Passionate data analyst with a focus on decoding the world through data and driving digital transformations. Currently pursuing a master's degree in digital transformations at the University of Applied Sciences and Arts Dortmund, Germany. Published a research paper on the use of software by digital businesses during the COVID-19 pandemic. Experienced in database development, data analysis, and statistical techniques. Proficient in Python, R, SQL, data-modeling, Deep learning, Tensorflow, and skilled in visualization tools like Tableau and Power BI. Previous roles include data analyst at Spandrel Design Lab and web developer at Preneur Lab. Actively seeking a student job to contribute skills to impactful projects and explore the vast potential of data. Let's connect and collaborate!
This project aims to detect whether a person is wearing a face mask or not in real-time using computer vision techniques. It combines a pre-trained face detector with a face mask classifier to analyze video streams from a webcam.
Tableau dashboard projects on World Demographic Analysis, LOD calculation, Crime rate geography analysis in New york, etc.
PowerBI dashboard projects on Amazon Fashion Dashboard, Health-Care Analysis, Superstore sales forecasting, HR Analytics, E-commerce Dashboard, etc.
This project utilizes deep learning techniques to detect pneumonia from chest X-ray images. It employs the VGG16 convolutional neural network, pre-trained on ImageNet, and fine-tuned for binary classification (normal vs. pneumonia). The model is trained on a dataset of chest X-ray images and achieves high accuracy in distinguishing pneumonia cases. The project includes:
Click on the link to check out the analytical dashboard projects prepared in MS Excel
In the Analyze International Debt Statistics project, I have written SQL queries to explore and analyze international debt using the World Bank dataset. SQL is the most popular and essential tool for performing data analytics on the go.
What makes a name timeless or trendy? In this project, you'll use data published by the U.S. Social Security Administration spanning over a hundred years to understand American baby name tastes. The ranking, grouping, joining, ordering, and pattern matching skills you'll use in this project are broadly applicable: understanding changing tastes is a key competency for businesses as well as parents searching for a baby name!
An important part of business is planning for the future and ensuring that the business survives changing market conditions. Some businesses do this remarkably well and last for hundreds of years. In this project, you'll explore data from BusinessFinancing.co.uk on the world's oldest businesses: when were they founded, and which industries do they belong to? Like many business problems, the data we'll explore is contained in several different datasets. In order to understand the world's oldest businesses, we will first need to use joining techniques to merge our data. From there, we can use manipulation tools such as grouping and filtering to answer questions about these historic businesses.
This project employs neural networks to develop a robust spam classification model for SMS messages. Through natural language processing, data preprocessing involves text transformation using NLTK. The neural network, built with TensorFlow and Keras, undergoes training and evaluation, showcasing accuracy improvements over epochs.
This project uses machine learning techniques to predict earthquake magnitude based on past earthquake data. The project includes data cleaning, exploratory data analysis, visualization, and model training using linear regression and random forest models. The project found that earthquake magnitude has a strong positive correlation with depth, and that there is a negative correlation between earthquake magnitude and latitude. Additionally, the project found that a random forest model was more accurate than a linear regression model in predicting earthquake magnitude. The insights gained from this project can be used to inform future earthquake preparedness efforts. By understanding the factors that contribute to earthquake magnitude, it may be possible to better prepare for and respond to future earthquakes.
In the Visualizing COVID-19 project, you will visualize COVID-19 data using the most popular R library ggplot. You will analyze confirmed cases worldwide, compare China with other countries, learn to annotate the graph, and add a logarithmic scale. The project will teach you skills that are in high demand for R programmers
In A/B testing, we analyze the results of two marketing strategies to choose the best one for future marketing campaigns. For example, when I started an ad campaign on Instagram to promote my Instagram post for the very first time, my target audience was different from the target audience of my second ad campaign. After analyzing the results of both ad campaigns, I always preferred the audience of the second ad campaign as it gave better reach and followers than the first one.
In the Mobile Games A/B Testing with Cookie Cats project, I have analyzed the result of A/B testing, where the first gate in Cookie Cats was moved from level 30 to level 40. In detail, I have applied data manipulation, statistics, bootstrap, data visualization, and critical thinking to decide which version is the best.
In the Find Movie Similarity from Plot Summaries project, I have used NLP and clustering to find the similarities between movie plots. It is the perfect project if you are looking to apply statistical techniques to text data. I have imported two datasets, combined them, performed tokenization and stemming, converted words into vectors, and used KMeans to perform clustering. Furthermore, you will calculate similarity distance and visualize the results using Matplotlib, Linkage, and Dendrograms.
This project analyzes a comprehensive dataset on bat viruses, uncovering insights into virus distribution, detection methods, and temporal trends. The findings highlight the significance of bats as virus reservoirs and emphasize the importance of ongoing research for zoonotic transmission prevention.
Mobile apps are everywhere. They are easy to create and can be lucrative. Because of these two factors, more and more apps are being developed. In this notebook, we will do a comprehensive analysis of the Android app market by comparing over ten thousand apps in Google Play across different categories. We'll look for insights in the data to devise strategies to drive growth and retention.
This analysis is based on the Divvy case study “‘Sophisticated, Clear, and Polished’: Divvy and Data Visualization” written by Kevin Hartman (found here: https://artscience.blog/home/divvy-dataviz-case-study. The purpose of this script is to consolidate downloaded Divvy data into a single dataframe and then conduct simple analysis to help answer the key question: “In what ways do members and casual riders use Divvy bikes differently?” In this case study, I have performed data analysis for a fictional bike-share company in order to help them attract more riders. Along the way, I performed numerous real-world tasks of a junior data analyst by following the steps of the data analysis process: Ask, Prepare, Process, Analyze, Share, and Act.
One of the main areas of research in machine learning is the analysis of water quality. It is also known as water potability analysis because the task here is to understand all the factors that affect water potability and train a machine learning model that can classify whether a specific water sample is safe or unfit for consumption. For the water quality analysis task, I will be using a Kaggle dataset that contains data on all of the major factors that affect the potability of water. All of the factors that affect water quality are very important, so we need to briefly explore each feature of this dataset before training a machine learning model to predict whether a water sample is safe or unsuitable for consumption
The objective of this project is to analyze the battery health percentage of different iPhone models over different iOS updates and regions. The data used for this analysis was collected through an online survey, in which participants were asked to provide information about their iPhone models, regions, iOS versions, battery health, and battery issues. The purpose of this project is to provide insights and recommendations on how to maintain better battery health for iPhone users.
As part of this project, a solution is developed, evaluated and documented on the basis of current existing libraries and / or our own implementations, which is able to evaluate comments on film trailers on platforms such as YouTube with regard to their sentiment
🎩🃏 Card Trick Game is a Python-based interactive magic card trick simulator built with Pygame. It features the classic 21-card trick with smooth animations, humorous commentary, and engaging narratives to create a fun and immersive gaming experience. Perfect for anyone who loves magic or wants to enjoy a quick and entertaining game!