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Gender: I can convert the column into two groups [male-female] to plot it latter w.r.t other variables
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Profession: I can do the same, but since there's around 124 null values in this column, we can't relay deeply on its outputs especially that the column values are texts, we can't simply use any method of filling the missing/empty values.
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Strong opportunities to design my analysis:
Gender - Age - Spending Score - Segmentation
- Medium opportunities to design analysis:
Graduated - Profession - Var_1 - Ever_Married
- Least opportunities to design analysis: [Nearly we can remove them from the analysis]
Family_Size - Work_Experience
- Data Wrangling section:
Hot-Encoding -
- Packages: Pandas - Numpy - MatPlotLib - Seaborn