Sudhir Rai

Visualization of food court data by using HTML, CSS, JavaScript and D3 library.
Data is visualized in form of Treemap.

Titanic dataset analysis is performed. Decision Tree and K-means clustering analysis using R.
Visualized results through Tableau.
Steps performed:
1)Data retrieval
2)Data pre-processing
3) Decision Tree using R
4) K-mean clustering using Tableau- R integration by invoking Rserve ()

Tools Used: R, Tableau

Dream Housing Finance company deals in all home loans. They have a presence across all urban, semi-urban and rural areas. Customers first apply for a home loan after that company validates the customer's eligibility. The company wants to automate the loan eligibility process (real-time) based on customer detail provided while filling online application form.

Tools Used: Python, Logistic Regression, XGboost, Random Forest, Decision Tree

Web Scrapping is a technique of extracting information from websites using computer software or applications.
Temperature data is acquired using a Python technique.

Tool Used: Python, BeautifulSoup4

Website used to collect data:
https://weather.com/weather/today/l/USTX1110:1:US

Academic Project:
Data exploration and data visualization of various types of data to create variety of charts for better understanding of data

This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. https://www.kaggle.com/mayanksrivastava/predict-housing-prices-simple-linear-regression/data

Used various regression methods and try to predict the house prices by using different features which contribute to the prediction of the house price. As we know we have different methods to get the solution but we need to find the best solution among the various… Show more

This dataset contains house sale prices for King County, which includes Seattle. It includes homes sold between May 2014 and May 2015. https://www.kaggle.com/mayanksrivastava/predict-housing-prices-simple-linear-regression/data

Used various regression methods and try to predict the house prices by using different features which contribute to the prediction of the house price. As we know we have different methods to get the solution but we need to find the best solution among the various method because each method has pros and cons. Once after visualizing some features and a data mining process. we tried to find the best regression model for this dataset. Show less

The objective of the project is to classify whether the customer will Churn or not for the Telecom Company

Examined consumer complaint database along with zip code dataset and IRS dataset using Hadoop framework

Used AWS EMR and S3 for the purpose of cloud storage and computing

Used Hive and Pyspark to conduct Descriptive analysis leading to meaningful insights

Data Camp -Case Study
Made schools more efficient by improving their budgeting decisions. Which Saves hundreds of hours each year that humans spent labeling line items and now they Can spend more time on the decisions that really matters. By preprocessing and splitting the data into train and test and feed into the different ML Algorithm. The accuracy of the model was 86%.(DataCamp)

Tool Used: Python,Linear Regression, Logistic Regression

Academic Project:
Performed Text and Sentiment Analysis for iPad Pro and Microsoft Surface Pro 4 using SAP HANA twitter API and visualized the findings using SAP Lumira.

Academic Project:
This is a Business Intelligence project using SAS enterprise miner in which the data will provide information that can be used to understand buying behavior of a customer and what factors contribute to a variation in this. Factors can be a mixture of internal (gender, income, location) or external (policy offered, renewal type). Also, using predictive analytics on the data, we would be able to predict the most profitable customers and would help company to retain and… Show more

Academic Project:
This is a Business Intelligence project using SAS enterprise miner in which the data will provide information that can be used to understand buying behavior of a customer and what factors contribute to a variation in this. Factors can be a mixture of internal (gender, income, location) or external (policy offered, renewal type). Also, using predictive analytics on the data, we would be able to predict the most profitable customers and would help company to retain and increase such customers to increase overall revenue of the company.

Tools Used: Enterprise SAS miner, SAS, Linear Regressions, Logistic Regression, Neural Network Show less