Project 2 - EDA on Vehicle Insurance Customer Data

I have completed the project 2 please find the attached document.

As an aspiring data analyst, I recently completed a project analyzing the career choices of alumni from two prestigious universities. Leveraging data from six CSV files, I explored the Higher Studies, Self-Employed, and Service/Job records of College A and College B. Project Highlights: 1. Data Import: I meticulously imported all six CSV files (representing alumni records from College A and College B) into MySQL. This step ensures that we have a clean and structured dataset for analysis. 2. Data Exploration: Using Python, I explored the structure of the six tables. Specifically, I examined the columns, data types, and relationships between tables. In a Jupyter Notebook, I displayed the first 1000 rows of each table, providing an initial glimpse into the data. 3. Data Cleaning and Views: Data quality is crucial. I performed data cleaning on each table to enhance accuracy and consistency. For instance, I removed null values from the tables: College_A_HS: Higher Studies records for College A College_A_SE: Self-Employed records for College A College_A_SJ: Service/Job records for College A College_B_HS: Higher Studies records for College B College_B_SE: Self-Employed records for College B College_B_SJ: Service/Job records for College B The cleaned data was stored in corresponding views (e.g., College_A_HS_V). 4. String Functions and Excel Integration: To improve readability, I converted the following fields to lowercase in the views: Student names Father names Mother names I seamlessly integrated the cleaned views into MS Excel, creating pivot charts to visualize alumni locations. 5. Procedure Creation: I crafted two procedures using cursors: get_name_collegeA: Fetches names of all students from College A get_name_collegeB: Fetches names of all students from College B Results: College A: Approximately 14.83% of alumni pursued Higher Studies, 13.02% were Self-Employed, and 72.14% opted for Service/Job roles. College B: Roughly 5.04% chose Higher Studies, 9.25% were Self-Employed, and 85.69% pursued Service/Job careers. ** My work contributes to understanding alumni career growth, ranking, and networking activities. Excited to continue my data journey! **

Hello mam/sirI have completed the project-2  please find the attachment and please provide the review or changes whatever needed so I can improvise on it.please find the shared link.https://docs.google.com/spreadsheets/d/1EPInzba5N5ba1nDJStZe50efyDNvNZuI/edit?usp=drive_link&ouid=117566329884595265911&rtpof=true&sd=trueThank…

Project Completion Announcement: Analyzing E-Commerce User Data I’m thrilled to share that I’ve successfully completed a fascinating project focused on understanding the customer-to-customer (C2C) business model within the dynamic realm of e-commerce. Let’s dive into the details! Project Overview: The C2C model empowers customers to engage in direct business transactions with each other. As e-commerce platforms continue to thrive, this model gains momentum. Sellers may or may not pay fees, while buyers can make purchases without any upfront payment. The heart of this ecosystem lies in the e-commerce website, where sellers and buyers converge. Key Tasks and Insights: Setting Up the Environment: Created a new MySQL schema named e-commerce. I imported a dataset from a users_data.csv file containing 98,913 rows and 24 columns. Exploring the User Database: Ran SQL commands to examine the table structure. I selected the first 100 rows of the database. Business Insights: Explored user behavior: Who uses profile pictures? Who engages via applications (web, Android, iOS)? Who are the buyers and sellers? Country-Specific Analysis: Calculated each country's total number of buyers and sellers. Identified the top 10 countries with the highest product pass rates. Language Preferences: Analyzed user language choices on the e-commerce platform. Gender Preferences: Explored female users' choices regarding wishlists and social likes. Investigated male users’ roles as sellers or buyers. Buyer Insights: Determined the country with the maximum number of buyers. Seller Insights: List countries with zero sellers. Recent Activity: Displayed records of the top 110 users who recently used the e-commerce platform. User Engagement: Calculated the number of female and male users per country. Computed the average number of products sold and bought by male users in each country. Thank you!

Project 2 - Literacy Rate In TamilNadu.

Please write a detailed insight on what the possible issues on gender gap in STEM Jobs. Answer: There is a significant gender gap in STEM jobs, with women making up less than a quarter of the workforce in these industries. We can see that women are more attracted to healthcare and biology-related fields in STEM because…

Project 1 is completed