data-manupulation

📚 This repository is my personal data science learning hub. Explore my journey from the very basics to advanced techniques. Dive into Python, data manipulation, analysis, visualization, and machine learning. Join me as I learn, grow, and experiment in the world of data science.

British Airways internship, focusing on leveraging data to enhance operational efficiency and customer experience.

This repository contains how to preprocess data, web scraping using requests library & beatifulsoup, reading csv files and Data manupulation techniques using Python

web scraping, data cleaning, data analysis

Live currency and gold table with profit added

Created a Billing System where user can login as an Admistrator or as a Buyer with their registered Email ID and Password. user can also do data adjustment, add , modify or delete any file record.

This analysis measures the performance of a call centre and assessed the satisfactions and sentiments of customers.

In this project, I explored data from BusinessFinancing.co.uk on the world's oldest businesses: when were they founded, and which industries do they belong to?

An interactive IBM HR Analytics Dashboard built using Google Looker Studio to analyze employee attrition, demographics, job satisfaction, income, and workforce trends.

This weeks project was to create an Express backend API with at least three RESTful endpoints, returning arrays or single items using hard-coded JSON data and array methods. The API was supposed to be RESTful and there should be API-documentation at the "/"-endpoint.

This repository showcases my work as a data analyst in the domain of supply chain management. Leveraging a diverse set of data analysis tools, including Power BI, Excel, and SQL, I have meticulously constructed a comprehensive supply chain management solution.

Explore honey production dynamics (1998-2012) in the U.S. amid declining bee populations using Python's seaborn and matplotlib. Visualize key attributes like colonies, yield, production, price, and stocks to draw insights into the impact on American honey agriculture.

In this project, we will perform Exploratory Data Analysis (EDA) on three datasets, `ufo`, `u.user` and `movies`. We will use the Python library Pandas for data cleaning, transforming, and manipulation and Matplotlib for data visualization.

In this notebook, we will explore this phenomenon using age distribution data to see if we can reproduce a difference in average age at death purely from the changing rates of left-handedness over time, refuting the claim of early death for left-handers.

"IPL Match Bidding App - Pie-in-the-Sky" is a SQL tool decoding IPL match data. It analyzes bidder success, popular stadiums, and toss impact. Tailored for IPL decision-makers, it simplifies complex data for strategic insights in just a few clicks.

In my various Python projects, I have covered all the steps in the data analysis process.

Data analysis tool to automate transaction risk assessments.

Pandas is a great library for data manipulation. Since it also provides some of the stunning features for the Data Visualisation.

Generic_DAO is a class that manages data access (PostgreSQL) within an application. It acts as an intermediary between the database and the business logic by encapsulating operations such as data retrieval, insertion, updating, and deletion.

Exploring a dataset from Kaggle containing a century's worth of Nobel Laureates. Who won? Who got snubbed?