data analysis R or Python

Data Analysis Project in R or Python I am looking for a freelancer who can help me with my data analysis project. I have no preference for the programming language to be used, but I need someone who is proficient in both R and Python. The data I am analyzing is both quantitative and qualitative, so I need someone who has experience working with both types of data. The main objective of this project is to perform predictive modeling, exploratory analysis, and data visualization. Therefore, I am looking for someone who has expertise in all three areas. Skills and Experience Required: - Proficiency in both R and Python - Experience with quantitative and qualitative data analysis - Expertise in predictive modeling, exploratory analysis, and data visualization - Excellent communication skills and ability to work collaboratively Work to do: Bank Account Fraud Description In a large consumer bank, individuals can apply online for a bank account opening and, if granted, benefit from the banking services. Leveraging this system, fraudsters attempt to gain access to such services by identity theft or by creating a fictional individual. Once access to a new bank account is granted, the fraudster quickly takes advantage of the accompanying line of credit or uses the account to receive illicit payments. In this scenario, where the fraudster succeeds, the bank sustains all costs as there is no way of tracing the fraudster's true identity. Using a dataset gathered from online bank account opening applications, the goal of this practical assignment is to provide informed and well-documented advice on which predictive modelling solution to use to detect fraudulent applications accurately. Each instance (row) of the dataset represents an application described by 32 features. The label of each instance is stored in the"is_fraud" target variable. A full description of the features that compose the dataset is available here. Tasks Using the [login to view URL] data set, you have a set of main tasks to accomplish as described next. Still, you are free to include other tasks to increase the value of your assignment. Task 1: Data Understanding and Preparation and Descriptive Analytics This task involves summarizing and visualizing the data to provide valuable insights. Consider questions that could be interesting to check with the available data and provide answers using textual summaries or data visualization. Based on this analysis, you should also check if it is necessary to carry out any data clean-up and pre-processing steps. Additionally, you can apply clustering techniques if you find them helpful to provide valuable insights. Task 2: Predictive Modelling From the available data, you should define the data set used for the classification task at hand. Different models should be considered, and the choice of the final model should be justified. Task 3: Kaggle Competition Additionally, you should submit your solution for the [login to view URL] data set to the Kaggle Competition. Note that the goal is not to overfit the test set data to achieve a good position in the public leaderboard. Your rank position in the private leaderboard will be accounted for in the final grade. Tools You can use R or Python. You can find material for dynamic reporting in R with markdown if you choose to use R. You can use the Colab Research Notebooks or Jupyter Notebooks if you decide to use Python. Deliverables file containing the following items: the source of two ready-to-execute dynamic reports or notebooks with all the code necessary to reproduce the results you present for i) Task 1; and ii) Task 2, including the solution submitted to Kaggle - Task 3, i.e. it should be possible to replicate your predictions; any complementary files needed to execute your report (e.g. data files, data objects) should be submitted as well.