Ansuman Patnaik's Projects
Welcome to Ansuman's Portfolio
In this project is a programming assignment wherein you have to build a multiple linear regression model for the prediction of demand for shared bikes. You will need to submit a Jupyter notebook for the same.
This case study provides a practical application of EDA techniques in a real business context. Beyond utilizing EDA methods, the goal is to foster a fundamental comprehension of risk analytics within the banking and financial services sector. The focus extends to understanding the role of data in mitigating financial risks associated with lending.
This project analyzes NYC restaurant inspections, leveraging business acceleration data to predict inspection grades using machine learning models. The Random Forest model performed best, demonstrating high accuracy and identifying key features influencing food safety compliance and inspection outcomes.
NaΓ―ve Bayes Text Classification
Decision Trees/Random Forests
Data Science Assignment Module 2 (K-Fold Cross Validation)
Cleaning Messy Data
Feature Selection & Dimensionality Reduction
Can We Predict Purchases from Web Sites?
Section1 DEMO_REPO
This project outlines the final project requirements for DAV6100 - Information Architectures, focusing on group assignments, scoring criteria, topic selection, core requirements, and project components such as design, development, visualization, and executive presentation.
An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.
RSVP Movies, aiming for a global audience in 2022, seeks data-driven insights. As a data analyst and SQL expert, I'll analyze past movie data, providing recommendations for their strategic planning and success.
This project aims to analyze and forecast the total funding amounts of startups using various regression and time series modeling techniques. Initially, we preprocess the dataset, which includes features such as funding amounts, company size, and number of funding rounds. The data is then scaled and split into training and testing sets.
In this project, you will analyse customer-level data of a leading telecom firm, build predictive models to identify customers at high risk of churn and identify the main indicators of churn.