Aspiring Data Analyst & Scientist
Lahore, Pakistan
I analyze large datasets for cleaning, and to derive as much information as possible. I have a keen interest in Python, R Programming, Power BI, and MS Excel.
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Stay current with CS and economics research without the search overload. I built an automated system using Google Apps Script that pulls papers from ArXiv, filters them against your research interests using keyword matching and topic classification, and delivers personalized email briefings (daily, weekly, or monthly) straight to your inbox.
Tech stack: Google Apps Script, Google Sheets database, Gmail API
Data source: ArXiv papers in computer science and economics
Unicorn Companies Analysis with Prediction using Python
I performed exploratory data analysis (EDA) to uncover insights on various aspects, including distribution by country, industry, financial stage, and investor activity. The analysis focused on trends, key metrics, and patterns that help explain the success and growth of unicorn companies.
Video Game Sales Dashboard Using SQL
Used SQL to upload the data and do EDA and then made a 5-part dashboard detailing all the information. The data was regarding video games and whether we are heading towards a point where companies only care about profit and not quality.
Data Survey Dashboard using Power BI
Used Power Query Editor to clean the data. The insights show how data professionals (Data Analysts & Scientists in the field) felt about their salaries, entrance, and how long it took them to develop skills.
Power BI Dashboard for Pakistan's Intellectual Capital
The aim for this project was to create an interactive dashboard using slicers. The number of Master's and the current titles that the respective faculties hold; whether they went into research after or became professors was examined.
This project classifies songs by genre using machine learning. Feature redundancy is checked, and dimensionality reduction is performed using PCA after normalization. The data is then split into training and testing sets, and two models—a decision tree and logistic regression—are evaluated to identify the best classifier.
World Population EDA with Prediction
Key trends and patterns are identified, and data visualization is used to highlight insights. After the initial exploration, the data is split into training and testing sets using train_test_split. A linear regression model is then applied to predict population growth, with model performance evaluated using mean-squared error and R-squared metrics.
In this project, we'll delve into the financial outcomes of this decision by applying clustering analysis in R to investigate salary growth associated with various college majors. Using K-means clustering, resulting clusters are visualized to identify which majors offer the best financial returns and to spot any significant outliers.
This project scrapes product data from Amazon, including details like product names, prices, ratings, and reviews. After extracting the data, it is cleaned and stored in a structured format such as CSV. The scraped data can be used for further analysis or for building product recommendation systems.
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