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australia_house_price_predcting-

assinment

automatic-ticket-classification

NLP case study to classify user mentioned issues into topics to assign the tickets to the responsible

bike-sharing-assignment

regression assignment

boosting

adaboost classifier

convolutional-neural-network-industry-applications

INDUSTRY APPLICATIONS- FLOWER DETECTION

convolutional-neural-networks

textttt

credit-card-fraud-detection

Fraud Detection model based on anonymized credit card transactions

datascience-assignment10-advancedregression

Australia house pricing-1

dl_content

transfer learning.

fraud_detection

building a machine learning model for fraud detection

gesture-recognition-case-study-iiitb-assignment-

A deeplearning based approach to classify human gestures for smart gadjets.

kccl_mines

Demo project for kccl

keras-resnet

Residual networks implementation using Keras-1.0 functional API

lending_club_case_study

Objective is to be able to identify the risky loan applicants to reduce the credit loss to the company

linear-regression

multipule linear regression

logistic-regression

implementation for diabetic patients

melanoma-cnn-prediction

PREDICTION ASSINMENT

model-selection

naive-bayes

forked file.

naive-bayes-imdb

In this segment, you will use the IMDB movie reviews dataset to classify reviews as 'positive' or 'negative'. We have divided the data into training and test sets. The training set contains 800 positive and 800 negative movie reviews whereas the test set contains 200 positive and 200 negative movie reviews.This was one of the first widely-available sentiment analysis datasets compiled by Pang and Lee's. The data was first collected in 2002, however, the text is similar to movies reviews you find on IMDB today. The dataset is in a CSV format. It has two categories: Pos (reviews that express a positive or favourable sentiment) and Neg (reviews that express a negative or unfavourable sentiment). For this exercise, we will assume that all reviews are either positive or negative; there are no neutral reviews. You will need to build a Multinomial Naive Bayes classification model in Python for solving the questions.

named_entity_recognition_healthcare_data

Named Entity Recognition in Healthcare data to identify possible diseases and their suggested treatments from a corpus of medical text containing both disease and treatment.

olist-business-analysis

A detialed analysis on the customers, products, orders and shipments of the Brazilian E-commerce giant Olist.

portfolio

Minimal is a Jekyll theme for GitHub Pages

price_housing_3

advanced regression assinment

resnet-1k-layers

Deep Residual Networks with 1K Layers

telecom-customer-churn-prediction

Customers in the telecom industry can choose from a variety of service providers and actively switch from one to the next. With the help of ML classification algorithms, we are going to predict the Churn.

telecomchurncasestudy

In the telecom industry, customers are able to choose from multiple service providers and actively switch from one operator to another. In this highly competitive market, the telecommunications industry experiences an average of 15-25% annual churn rate.

tree-models

sample questiones

unsupervised-learning-clustering

unsupervised-learning-principal-component-analysis

PCA