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x=cc_df_ss['Total_Trans_Amt'].mean(),\n ymin=0, ymax=130, colors='0.75',\n linestyles='dashed', label='MEAN'\n)\n\nplt.vlines(\n x=cc_df_ss['Total_Trans_Amt'].median(),\n ymin=0, ymax=130, colors='0.75',\n linestyles='dotted', label='MEDIAN'\n)\n\nplt.title('Customer Total Transaction Amount (Last 12 months)')\nplt.xlabel('Total Transaction Amount')\nplt.ylabel('Count')\nplt.legend()\n"})}),"\n",(0,s.jsx)(e.p,{children:(0,s.jsx)(e.img,{alt:"Credit Card Customer Churn Prediction",src:t(63777).A+"",width:"571",height:"455"})}),"\n",(0,s.jsx)(e.h2,{id:"data-transformation-1",children:"Data Transformation"}),"\n",(0,s.jsx)(e.h3,{id:"normalization",children:"Normalization"}),"\n",(0,s.jsx)(e.pre,{children:(0,s.jsx)(e.code,{className:"language-python",children:"def normalize(column):\n upper = column.max()\n lower = column.min()\n norm = (column - lower)/(upper - lower)\n \n return 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5))\nplt.title('Relative Difference between Existing and Attrited Customers')\nsns.set(style='darkgrid')\nsns.barplot(\n data=cc_df_attr_trans,\n x='Diff',\n y='index',\n estimator=np.median,\n errorbar='sd',\n palette='winter',\n orient='h'\n)\n\nplt.savefig('../assets/CC_Customer_Churn_12.webp', bbox_inches='tight')\n"})}),"\n",(0,s.jsx)(e.p,{children:(0,s.jsx)(e.img,{alt:"Credit Card Customer Churn Prediction",src:t(653081).A+"",width:"1024",height:"479"})})]})}function x(n={}){const{wrapper:e}={...(0,r.R)(),...n.components};return 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