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Exploratory data analysis in python case study

As billions of dollars of loss are caused every year due to fraudulent credit card transactions, the financial industry has switched from a case by case a posteriori investigation approach to an a priori predictive approach with the design of fraud detection algorithms to alert and assist fraud investigators. For an overview of data science for fraud detection and financial services, I have written a white paper available on this website. This project will focus on the step by step implementation of credit card fraud detection algorithms. Being able to spot fraudulent activities in large volume of transaction such as the credit card uses can have the following benefits:.
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Analysis of COVID-19 in India using Exploratory Method

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GitHub - anurag/Gramener-Case-Study-EDA--Python-and-R: Exploratory Data Analysis

The answer to the following research questions will be searched for, using exploratory analysis and visualization :. How can the average ratings for different genres be compared among themselves? For example, are the average ratings for Comedy and Sci-Fi movies positively associated with each other? What is the strength of association? The next table shows the first few rows of the movies dataset, loaded in a pandas DataFrame. The next table shows the first few rows of the ratings dataset, again loaded with pandas.
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Reproducible Data Analysis in Jupyter

To understand EDA using python, we can take the sample data either directly from any website or from your local disk. To find what all columns it contains, of what types and if they contain any value in it or not, with the help of info function. Another useful function provided by pandas is describe which provides the count, mean, standard deviation, minimum and maximum values and the quantities of the data. Above two observations, gives an indication that there are extreme values- deviations in our data set. From above we can conclude, none of the observation score 1 poor , 2 and 9, 10 best score.
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This course covers the essential exploratory techniques for summarizing data. These techniques are typically applied before formal modeling commences and can help inform the development of more complex statistical models. Exploratory techniques are also important for eliminating or sharpening potential hypotheses about the world that can be addressed by the data.
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