Explore a compilation of my projects in the realm of Data Science, Here, I showcase various projects that cover all aspects of a data scientist's journey, including the stages of business understanding, data understanding, data preparation, modeling, evaluation, deployment, and dashboard creation.
This project serves as a case study for the Breast Cancer Diagnosis Prediction Model. You can find the project here.
- Business Understanding:
- Explores the initial step in the Data Science Methodology.
- Demonstrates the application of data science practices at each stage to address the identified business problem.
This project involves extracting and visualizing stock data for Tesla and GameStop. The data is obtained using the yfinance library and web scraping for historical stock prices and revenue data, respectively. Visualizations are created using plotly for an interactive and informative presentation.
This project analyzes three datasets from the city of Chicago using SQLite. It delves into the city's data to extract insights and patterns.
An analysis of an insurance cost dataset using Python and various data analysis tools. The dataset includes information about individuals, such as age, gender, BMI, number of children, smoking status, region, and insurance charges.
This project involves the analysis and visualization of historical wildfire data in Australia. The dataset includes information on various aspects of wildfires, such as the region, date, estimated fire area, mean estimated fire brightness, and more.
Visualizations explore different aspects of wildfire data, highlighting trends over time, distribution across regions, and relationships between various parameters.
This project focuses on predicting the landing success of SpaceX Falcon 9 first-stage rockets. The process involves data collection, data wrangling, exploratory data analysis (EDA), and machine learning modeling. The project utilizes a variety of technologies and tools, including HTTP requests, Beautiful Soup for web scraping, Pandas DataFrames for data manipulation, SQL for analysis, and machine learning algorithms such as logistic regression, SVM, decision trees, and KNN for prediction.
Feel free to explore each project for a deeper understanding of the methodologies and insights gained.