We performed a quick data cleaning on a dataset with 20 variables and performed PCA for dimensionality redunction.

In this project, I performed a series of data analysis steps to explore and analyze the impact of the recession and layoffs in 2022. The key data analysis steps carried out include: Data Cleaning: prepared and cleaned the data from various CSV files, ensuring data accuracy and consistency for analysis. Exploratory Data Analysis (EDA): performed exploratory data analysis to understand the impact of the recession and layoffs, using various visualizations and indicators such as Real GDP, unemployment rates, and industrial production. Data Transformation: transformed and reshaped the data to create informative visualizations and insights regarding economic indicators and layoffs in 2022. Data Visualization: created various visualizations, including line charts for Real GDP growth, unemployment rate changes, and industry production. Additionally, visualized the top companies with layoffs in 2022, the percentage of layoffs, and the industries affected. Geospatial Analysis: analyzed the geographic distribution of companies with layoffs, providing insights into the locations of layoffs. Conclusion: concluded the project by discussing the implications of layoffs and the need for individuals to take care of their physical and mental health while navigating a job market impacted by layoffs.

In this project, I demonstrated my proficiency in various data analysis skills: Data Cleaning: meticulously ensured data accuracy by converting body weight to grams for consistent calculations and maintained data cleanliness throughout the project. Data Transformation: transformed data by separating women from men, enabling gender-specific analysis, and calculating BAC levels based on the type of alcohol consumed (shots, beer, wine). Data Wrangling: efficiently prepared and cleaned the data, creating distinct datasets for men and women, and labeling BAC levels to categorize intoxication. Exploratory Data Analysis (EDA): engaged in exploratory data analysis to uncover patterns and trends, considering factors like gender, alcohol type, and BAC levels. This project demonstrates proficiency in data analysis, data cleaning, transformation, and wrangling, ultimately providing valuable insights into BAC levels.

In this document, an ensemble model is prepared to predict admissions using Support Vector Machines (SVM) and Random Forests. The dataset used is "binary.csv" and it is split into training and testing sets. The confusion matrix for the Random Forest and SVM models are shown separately, and then for the ensemble model. The accuracy of the models is also calculated. The aim of this document is to demonstrate the process of creating an ensemble model and evaluating its performance using confusion matrices.