shwetamustare,Shweta Mustare,github

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.

binary-search-cpp

Binary Search of numerical items within a list. C++

binary-search-java

Binary Search of numerical items within a list. Java

breadth-first-search

Implementing Breadth First Search for minimum distance between cities. C++

cash-cow

Predicting the profits of start-up companies based on the location and expenditure for innovative research, admin, and marketing, using Multiple Linear Regression.

churn-rate-prediction-with-artificial-neural-network

Predicting the customers leave/stay event with Artificial Neural Network, on bank customer data.

concurrent-quick-sort-with-openmp

Implementing concurrent Quick Sort with #pragma omp

convolutional-neural-network

Recognising cats and dogs in images using CNN with Tensor Flow library.

decision-tree-classification

Classifying purchase event data with Decision Tree built on previous data points, predicting relations between age, salary and purchase event.

decision-tree-regression

Predicting salaries by splitting the multi-dimensional data in terminal leaves using Decision Tree Regression.

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

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.

fair-exchange-protocol

Demonstrating Consent in Online Collection of Private Data

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.

pay-day

Predicting the salary of employees based on years of experience, using Simple Linear Regression

polynomial-regression

Predicting the salaries of employees based on their job role and level, using polynomial regression.

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.

random-forest-classification

Classifying purchase events based on majority vote from various decision trees generated with K random points.

random-forest-regression

Predicting the salaries with Ensemble learning using Random Forest Regression.

recursive-search-cpp

Recursive Search of numerical items in a list. C++

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