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Credit Card Fraud Analysis Using Data Science (1).pdf
1.
Credit Card Fraud
Analysis Using Data Science Sampriti Chatterjee (Great Learning) Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
2.
Agenda Why do we
need data science? 1 What is Data science? 2 Why Python is so popular? 4 Life cycle of Data science 3 Install python 5 Statistical visualization on Python user 6 Basic Syntax for Python 7 List,Set.Tuple 8 Data manipulation using Numpy and Pandas 9 What is machine Learning? 10 Supervised Learning: Logistic Regression 11 Credit card Fraud analysis using Python 12 Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
3.
Why do we
need Data Science? • In the past, we used to have data in a structured format but now as the volume of the data is increasing, so the number of structured data becomes very less, so to handle the massive amount of data we need data science techniques • Those data can be used to get the proper business insights and the hidden trends from them. • These insights helps the organization to predict the Future • Using data science decision making can be faster and effective • Helps to reduce the production cost • Build model based on the data to give the ability to the machine to predicts on its own How data science is effecting our everyday life? Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
4.
What is Data
Science? Data science is a process to get some meaningful information from the massive amount of data. In simple terms, read and study the data to get proper intuitive insights. Data Science is a mixture of various tools, algorithms, and machine learning and deep learning concepts to discover hidden patterns from the raw and unstructured data Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
5.
Life cycle of
Data Science? Understand the business problem Data Acquisition Data Cleaning Exploratory data Analysis Machine Learning Algorithm Predict your model accuracy Deploy the model Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
6.
Most Popular Programming
Languages For Data Science? Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
7.
Introduction to Python High
level Object oriented Interpreted Python is a popular high level, object oriented and interpreted language Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
8.
History of Python But,
why the language called as Python? • Python is invented by Guido van Rossum in 1989 • Rossum used to love watching comedy movies from late seventies • He needed a short, unique, and slightly mysterious name for his language • In that time he was watching Monty Python’s Flying Circus and from that series he decided to keep his language name python. • This how Python invented Important Facts Inventor of Python Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
9.
Graphical user interface Why
should you learn Python? Web development using Python Length of the program is short Mathematical computation can be done easily 1 2 3 Python is simple and beginner friendly language 4 5 Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
10.
Why Python is
so popular? 1 Largest community for Learners and Collaborators Open source 3 Easy to learn and usable flexibility 4 Huge numbers of Python libraries and Frame work 2 5 Supports Big Data, Machine Learning and Cloud computing 6 Supports Automation Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
11.
This is the
site to install Python -> https://www.python.org/downloads/ Installing Python Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
12.
Site to install
Python -> https://www.jetbrains.com/pycharm/download/#section=mac Popular IDE for Python: Pycharm Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
13.
Anaconda installation site-> https://www.anaconda.com/products/individual Popular
IDE for Python: Anaconda Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
14.
Google collaboratory link-> https://colab.research.google.com/notebooks/intro.ipynb Popular
IDE for Python: Google colab Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
15.
Statistical measurement on
Python user In recent time it is prominent that Python is one of the most popular language because of it’s simplicity Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
16.
Getting started with
Python Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
17.
How to Store
Data? How do I store data? 123 “John” TRUE Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
18.
Need of Variables Data/Values
can be stored in temporary storage spaces called variables “John” Student 0x1098ab Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
19.
Basic syntax for
Python Variable 1 Variables are used as containers to store the data values 2 Python has no command to declare a variable 3 A variable is created when you assigned a value to it Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
20.
DataTypes in Python Every
variable is associated with a data-type 10, 500 3.14, 15.97 TRUE, FALSE “Sam”, “Matt” int float Boolean String Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
21.
Basic syntax for
Python Datatypes 1 Data types are used to classify or categorize of data items 2 Data types represent value which determines what operations can be performed on that data 3 In python data types is assigned when you assign the value to the variable 4 We can check the data type of the variable by calling the type() function Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
22.
Basic syntax for
Python Python string 1 Single line string: 2 Multiline string: 3 String as arrays: Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
23.
Basic syntax for
Python Python string 4 String slicing: 5 String negative indexing: 6 Length of the String: Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
24.
Python Libraries Proprietary content.
©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
25.
Data manipulation using
Python Data manipulation is a technique which allows to transform, extract, and filter your data efficiently with less time. Main two python libraries are used to manipulate the data Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
26.
Data manipulation using
Numpy Numpy stands for Numerical Python and it is used to perform mathematical and logical operations on arrays 1 Numpy is a python library 2 Install Numpy: !pip install numpy 3 Import the Library: import numpy as np Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
27.
Data manipulation using
Pandas Pandas is a popular data manipulation and analysis library in python which is based on Numpy 1 Python is a python library built on top of Numpy 2 Install Numpy: !pip install pandas 3 Import the Library: import pandas as pd Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
28.
Demo on Numpy
and pandas Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
29.
Creating a Dataframe What
is a dataframe? Panda’s data frame is a two-dimensional data structure which is aligned in a tabular fashion with rows and columns Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
30.
Dataframe In-Built Functions head() tail() shape()
describe() Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
31.
Dropping Columns Proprietary content.
©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
32.
Dropping Rows Proprietary content.
©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
33.
Machine Learning to
build the Model Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
34.
What is Machine
Learning? Machine learning is a sub-set of artificial intelligence (AI) that allows the system to automatically learn and improve from experience without being explicitly programmed Training Data Model Building Testing Data Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
35.
Traditional Programming Data Program Machine Learning Output Model Data Output Traditional
Vs Machine Learning Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
36.
Types Of Machine
Learning Supervised Learning Unsupervised Learning Reinforcement Learning Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
37.
What is Supervised
Learning? Supervised learning works as a supervisor or teacher. Basically, In supervised learning, we teach or train the machine with labeled data (that means data is already tagged with some predefined class). Then we test our model with some unknown new set of data and predict the level for them ▪ Learning from the labelled data and applying the knowledge to predict the label of the new data(test data), is known as Supervised Learning ▪ Types of Supervised Learning: • Linear Regression • Logistic regression • Decision Tree • Random Forest • Naïve Bayes Classifier Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
38.
What is Logistic
Regression? Logistic regression is also a part of supervised learning classification algorithm. It is used to predict the probability of a target variable and the nature of target or dependent variable is discrete, so for the output there will be only two class will be present ▪ The dependent variable is binary in nature so that can be either 1 (stands for success/yes) or 0 (stands for failure/no). ▪ Logistic regression is also known as sigmoid function ▪ Sigmoid function = 1 / (1 + e^-value) Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
39.
Credit Card Fraud
Analysis Using Python Proprietary content. ©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
40.
Thank You Proprietary content.
©Great Learning. All Rights Reserved. Unauthorized use or distribution prohibited
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