Pravin Borate's repositories
Fashion_Search_App
Fashion Search Application
LLM_MODELING_QNA
ZS LLM CHALLENGE
ML-From-Scratch
Machine Learning From Scratch
Optimization
Optimization
LangChain
LangChain
Data-Visualizations---Business-Case-Study
Data Visualizations - Business Case Study
Data-Science-Free-Courses
Free of cost Data Science Courses
8WeeksSqlChallenge
The Repository contains sql case studies which will help to practice and learn effectively
text_preprocess_package
Text Prepossessing Package
gitignore
A collection of useful .gitignore templates
StackOverflowTagging
StackOverflow
Public-Relations-Department
Public Relations Department
Marketing-Segementation
Case Study : You have hired as a consultant to a bank in New York City.
COVID-19-DETECTION
Covid-19 Detection via x-ray
pyspark-examples
Pyspark RDD, DataFrame and Dataset Examples in Python language
Pima-Indians-Diabetes-Dataset
Personal project using Pima Indians Diabetes to analyse it and make predictions using Machine Learning techniques.
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.
Neural-style-transfer
Neural-style-transfer
Flight-Fare-Prediction
Flight Fare Prediction
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.
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.