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carprice-analysis

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. It is required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market. In this a multivariate Linear regression model was made for the prediction of car prices.

conversationalbot-restaurant

An Indian startup named 'Foodie' wants to build a conversational bot (chatbot) which can help users discover restaurants across several Indian cities. The main purpose of the bot is to help users discover restaurants quickly and efficiently and to provide a good restaurant discovery experience. Zomato apis are used for searching the restaurants. https://developers.zomato.com/documentation#/ Assuming that Foodie works for Tier-1 and Tier-2 restaurants build chatbot that asks for cuisine preferences , location and budget and return top 5 restaurants in sorted order of the average Zomato user rating(Scale 1-5). Send the details of the top 10 restaurants to user’s email if he asks for it. Using Rasa framework build a bot. Also handle the specific nuances of the conversation and greetings in graceful fashion. At last deploy the model on slack and make a you tube video showcasing conversations.

cypher-for-gremlin

Cypher for Gremlin adds Cypher support to any Gremlin graph database.

datasets

This repository contains different datasets we are going to use for our sessions in Journey to the core or data science

datasetsfordemo

This repository contains datasets for public usage and demo

elasticsearch-py

Official Python low-level client for Elasticsearch.

flowerdatarecognition

global-sales-data-for-pandas

This repository contains the global sales dataset segregated in various sheets with customer, market , orders, product and shipping information

handgesturerecognition

investment-analysis--spark-funds

This repository comprises of Spark funds case study which analyses data and derive beautiful insights from it

lending-club---analysis

If one is able to identify risky loan applicants, this is the place to look for

maximize-cab-profits

notebooks-for-introductory-session-in-journeytothecoreofds

This repository contains the notebooks for introduction sessions- Python, DateTime, EDA, Numpy, Pandas, Visualizations

questions

This repository contains notebook with questions on various topics for practice.

restrited-boltzman-machine

samplemeantestdatasets

This repository contains datasets for sample mean tests and other forms of tests used in Hypothesis Testing

spring-boot-example

spring-boot-examples

Code Examples for everything thats written on www.springboottutorial.com

sqlcontent

This repository contains document for sql installation and tutorial with sample db and questions

syntacticprocessing-postagger

Using Treebank dataset of NLTK with the 'universal' tagset comprising 12 coarse tag classes modify the Viterbi and Hidden Markov Model algorithm to solve the problem of unknown words using at least two techniques. Using any of the approaches example lexicon, rule-based, probabilistic etc solve the problem of unknown words. Compare the tagging accuracy after modifying vanilla Viterbi and list down at least 3 cases from sample test where original Viterbi POS tagger incorrectly tagged words and got corrected after modifications.

telecom-churn-prediction

Build models to predict churn. The predictive model that you’re going to build will serve two purposes: It will be used to predict whether a high-value customer will churn or not, in near future (i.e. churn phase). By knowing this, the company can take action steps such as providing special plans, discounts on recharge etc. It will be used to identify important variables that are strong predictors of churn. These variables may also indicate why customers choose to switch to other networks.

tic-tac-toe

One of the most popular and enduring games of all time is Tic-Tac-Toe. Because of its familiarity, this game is often used as a starting example to mathematically analyze a decision-making process. Its brevity makes it a perfect game to illustrate the rewards of thinking ahead and learning the consequence of each decision. There are many variants of Tic-Tac-Toe. The most classic one is of X’s and O’s, where each player aims to place three of their marks in a horizontal, vertical, or diagonal row in a 3x3 grid. The other popular variant of this game is Numerical Tic-Tac-Toe. Instead of X’s and O’s, the numbers 1 to 9 are used. In the 3x3 grid, numbers 1 to 9 are filled, with one number in each cell. The first player plays with the odd numbers, the second player plays with the even numbers, i.e. player 1 can enter only an odd number in the cell while player 2 can enter an even number in one of the remaining cells. Each number can be used exactly once in the entire grid. The player who puts down 15 points in a line - (column, row or a diagonal) wins the game. It is recommended that you play the game here for more clarity. Rules of the Game: The game will be played on a 3x3 grid (9 cells) using numbers from 1 to 9. Each number can be used exactly once in the entire grid. There are two players: one is the Reinforcement Learning (RL) agent and other is the environment. The RL agent is given odd numbers {1, 3, 5, 7, 9} and the environment is given the even numbers {2, 4, 6, 8} Each of them takes a turn. The player with odd numbers always goes first. At each round, a player puts one unused number on a blank spot. The objective is to make 15 points in a row, column or a diagonal. The player can use the opponent's numbers in the grid to make 15. The game terminates when any one of the players makes 15. In this assignment, you need to build an RL agent that learns to play Numerical Tic-Tac-Toe with odd numbers (the agent will always make the first move). You need to train your agent using Q-Learning. The environment is playing randomly with the agent, i.e. its strategy is to put an even number randomly in an empty cell. If your agent wins the game, it gets 10 points, if the environment wins, the agent loses 10 points. And if the game ends in a draw, it gets 0. Also, you want the agent to win in as few moves as possible, so for each move, it gets a -1 point.

tic_tac_toe

varuntahin

workshops

IQSS RTC workshop materials

xray-effusion-detection

X-ray classification: Chest X-ray images. Here, we will learn how to identify and debug problems often encountered during training.