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arima

captures a suite of different standard temporal structures in time series data

bank-marketing-campaign-analysis

Analyzed the prior marketing campaigns of a Portuguese Bank using various ML techniques like Logistic Regression, Random Forests, Decision Trees and predicted if the user will buy the Bank’s term deposit or not. Recommended, the marketing team, ways to better target customers using feature importance maps and business intuition.

color-treshold

decision-tree

Decision tree is one of the most popular machine learning algorithms. Decision trees are used for both classification and regression problems

github-slideshow

A robot powered training repository :robot:

greyatom-python-for-data-science

A collection of projects as part of the Python for Data Science program at GreyAtom EduTech Pvt Ltd

k-means-clustering

The goal of this algorithm is to find groups in the data, with the number of groups represented by the variable K.

k-nearest-neighbors-algorithm

K-Nearest Neighbours , Dataset: glass , Use knn on short datasets for quicker processing

logistic-regression

Bank products.. will a customer buy a product or not

lstm

market-basket-analysis

Market Basket Analysis is a modelling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items.

mlp-to-classify-images-

MLP to classify images from the MNIST database.

mnist

Computer vision fundamentals with the famous MNIST data

mnist-mlp

natural-language-processing

Natural Language Processing (NLP) is a sub-field of Artificial Intelligence that is focused on enabling computers to understand and process human languages, to get computers closer to a human-level understanding of language.

predicting-hospital-readmission-for-diabetes-patients

Hospital readmission for diabetic patients is a major concern in the United States. This disease is chronic and does not have any specific cure. Hospital readmissions are expensive as hospitals face penalties if their readmission rate is higher than expected. This study attempts to identify the key factors that influence readmission for diabetes and to predict the probability of patient readmission.

predicting-housing-prices-using-advanced-regression-

The goal of this project was to use EDA, Visualization, Data cleaning, Preprocessing, and Linear models to predict home prices given the features of the home and interpret your linear models to find out what features add value to a home.

python

Various Machine Learning Models

r-

My R programming

random-forest

Random Forest is a supervised learning algorithm. Like you can already see from it’s name, it creates a forest and makes it somehow random. The „forest“ it builds, is an ensemble of Decision Trees, most of the time trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result.

sentiment-analysis-of-jw-marriot-hotel-reviews

implemented a web scraper to fetch live hotel reviews from TripAdvisor website. Conducted data pre-processing and cleaning by removing stop words, punctuation, special characters, numbers, and white-spaces from reviews. Performed tokenization and stemming of reviews, and built a corpus out of it. Calculated the sentiment score of each review by comparing it's tokens with positive and negative lexicon and the overall sentiment score of the hotel depending on the number of positive and negative reviews it received.

support-vector-machine

Implementation of SVM

titanic-data--gradient-boosting

Finding accuracy using Gradient boosting algorithm

titanic-logistic-model

Titanic Survival Analysis Using Logistic Regression

titanic-machine-learning-from-disaster

Kaggle Competition Description The sinking of the RMS Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This sensational tragedy shocked the international community and led to better safety regulations for ships. One of the reasons that the shipwreck led to such loss of life was that there were not enough lifeboats for the passengers and crew. Although there was some element of luck involved in surviving the sinking, some groups of people were more likely to survive than others, such as women, children, and the upper-class. In this challenge, we ask you to complete the analysis of what sorts of people were likely to survive. In particular, we ask you to apply the tools of machine learning to predict which passengers survived the tragedy.