In this project, we are introduced to exploratory data analysis using some of the Python data science libraries such as pandas, NumPy, and matplotlib in Jupyter Notebook.
Using the Titanic passengers dataset, we use simple probability methods to predict whether a passenger would survive or not.
In this project, a problem is solved using various searching methods including BFS, IDS, and weighted A*.
The problem is searching a graph for recipes, and giving them to the ones who need them in minimum time.
In this project, the mathematical equality problem is solved using a genetic algorithm.
Given an equation of length n and its answer, we use genetic methods to find what combination of operands and operators satisfy the equation.
In this project, the Sim pencil game is played by two agents.
One of them uses the alpha-beta minimax algorithm to play optimally, while the other agent plays randomly.
In this project, the class of a news is predicted using an implementation of the Naive Bayes classifier.
In this project, different classifiers are trained on a dataset and the models are tested.
A diabetes dataset is preprocessed, split, and used to train a Decision Tree, K-Nearest Neighbors, Logistic Regression, and Random Forest model to predict whether someone has diabetes.
In this project, a neural network is implemented and trained to classify images of Arabic handwritten characters.
The feedforward neural network and various activation functions are implemented and the effects of different parameters are checked.
The TensorFlow library is used afterwards to classify the CIFAR-10 dataset.