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26-weeks-of-data-science

Email Newsletter

algoperformancechecker

Java Based Design to check the performance of different sorting algorithms

analytics_vidhya

Codes related to activities on AV including articles, hackathons and discussions.

analyzing-visualizing-data-powerbi

This repository contains the lab files and other resources for the free Microsoft course DAT207x: Analyzing and Visualizing Data with Power BI. To learn how to connect, explore, and visualize data with Power BI, sign up for this course on edX.

audit-data

Building a classification ML model to help auditors to predict a fraudulent firm

capital-one_ds-_ch

A capital one data science challenge where we have to analyze data patterns in NYC dataset

classification-problem

Identifying wthether a person will be able to complete training or not

credit-card-default

The project helps you detect whether a person will default the next month or not

data

Data and code behind the articles and graphics at FiveThirtyEight

data-analysis

Data Science Using Python

database-project

Building a Library Management System with dynamic actions capability

detecting-spam

Out of the mail list, find an algo to detect whether a mail is a spam or not

energy-efficiency

Goal: Implemented a linear regression model on the dataset to predict the energy usage of appliances. We are not allowed to use any available implementation of the regression model. We should implement the gradient descent algorithm with batch update (all training examples used at once). Using the sum of squared error normalized by 2*number of samples [J(β0, β1) = (1/2m)[∑(yᶺ(i) – y(i))2 ] as our cost and error measures, where m is number of samples. We can use any number of features greater than 15. We can decide on which features to use using some experimentation and exploratory analysis. Also implemented a logistic regression model as described in Part 4. For logistic regression, we can write our own implementation similar to linear regression, or can use any R or Python library. You should use the logistic regression cost/error function from the class. In addition you can also use accuracy/ROC/etc.

eta---uber

Calculating ETA of Uber in london using Uber movement data

learn-pandas

Tutorials on how to use pandas effectively to do data analysis

machine-learning-101

A repository related to datasets for Machine Learning.

mlcourse.ai

Open Machine Learning Course

multidimensionalsearch

A Java project to optimize search operations for different attributes for the same line of products.

nycflights13

An R data package containing all out-bound flights from NYC in 2013 + useful metdata

pandas-intro

product_design

uber_ds_ch

Uber data science challenge

uni-variate-plot

Uni variate plots in python