Hello, and thanks for stopping by! This repository contains personal data analysis projects that I have done utilizing Python, SQL, Excel and PowerBI. Below are some of my highlighted projects, including data visualization, statistical analysis, and data-driven insights.
For these projects, I start by conducting most of my analysis using Python. Additionally, I perform some minor analysis or adjustments in Excel. To replicate the analysis in a database environment, I simulated it in MySQL by uploading the data to my local MySQL instance. Ultimately, I visualized the results and findings by creating a PowerBI dashboard.
These projects reflect my skills in handling diverse datasets and extracting meaningful conclusions to support decision-making. Feel free to check them out and reach out for any questions or feedback.
In this project, I used a dataset found on kaggle to analyze automotive sales transactions, examining details like Order Number, Quantity Ordered, Price, Product Line, Customer Information, and other pertinent factors. By closely examining important elements such as sales volume, transaction sizes, customer demographics, and specific order details among different automobile models, my aim is to discover significant insights. The goal of this analysis is to offer the management, potential buyers and stakeholders a comprehensive comprehension of automotive sales trends, empowering them to make informed decisions.
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Sales Transaction Insights: Examining key sales transaction details such as quantity ordered, pricing, and deal sizes to understand the sales landscape.
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Product Line Performance: Evaluating the performance of various product lines across different deal sizes.
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Customer and Geographic Analysis: Assessing customer demographics and geographical trends influencing automobile sales.
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Potential Sales Forecasting: Utilizing historical sales data and identified trends to develop potential sales forecasts, aiding in strategic planning and decision-making for future sales initiatives.
In this project, I used a dataset found on kaggle to delve into the demographics and clinical aspects of Obsessive-Compulsive Disorder (OCD). This data exploration aimed to understand various facets of OCD, including patient profiles, symptomatology, severity measures, and potential correlations between demographic factors and OCD symptoms. By closely scrutinizing factors such as patient demographics, severity scores, comorbid conditions, and correlation analyses, the objective was to uncover substantial insights on the disorder.
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Patient Profile and Demographics: Analyzing patient demographics including age, gender, ethnicity, marital status, and educational levels to identify potential correlations with OCD manifestation.
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Duration and Symptomatology: Examining the duration of symptoms, specific types of obsessions and compulsions, along with associated severity scores (Y-BOCS), and any comorbid mental health diagnoses such as depression and anxiety.
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Correlation Analysis: Exploring potential correlations between demographic factors, symptom types, severity scores, and treatment methods to understand patterns or associations within the OCD patient dataset.
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Diagnosis and Treatment Trends: Investigating the timeline of OCD diagnoses, previous diagnoses, family history correlations, and the usage of medications among patients.
In this project, I used personal data from Google Fit and Youtube that was extracted with the help of Google Takeout. It delves into diverse aspects of my activity within these platforms, examining information such as fitness metrics, video consumption patterns, user engagement, and more. By scrutinizing critical data points, the objective is to extract valuable insights that shed light on my behaviors and preferences.
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Fitness and Activity Trends: Analyzing data from Google Fit to uncover patterns related to my fitness activities, such as step counts, workout duration, speed analysis, and activity types. This exploration aims to understand prevalent fitness trends.
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Video Consumption Patterns: Investigating YouTube data to discern preferences, viewing habits, and popular content categories. This includes metrics like watch time, aiming to grasp trends in video consumption behavior.