Data Cleaning in SQL
This SQL data cleaning improves an e-commerce database by fixing date formats, filling missing customer addresses, and organizing address details. It also makes the "Shipped" status clearer and removes duplicates for data integrity. Unused columns like billing address and tax info are removed, streamlining the dataset for better efficiency.
Data Exploration in sql
This data exploration project on flights data begins by selecting relevant information such as departure dates and airport codes. It then delves into key insights, including the total number of flights, average departure and arrival delays from various airports. Monthly flight trends are examined to understand patterns over time. The project also identifies airlines with the highest average departure delays. Lastly, on-time arrival performance is assessed by analyzing flights to destinations based on their punctuality. The findings are presented in a clear and organized manner, providing valuable insights into the performance and trends within the dataset.
Python-Driven Data Extraction with SQL
The SQL query analyzes sales transactions from a dataset called sales_transactions, focusing on customer-level insights within a specified date range. It calculates aggregated metrics such as total purchases and returns for each customer, categorized by product type (Grocery, Electronics, and Clothing). The query includes conditional logic to handle different currencies, converting values to a common unit where necessary. The results are grouped by customer ID to provide a comprehensive overview of customer behavior and transaction patterns. This analysis aids in understanding customer purchasing trends and informs strategic decision-making for the business.
EDA,Correlation,Feature Engineering,Regression Analysis using Python
In developing a regression model for delivery time, I conducted a comprehensive analysis that encompassed Exploratory Data Analysis (EDA), data visualization, feature engineering, correlation analysis, model building, and thorough model testing. This rigorous approach aimed to understand the factors influencing delivery time and to create an effective predictive model for optimizing delivery efficiency.
Similarly, in crafting a prediction model for salary hikes, I undertook a detailed process involving EDA, data visualization, feature engineering, correlation analysis, model building, and meticulous model testing. This methodical exploration sought to uncover patterns and relationships within the salary data, ultimately leading to the construction of a robust model capable of forecasting salary hikes accurately.