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analysis-of-child-mortaity-rates-using-python

Child mortality rates for all countries under UNICEF across the years are analysed and visualised using Python.

anomaly-detection-nab-dataset

Deep learning approaches that include building a sequence to sequence MLP and also building an Autoencoder with the help of Dense, LSTM, Conv1D layers individually to reconstruct and detect the anomalies in the benchmark dataset.

basic-machine-learning-algorthims

Exploring few basic Machine Learning Algorithms

big-data-processing-with-hadoop

Mapper and Reducer algorithms were implemented for basic wordcount, N-grams, Inverted Index, Relational Join and KNN(K-Nearest Neighbours) using NumPy in Python.

data-models-n-query-languages

This repository includes implementation of all kinds of SQL queries including recursive SQL on employees DB, Implementation of XQuery on a private books.xml data and a small project on designing and implementing database schema for TinyHub complying to given requirements along with creation of Entity-Relationship Diagram for the designed schema. Additionally, it also has the applications and tools to implement the added .sql or .xq files.

data-visualisation

Exploring data visualisations.

data_analysis_of_online_retail_datasets

Online Retails datasets are joined, analyzed with the help of Pandas (performing all SQL operations) and the resultant dataset was grouped by Stock Description ordered with respect to frequency.

decision-tree-algorithm-and-bagging-concepts-using-python

Implementation of decision tree algorithm using NumPy and Bagging concepts of bootstrapping application code using python

digit-recognizer-app

This AWS deployed application which recognizes the handwritten digit written by user in front-end with the help of trained Deep learning model in the back-end.

eas503

en-fr_translation-toolbar_with_autocorrect-autofill

Translation Toolbar with Autocorrect and Autofill features supporting English language used to translate English language to French Language

exploring-numpy-and-pandas

Examples exploring NumPy and Pandas

fashion-mnist-image-classification

Convolutional Neural Networks were built, trained for image classification task for Fashion-MNIST dataset.

gdp-prediction-using-r

GDP prediction using Supervised models in R

googlecodejam

Google CodeJam questions from each round are solved and saved

hello-world

imagenet-image-classification

Convolutional Neural Networks were built, trained for image classification task on 20 classes of ImageNet dataset. Data Augmentation tools and CNN models were explored to achieve more than 51% accuracy on Validation data.

k-means-algorithm-using-python

Implementation and visualisation of K-Means algorithm from scratch using NumPy

keras-io

Keras documentation, hosted live at keras.io

knn-algorithm-using-python

Implementation of KNN algorithm using Python from scratch, which for huge datasets gets results faster than existing sk learn KNN.

linear-and-logistic-regression-algorithm-using-python

Linear and Logistic regression algorithm has been implemented from scratch using Python. Polynomial functions of order 1,2,3,4,5 which are equivalent to linear regression were plotting against true cubic relation to demonstrate under fitting and over fitting of the models. For Logistic regression, classifier has been built using Batch gradient descent and Stochastic gradient descent to compare them in terms of accuracy

matplotlib-tutorial

Matplotlib tutorial for beginner

mlcourse-ub-notebooks

Collection of iPython notebooks for UB CSE Machine Learning course (CSE 474/574)

naive-bayes-theorem-using-matlab

Classifier has been built with the help probability theories and Discriminant Analysis using MATLAB

predictive-analytics-using-spark

Feature Engineering techniques like Term-Document Matrix, Term Frequency-Inverse Document Frequency (TF- IDF) and Word2vec were tried to get features to implement movie genre prediction machine learned model (Random Forest) using Apache Spark.

principal-component-analysis-using-python

Dimension reduction of a dataset using Principal Component Analysis to produce a low dimension dataset using Python

python-hands-on

Examples and labs done using python

random-forest-algorithm-using-python

Random Forest algorithm has been implemented from scratch using NumPy

recommender-system-on-movielens-dataset

Knowledge-based, Content-based and Collaborative Recommender systems are built on MovieLens dataset with 100,000 movie ratings. These Recommender systems were built using Pandas operations and by fitting KNN, SVD & deep learning models which use NLP techniques and NN architecture to suggest movies for the users based on similar users and for queries specific to genre, user, movie, rating, popularity.

shape_detection_using_opencv

Task was to output a set of rectangles corresponding to the possible bar locations, given an image and number of bars.