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Here's a step-by-step approach to the project:

Load the data into Excel and perform some basic data cleaning such as removing unnecessary columns, handling missing values, and removing duplicates.

Identify the missing data and use appropriate methods to deal with it. You can either remove the columns with too many missing values or replace the missing values with an appropriate value (e.g. mean, median, or mode). Be sure to document your approach.

Identify outliers in the dataset. To do this, you can use box plots or scatter plots to visualize the data and identify any data points that are significantly different from the rest of the data. Determine if these data points are truly outliers or if they are valid data points that should be included in the analysis.

Identify data imbalance in the data. You can do this by calculating the ratio of the number of observations in each class of the target variable. If there is a significant imbalance, you may need to use stratified sampling or other techniques to ensure that the model is trained on a representative sample of the data.

Perform univariate, segmented univariate, and bivariate analyses to identify any patterns or trends in the data. Use visualizations such as histograms, bar charts, scatter plots, and heat maps to help you identify any relationships between the variables.

Find the top 10 correlations for the client with payment difficulties and all other cases. To do this, segment the data frame w.r.t. to the target variable and then find the top correlation for each of the segmented data. Use the correlation matrix to identify the top correlations between the variables.

Include visualizations and summarize the most important results in the presentation. Use charts and graphs to help communicate your findings to your audience.

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