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couchbasepushfetchmetrics

Java Utility to check the couchbase metrics on AWS cloud

data-visualization-in-python-talk

digit-recognition

Handwritten Digit Recognition using OpenCV, sklearn and Python

foodiebot

Rasa based chatbot that can help users discover restaurants across Indian cities

geely_automotive_pricing_model

A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts. They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know: Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the American market.

hello-world

First git hub test repository for learning

kaggle-titanic_survivor_prediction

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.

lending-club---exploratory-data-analysis

Conducting Exploratory Data Analysis on the Lending Club data set as part of the Upgrad MLAI course.

mnsist_handwritten_digit_recognition-svm

Explore the popular MNIST dataset and build an SVM model to classify handwritten digits

python-learning

spark-fund_investment-analysis

A case study for suggesting Spark Funds, an investment company, the best possible investment solution according to there business needs.

telecom_churn_model

Analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.

telstra