Pravin1Borate

8WeeksSqlChallenge

The Repository contains sql case studies which will help to practice and learn effectively

COVID-19-DETECTION

Covid-19 Detection via x-ray

Credit-Card-Fraud-Detection

The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Data-Visualizations---Business-Case-Study

Data Visualizations - Business Case Study

Flight-Fare-Prediction

Flight Fare Prediction

Hackathon

Details :

Pravin1Borate/Hackathon

Details :

Pravin1Borate/8WeeksSqlChallenge

The Repository contains sql case studies which will help to practice and learn effectively

Pravin1Borate/COVID-19-DETECTION

Covid-19 Detection via x-ray

Pravin1Borate/Credit-Card-Fraud-Detection

The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, … V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.

Pravin1Borate/Data-Visualizations---Business-Case-Study

Data Visualizations - Business Case Study

Pravin1Borate/Flight-Fare-Prediction

Flight Fare Prediction

Pravin1Borate/Bank_Customer_Churn

A bank is investigating a very high rate of customer leaving the bank. Here is a 10.000 records dataset to investigate and predict which of the customers are more likely to leave the bank soon.

Pravin1Borate/Cars24

Pravin1Borate/Data-Science-Free-Courses

Free of cost Data Science Courses

Pravin1Borate/Fashion_Search_App

Fashion Search Application

Pravin1Borate/gitignore

A collection of useful .gitignore templates

Pravin1Borate/Happiness-Salaries

Pravin1Borate/LangChain

LangChain

Pravin1Borate/LLM_MODELING_QNA

ZS LLM CHALLENGE

Pravin1Borate/Marketing-Segementation

Case Study : You have hired as a consultant to a bank in New York City.

Pravin1Borate/ML-From-Scratch

Machine Learning From Scratch

Pravin1Borate/Neural-style-transfer

Neural-style-transfer

Pravin1Borate/NLP_Case_Studies

Pravin1Borate/Optimization

Optimization

Pravin1Borate/Pima-Indians-Diabetes-Dataset

Personal project using Pima Indians Diabetes to analyse it and make predictions using Machine Learning techniques.

Pravin1Borate/Pravin1Borate

Pravin1Borate/pravinborate.github.io

Pravin1Borate/Public-Relations-Department

Public Relations Department

Pravin1Borate/pyspark-examples

Pyspark RDD, DataFrame and Dataset Examples in Python language

Pravin1Borate/San-Francisco-Crime-Classification

From 1934 to 1963, San Francisco was infamous for housing some of the world's most notorious criminals on the inescapable island of Alcatraz. Today, the city is known more for its tech scene than its criminal past. But, with rising wealth inequality, housing shortages, and a proliferation of expensive digital toys riding BART to work, there is no scarcity of crime in the city by the bay. From Sunset to SOMA, and Marina to Excelsior, this competition's dataset provides nearly 12 years of crime reports from across all of San Francisco's neighborhoods. Given time and location, you must predict the category of crime that occurred. We're also encouraging you to explore the dataset visually. What can we learn about the city through visualizations like this Top Crimes Map? The top most up-voted scripts from this competition will receive official Kaggle swag as prizes.

Pravin1Borate/StackOverflowTagging

StackOverflow

Pravin1Borate/StudentPerformanceIndicator

Pravin1Borate/text_preprocess_package

Text Prepossessing Package

Pravin1Borate/TF_certification_Practice

Pravin1Borate/transformers