Credit-card-fraud-detection

roblem Statement The problem statement chosen for this project is to predict fraudulent credit card transactions with the help of machine learning models.

In this project, you will analyse customer-level data which has been collected and analysed during a research collaboration of Worldline and the Machine Learning Group.

The dataset is taken from the Kaggle website and it has a total of 2,84,807 transactions, out of which 492 are fraudulent. Since the dataset is highly imbalanced, so it needs to be handled before model building.

Business Problem Overview For many banks, retaining high profitable customers is the number one business goal. Banking fraud, however, poses a significant threat to this goal for different banks. In terms of substantial financial losses, trust and credibility, this is a concerning issue to both banks and customers alike.

It has been estimated by Nilson report that by 2020 the banking frauds would account to $30 billion worldwide. With the rise in digital payment channels, the number of fraudulent transactions is also increasing with new and different ways.

In the banking industry, credit card fraud detection using machine learning is not just a trend but a necessity for them to put proactive monitoring and fraud prevention mechanisms in place. Machine learning is helping these institutions to reduce time-consuming manual reviews, costly chargebacks and fees, and denials of legitimate transactions.

Understanding and Defining Fraud Credit card fraud is any dishonest act and behaviour to obtain information without the proper authorization from the account holder for financial gain. Among different ways of frauds, Skimming is the most common one, which is the way of duplicating of information located on the magnetic strip of the card. Apart from this, the other ways are:

Manipulation/alteration of genuine cards Creation of counterfeit cards Stolen/lost credit cards Fraudulent telemarketing

Data Dictionary The dataset can be download using this link

The data set includes credit card transactions made by European cardholders over a period of two days in September 2013. Out of a total of 2,84,807 transactions, 492 were fraudulent. This data set is highly unbalanced, with the positive class (frauds) accounting for 0.172% of the total transactions. The data set has also been modified with Principal Component Analysis (PCA) to maintain confidentiality. Apart from ‘time’ and ‘amount’, all the other features (V1, V2, V3, up to V28) are the principal components obtained using PCA. The feature 'time' contains the seconds elapsed between the first transaction in the data set and the subsequent transactions. The feature 'amount' is the transaction amount. The feature 'class' represents class labelling, and it takes the value 1 in cases of fraud and 0 in others.

Project Pipeline The project pipeline can be briefly summarized in the following four steps:

Data Understanding: Here, you need to load the data and understand the features present in it. This would help you choose the features that you will need for your final model. Exploratory data analytics (EDA): Normally, in this step, you need to perform univariate and bivariate analyses of the data, followed by feature transformations, if necessary. For the current data set, because Gaussian variables are used, you do not need to perform Z-scaling. However, you can check if there is any skewness in the data and try to mitigate it, as it might cause problems during the model-building phase. Train/Test Split: Now you are familiar with the train/test split, which you can perform in order to check the performance of your models with unseen data. Here, for validation, you can use the k-fold cross-validation method. You need to choose an appropriate k value so that the minority class is correctly represented in the test folds. Model-Building/Hyperparameter Tuning: This is the final step at which you can try different models and fine-tune their hyperparameters until you get the desired level of performance on the given dataset. You should try and see if you get a better model by the various sampling techniques. Model Evaluation: Evaluate the models using appropriate evaluation metrics. Note that since the data is imbalanced it is is more important to identify which are fraudulent transactions accurately than the non-fraudulent. Choose an appropriate evaluation metric which reflects this business goal.

NLP---POS-tagger-

HMMs and Viterbi algorithm for POS tagging

You have learnt to build your own HMM-based POS tagger and implement the Viterbi algorithm using the Penn Treebank training corpus. The vanilla Viterbi algorithm we had written had resulted in ~87?curacy. The approx. 13% loss of accuracy was majorly due to the fact that when the algorithm encountered an unknown word (i.e. not present in the training set, such as 'Twitter'), it assigned an incorrect tag arbitrarily. This is because, for unknown words, the emission probabilities for all candidate tags are 0, so the algorithm arbitrarily chooses (the first) tag.

In this assignment, you need to modify the Viterbi algorithm to solve the problem of unknown words using at least two techniques. Though there could be multiple ways to solve this problem, you may use the following hints:

Which tag class do you think most unknown words belong to? Can you identify rules (e.g. based on morphological cues) that can be used to tag unknown words? You may define separate python functions to exploit these rules so that they work in tandem with the original Viterbi algorithm. Why does the Viterbi algorithm choose a random tag on encountering an unknown word? Can you modify the Viterbi algorithm so that it considers only one of the transition or emission probabilities for unknown words?

Problem Statement
An education company named X Education sells online courses to industry professionals. On any given day, many professionals who are interested in the courses land on their website and browse for courses.
The company markets its courses on several websites and search engines like Google. Once these people land on the website, they might browse the courses or fill up a form for the course or watch some videos. When these people fill up a form providing their email address or phone number, they are classified to be a lead. Moreover, the company also gets leads through past referrals. Once these leads are acquired, employees from the sales team start making calls, writing emails, etc. Through this process, some of the leads get converted while most do not. The typical lead conversion rate at X education is around 30%.
Now, although X Education gets a lot of leads, its lead conversion rate is very poor. For example, if, say, they acquire 100 leads in a day, only about 30 of them are converted. To make this process more efficient, the company wishes to identify the most potential leads, also known as ‘Hot Leads’. If they successfully identify this set of leads, the lead conversion rate should go up as the sales team will now be focusing more on communicating with the potential leads rather than making calls to everyone. A typical lead conversion process can be represented using the following funnel:
As you can see, there are a lot of leads generated in the initial stage (top) but only a few of them come out as paying customers from the bottom. In the middle stage, you need to nurture the potential leads well (i.e. educating the leads about the product, constantly communicating etc. ) in order to get a higher lead conversion.
X Education has appointed you to help them select the most promising leads, i.e. the leads that are most likely to convert into paying customers. The company requires you to build a model wherein you need to assign a lead score to each of the leads such that the customers with higher lead score have a higher conversion chance and the customers with lower lead score have a lower conversion chance. The CEO, in particular, has given a ballpark of the target lead conversion rate to be around 80%.
Data
You have been provided with a leads dataset from the past with around 9000 data points. This dataset consists of various attributes such as Lead Source, Total Time Spent on Website, Total Visits, Last Activity, etc. which may or may not be useful in ultimately deciding whether a lead will be converted or not. The target variable, in this case, is the column ‘Converted’ which tells whether a past lead was converted or not wherein 1 means it was converted and 0 means it wasn’t converted. You can learn more about the dataset from the data dictionary provided in the zip folder at the end of the page. Another thing that you also need to check out for are the levels present in the categorical variables. Many of the categorical variables have a level called 'Select' which needs to be handled because it is as good as a null value (think why?).

Goals of the Case Study
There are quite a few goals for this case study.

Build a logistic regression model to assign a lead score between 0 and 100 to each of the leads which can be used by the company to target potential leads. A higher score would mean that the lead is hot, i.e. is most likely to convert whereas a lower score would mean that the lead is cold and will mostly not get converted.
There are some more problems presented by the company which your model should be able to adjust to if the company's requirement changes in the future so you will need to handle these as well. These problems are provided in a separate doc file. Please fill it based on the logistic regression model you got in the first step. Also, make sure you include this in your final PPT where you'll make recommendations.

Car-Price-Prediction---Linear-Regression

Problem Statement This assignment is a programming assignment wherein you have to build a multiple linear regression model for the prediction of car prices. You will need to submit a Jupyter notebook for the same.

Problem Statement A Chinese automobile company Geely Auto aspires to enter the US market by setting up their manufacturing unit there and producing cars locally to give competition to their US and European counterparts.

They have contracted an automobile consulting company to understand the factors on which the pricing of cars depends. Specifically, they want to understand the factors affecting the pricing of cars in the American market, since those may be very different from the Chinese market. The company wants to know:

Which variables are significant in predicting the price of a car How well those variables describe the price of a car Based on various market surveys, the consulting firm has gathered a large dataset of different types of cars across the Americal market.

Business Goal

You are required to model the price of cars with the available independent variables. It will be used by the management to understand how exactly the prices vary with the independent variables. They can accordingly manipulate the design of the cars, the business strategy etc. to meet certain price levels. Further, the model will be a good way for management to understand the pricing dynamics of a new market.

Data Preparation

There is a variable named CarName which is comprised of two parts - the first word is the name of 'car company' and the second is the 'car model'. For example, chevrolet impala has 'chevrolet' as the car company name and 'impala' as the car model name. You need to consider only company name as the independent variable for model building.

Model Evaluation:

When you're done with model building and residual analysis, and have made predictions on the test set, just make sure you use the following two lines of code to calculate the R-squared score on the test set.

from sklearn.metrics import r2_score r2_score(y_test, y_pred) where y_test is the test data set for the target variable, and y_pred is the variable containing the predicted values of the target variable on the test set.

Please don't forget to perform this step as the R-squared score on the test set holds some marks. The variable names inside the 'r2_score' function can be different based on the variable names you have chosen.