These are the tasks that were assigned during the one month internship.

Build a classifier to predict the Pass/Fail yield of a particular process entity and analyse whether all the features are required to build the model or not.

The data at hand is of flight satisfaction survey along with the customer flight information, the task at hand is to build a model that predicts satisfaction/dissatisfaction given the various attri…

The objective of the project is to create a machine learning model. We are doing a supervised learning and our aim is to do predictive analysis to predict housing price.

A data set of 30000 records and 24 variables containing information on defaults, demographic factors, credit data, delinquency, repayment and billed amounts of a credit card client in Taiwan from A…

It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Here, the aim is to anal…

This Online Retail data set contains all the transactions occurring for a UK-based and registered, non-store online retail between 01/12/2009 and 09/12/2011.

This project used Hypothesis Testing and visualization to evaluate a new golf ball coating designed to resist cuts and provide durability by comparing the driving distances of the old and new golf …

Goal is to classify the patients into the respective labels ( Healthy/ Parkinson) using the attributes from their voice recordings

Identified potential loan customers for Thera Bank using classification techniques. Compared models built with Logistic Regression and KNN algorithm in order to select the best performing one.

This 2018 Developer Survey results are organized on Kaggle in two tables and Telecom churn dataset on Kaggle, objective was EDA

The objective of the project is to create a machine learning model. We are doing a supervised learning and our aim is to do predictive analysis to predict median housing price.

5 Projects based on Unsupervised learning

The project involves deciding on the mode of transport that the employees prefer while commuting to office. For this, multiple models such as KNN, Naive Bayes, Logistic Regression have been created…

The objective of the project is to use the dataset to build a regression model to predict satisfaction. Project Approach, Data Exploration, Collinearity of the variables ,Initial Regression analysi…

This project used Hypothesis Testing and Visualization to leverage customer's health information like smoking habits, bmi, age, and gender for checking statistical evidence to make valuable decisio…

The data of a driver’s uber trips are available for year 2016. Your manager wants you to explore this data to give him some useful insights about the trip behaviour of a Uber driver.The dataset con…

This dataset consist of data From 1985 Ward's Automotive Yearbook Honey production data was published by the National Agricultural Statistics Service (NASS) of the U.S. Department of Agriculture.

The project involves conducting a thorough analysis of Point of Sale (POS) Data for providing recommendations through which a coffee chain restaurant can increase its revenue by either taking off c…

The project involved developing a credit risk default model using a given data which had to be checked for outliers, missing values, multicollinearity etc. Univariate and Bivariate Analysis had to …

Initial text mining exercise was performed on a dataset of Shark tank episodes with 495 entrepreneurs making their pitch to VCs. Used that to build multiple models (CART, Random Forest, Logistic Re…

The project involved developing an ARIMA model to forecast the monthly Australian gas production level for the next 12 months.

The primary objective was to investigate the parameters contributing for customer churn (attrition) in the Telecom Industry. A Logistic Regression Model was developed and validated with test data t…

The objective of this exercise was to build a model using a Supervised learning technique to figure out profitable segments to target for cross-selling personal loans. A Pilot campaign data of 2000…