Always know what to expect from your data.

A collection of Jupyter/IPython notebooks

Speed Up Exploratory Data Analysis (EDA)

A Toolkit for Interactive Statistical Data Visualization

Machine Learning Applications using Sklearn, matplotlib, pandas, and seaborn

Pre-Modelling Analysis of the data, by doing various exploratory data analysis and Statistical Test.

Solr client and user interface for search

Amazon Recommendation System build on BPR TensorFlow implementation

Mini-project based on activity detection dataset at UCIML. Checkout branches for latest push.

OCS: Examine the relationship between healthcare spending and healthcare coverage

Python Package for Quick Exploratory Data Analysis

In this project I make machine learning models to predict market value of professional football player from the "Complete FIFA 19 dataset" from Kaggle. It contains a pdf with the results, a youtube video of me explaining it, the codes and a read me file. Enjoy :)

World Cup Data ETL

Descriptive Analysis of Pride Parade Lineups, 2011-2018

Investigate a dataset of appoinment records for Brasil public hospitals

TP final de la materia 7550 - Introduccion a los sistemas inteligentes

Consists all the try outs and assignments in AIML program of great lakes

collection of some of the public data-related tutorials and reports I've made

explanatory analysis of news articles with gensim

This repository has some researches about the Brazilian dairy milk production.

Simple predictive modelling task using time series data.

Geospatial and Exploratory Analysis of Starbucks Locations

Exploratory Data Analysis on IPL data (2008 - 2018). Finding patterns in data

Sentiment Analysis on Amazon Food Reviews using Machine Learning Techniques

R package for exploratory analysis of ChIPseq data

Udacity exploratory analysis in R

A folder for the Inter-American Development Bank's 2018 Costa Rican Household Poverty Prediction Challenge

Customer Churn is a burning problem for Telecom companies. In this project, we simulate one such case of customer churn where we work on a data of postpaid customers with a contract. The data has information about the customer usage behavior, contract details and the payment details. The data also indicates which were the customers who canceled their service. Based on this past data, we need to build a model which can predict whether a customer will cancel their service in the future or not.