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1.-hackerearth_novartis

https://www.hackerearth.com/challenges/hiring/novartis-data-science-hiring-challenge/problems/6f3530e0811f499c9b5982ee99c5441d/

a-to-z-resources-for-students

Curated list of resources for college students

av-janatahack-insurance-cross-sell

Your client is an Insurance company that has provided Health Insurance to its customers now they need your help in building a model to predict whether the policyholders (customers) from past year will also be interested in Vehicle Insurance provided by the company. An insurance policy is an arrangement by which a company undertakes to provide a guarantee of compensation for specified loss, damage, illness, or death in return for the payment of a specified premium. A premium is a sum of money that the customer needs to pay regularly to an insurance company for this guarantee. For example, you may pay a premium of Rs. 5000 each year for a health insurance cover of Rs. 200,000/- so that if, God forbid, you fall ill and need to be hospitalised in that year, the insurance provider company will bear the cost of hospitalisation etc. for upto Rs. 200,000. Now if you are wondering how can company bear such high hospitalisation cost when it charges a premium of only Rs. 5000/-, that is where the concept of probabilities comes in picture. For example, like you, there may be 100 customers who would be paying a premium of Rs. 5000 every year, but only a few of them (say 2-3) would get hospitalised that year and not everyone. This way everyone shares the risk of everyone else. Just like medical insurance, there is vehicle insurance where every year customer needs to pay a premium of certain amount to insurance provider company so that in case of unfortunate accident by the vehicle, the insurance provider company will provide a compensation (called ‘sum assured’) to the customer. Building a model to predict whether a customer would be interested in Vehicle Insurance is extremely helpful for the company because it can then accordingly plan its communication strategy to reach out to those customers and optimise its business model and revenue. Now, in order to predict, whether the customer would be interested in Vehicle insurance, you have information about demographics (gender, age, region code type), Vehicles (Vehicle Age, Damage), Policy (Premium, sourcing channel) etc.

av_no.16_janatahack_independence-day-2020-ml-hackathon_public_lb_top_20

Janatahack Independence Day 2020 ML Hackathon Topic Modeling for Research Articles

cs224n-squad-project

datamining-usecases

ds-with-python

feature-engineering-book

Code repo for the book "Feature Engineering for Machine Learning," by Alice Zheng and Amanda Casari, O'Reilly 2018

githubtest

test github

go

The Open Source Data Science Masters

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

how_to_make_a_chatbot

This is the code for "How to Make a Chatbot - Intro to Deep Learning #12' by Siraj Raval on YouTube

hr_analytics_pratise_problem

Your client is a large MNC and they have 9 broad verticals across the organisation. One of the problem your client is facing is around identifying the right people for promotion (only for manager position and below) and prepare them in time. Currently the process, they are following is: They first identify a set of employees based on recommendations/ past performance Selected employees go through the separate training and evaluation program for each vertical. These programs are based on the required skill of each vertical At the end of the program, based on various factors such as training performance, KPI completion (only employees with KPIs completed greater than 60% are considered) etc., employee gets promotion For above mentioned process, the final promotions are only announced after the evaluation and this leads to delay in transition to their new roles. Hence, company needs your help in identifying the eligible candidates at a particular checkpoint so that they can expedite the entire promotion cycle. They have provided multiple attributes around Employee's past and current performance along with demographics. Now, The task is to predict whether a potential promotee at checkpoint in the test set will be promoted or not after the evaluation process.

iot_analytics_ml

You are working with the government to transform your city into a smart city. The vision is to convert it into a digital and intelligent city to improve the efficiency of services for the citizens. One of the problems faced by the government is traffic. You are a data scientist working to manage the traffic of the city better and to provide input on infrastructure planning for the future. The government wants to implement a robust traffic system for the city by being prepared for traffic peaks. They want to understand the traffic patterns of the four junctions of the city. Traffic patterns on holidays, as well as on various other occasions during the year, differ from normal working days. This is important to take into account for your forecasting. Your task To predict traffic patterns in each of these four junctions for the next 4 months. The sensors on each of these junctions were collecting data at different times, hence you will see traffic data from different time periods. To add to the complexity, some of the junctions have provided limited or sparse data requiring thoughtfulness when creating future projections. Depending upon the historical data of 20 months, the government is looking to you to deliver accurate traffic projections for the coming four months. Your algorithm will become the foundation of a larger transformation to make your city smart and intelligent.

janatahack-computer-vision-hackathon

Emergency vs Non-Emergency Vehicle Classification Fatalities due to traffic delays of emergency vehicles such as ambulance & fire brigade is a huge problem. In daily life, we often see that emergency vehicles face difficulty in passing through traffic. So differentiating a vehicle into an emergency and non emergency category can be an important component in traffic monitoring as well as self drive car systems as reaching on time to their destination is critical for these services. In this problem, you will be working on classifying vehicle images as either belonging to the emergency vehicle or non-emergency vehicle category. For the same, you are provided with the train and the test dataset. Emergency vehicles usually includes police cars, ambulance and fire brigades.

janatahack-customer-segmentation

Customer segmentation is the practice of dividing a customer base into groups of individuals that are similar in specific ways relevant to marketing, such as age, gender, interests and spending habits. Companies employing customer segmentation operate under the fact that every customer is different and that their marketing efforts would be better served if they target specific, smaller groups with messages that those consumers would find relevant and lead them to buy something. Companies also hope to gain a deeper understanding of their customers' preferences and needs with the idea of discovering what each segment finds most valuable to more accurately tailor marketing materials toward that segment. This weekend, we are back with another Janatahack, this time dealing in a problem statement on customer segmentation. Stay tuned and make the maximum out of this learning opportunity.

janatahack-hr-analytics

HR Analytics Challenge A training institute which conducts training for analytics/ data science wants to expand their business to manpower recruitment (data science only) as well. Company gets large number of signups for their trainings. Now, company wants to connect these enrollees with their clients who are looking to hire employees working in the same domain. Before that, it is important to know which of these candidates are really looking for a new employment. They have student information related to demographics, education, experience and features related to training as well. To understand the factors that lead a person to look for a job change, the agency wants you to design a model that uses the current credentials/demographics/experience to predict the probability of an enrollee to look for a new job. Github Link: https://github.com/bilalProgTech/online-data-science-ml-challenges/tree/master/AV-Janata-Hack-HR-Analytics

janatahack-ml-for-banking

Have you ever wondered how lenders use various factors such as credit score, annual income, the loan amount approved, tenure, debt-to-income ratio etc. and select your interest rates The process, defined as ‘risk-based pricing’, uses a sophisticated algorithm that leverages different determining factors of a loan applicant. Selection of significant factors will help develop a prediction algorithm which can estimate loan interest rates based on clients’ information. On one hand, knowing the factors will help consumers and borrowers to increase their credit worthiness and place themselves in a better position to negotiate for getting a lower interest rate. On the other hand, this will help lending companies to get an immediate fixed interest rate estimation based on clients information. Here, your goal is to use a training dataset to predict the loan rate category (1 / 2 / 3) that will be assigned to each loan in our test set. You can use any combination of the features in the dataset to make your loan rate category predictions. Some features will be easier to use than others. Variable Definition Loan_ID A unique id for the loan. Loan_Amount_Requested The listed amount of the loan applied for by the borrower. Length_Employed Employment length in years Home_Owner The home ownership status provided by the borrower during registration. Values are: Rent, Own, Mortgage, Other. Annual_Income The annual income provided by the borrower during registration. Income_Verified Indicates if income was verified, not verified, or if the income source was verified Purpose_Of_Loan A category provided by the borrower for the loan request. Debt_To_Income A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested loan, divided by the borrower’s self-reported monthly income. Inquiries_Last_6Mo The number of inquiries by creditors during the past 6 months. Months_Since_Deliquency The number of months since the borrower's last delinquency. Number_Open_Accounts The number of open credit lines in the borrower's credit file. Total_Accounts The total number of credit lines currently in the borrower's credit file Gender Gender Interest_Rate Target Variable: Interest Rate category 1 2 3 of the loan application

knocktober-2016

Codes related to Knocktober 2016

master

ml-customer-segments

Customer Segments Exploration w. Unsupervised Machine Learning

mobilityanalytics

With the upcoming cab aggregators and demand for mobility solutions, the past decade has seen immense growth in data collected from commercial vehicles with major contributors such as Uber, Lyft and Ola to name a few. There are loads of innovative data science and machine learning solutions being implemented using such data and that has led to tremendous business value for such organizations. This weekend we bring to you another JanataHack, this time relating to mobility business. Participate, compete and earn bragging rights against the best hackers globally.

nlp-with-python

Scikit-Learn, NLTK, Spacy, Gensim, Textblob and more

squad

Building QA system for Stanford Question Answering Dataset

thinkstats2

Text and supporting code for Think Stats, 2nd Edition

vetrivel-ps