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association-rules

For my Data Mining lab where we had to execute algorithms like apriori, it was very difficult to get a small data set with only a few transactions. It was infeasible to run the algorithm with datasets containing over 10000 transactions. This dataset contains 11 items : JAM, MAGGI, SUGAR, COFFEE, CHEESE, TEA, BOURNVITA, CORNFLAKES, BREAD, BISCUIT and MILK.

customer-churn-prediction

Assignment No. 2 NTI Data Science

customer-segments

A wholesale distributor recently tested a change to their delivery method for some customers, by moving from a morning delivery service five days a week to a cheaper evening delivery service three days a week.Initial testing did not discover any significant unsatisfactory results, so they implemented the cheaper option for all customers. Almost immediately, the distributor began getting complaints about the delivery service change and customers were canceling deliveries — losing the distributor more money than what was being saved. You’ve been hired by the wholesale distributor to find what types of customers they have to help them make better, more informed business decisions in the future. Your task is to use unsupervised learning techniques to see if any similarities exist between customers, and how to best segment customers into distinct categories.

deep-learning-v2-pytorch

Projects and exercises for the latest Deep Learning ND program https://www.udacity.com/course/deep-learning-nanodegree--nd101

dog-breed-classifier

Build a pipeline to process real-world, user-supplied images. Given an image of a dog, my algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed.

finding-donors-for-charity-ml

generate-tv-scripts

In this project, I generate my own Seinfeld TV scripts using RNNs. I used a Seinfeld dataset of scripts from 9 seasons. The Neural Network I built generates a new, "fake" TV script.

heart-attack

Machine learning model to detect a heart attack before they happen.

identify-monkey

Monkey classification is to classify major types of monkeys based on biometric cues. Usually facial images are used to extract features and then a classifier is applied to the extracted features to learn a type of monkey recognizer. That’s in Computer Vision and Biometrics fields. The monkey classification result is a type of monkey. I used two pre-trained model to compare between them and to find the best accuracy

missing-spaces-task

predict-wine-type-quality

Data Set Information: The dataset was downloaded from the UCI Machine Learning Repository. The two datasets are related to red and white variants of the Portuguese "Vinho Verde" wine. The reference [Cortez et al., 2009]. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). These datasets can be viewed as classification or regression tasks. The classes are ordered and not balanced (e.g. there are munch more normal wines than excellent or poor ones). Outlier detection algorithms could be used to detect the few excellent or poor wines. Also, we are not sure if all input variables are relevant. So it could be interesting to test feature selection methods. Two datasets were combined and few values were randomly removed. Attribute Information: For more information, read [Cortez et al., 2009]. Input variables (based on physicochemical tests): 1 - fixed acidity 2 - volatile acidity 3 - citric acid 4 - residual sugar 5 - chlorides 6 - free sulfur dioxide 7 - total sulfur dioxide 8 - density 9 - pH 10 - sulphates 11 - alcohol Output variable (based on sensory data): 12 - quality (score between 0 and 10) Acknowledgements: P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009.

predicting-boston-housing-prices

In this project, you will create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age. You will start with a simple algorithm and increase its complexity until you are able to accurately predict the outcomes for at least 80% of the passengers in the provided data. This project will introduce you to some of the concepts of machine learning as you start the Nanodegree program.

project-bikesharing

Built and trained a simple neural network to predict the number of bike-share users on a given day.

project-face-generation

smartcab

In the not-so-distant future, taxicab companies across the United States no longer employ human drivers to operate their fleet of vehicles. Instead, the taxicabs are operated by self-driving agents — known as smartcabs — to transport people from one location to another within the cities those companies operate. In major metropolitan areas, such as Chicago, New York City, and San Francisco, an increasing number of people have come to rely on smartcabs to get to where they need to go as safely and efficiently as possible. Although smartcabs have become the transport of choice, concerns have arose that a self-driving agent might not be as safe or efficient as human drivers, particularly when considering city traffic lights and other vehicles. To alleviate these concerns, your task as an employee for a national taxicab company is to use reinforcement learning techniques to construct a demonstration of a smartcab operating in real-time to prove that both safety and efficiency can be achieved.

tags-recommendation-system-

Using NLP ,Classification and frequent pattern

tagsrecommendernlp

titanic-survival-exploration

Titanic Survival Exploration as part of Udacity's Machine Learning Nanodegree. create decision functions that attempt to predict survival outcomes from the 1912 Titanic disaster based on each passenger’s features, such as sex and age.