{

The animal guessing game is a program that allows users to guess the name of an animal based on a series of clues. For this project I wrote a python program that through a series of questions about the animal's characteristics, the program narrows down the possible options until the user correctly guesses the animal's name. 

This Python program solves the knapsack problem using an algorithm that generates a table of all possible combinations of items and their corresponding values and weights, and then determines the optimal combination of items for a given knapsack capacity. 

The knapsack problem has a wide range of applications, including inventory management, resource allocation, and logistics. 

The dolphin network visualization program is a R that uses network analysis techniques to visualize the relationships between dolphins in a community. It creates graphs showing the connections between individual dolphins, based on data on their interactions, such as observations of play behavior or vocalizations. The program can display various types of information about the relationships, such as the strength of the connections or the patterns of interaction between different groups of dolphins.

The WeatherData program is a Java program that stores and manages weather data for a specific location. It includes data on temperature, humidity, wind speed, and cloudiness, and provides methods for setting and accessing this data. The program also includes a feature that allows it to push the data out to observers, enabling other programs to access the data in real-time. This program is useful for storing and managing weather data as part of a larger weather forecasting system.

This is my implementation of the Vehicle Routing Problem (VRP) in Python. The code takes in a file containing information about customers, vehicles, and their capacities, and uses a greedy algorithm to assign customers to vehicles in a way that minimizes the total distance traveled by all vehicles. The program first calculates the difference in coordinates between all customer points using my "coor_dif" function, then uses a algorithm to assign customers to vehicles based on their demand. The total cost of the routes is calculated, and the program returns the final cost and the routes assigned to each vehicle.

The nephritis prediction program is an R tool that uses machine learning algorithms to predict the likelihood of a patient developing nephritis. It applies the k nearest neighbors and classification tree algorithms to a dataset of patient records to create a prediction model, and can then analyze new patient records to predict the likelihood of nephritis.

This 8 puzzle program using the A* algorithm is a Python program that solves the 8 puzzle game. The 8 puzzle is a game that consists of a 3x3 grid of numbered tiles, with one tile missing. The goal is to rearrange the tiles so that they are in numerical order, with the missing tile at the bottom right of the grid.

To solve the 8 puzzle, the program uses the A* algorithm to generate a list of all possible configurations of the tiles and find a sequence of moves that leads to the solution.

The peace treaty prediction program is a R program that uses a linear model to predict the likelihood of a country signing a peace treaty based on the country's carbon dioxide (CO2) emissions per capita and its GDP. It applies a linear model to data on CO2 emissions and peace treaty signing for a number of countries to create a model that can predict the likelihood of a country signing a peace treaty based on its CO2 emissions.

This project solution to the Traveling Salesman Problem (TSP) implemented in Python. The TSP class has a constructor that takes a list of City objects as input and initializes an instance variable "self.unvisited" with all the cities except the first one. It also initializes another instance variable "self.route" with the first city in the list. The TSP class has a method called "next" that finds the closest city to the current route and adds it to the route. The method continues to do this until all cities have been visited and then returns the total cost of the route.

This project is using the R programming language to analyze a dataset of car prices using the Random Forest algorithm. The project involves reading and cleaning the data, creating visualizations to understand the distribution of car prices, and building a linear multivariate model of price using various car features. The project also includes using the Random Forest algorithm to fit a model to the data and analyze the importance of each feature in predicting the price of a car. The project goal is to identify which variables are significant in predicting the price of a car and how well those variables describe the price of a car.