Shweta Mustare's repositories
Churn-Rate-Prediction-with-Artificial-Neural-Network
Predicting the customers leave/stay event with Artificial Neural Network, on bank customer data.
Random-Forest-Classification
Classifying purchase events based on majority vote from various decision trees generated with K random points.
Token-Generator-in-Lex
Generate a token table for a given file using Unix Lex.
A-Star-Search
Finding the least expensive path with A* search in C++.
Access-to-Genius
Bachelors' dissertation project - The development of a portfolio website which gathers public information about a person from web scraping and social media links.
Convolutional-Neural-Network
Recognising cats and dogs in images using CNN with Tensor Flow library.
Predicting-Negative-Positive-Reviews-of-Restaurants-using-NLP
Implementing an NLP model to understand restaurant reviews and predict the positive/negative factor with Bag of Words Model.
Support-Vector-Machine
Classifying customer purchase event, with supporting vector data points containing age and salary information using Support Vector Machine algorithm.
Thompson-Sampling-Algorithm
Selecting the ads to run on a website with maximum yield of customer clicks. The success distribution of ads is calculated with Thompson Sampling Reinforcement algorithm, which actions the exploration based on maximum exploit rewards, in lieu of randomly exploring the actions on the distributions.
Breadth-First-Search
Implementing Breadth First Search for minimum distance between cities. C++
Concurrent-Quick-Sort-with-OpenMP
Implementing concurrent Quick Sort with #pragma omp
Decision-Tree-Classification
Classifying purchase event data with Decision Tree built on previous data points, predicting relations between age, salary and purchase event.
Depth-First-Search-CPP
Implementing Depth First Search to find minimum distance within cities. C++
Depth-First-Search-Python
Implementing Depth First Search to find minimum distance within cities. Python
Eight-Queens
The eight queens problem statement dictates placement of eight chess queens on an 8×8 chessboard so that no two queens threaten each other; thus, a solution requires that no two queens share the same row, column, or diagonal. This code is using Python to resolve the problem.
Hierarchical-Clustering
Clustering data items with Agglomerate Hierarchical Clustering. Utilising Dendrograms.
K-Means-Clustering
Clustering data items with height, weight and foot size into clusters with K-means, utilising Python.
K-Nearest-Neighbour
Classifying the customer purchase event using K-Nearest Neighbour, based on their age and salary.
Kernel-SVM
Classifying purchase events with introduction of dimensions to linearly separate the data points. The SVM algorithm uses Radial basis Function (RBF) Kernel.
Knapsack-Algorithm
The Knapsack problem dictates placement of weighted items with gains in a limited capacity knapsack to maximise the gain.
Lexical-Analysis-CPP
Counting number of lines, words and letters in a give input file using C++.
Lexical-Analysis-Unix-Lex
Counting of number of lines, words and letters using Unix Lex.
Logistic-Regression
Calculating the probability of customers purchasing a product from a social media ad, based on their age and salary, using Logistic Regression. Python.
Naive-Bayes-Classifer
Calculating the most probable output with Naive Bayes Classifier, as it assumes the attributes are not related to each other. This code determines whether a person is a male or female based on measured features including height, weight and footsize.
Recursive-Search-CPP
Recursive Search of numerical items in a list. C++
Recursive-Search-Java
Recursive Search of numerical items in a list. Java
Shopping-Basket-Optimisation-with-Apriori
Mining association rules within a dataset for frequent items, using Apriori.
Shopping-Basket-Optimisation-with-Eclat
Mining association rules for smaller datasets using Eclat (Equivalence Class Clustering and bottom-up Lattice Traversal).
Tic-Tac-Toe-Game
Implementing the classic Tic Tac Toe game using Minimax recursive algorithm. Python
Upper-Confidence-Bound-Algorithm
Selecting the ads to run on a website based on the distribution of customer clicking on it. The distributions of the ads are explored and exploited using a Reinforcement algorithm, UCB.