- Project 1A: Pandas

In this assignment, I engaged in hands-on data exploration using the Pandas library. The focus was on utilizing various Pandas functions to analyze and manipulate data effectively. I was required to answer each question with a single line of Pandas code unless multiple lines were explicitly permitted. Throughout the assignment, I used functions such as read_csv, head, unique, sum, mean, and others to interact with the dataset.

- Project 1B: SQL

In this assignment, I practiced SQL queries using SQLite to interact with a provided database. My tasks involved querying the database to extract and analyze information based on specific instructions.

- Project 2A: Hypothesis Testing

In this assignment, I practiced using Chi-Squared Test, Z test, T test, Mann-Whitney U Test and Anova.

- Project 2B: Data Exploration

In this assignment, I delved into a mystery involving the theft of Dr. Fardina's markers. My task was to analyze four distinct datasets to piece together the clues and identify the culprit. The datasets I worked with were:

+ Profiles: Contains detailed profiles of individuals involved.

+ Witnesses: Includes statements and information from witnesses.

+ Zichao's Magic Show: Provides data related to the event where the theft occurred.

+ Interrogation Statements: Contains statements from individuals who were interrogated.

- Project 3: Classification

In this assignment, I practiced training 4 different classifiers KNN, DecisionTree, Logistic Regression, and Random Forest when analyzing and classifying the quality of Mr. Gaurav Grapeglamour's red wines.

- Project 4: Regression, Gradient Descent and Neural Network

In this assignment, you explored the concept of Regression through a structured tutorial. This assignment was designed to help me understand various regression techniques and their applications in machine learning.

Main Points:

+ Regression on Different Data Relationships: Begin by examining how regression could be applied to different types of relationships in data, such as linear and quadratic. This foundational step helped me understand how different regression models fit and predicted data.

+ Building a Regression Tool: I applied my knowledge by constructing my own regression tool. This hands-on activity woulf allow me to implement regression algorithms and gain practical experience in building and using these models.

+ Connecting Regression to Neural Networks: Finally, I integrated my understanding of linear and quadratic regression into neural networks.