VIJAY-GADRE

1_Simple_Linear_Regression

2nd_Hand_Car_Price_Prediction

The prices of new cars in the industry are fixed by the manufacturer with some additional costs incurred by the Government in the form of taxes. So, customers buying a new car can be assured of the money they invest to be worthy. But due to the increased price of new cars and the incapability of customers to buy new cars due to the lack of funds, used cars sales are on a global increase (Pal, Arora, and Palakurthy, 2018). There is a need for a used car price prediction system to effectively determine the worthiness of the car using a variety of features. Even though there are websites that offer this service, their prediction method may not be the best. Besides, different models and systems may contribute to predicting power for a used car’s actual market value. It is important to know their actual market value while both buying and selling.

Audio_Books_Classification

The Data is from an Audiobook App. Logically, it relates to the audio versions of books ONLY. Each customer in the database has made a purchase at least once, that's why he/she is in the database. We want to create a machine learning algorithm based on our available data that can predict if a customer will buy again from the Audiobook company.

Bank_Loan_Prediction

Loans are the core business of banks. The main profit comes directly from the loan’s interest. The loan companies grant a loan after an intensive process of verification and validation. However, they still don’t have assurance if the applicant is able to repay the loan with no difficulties.

Barcode_Face_attendance

Customer_Personality_Analysis

Customer Personality Analysis is a detailed analysis of a company’s ideal customers. It helps a business to better understand its customers and makes it easier for them to modify products according to the specific needs, behaviors, and concerns of different types of customers.

Market_Segmentation

MNIST_HANDWRITTEN_DIGIT_CLASSIFICATION

Titanic_Survival_Prediction

The sinking of the Titanic is one of the most infamous shipwrecks in history. On April 15, 1912, during her maiden voyage, the widely considered “unsinkable” RMS Titanic sank after colliding with an iceberg. Unfortunately, there weren’t enough lifeboats for everyone on board, resulting in the death of 1502 out of 2224 passengers and crew. While there was some element of luck involved in surviving, it seems some groups of people were more likely to survive than others. In this challenge, we ask you to build a predictive model that answers the question: “what sorts of people were more likely to survive?”

Waste_Classification_using_ResNet152

VIJAY-GADRE/Barcode_Face_attendance

VIJAY-GADRE/opencv

Open Source Computer Vision Library

VIJAY-GADRE/portfolio-2

VIJAY-GADRE/iPhone-Demo-Design

VIJAY-GADRE/Hotel-Design2

VIJAY-GADRE/Attendance

VIJAY-GADRE/Web-design-Hotel-Design-

VIJAY-GADRE/Web-Design-BlackBoard

VIJAY-GADRE/Web-Design-Portfolio

VIJAY-GADRE/Web-Design-Weather