yashaswibiyahut

Credit-Card-Fraud-Detection

Working on scikit-learn library in Python to classify - Anonymized credit card transactions labeled as fraudulent or genuine. 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. Here in this module I'm working on three classification models : Decision Tree Classifier K-nearest neighbors (KNN) Classifier Random Forest Classifer to predict Anonymized credit card transactions as fraudulent or genuine. In order to do so I have done my data Processing : Handled Missing values : Using mean Standarised the data : Using Standardisation At the end, calculate the acuracy rate and error rate of all three classification. Decision Tree Accuracy : Accuracy_Decison : 99.87361325656508 -- (A better approach to follow) Error_rate : 0.12638674343491083 Random Forest Accuracy : Accuracy_Decison : 99.92978514253616 Error_rate : 0.07021485746383935 K-nearest neighbors (KNN) Accuracy : Accuracy_Decison : 99.9578710855217 Error_rate : 0.04212891447830361 Thanks for looking it :) .... Feel free to like :)

Data-Structures-and-Algorithms

Data Structures and Algorithms implemented In Python, C/C++, Java or any other language. Give a Star 🌟 if it helps you.

DS-Algo-Point

This repository contains codes for various data structures and algorithms in C, C++, Java, Python, C#, Go, JavaScript, PHP, Kotlin and Scala

DSA

E-shopping-Hub

hacking-tools-scripts

Hacktoberfest2020

HacktoberFese2020

NeoAlgo

Bringing all Data Structures and Algorithms under one Roof ⚡

people

python_blackjack.py

yashaswibiyahut/Credit-Card-Fraud-Detection

Working on scikit-learn library in Python to classify - Anonymized credit card transactions labeled as fraudulent or genuine. 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. Here in this module I'm working on three classification models : Decision Tree Classifier K-nearest neighbors (KNN) Classifier Random Forest Classifer to predict Anonymized credit card transactions as fraudulent or genuine. In order to do so I have done my data Processing : Handled Missing values : Using mean Standarised the data : Using Standardisation At the end, calculate the acuracy rate and error rate of all three classification. Decision Tree Accuracy : Accuracy_Decison : 99.87361325656508 -- (A better approach to follow) Error_rate : 0.12638674343491083 Random Forest Accuracy : Accuracy_Decison : 99.92978514253616 Error_rate : 0.07021485746383935 K-nearest neighbors (KNN) Accuracy : Accuracy_Decison : 99.9578710855217 Error_rate : 0.04212891447830361 Thanks for looking it :) .... Feel free to like :)

yashaswibiyahut/Hacktoberfest2020

HacktoberFese2020

yashaswibiyahut/Data-Structures-and-Algorithms

Data Structures and Algorithms implemented In Python, C/C++, Java or any other language. Give a Star 🌟 if it helps you.

yashaswibiyahut/DS-Algo-Point

This repository contains codes for various data structures and algorithms in C, C++, Java, Python, C#, Go, JavaScript, PHP, Kotlin and Scala

yashaswibiyahut/DSA

yashaswibiyahut/E-shopping-Hub

yashaswibiyahut/hacking-tools-scripts

yashaswibiyahut/NeoAlgo

Bringing all Data Structures and Algorithms under one Roof ⚡

yashaswibiyahut/people

yashaswibiyahut/python_blackjack.py

yashaswibiyahut/Shree-Boutique

yashaswibiyahut/Tic-Tac-Toe

Tic-tac-toe is implemented using python. The game is automatically played by the program and hence, no user input is needed. Numpy and random Python libraries are used to build this game. Instead of asking the user to put a mark on the board, code randomly chooses a place on the board and put the mark. It will display the board after each turn unless a player wins. If the game gets draw, then it returns -1.