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"}}},{"cell_type":"markdown","source":"<p style=\"font-family: Arials; font-size: 20px;text-align: center;; font-style: normal;line-height:1.3\">The objective of this competition is to predict the probability that a customer does not pay back their credit card balance amount in the future based on their monthly customer profile. </p>","metadata":{}},{"cell_type":"markdown","source":"The dataset contains aggregated profile features for each customer at each statement date. Features are anonymized and normalized, and fall into the following general categories:\n\n- `D_*` = Delinquency variables\n- `S_*` = Spend variables\n- `P_*` = Payment variables\n- `B_*` = Balance variables\n- `R_*` = Risk variables\n\nWith the following features being categorical:`B_30`,`B_38`,`D_114`,`D_116`,`D_117`,`D_120`,`D_126`,`D_63`,`D_64`, `D_66`,`D_68`\n\nYour task is to predict, for each customer_ID, the probability of a future payment default (target = 1).","metadata":{}},{"cell_type":"markdown","source":"![Importing Libraries & 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"}}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport plotly.graph_objects as go\nimport plotly.express as px\nfrom itertools import cycle\n\nimport warnings, gc\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:18:13.256082Z","iopub.execute_input":"2022-07-11T07:18:13.256499Z","iopub.status.idle":"2022-07-11T07:18:17.160266Z","shell.execute_reply.started":"2022-07-11T07:18:13.256401Z","shell.execute_reply":"2022-07-11T07:18:17.159138Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Importing Libraries & 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"}}},{"cell_type":"code","source":"df_train = pd.read_feather('../input/amexfeather/train_data.ftr')\ndf_train = df_train.groupby('customer_ID').tail(1).set_index('customer_ID')\n\ndf_test = pd.read_feather('../input/amexfeather/test_data.ftr')\ndf_test = df_test.groupby('customer_ID').tail(1).set_index('customer_ID')\n\ndf_subm = pd.read_csv(\"../input/amex-default-prediction/sample_submission.csv\")","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:18:17.162162Z","iopub.execute_input":"2022-07-11T07:18:17.162526Z","iopub.status.idle":"2022-07-11T07:19:26.811842Z","shell.execute_reply.started":"2022-07-11T07:18:17.162481Z","shell.execute_reply":"2022-07-11T07:19:26.810834Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Custom Color Palette 🎨\ncustom_colors = [\"#ffd670\",\"#70d6ff\",\"#ff4d6d\",\"#8338ec\",\"#90cf8e\"]\ncustomPalette = sns.set_palette(sns.color_palette(custom_colors))\nsns.palplot(sns.color_palette(custom_colors),size=1.2)\nplt.tick_params(axis='both', labelsize=0, length = 0)","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:26.813422Z","iopub.execute_input":"2022-07-11T07:19:26.813813Z","iopub.status.idle":"2022-07-11T07:19:26.907614Z","shell.execute_reply.started":"2022-07-11T07:19:26.813775Z","shell.execute_reply":"2022-07-11T07:19:26.906652Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train_row_count, df_train_column_count=df_train.shape\nprint('Total number of rows (Train):', df_train_row_count)\nprint('Total number of columns (Train):', df_train_column_count)","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:26.910555Z","iopub.execute_input":"2022-07-11T07:19:26.911257Z","iopub.status.idle":"2022-07-11T07:19:26.918137Z","shell.execute_reply.started":"2022-07-11T07:19:26.911221Z","shell.execute_reply":"2022-07-11T07:19:26.917040Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test_row_count, df_test_column_count=df_test.shape\nprint('Total number of rows (Test):', df_test_row_count)\nprint('Total number of columns (Test):', df_test_column_count)","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:26.919868Z","iopub.execute_input":"2022-07-11T07:19:26.920206Z","iopub.status.idle":"2022-07-11T07:19:26.931141Z","shell.execute_reply.started":"2022-07-11T07:19:26.920172Z","shell.execute_reply":"2022-07-11T07:19:26.929799Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train['S_2'] = pd.to_datetime(df_train['S_2'])\ndf_test['S_2'] = pd.to_datetime(df_test['S_2'])","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:19:26.932907Z","iopub.execute_input":"2022-07-11T07:19:26.933393Z","iopub.status.idle":"2022-07-11T07:19:27.032094Z","shell.execute_reply.started":"2022-07-11T07:19:26.933357Z","shell.execute_reply":"2022-07-11T07:19:27.031105Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Duration (Train Date): ', df_train['S_2'].min(), df_train['S_2'].max())\nprint('Duration (Test Date): ', df_test['S_2'].min(), df_test['S_2'].max())","metadata":{"_kg_hide-input":false,"execution":{"iopub.status.busy":"2022-07-11T07:19:27.033540Z","iopub.execute_input":"2022-07-11T07:19:27.033909Z","iopub.status.idle":"2022-07-11T07:19:27.050177Z","shell.execute_reply.started":"2022-07-11T07:19:27.033872Z","shell.execute_reply":"2022-07-11T07:19:27.049118Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.head()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:27.052025Z","iopub.execute_input":"2022-07-11T07:19:27.052389Z","iopub.status.idle":"2022-07-11T07:19:27.084166Z","shell.execute_reply.started":"2022-07-11T07:19:27.052353Z","shell.execute_reply":"2022-07-11T07:19:27.083119Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.info()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:27.085814Z","iopub.execute_input":"2022-07-11T07:19:27.086234Z","iopub.status.idle":"2022-07-11T07:19:27.116173Z","shell.execute_reply.started":"2022-07-11T07:19:27.086196Z","shell.execute_reply":"2022-07-11T07:19:27.115327Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cat_cols = ['B_30', 'B_38', 'D_114', 'D_116', 'D_117', 'D_120', 'D_126', 'D_63', 'D_64', 'D_66', 'D_68']","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:27.120452Z","iopub.execute_input":"2022-07-11T07:19:27.120733Z","iopub.status.idle":"2022-07-11T07:19:27.125553Z","shell.execute_reply.started":"2022-07-11T07:19:27.120708Z","shell.execute_reply":"2022-07-11T07:19:27.124465Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"feat_Delinquency = [c for c in df_train.columns if c.startswith('D_')]\nfeat_Spend = [c for c in df_train.columns if c.startswith('S_')]\nfeat_Payment = [c for c in df_train.columns if c.startswith('P_')]\nfeat_Balance = [c for c in df_train.columns if c.startswith('B_')]\nfeat_Risk = [c for c in df_train.columns if c.startswith('R_')]\nprint(f'Total number of Delinquency variables: {len(feat_Delinquency)}')\nprint(f'Total number of Spend variables: {len(feat_Spend)}')\nprint(f'Total number of Payment variables: {len(feat_Payment)}')\nprint(f'Total number of Balance variables: {len(feat_Balance)}')\nprint(f'Total number of Risk variables: {len(feat_Risk)}')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:19:27.127068Z","iopub.execute_input":"2022-07-11T07:19:27.127977Z","iopub.status.idle":"2022-07-11T07:19:27.138572Z","shell.execute_reply.started":"2022-07-11T07:19:27.127939Z","shell.execute_reply":"2022-07-11T07:19:27.137262Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Importing Libraries & 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"}}},{"cell_type":"code","source":"labels=['Delinquency', 'Spend','Payment','Balance','Risk']\nvalues= [len(feat_Delinquency), len(feat_Spend),len(feat_Payment), len(feat_Balance),len(feat_Risk)]","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:27.139866Z","iopub.execute_input":"2022-07-11T07:19:27.140325Z","iopub.status.idle":"2022-07-11T07:19:27.153475Z","shell.execute_reply.started":"2022-07-11T07:19:27.140285Z","shell.execute_reply":"2022-07-11T07:19:27.152460Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig_1 = go.Figure()\nfig_1.add_trace(go.Pie(values = values,labels = labels,hole = 0.6, \n                     hoverinfo ='label+percent'))\nfig_1.update_traces(textfont_size = 12, hoverinfo ='label+percent',textinfo ='label', \n                  showlegend = False,marker = dict(colors =[\"#70d6ff\",\"#ff9770\"]),\n                  title = dict(text = 'Feature Distribution'))  \nfig_1.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:27.155141Z","iopub.execute_input":"2022-07-11T07:19:27.155558Z","iopub.status.idle":"2022-07-11T07:19:27.248408Z","shell.execute_reply.started":"2022-07-11T07:19:27.155524Z","shell.execute_reply":"2022-07-11T07:19:27.247398Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:27.250511Z","iopub.execute_input":"2022-07-11T07:19:27.251161Z","iopub.status.idle":"2022-07-11T07:19:27.640473Z","shell.execute_reply.started":"2022-07-11T07:19:27.251125Z","shell.execute_reply":"2022-07-11T07:19:27.639603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sum(df_train.isna().sum())","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:27.641894Z","iopub.execute_input":"2022-07-11T07:19:27.642392Z","iopub.status.idle":"2022-07-11T07:19:28.026639Z","shell.execute_reply.started":"2022-07-11T07:19:27.642350Z","shell.execute_reply":"2022-07-11T07:19:28.025599Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"background_color = 'white'\nmissing = pd.DataFrame(columns = ['% Missing values'],data = df_train.isnull().sum()/len(df_train))\nfig = plt.figure(figsize = (20, 60),facecolor=background_color)\ngs = fig.add_gridspec(1, 2)\ngs.update(wspace = 0.5, hspace = 0.5)\nax0 = fig.add_subplot(gs[0, 0])\nfor s in [\"right\", \"top\",\"bottom\",\"left\"]:\n    ax0.spines[s].set_visible(False)\nsns.heatmap(missing,cbar = False,annot = True,fmt =\".2%\", linewidths = 2,cmap = custom_colors,vmax = 1, ax = ax0)\nplt.show()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:19:28.029215Z","iopub.execute_input":"2022-07-11T07:19:28.029634Z","iopub.status.idle":"2022-07-11T07:19:31.080258Z","shell.execute_reply.started":"2022-07-11T07:19:28.029607Z","shell.execute_reply":"2022-07-11T07:19:31.076031Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"target_class = pd.DataFrame({'count': df_train.target.value_counts(),\n                             'percentage': df_train['target'].value_counts() / df_train.shape[0] * 100\n})","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:19:31.081377Z","iopub.execute_input":"2022-07-11T07:19:31.081759Z","iopub.status.idle":"2022-07-11T07:19:31.096350Z","shell.execute_reply.started":"2022-07-11T07:19:31.081724Z","shell.execute_reply":"2022-07-11T07:19:31.095345Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"target_class ","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:31.098903Z","iopub.execute_input":"2022-07-11T07:19:31.099491Z","iopub.status.idle":"2022-07-11T07:19:31.112303Z","shell.execute_reply.started":"2022-07-11T07:19:31.099451Z","shell.execute_reply":"2022-07-11T07:19:31.111029Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import plotly.graph_objects as go\nfig = go.Figure()\nfig.add_trace(go.Pie(values = target_class['count'],labels = target_class.index,hole = 0.6, \n                     hoverinfo ='label+percent'))\nfig.update_traces(textfont_size = 12, hoverinfo ='label+percent',textinfo ='label', \n                  showlegend = False,marker = dict(colors =[\"#90cf8e\",\"#ff70a6\"]),\n                  title = dict(text = 'Target Distribution'))  \nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:31.115913Z","iopub.execute_input":"2022-07-11T07:19:31.116509Z","iopub.status.idle":"2022-07-11T07:19:31.132903Z","shell.execute_reply.started":"2022-07-11T07:19:31.116476Z","shell.execute_reply":"2022-07-11T07:19:31.131848Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"stat_plot = df_train.reset_index().groupby('S_2')['customer_ID'].nunique().reset_index()\nfig = go.Figure()\nfig.add_trace(go.Scatter(x = stat_plot['S_2'], y = stat_plot['customer_ID']))\nfig.update_layout(title=\"Customer Statements\", width = 800, height = 600,xaxis_title ='Statement Date',\n                  paper_bgcolor='rgb(0,0,0,0)',plot_bgcolor='rgb(0,0,0,0)') \nfig['data'][0]['line']['color']=\"#ff9770\"\nfig.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:31.134126Z","iopub.execute_input":"2022-07-11T07:19:31.134746Z","iopub.status.idle":"2022-07-11T07:19:31.537310Z","shell.execute_reply.started":"2022-07-11T07:19:31.134710Z","shell.execute_reply":"2022-07-11T07:19:31.536415Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"gc.collect()","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-07-11T07:19:31.538641Z","iopub.execute_input":"2022-07-11T07:19:31.539103Z","iopub.status.idle":"2022-07-11T07:19:31.688598Z","shell.execute_reply.started":"2022-07-11T07:19:31.539066Z","shell.execute_reply":"2022-07-11T07:19:31.687653Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"del_cols = [c for c in df_train.columns if (c.startswith(('D','t'))) & (c not in cat_cols)]\ndf_del = df_train[del_cols]\nspd_cols = [c for c in df_train.columns if (c.startswith(('S','t'))) & (c not in cat_cols)]\ndf_spd = df_train[spd_cols]\npay_cols = [c for c in df_train.columns if (c.startswith(('P','t'))) & (c not in cat_cols)]\ndf_pay = df_train[pay_cols]\nbal_cols = [c for c in df_train.columns if (c.startswith(('B','t'))) & (c not in cat_cols)]\ndf_bal = df_train[bal_cols]\nris_cols = [c for c in df_train.columns if (c.startswith(('R','t'))) & (c not in cat_cols)]\ndf_ris = df_train[ris_cols]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:19:31.690029Z","iopub.execute_input":"2022-07-11T07:19:31.690589Z","iopub.status.idle":"2022-07-11T07:19:32.054780Z","shell.execute_reply.started":"2022-07-11T07:19:31.690547Z","shell.execute_reply":"2022-07-11T07:19:32.053803Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, axes = plt.subplots(29, 3, figsize = (35,150))\nfor i, ax in enumerate(axes.reshape(-1)):\n    if i < len(del_cols) - 1:\n        sns.kdeplot(x = del_cols[i], hue='target', data = df_del, fill = True, ax = ax, palette =[\"#e63946\",\"#8338ec\"])\n        ax.tick_params()\n        ax.xaxis.get_label()\n        ax.set_ylabel('')\nfig.suptitle('Distribution of Delinquency Variables', fontsize = 35, x = 0.5, y = 1)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:19:32.056084Z","iopub.execute_input":"2022-07-11T07:19:32.056702Z","iopub.status.idle":"2022-07-11T07:22:11.638872Z","shell.execute_reply.started":"2022-07-11T07:19:32.056650Z","shell.execute_reply":"2022-07-11T07:22:11.637681Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize =(11,11))\ncorr = df_del.corr()\nmask = np.triu(np.ones_like(corr, dtype = bool))\nsns.heatmap(corr, mask = mask, robust = True, center = 0,square = True, linewidths =.6, cmap = custom_colors)\nplt.title('Correlation of Delinquency Variables')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:22:11.640343Z","iopub.execute_input":"2022-07-11T07:22:11.641247Z","iopub.status.idle":"2022-07-11T07:22:20.271548Z","shell.execute_reply.started":"2022-07-11T07:22:11.641208Z","shell.execute_reply":"2022-07-11T07:22:20.270622Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, axes = plt.subplots(8, 3, figsize = (16,18))\nfig.suptitle('Distribution of Spend Variables', fontsize = 15, x = 0.5, y = 1)\nfor i, ax in enumerate(axes.reshape(-1)):\n    if i < len(spd_cols) - 1:\n        sns.kdeplot(x = spd_cols[i], hue ='target', data = df_spd, fill = True, ax = ax, palette =[\"#e63946\",\"#8338ec\"])\n        ax.tick_params()\n        ax.xaxis.get_label()\n        ax.set_ylabel('')\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:22:20.272951Z","iopub.execute_input":"2022-07-11T07:22:20.273949Z","iopub.status.idle":"2022-07-11T07:23:07.805369Z","shell.execute_reply.started":"2022-07-11T07:22:20.273905Z","shell.execute_reply":"2022-07-11T07:23:07.804300Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"S_cols = [c for c in df_train.columns if (c.startswith(('S')))]\ndf_S = df_train[S_cols]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:23:07.807221Z","iopub.execute_input":"2022-07-11T07:23:07.807596Z","iopub.status.idle":"2022-07-11T07:23:07.827138Z","shell.execute_reply.started":"2022-07-11T07:23:07.807554Z","shell.execute_reply":"2022-07-11T07:23:07.826142Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize = (11,11))\ncorr = df_S.corr()\nmask = np.triu(np.ones_like(corr, dtype=bool))\nsns.heatmap(corr, mask = mask, robust = True, center = 0,square = True, linewidths = .6, cmap = custom_colors)\nplt.title('Correlation of Spend Variables')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:23:07.828921Z","iopub.execute_input":"2022-07-11T07:23:07.829618Z","iopub.status.idle":"2022-07-11T07:23:08.902395Z","shell.execute_reply.started":"2022-07-11T07:23:07.829578Z","shell.execute_reply":"2022-07-11T07:23:08.901447Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, axes = plt.subplots(1, 3, figsize = (12,4))\nfig.suptitle('Distribution of Payment Variables',fontsize = 15)\nfor i, ax in enumerate(axes.reshape(-1)):\n    if i < len(pay_cols) - 1:\n        sns.kdeplot(x = pay_cols[i], hue ='target', data = df_pay, fill = True, ax = ax, palette =[\"#e63946\",\"#8338ec\"])\n        ax.tick_params()\n        ax.xaxis.get_label()\n        ax.set_ylabel('')\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:23:08.908355Z","iopub.execute_input":"2022-07-11T07:23:08.908999Z","iopub.status.idle":"2022-07-11T07:23:15.924557Z","shell.execute_reply.started":"2022-07-11T07:23:08.908958Z","shell.execute_reply":"2022-07-11T07:23:15.923586Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"P_cols = [c for c in df_train.columns if (c.startswith(('P')))]\ndf_P = df_train[P_cols]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:23:15.926126Z","iopub.execute_input":"2022-07-11T07:23:15.927147Z","iopub.status.idle":"2022-07-11T07:23:15.935447Z","shell.execute_reply.started":"2022-07-11T07:23:15.927106Z","shell.execute_reply":"2022-07-11T07:23:15.934492Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize = (6,6))\ncorr = df_P.corr()\nmask = np.triu(np.ones_like(corr, dtype = bool))\nsns.heatmap(corr, mask = mask, robust = True, center = 0,square = True, linewidths = .6, cmap = custom_colors)\nplt.title('Correlation of Payment Variables')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:23:15.936828Z","iopub.execute_input":"2022-07-11T07:23:15.937282Z","iopub.status.idle":"2022-07-11T07:23:16.171591Z","shell.execute_reply.started":"2022-07-11T07:23:15.937244Z","shell.execute_reply":"2022-07-11T07:23:16.170598Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, axes = plt.subplots(10, 4, figsize = (15,24))\nfig.suptitle('Distribution of Balance Variables',fontsize = 15, x = 0.5, y = 1)\nfor i, ax in enumerate(axes.reshape(-1)):\n    if i < len(bal_cols) - 1:\n        sns.kdeplot(x = bal_cols[i], hue ='target', data = df_bal, fill = True, ax = ax, palette =[\"#e63946\",\"#8338ec\"])\n        ax.tick_params()\n        ax.xaxis.get_label()\n        ax.set_ylabel('')\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:23:16.174614Z","iopub.execute_input":"2022-07-11T07:23:16.174938Z","iopub.status.idle":"2022-07-11T07:24:33.997736Z","shell.execute_reply.started":"2022-07-11T07:23:16.174909Z","shell.execute_reply":"2022-07-11T07:24:33.996692Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"B_cols = [c for c in df_train.columns if (c.startswith(('B')))]\ndf_B = df_train[B_cols]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:24:33.999085Z","iopub.execute_input":"2022-07-11T07:24:33.999846Z","iopub.status.idle":"2022-07-11T07:24:34.064061Z","shell.execute_reply.started":"2022-07-11T07:24:33.999798Z","shell.execute_reply":"2022-07-11T07:24:34.063087Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize = (11,11))\ncorr = df_B.corr()\nmask = np.triu(np.ones_like(corr, dtype = bool))\nsns.heatmap(corr, mask = mask, robust=True, center = 0,square = True, linewidths =.6, cmap = custom_colors)\nplt.title('Correlation of Balance Variables')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:24:34.065727Z","iopub.execute_input":"2022-07-11T07:24:34.066145Z","iopub.status.idle":"2022-07-11T07:24:36.348701Z","shell.execute_reply.started":"2022-07-11T07:24:34.066104Z","shell.execute_reply":"2022-07-11T07:24:36.347657Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, axes = plt.subplots(10, 3, figsize = (18,23))\nfig.suptitle('Distribution of Risk Variables',fontsize=15, x = 0.5, y = 1)\nfor i, ax in enumerate(axes.reshape(-1)):\n    if i < len(ris_cols) - 1:\n        sns.kdeplot(x = ris_cols[i], hue ='target', data = df_ris, fill = True, ax = ax, palette =[\"#e63946\",\"#8338ec\"])\n        ax.tick_params()\n        ax.xaxis.get_label()\n        ax.set_ylabel('')\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:24:36.350086Z","iopub.execute_input":"2022-07-11T07:24:36.350550Z","iopub.status.idle":"2022-07-11T07:25:36.139035Z","shell.execute_reply.started":"2022-07-11T07:24:36.350508Z","shell.execute_reply":"2022-07-11T07:25:36.138040Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"R_cols = [c for c in df_train.columns if (c.startswith(('R')))]\ndf_R = df_train[R_cols]","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:25:36.140479Z","iopub.execute_input":"2022-07-11T07:25:36.141276Z","iopub.status.idle":"2022-07-11T07:25:36.168402Z","shell.execute_reply.started":"2022-07-11T07:25:36.141233Z","shell.execute_reply":"2022-07-11T07:25:36.167432Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(11,11))\ncorr = df_R.corr()\nmask = np.triu(np.ones_like(corr, dtype=bool))\nsns.heatmap(corr, mask = mask, robust = True, center = 0, square = True, linewidths =.6, cmap = custom_colors)\nplt.title('Correlation of Risk Variables')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:25:36.170112Z","iopub.execute_input":"2022-07-11T07:25:36.170921Z","iopub.status.idle":"2022-07-11T07:25:37.746961Z","shell.execute_reply.started":"2022-07-11T07:25:36.170876Z","shell.execute_reply":"2022-07-11T07:25:37.745835Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"palette = cycle([\"#ffd670\",\"#70d6ff\",\"#ff4d6d\",\"#8338ec\",\"#90cf8e\"])\ntarg = df_train.corrwith(df_train['target'], axis=0)\nval = [str(round(v ,1) *100) + '%' for v in targ.values]\nfig = go.Figure()\nfig.add_trace(go.Bar(y=targ.index, x= targ.values, orientation='h',text = val, marker_color = next(palette)))\nfig.update_layout(title = \"Correlation of variables with Target\",width = 750, height = 3500,\n                  paper_bgcolor='rgb(0,0,0,0)',plot_bgcolor='rgb(0,0,0,0)')","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:25:37.752028Z","iopub.execute_input":"2022-07-11T07:25:37.752628Z","iopub.status.idle":"2022-07-11T07:25:39.295217Z","shell.execute_reply.started":"2022-07-11T07:25:37.752586Z","shell.execute_reply":"2022-07-11T07:25:39.294316Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"gc.collect()","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:25:39.296706Z","iopub.execute_input":"2022-07-11T07:25:39.297074Z","iopub.status.idle":"2022-07-11T07:25:39.556346Z","shell.execute_reply.started":"2022-07-11T07:25:39.297038Z","shell.execute_reply":"2022-07-11T07:25:39.555286Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Importing Libraries & 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"}}},{"cell_type":"code","source":"from sklearn.preprocessing import LabelEncoder\nlab_enc = LabelEncoder()\nfor cat_feat in cat_cols:\n    df_train[cat_feat] = lab_enc.fit_transform(df_train[cat_feat])\n    df_test[cat_feat] = lab_enc.transform(df_test[cat_feat])","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:25:39.558141Z","iopub.execute_input":"2022-07-11T07:25:39.558803Z","iopub.status.idle":"2022-07-11T07:25:40.738955Z","shell.execute_reply.started":"2022-07-11T07:25:39.558764Z","shell.execute_reply":"2022-07-11T07:25:40.737967Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#Reference: https://www.kaggle.com/code/inversion/amex-competition-metric-python/notebook\ndef amex_metric(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:\n\n    def top_four_percent_captured(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:\n        df = (pd.concat([y_true, y_pred], axis='columns')\n              .sort_values('prediction', ascending=False))\n        df['weight'] = df['target'].apply(lambda x: 20 if x==0 else 1)\n        four_pct_cutoff = int(0.04 * df['weight'].sum())\n        df['weight_cumsum'] = df['weight'].cumsum()\n        df_cutoff = df.loc[df['weight_cumsum'] <= four_pct_cutoff]\n        return (df_cutoff['target'] == 1).sum() / (df['target'] == 1).sum()\n        \n    def weighted_gini(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:\n        df = (pd.concat([y_true, y_pred], axis='columns')\n              .sort_values('prediction', ascending=False))\n        df['weight'] = df['target'].apply(lambda x: 20 if x==0 else 1)\n        df['random'] = (df['weight'] / df['weight'].sum()).cumsum()\n        total_pos = (df['target'] * df['weight']).sum()\n        df['cum_pos_found'] = (df['target'] * df['weight']).cumsum()\n        df['lorentz'] = df['cum_pos_found'] / total_pos\n        df['gini'] = (df['lorentz'] - df['random']) * df['weight']\n        return df['gini'].sum()\n\n    def normalized_weighted_gini(y_true: pd.DataFrame, y_pred: pd.DataFrame) -> float:\n        y_true_pred = y_true.rename(columns={'target': 'prediction'})\n        return weighted_gini(y_true, y_pred) / weighted_gini(y_true, y_true_pred)\n\n    g = normalized_weighted_gini(y_true, y_pred)\n    d = top_four_percent_captured(y_true, y_pred)\n\n    return 0.5 * (g + d)","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2022-07-11T07:25:40.740372Z","iopub.execute_input":"2022-07-11T07:25:40.740729Z","iopub.status.idle":"2022-07-11T07:25:40.753863Z","shell.execute_reply.started":"2022-07-11T07:25:40.740692Z","shell.execute_reply":"2022-07-11T07:25:40.752744Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# define dataset\nX = df_train.drop('target', axis=1)\ny = df_train['target']","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:25:40.755576Z","iopub.execute_input":"2022-07-11T07:25:40.755946Z","iopub.status.idle":"2022-07-11T07:25:41.065708Z","shell.execute_reply.started":"2022-07-11T07:25:40.755910Z","shell.execute_reply":"2022-07-11T07:25:41.064715Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\n# creating dataset split for prediction\nX_train, X_test , y_train , y_test = train_test_split(X,y,test_size=0.2,random_state=42) # 80-20 split\n\n# Checking split \nprint('X_train:', X_train.shape)\nprint('y_train:', y_train.shape)\nprint('X_test:', X_test.shape)\nprint('y_test:', y_test.shape)","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:25:41.067230Z","iopub.execute_input":"2022-07-11T07:25:41.067595Z","iopub.status.idle":"2022-07-11T07:25:42.150440Z","shell.execute_reply.started":"2022-07-11T07:25:41.067557Z","shell.execute_reply":"2022-07-11T07:25:42.149430Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from catboost import CatBoostClassifier\nclf = CatBoostClassifier(iterations = 3000, random_state = 42, nan_mode ='Min',task_type =\"GPU\")\nclf.fit(X_train, y_train, eval_set = [(X_test, y_test)], cat_features=cat_cols,  verbose = 100)\npreds = clf.predict_proba(X_test)[:, 1]","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-07-11T07:25:42.151809Z","iopub.execute_input":"2022-07-11T07:25:42.152769Z","iopub.status.idle":"2022-07-11T07:30:32.161510Z","shell.execute_reply.started":"2022-07-11T07:25:42.152726Z","shell.execute_reply":"2022-07-11T07:30:32.160461Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_preds = clf.predict_proba(df_test)[:, 1]\ny_preds","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-07-11T07:30:32.162959Z","iopub.execute_input":"2022-07-11T07:30:32.163307Z","iopub.status.idle":"2022-07-11T07:31:48.433626Z","shell.execute_reply.started":"2022-07-11T07:30:32.163271Z","shell.execute_reply":"2022-07-11T07:31:48.432571Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_subm[\"prediction\"] = y_preds\ndf_subm.to_csv('submission_catb.csv', index=False)\ndf_subm","metadata":{"execution":{"iopub.status.busy":"2022-07-11T07:31:48.435468Z","iopub.execute_input":"2022-07-11T07:31:48.435917Z","iopub.status.idle":"2022-07-11T07:31:53.489652Z","shell.execute_reply.started":"2022-07-11T07:31:48.435880Z","shell.execute_reply":"2022-07-11T07:31:53.488050Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![Importing Libraries & 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"}}}]}