Illegal transactions detection model to prevent money laundering

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here.

In order to create different prediction models to identify illegal transactions, the first step is to understand the characteristics of the dataset. The purpose of this milestone is to use Python to create frequency tables and charts to assess the number of payments received and made, payment status and currency. This is a part of ‘exploratory analysis’ i.e., simple functions in Python.

Method: Use the Python application (Jupyter Notebook) and follow the below steps.

• Go through the dataset and understand it.
• Create a bar chart to define the frequency of transactions from each bank,
• Create a bar chart to define the frequency of transactions to which bank
• Create a bar chart for ‘receiving current’
• Create a bar chart for ‘payment current’
• Create a pie chart for defining payment format
• Create a pie chart for defining laundering status
• Create a frequency table to show the number of transactions for each currency

Structure: after generating all the charts and table, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Introduction to the dataset
• Explanation of the bar chart ‘frequency of transactions from each bank’
• Explanation of the bar chart ‘frequency of transactions to which bank’
• Explanation of the bar chart ‘receiving current’
• Explanation of the bar chart ‘payment current’
• Explanation of the pie chart ‘defining payment format’
• Explanation of the bar chart ‘defining laundering status’
• Conclusion

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb) for charts and graphs

 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here.

Although the ultimate goal of this study is to create a different prediction model to detect illegal transactions, the first step is to understand the characteristics of the dataset. The purpose of this milestone is to use Python to create pie charts to distribute the amounts received and amounts paid according to a range. This is a part of ‘exploratory analysis’ i.e., simple functions in Python.

Methodology: Use the Python application (Jupyter Notebook) and follow the below steps.

• Go through the dataset and understand it.
• Create a pie chart for distributing the ‘payments received’ column into the following ranges:
  • Below 50000
  • 50000-100000
  • 1000000-5000000
  • 5000000-10000000
  • 10000000-50000000,
  • Above 50000000
• Create a pie chart for the ‘payments made’ in the same ranges.

Structure: after generating all the charts and table, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Introduction to the dataset
• Explanation of the pie chart ‘payments received’.
• Explanation of the pie chart ‘payments made’.
• Conclusion

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here.

Although the ultimate goal of this study is to create a different prediction model to detect illegal transactions, the first step is to understand the characteristics of the dataset. The purpose of this milestone is to use Python to create new features in this dataset like generating the mean values. This is a part of ‘feature extraction’ i.e., intermediate-level functions in Python.

Methodology: Use the Python application (Jupyter Notebook) and follow the below steps.

• Go through the dataset and understand it.
• Based on different currency types, define a new feature for recording the aggregate of the mean of ‘received’ and ‘paid’ amounts. For example- you compute the mean of ‘paid’ amounts for the US dollar, similarly for Euro, and so on.
• Based on the frequency of transactions between the two accounts, add a new feature of aggregate number of transactions, i.e., wherein the if recipient account has more than 50 transactions, then the value is ‘1’, else ‘0’.

 

Structure: after generating all the charts and table, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Introduction to the dataset
• Explanation of the ‘mean’ feature which you generated.
• Explanation of the ‘aggregate number of transactions’ feature which you generated
• Conclusion

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here.

Although the ultimate goal of this study is to create a different prediction model to detect illegal transactions, the first step is to clean and organise the dataset. The purpose of this milestone is to clean the data to make it relevant for model building at a later stage. This is a part of ‘data preprocessing’ i.e., intermediate-level functions in Python.

Methodology: Use the Python application (Jupyter Notebook) and follow the below steps.

• Transform the Timestamp using normalization.
• Create a Unique ID for each transaction by adding bank code with the account number.
• Create a new dataframe for recording all receiving information like receiving amount, currency, and account.
• Create a separate dataframe for recording all the payment information like paid amount, currency, and account.
• Create a list of currencies which are used for all transactions.
• Using labelencoder, label the receiving currency, payment currency, and the payment format

Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Introduction to/ Characteristics of the dataset
• Process of transforming timestamp (explain in a few simple steps)
• Process of creating a UniqueID for each transaction (explain in a few simple steps)
• Process of creating a new dataframe for recording RECEIVED AMOUNTS information (explain in a few simple steps)
• Process of creating a separate dataframe for recording PAID AMOUNTS information (explain in a few simple steps)
• Process of creating a list of currencies (explain in a few simple steps)
• Process of labelling the currency and payment format (explain in a few simple steps)

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Intermediate’ level of expertise in Python programming. In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using SVM with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a SVM based model for prediction.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

Note: This is a part of ‘Intermediate’ level of expertise in Python programming. In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using Naïve Bayes with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a Naïve Bayes based model for prediction.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

Note: This is a part of ‘Intermediate’ level of expertise in Python programming. In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a Logistic Regression based model for prediction.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Intermediate’ level of expertise in Python programming. In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using KNN with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a KNN based model for prediction.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Intermediate’ level of expertise in Python programming. In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using Random Forest with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a Random Forest based model for prediction.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

 

Note: This is a part of ‘Intermediate’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using Decision Tree with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a Decision Tree based model for prediction.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

Note: This is a part of ‘Advanced’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using LSTM with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a LSTM based model for prediction.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Advanced’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using Bi-LSTM with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a Bi-LSTM based model for prediction.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Advanced’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using CNN with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a CNN based model for prediction.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Advanced’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using ANN with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a ANN based model for prediction.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Expert’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using SVM with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a SVM based model for prediction and tune it with GridSearchCV
• Derive the most optimal SVM model.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Expert’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using decision trees with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a decision trees based model for prediction and tune it with GridSearchCV
• Derive the most optimal decision trees model.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

 

Note: This is a part of ‘Expert’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using Naïve Bayes with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a Naïve Bayes based model for prediction and tune it with GridSearchCV
• Derive the most optimal Naïve Bayes model.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Expert’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using logistic regression with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a logistic regression based model for prediction and tune it with GridSearchCV
• Derive the most optimal logistic regression model.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Expert’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using random forest with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a random forest based model for prediction and tune it with GridSearchCV
• Derive the most optimal random forest model.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Expert’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using KNN with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a KNN based model for prediction and tune it with GridSearchCV
• Derive the most optimal KNN model.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

Note: This is a part of ‘Expert’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using LSTM with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a LSTM based model for prediction and tune it with GridSearchCV
• Derive the most optimal LSTM model.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)

 

 

Note: This is a part of ‘Expert’ level of expertise in Python programming.  In order to work on this milestone, you need to perform all the steps related to data processing explained in the previous milestones:

• Milestone 1: Understanding the nature of banks’ financial transactions using Python
• Milestone 2: Using Python to classify financial transaction data according to amount
• Milestone 3: Generating new features in a financial transactions dataset using Python
• Milestone 4: Cleaning financial transactions dataset using Python

However, you do not need to write the articles. 

Aim: Our dataset consists of over 10 lakh synthetic transactions of a bank. More information about the dataset can be found here. The ultimate goal of this module is to create a prediction model for detecting illegal transactions.

After cleaning and processing the data, prediction models can be generated to detect money laundering (illegal) transactions. The purpose of this milestone is to create the predictive model using CNN with accuracy of at least 85%.

Methodology: Use Jupyter Notebook (Python) to perform the following steps:

• Perform the data processing steps mentioned in Milestone 1.
• Perform the data processing steps mentioned in Milestone 2.
• Perform the data processing steps mentioned in Milestone 3.
• Perform the data processing steps mentioned in Milestone 4.
• Then, use the dataset created after Milestone 4. Balance and format the dataset by splitting the data with test split of 0.3, standardized, and generate tokenizers.
• Create a random CNN model for prediction and tune it with GridSearchCV
• Derive the most optimal CNN model.
• Evaluate the performance of model using performance metrics i.e. accuracy, F1-score, precision, recall, confusion matrix, and classification report.

 Structure: after organising the data, write a 600 words article in the below format.

• Introduction to the problem of money laundering
• Major shortcomings of existing money laundering detection models
• Findings of the created prediction model (explain the findings of the performance metrics and compare it to the accuracy of previously developed models).
• References (of previous models which you are referring to).

Note:

Do not copy paste information from any source. It goes against our plagiarism rules.

Your submission must consist of the following:

• Article in MS Word format
• Jupyter notebook files (.ipynb)