthly sales trend analysis.
- Identification of the top 10 best-selling products.
- Revenue breakdown by product category.
- Visualizations for better understanding of sales data.
- Data cleansing involves removing unnecessary columns.
- Giving the columns new names.
- Eliminating redundant entries.
- sanitizing specific columns.
- Eliminate the dataset's NaN values.
- Look for a few more changes if necessary
This project aims to predict whether a passenger aboard the RMS Titanic survived or not, using a dataset containing demographic and other information about individual passengers. The Titanic's sinking on April 15, 1912, resulted in the loss of 1502 lives out of 2224 passengers and crew. The goal is to create a machine learning model that predicts survival based on features like passenger age, gender, class, and fare.