{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# American Express - Default Prediction\n\n## Description\nWhether out at a restaurant or buying tickets to concert, modern life counts on the convenience of a credit card to make daily purchases. It saves us from carrying large amounts of cash and also can advance a full pruchase that can be paid over time. How do card issuers know we will pay back what we charge? That's a complex problem with many existing solutions - and even more potential improvements, to be explored in this competition.\n    \nCredit default prediction is central to managing risk in a consumer lending business. Credit default prediction allows lenders to optimize lending decisions, which leads to a better customer experience and sound business economics. Current model exist to help manage risk. But it is possible to create better models that can outperform those currently in use.\n    \nAmerican Express is a globally integrated payments company. The largest payment card issuer in the world, they provide customers with access to products, insights, and experiences that enrich lives an build business success.\n    \nIn this competition, you will apply your machine learning  skills to predict credit default. Specifically, you will leverage an industrial scale data set to build a machine learning model that challenges the current model in production. Training, validation, and testing datasets include time-series behavioral data and anonymized customer profile information. You are free to explore any technique  to create the most powerfull model, from creating features to using the data in a more organic way within a model.\n\nThe evaluation metric, `M`, for this competition is the mean of two measures of rank ordering: Normalized Gini Coefficient, G, and default rate captured at 4%, D.\n\n$$M = 0.5*(G + D)$$\n    \n**Gini Coefficient** - The Gini coefficient is popular metric on Kaggle, especially for imbalanced class values. But googling `Gini Coefficient` gives you mostly economic explanations. But \"what is it Gini?\" `Gini Coefficient` is one of the most popular metrics used by the financial industry for evaluating the performance of credit score models.\n\nThe Gini coefficient is a metric that indicates the model's discriminatory power, namely, the effectiveness of the model in differentiating between \"bad\" borrowers, who will default in the future, and \"good\" borrowers, who will not default in the future. This metric is often used in compare the quality of different models and evaluate their prediction power.\n![image.png](attachment:image.png)\n\n**The default rate** captured at 4% is the percentage of the positive labels (defaults) captured within the highest-ranked 4% of the preidictions, and represents a Sensetive/Recall statistic.\n\nFor both of the sub-metrics `G` and `D`, the negative labels are given a weight of 20 to adjust for downsampling.\n\n## Data Overview\n\nThe target binary variable is calculated by observing 18 months performance window after the latest credit card statement, and if the customer does not pay due amount in 120 days after their statement date it is considered a default event.\n\nThe dataset contains aggregated profile features for each customer at each statement date. Features are anonumized and normalized, and fall into the following general categories:\n\n`D_*`: Delinquency variables\n\n`S_*`: Spend variables\n\n`P_*`: Payment variables\n\n`B_*`: Balance variables\n\n`R_*`: Risk variables\n\n\nWith the follwing features being categorical: `B_30`,`B_38`,`D_63`, `D_64`, `D_66`, `D_114`, `D_116`, `D_117`, `D_120`, `D_126`.\n\nThere are a total of 190 variables in the dataset with approximately 450,000 customers in the training set and 925,000 in the test set. Due to the dataset size, I will the compressed version of the train and test sets provided by `Amex-Feather-Dataset` and take the last statement for each customer.\n\n","metadata":{},"attachments":{"image.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"# Let there be light\n","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib.colors\nimport plotly.express as px\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\nfrom plotly.offline import init_notebook_mode\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.metrics import roc_auc_score, roc_curve, auc\nfrom lightgbm import LGBMClassifier, early_stopping, log_evaluation\nimport warnings, gc\nwarnings.filterwarnings(\"ignore\")\ninit_notebook_mode(connected=True)","metadata":{"execution":{"iopub.status.busy":"2023-03-13T20:25:02.485306Z","iopub.execute_input":"2023-03-13T20:25:02.485652Z","iopub.status.idle":"2023-03-13T20:25:06.528068Z","shell.execute_reply.started":"2023-03-13T20:25:02.485620Z","shell.execute_reply":"2023-03-13T20:25:06.527040Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"temp = dict(layout = go.Layout(font = dict(family = \"Franklin Gothic\", size = 12),\n                              height=500, width = 1000))","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_feather('./input/train_data.ftr')\n# taking last statement of costumer for traning set\ntrain = train.groupby('customer_ID').tail(1).set_index('customer_ID')\n#printing metadata\nprint(\"The training data begins on {} and ends on {}.\". format(train['S_2'].min().strftime('%m-%d-%Y'), train['S_2'].max().strftime('%m-%d-%Y')))\n\nprint(\"\\nThere are {:,.0f} customers in the traning set and {} features.\".format(train.shape[0],train.shape[1]))\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.read_feather(\"./input/test_data.ftr\")\n# taking last statement of costumer for test set\ntest = test.groupby('customer_ID').tail(1).set_index('customer_ID')\n# printing metadata \nprint(\"\\nThe test data begins on {} and ends on {}.\".format(test['S_2'].min().strftime('%m-%d-%Y'),test['S_2'].max().strftime('%m-%d-%Y')))\n\nprint(\"\\nThere are {:,.0f} customer in the test set and {} features.\".format(test.shape[0],test.shape[1]))\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# deleting date column from test data set\ndel test['S_2']\n# removing garbage\ngc.collect()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# renaming titles to understandable format\ntitels = ['Delinquency '+ str(i).split('_')[1] if i.startswith('D') else 'Spend '+ str(i).split('_')[1]\n          if i.startswith('S') else 'Payment ' + str(i).split('_')[1]\n          if i.startswith('P') else 'Balance ' + str(i).split('_')[1]\n          if i.startswith('B') else 'Risk '    + str(i).split('_')[1] for i in train.columns[:-1]]\n\n# categorical columns\ncat_cols=['Balance 30', 'Balance 38', 'Delinquency 63', 'Delinquency 64', 'Delinquency 66', 'Delinquency 68',\n          'Delinquency 114', 'Delinquency 116', 'Delinquency 117', 'Delinquency 120', 'Delinquency 126', 'Target']\n\ntest.columns = titels[1:]\ntitels.append('Target')\ntrain.columns = titels\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Exploratory Data Analysis","metadata":{}},{"cell_type":"code","source":"target = train.Target.value_counts(normalize=True)\ntarget.rename(index = {1: 'Default', 0: 'Paid'}, inplace = True)\npal, color = ['#016CC9','#DEB078'], ['#8DBAE2','#EDD3B3']\nfig = go.Figure()\nfig.add_trace(go.Pie(labels = target.index, values = target * 100, hole = 0.45,\n                    showlegend = True, sort = False,\n                    marker = dict(colors = color, line = dict(color = pal, width = 2.5)),\n                    hovertemplate = \"%{label} Accounts: %{value:.2f}%<extra></extra>\"))\nfig.update_layout(template = temp, title = 'Target Distribution',\n                 legend = dict(traceorder = 'reversed', y = 1.05, x = 0),\n                 uniformtext_minsize = 15, uniformtext_mode = 'hide', width = 700)\nfig.show()\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"target = pd.DataFrame(data = {'Default': train.groupby('Spend 2')['Target'].mean()*100})\n\ntarget['Paid'] = np.abs(train.groupby('Spend 2')['Target'].mean() - 1)*100\n\nrgb = ['rgba' + str(matplotlib.colors.to_rgba(i, 0.7)) for i in pal]\n\nfig = go.Figure()\n\nfig.add_trace(go.Bar(x=target.index, y=target.Paid, name = 'Paid',\n                    text = target.Paid, texttemplate='%{text:.0f}%',\n                    textposition='inside', insidetextanchor = \"middle\",\n                    marker = dict(color = color[0], line = dict(color=pal[0], width = 1.5)),\n                    hovertemplate=\"<b>%{x}</b><br>Default accounts: %{y:.2f}%\" \n                    ))\n\nfig.add_trace(go.Bar(x = target.index, y = target.Default, name = 'Default',\n                    text = target.Default, texttemplate='%{text:.0f}%',\n                    textposition='inside', insidetextanchor='middle',\n                    marker = dict(color = color[1], line = dict(color=pal[1], width=1.5)),\n                    hovertemplate=\"<b>%{x}</b><br>Default accounts: %{y:.2f}%\"))\n\nfig.update_layout(template = temp, title = 'Distribution of Default by Day',\n                 barmode = 'relative', yaxis_ticksuffix = '%',\n                 width = 1400,\n                 legend = dict(orientation =\"h\", traceorder=\"reversed\", yanchor = \"bottom\", y = 1.1,\n                              xanchor = \"left\", x = 0))\n\nfig.show()\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"About 25% of customers in the training data have defaulted. This proportion is consistent across each day in the training set, with a weekly seasonal trend in the day the month when customers receive their statements.","metadata":{}},{"cell_type":"code","source":"plot_df = train.reset_index().groupby('Spend 2')['customer_ID'].nunique().reset_index()\nfig = go.Figure()\nfig.add_trace(go.Scatter(x=plot_df['Spend 2'],\n                         y=plot_df['customer_ID'],mode='lines',\n                         line=dict(color=pal[0], width = 3),\n                         hovertemplate = ''))\nfig.update_layout(template = temp, title = \"Frequency of Customer Statements\",\n                 hovermode = \"x unified\", width = 800, height = 500,\n                 xaxis_title = 'Statement Date', yaxis_title = 'Number of Statements Issued')\n\nfig.show()\n\n\ndel train['Spend 2']\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# EDA of Delinquency Variables","metadata":{}},{"cell_type":"code","source":"cols = [col for col in train.columns if (col.startswith(('D','T'))) \n       & (col not in cat_cols[:-1])]\n\nplot_df = train[cols]\nfig, ax = plt.subplots(18,5, figsize=(16,54))\nfig.suptitle('Distribution of Delinquency Variables', fontsize = 16)\nrow = 0\ncol = [0,1,2,3,4]*18\nfor i,column in enumerate(plot_df.columns[:-1]):\n    if(i != 0)&(i%5==0):\n        row+=1\n    sns.kdeplot(x=column, hue='Target', palette = pal[::-1], hue_order=[1,0],\n               label = ['Default','Paid'], data=plot_df,\n               fill = True, linewidth = 2, legend = False, ax=ax[row,col[i]])\n    \n    ax[row,col[i]].tick_params(left=False,bottom=False)\n    ax[row,col[i]].set(title='\\n\\n{}'.format(column), xlabel='',ylabel = ('Density' if i%5==0 else ''))\n\nfor i in range(2,5):\n    ax[17,i].set_visible(False)\n    \nhandles, _ = ax[0,0].get_legend_handles_labels()\nfig.legend(labels=['Default','Paid'], handles = reversed(handles), ncol = 2, bbox_to_anchor=(0.18,0.983))\nsns.despine(bottom=True, trim=True)\nplt.tight_layout(rect=[0,0.2,1,0.99])\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr = plot_df.iloc[:,:-1].corr()\nmask = np.triu(np.ones_like(corr, dtype=bool))[1:,:-1]\ncorr = corr.iloc[1:,:-1].copy()\nfig, ax = plt.subplots(figsize=(48,48))\nsns.heatmap(corr, mask=mask, vmin = -1, vmax = 1, center = 0, annot = True, fmt='.2f',\n           cmap = 'coolwarm', annot_kws = {'fontsize':10, 'fontweight':'bold'}, cbar = False)\n\nax.tick_params(left=False, bottom=False)\nax.set_xticklabels(ax.get_xticklabels(), rotation = 45, horizontalalignment='right',fontsize = 12)\nax.set_yticklabels(ax.get_yticklabels(), fontsize = 12)\nplt.title('Correlations between Payment Variables\\n',fontsize=16)\n\nfig.show()\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are several highly correlated Delinquency variables, with a few pairs perfectly positively correlated at 1.0. There are also a number of missing correlations, particularly in `Delinquency 87`, due to null values in the data. Bellow are the relationships between some the ost correleated Delinquency variables.","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(1,4, figsize=(16,5))\nfig.suptitle('Relationships between Delinquency Variables, \\n Log-Transformed', fontsize = 16)\n\nax[0].hexbin(x='Delinquency 74', y='Delinquency 75', data=plot_df, bins = 'log', gridsize = 40, cmap = 'coolwarm')\nax[0].set(xlabel='Delinquency 74', ylabel='Delinquency 75')\nax[0].text(1,4,'Correlation: {:.2f}'.format(plot_df[['Delinquency 74','Delinquency 75']].corr().iloc[1,0]),\n          ha='center', va = 'center', bbox = dict(boxstyle=\"round,pad=0.3\",fc='white'))\n\nax[1].hexbin(x='Delinquency 58', y='Delinquency 74', data=plot_df, bins = 'log', gridsize = 40, cmap = 'coolwarm')\nax[1].set(xlabel='Delinquency 58', ylabel='Delinquency 74')\nax[1].text(0.3,4.2,'Correlation: {:.2f}'.format(plot_df[['Delinquency 58','Delinquency 74']].corr().iloc[1,0]),\n          ha='center', va = 'center', bbox = dict(boxstyle=\"round,pad=0.3\",fc='white'))\n\nax[2].hexbin(x='Delinquency 113', y='Delinquency 115', data=plot_df, bins = 'log', gridsize = 40, cmap = 'coolwarm')\nax[2].set(xlabel='Delinquency 113', ylabel='Delinquency 115')\nax[2].text(2.15, 1.95, 'Correlation: {:.2f}'.format(plot_df[['Delinquency 113','Delinquency 115']].corr().iloc[1,0]),\n          ha='center', va = 'center', bbox = dict(boxstyle=\"round,pad=0.3\",fc='white'))\n\nax[3].hexbin(x='Delinquency 131', y='Delinquency 132', data=plot_df, bins = 'log', gridsize = 40, cmap = 'coolwarm')\nax[3].set(xlabel='Delinquency 131', ylabel='Delinquency 132')\nax[3].text(1.1, 5.9, 'Correlation: {:.2f}'.format(plot_df[['Delinquency 131','Delinquency 132']].corr().iloc[1,0]),\n          ha='center', va = 'center', bbox = dict(boxstyle=\"round,pad=0.3\",fc='white'))\n\n\n\n\n\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# EDA of Spend Variables","metadata":{}},{"cell_type":"code","source":"cols = [col for col in train.columns if (col.startswith(('S','T'))) & (col not in cat_cols[:-1])]\nplot_df = train[cols]\nfig, ax = plt.subplots(5,5, figsize=(16,20))\nfig.suptitle('Distribution of Spend Variables', fontsize = 16)\nrow = 0\ncol = [0,1,2,3,4]*5\n\nfor i, column in enumerate(plot_df.columns[:-1]):\n    if(i!=0)&(i%5==0):\n        row += 1\n    sns.kdeplot(x=column, hue='Target', palette = pal[::-1], hue_order=[1,0],\n               label = ['Default','Paid'], data=plot_df,\n               fill = True, linewidth = 2, legend=False, ax=ax[row,col[i]])\n    \n    ax[row,col[i]].tick_params(left=False,bottom=False)\n    ax[row,col[i]].set(title='\\n\\n{}'.format(column),xlabel='',\n                      ylabel=('Density' if i%5==0 else ''))\n\nfor i in range(1,5):\n    ax[4,i].set_visible(False)\n    \nhandles, _ = ax[0,0].get_legend_handles_labels()\nfig.legend(labels=['Default','Paid'], handles=reversed(handles), ncol = 2, bbox_to_anchor=(0.18,0.985))\nsns.despine(bottom=True, trim=True)\nplt.tight_layout(rect=[0, 0.2, 1, 0.99])\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr = plot_df.corr()\nmask = np.triu(np.ones_like(corr,dtype=bool))[1:,:-1]\ncorr = corr.iloc[1:, :-1].copy()\nfig, ax = plt.subplots(figsize=(16,12))\nsns.heatmap(corr,mask=mask, vmin = -1, vmax = 1, center = 0, annot = True, fmt = '.2f',\n           cmap = 'coolwarm', annot_kws={'fontsize':10, 'fontweight':'bold'}, cbar = False)\nax.tick_params(left = False, bottom = False)\nax.set_xticklabels(ax.get_xticklabels(), rotation = 45, horizontalalignment='right',fontsize=12)\nax.set_yticklabels(ax.get_yticklabels(), fontsize = 12)\nplt.title('Correlations between Spend Variables\\n', fontsize = 16)\nfig.show()\n\n\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(1,4, figsize = (16,5))\nfig.suptitle('Relationships between Spend Variables, \\nLog-Transformed', fontsize = 16)\n\nax[0].hexbin(x = 'Spend 24', y = 'Spend 22', data = plot_df, bins='log',\n            gridsize = 40, cmap = 'coolwarm')\nax[0].set(xlabel='Spend 24', ylabel = 'Spend 22')\nax[0].text(-70, 4, 'Correlation: {:.2f}'.format(plot_df[['Spend 24','Spend 22']].corr().iloc[1,0]),\n           ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\n\nax[1].hexbin(x = 'Spend 7', y = 'Spend 3', data = plot_df, bins='log',\n            gridsize = 40, cmap = 'coolwarm')\nax[1].set(xlabel='Spend 7', ylabel = 'Spend 3')\nax[1].text(0.4, 4.15, 'Correlation: {:.2f}'.format(plot_df[['Spend 7','Spend 3']].corr().iloc[1,0]),\n           ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\n\nax[2].hexbin(x = 'Spend 15', y = 'Spend 8', data = plot_df, bins='log',\n            gridsize = 40, cmap = 'coolwarm')\nax[2].set(xlabel='Spend 15', ylabel = 'Spend 8')\nax[2].text(1.2, 1.28, 'Correlation: {:.2f}'.format(plot_df[['Spend 15','Spend 8']].corr().iloc[1,0]),\n           ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\n\nax[3].hexbin(x = 'Spend 11', y = 'Spend 15', data = plot_df, bins='log',\n            gridsize = 40, cmap = 'coolwarm')\nax[3].set(xlabel='Spend 11', ylabel = 'Spend 15')\nax[3].text(0.5, 5.5, 'Correlation: {:.2f}'.format(plot_df[['Spend 11','Spend 15']].corr().iloc[1,0]),\n           ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\n\n\nfor i in range(4):\n    ax[i].tick_params(left = False, bottom = False)\nsns.despine()\nplt.tight_layout(rect=[0, 0, 1, 0.99])\nplt.show()\n\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# EDA of Payment Variables","metadata":{}},{"cell_type":"code","source":"cols = [col for col in train.columns if (col.startswith(('P','T'))) & (col not in cat_cols[:-1])]\nplot_df=train[cols]\nfig, ax = plt.subplots(1, 3, figsize=(16,5))\nfig.suptitle('Distribution of Payment Variables', fontsize=16)\n\nfor i, col in enumerate(plot_df.columns[:-1]):\n    sns.kdeplot(x=col, hue = 'Target', palette=pal[::-1], hue_order=[1,0],\n               label = ['Default','Paid'], data=plot_df,\n               fill = True, linewidth=2, legend=False, ax=ax[i])\n    ax[i].tick_params(left=False, bottom=False)\n    ax[i].set(title='{}'.format(col), xlabel='',ylabel=('Density' if i==0 else ''))\n\nhandles, _ = ax[0].get_legend_handles_labels()    \nfig.legend(labels=['Default','Paid'], handles = reversed(handles), ncol=2, bbox_to_anchor=(0.18,1))    \nsns.despine(bottom=True,trim=True)\nplt.tight_layout(rect=[0, 0.2, 1, 0.99])\n    \n    ","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr = plot_df.corr()\nmask = np.triu(np.ones_like(corr, dtype=bool))[1:,:-1]\ncorr = corr.iloc[1:,:-1].copy()\nfig, ax = plt.subplots(figsize=(7,5))\nsns.heatmap(corr, mask=mask, vmin = -1, vmax = 1, center = 0, annot=True, fmt = '.2f',\n           cmap = 'coolwarm', annot_kws={'fontsize':12,'fontweight':'bold'})\nax.tick_params(left=False, bottom=False)\nax.set_xticklabels(ax.get_xticklabels(), rotation = 45, horizontalalignment = 'right', fontsize = 12)\nax.set_yticklabels(ax.get_yticklabels(),fontsize = 12)\nplt.title('Correlations between Payment Variables\\n', fontsize=16)\nfig.show()\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(1,3, figsize = (16,5))\nfig.suptitle('Relationships between Payment Variables, \\nLog-Transformed', fontsize=16)\n\nax[0].hexbin(x='Payment 2', y='Payment 3', data=plot_df, bins='log',\n            gridsize=40, cmap = 'coolwarm')\nax[0].text(-.2,2.2, 'Correlation: {:.2f}'.format(plot_df[['Payment 2',\n                                                         'Payment 3']].corr().iloc[1,0]),\n          ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\nax[0].set(xlabel='Payment 2', ylabel='Payment 3')\n\nax[1].hexbin(x='Payment 3', y='Payment 4', data=plot_df, bins='log',\n            gridsize=40, cmap = 'coolwarm')\nax[1].text(-.6,1.35, 'Correlation: {:.2f}'.format(plot_df[['Payment 3',\n                                                         'Payment 4']].corr().iloc[1,0]),\n          ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\nax[1].set(xlabel='Payment 3', ylabel='Payment 4')\n\n\nax[2].hexbin(x='Payment 4', y='Payment 2', data=plot_df, bins='log',\n            gridsize=40, cmap = 'coolwarm')\nax[2].text(.25,1.1, 'Correlation: {:.2f}'.format(plot_df[['Payment 4',\n                                                         'Payment 2']].corr().iloc[1,0]),\n          ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\nax[2].set(xlabel='Payment 4', ylabel='Payment 2')\n\n\nfor i in range(3):\n    ax[i].tick_params(left=False, bottom=False)\nsns.despine()\nplt.tight_layout(rect=[0, 0, 1, 0.99])\nplt.show()\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# EDA of Balance Variables","metadata":{}},{"cell_type":"code","source":"cols = [col for col in train.columns if (col.startswith(('B','T'))) & (col not in cat_cols[:-1])]\nplot_df = train[cols]\nfig, ax = plt.subplots(8, 5, figsize = (16,32))\nfig.suptitle('Distribution of Balance Variables', fontsize = 16)\nrow = 0\ncol = [0,1,2,3,4]*8\nfor i, column in enumerate(plot_df.columns[:-1]):\n    if(i!=0)&(i%5==0):\n        row += 1\n    sns.kdeplot(x=column, hue = 'Target', palette = pal[::-1], hue_order=[1,0],\n               label = ['Default','Paid'], data=plot_df,\n               fill = True, linewidth = 2, legend = False, ax=ax[row,col[i]])\n    ax[row,col[i]].tick_params(left=False, bottom=False)\n    ax[row,col[i]].set(title='\\n\\n{}'.format(column),xlabel='',ylabel=('Density' if i%5==0 else ''))\n    \nfor i in range(3,5):\n    ax[7,i].set_visible(False)\n\nhandles, _ = ax[0,0].get_legend_handles_labels()\nfig.legend(labels=['Default','Paid'],handles=reversed(handles), ncol=2, bbox_to_anchor=(0.18,0.984))\nsns.despine(bottom=True, trim=True)\nplt.tight_layout(rect=[0, 0.2, 1, 0.99])\n\n\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr = plot_df.corr()\nmask = np.triu(np.ones_like(corr, dtype = bool))[1:,:-1]\ncorr = corr.iloc[1:,:-1].copy()\nfig, ax = plt.subplots(figsize=(24,22))\nsns.heatmap(corr, mask=mask, vmin = -1, vmax = 1, center = 0, annot = True, fmt = '.2f',\n           cmap = 'coolwarm', annot_kws = {'fontsize':12, 'fontweight':'bold'}, cbar=False)\nax.tick_params(left=False, bottom = False)\nax.set_xticklabels(ax.get_xticklabels(), rotation = 45, horizontalalignment='right', fontsize = 12)\nax.set_yticklabels(ax.get_yticklabels(), fontsize=12)\nplt.title('Correlations between Balance Variables\\n', fontsize=16)\nfig.show()\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(1,3, figsize=(16,5))\nfig.suptitle('Relationships between Balance Variables, \\nLog-Transformed', fontsize = 16)\n\nax[0].hexbin(x='Balance 23', y='Balance 7', data=plot_df, bins='log', gridsize=40, cmap = 'coolwarm')\nax[0].text(0.23,1.42, 'Correlation: {:.2f}'.format(plot_df[['Balance 23','Balance 7']].corr().iloc[1,0]),\n          ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\nax[0].set(xlabel = 'Balance 23', ylabel = 'Balance 7')\n\nax[1].hexbin(x='Balance 3', y='Balance 11', data=plot_df, bins='log', gridsize=40, cmap = 'coolwarm')\nax[1].text(0.3,1.85, 'Correlation: {:.2f}'.format(plot_df[['Balance 3','Balance 11']].corr().iloc[1,0]),\n          ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\nax[1].set(xlabel = 'Balance 3', ylabel = 'Balance 11')\n\nax[2].hexbin(x='Balance 11', y='Balance 2', data=plot_df, bins='log', gridsize=40, cmap = 'coolwarm')\nax[2].text(0.3,1.07, 'Correlation: {:.2f}'.format(plot_df[['Balance 11','Balance 2']].corr().iloc[1,0]),\n          ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\nax[2].set(xlabel = 'Balance 11', ylabel = 'Balance 2')\n\nfor i in range(3):\n    ax[i].tick_params(left=False, bottom=False)\nsns.despine()\nplt.tight_layout(rect=[0, 0, 1, 0.99])\nplt.show()\n\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# EDA of Risk Variables","metadata":{}},{"cell_type":"code","source":"cols = [col for col in train.columns if (col.startswith(('R','T'))) & (col not in cat_cols[:-1])]\nplot_df = train[cols]\nfig, ax = plt.subplots(6,5, figsize=(16,24))\nrow = 0\ncol = [0, 1, 2, 3, 4]*6\nfor i, column in enumerate(plot_df.columns[:-1]):\n    if(i!=0) & (i%5==0):\n        row+=1\n    sns.kdeplot(x=column, hue = 'Target', palette=pal[::-1], hue_order=[1,0],\n               label = ['Default','Paid'], data = plot_df,\n               fill = True, linewidth = 2, legend = False, ax=ax[row,col[i]])\n    ax[row, col[i]].tick_params(left=False, bottom=False)\n    ax[row, col[i]].set(title='\\n\\n{}'.format(column), xlabel='', ylabel=('Density' if i%5==0 else ''))\n\nfor i in range(3,5):\n    ax[5,i].set_visible(False)\nhandles, _ = ax[0,0].get_legend_handles_labels()\nfig.legend(labels=['Default','Paid'], handles=reversed(handles), ncol=2, bbox_to_anchor=(0.18,0.94))\nsns.despine(bottom=True, trim = True)\nplt.tight_layout(rect=[0, 0.2, 1, 0.99])\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr=plot_df.corr()\nmask=np.triu(np.ones_like(corr, dtype=bool))[1:,:-1]\ncorr=corr.iloc[1:,:-1].copy()\nfig, ax = plt.subplots(figsize=(24,18))   \nsns.heatmap(corr, mask=mask, vmin=-1, vmax=1, center=0, annot=True, fmt='.2f', \n            cmap='coolwarm', annot_kws={'fontsize':12,'fontweight':'bold'}, cbar=False)\nax.tick_params(left=False,bottom=False)\nax.set_xticklabels(ax.get_xticklabels(), rotation=45, horizontalalignment='right',fontsize=12)\nax.set_yticklabels(ax.get_yticklabels(), fontsize=12)\nplt.title('Correlations between Risk Variables\\n', fontsize=16)\nfig.show()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(1,3, figsize=(16,5))\nfig.suptitle('Relationships between Risk Variables, \\nLog-Transformed', fontsize = 16)\n\nax[0].hexbin(x='Risk 8', y='Risk 5', data=plot_df, bins = 'log', gridsize = 40, cmap = 'coolwarm')\nax[0].text(5, 35.7, 'Correlation: {:.2f}'.format(plot_df[['Risk 8', 'Risk 5']].corr().iloc[1,0]),\n          ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\nax[0].set(xlabel='Risk 8', ylabel = 'Risk 5')\n\nax[1].hexbin(x='Risk 3', y='Risk 16', data=plot_df, bins = 'log', gridsize = 40, cmap = 'coolwarm')\nax[1].text(1.3, 14.3, 'Correlation: {:.2f}'.format(plot_df[['Risk 3', 'Risk 16']].corr().iloc[1,0]),\n          ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\nax[1].set(xlabel='Risk 3', ylabel = 'Risk 16')\n\nax[2].hexbin(x='Risk 20', y='Risk 17', data=plot_df, bins = 'log', gridsize = 40, cmap = 'coolwarm')\nax[2].text(7, 1.02, 'Correlation: {:.2f}'.format(plot_df[['Risk 20', 'Risk 17']].corr().iloc[1,0]),\n          ha=\"center\", va=\"center\", bbox=dict(boxstyle=\"round,pad=0.3\", fc=\"white\"))\nax[2].set(xlabel='Risk 20', ylabel = 'Risk 17')\n\nfor i in range(3):\n    ax[i].tick_params(left = False, bottom = False)\nsns.despine()\nplt.tight_layout(rect=[0, 0, 1, 0.99])\nplt.show()\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# EDA of Categorical Variables","metadata":{}},{"cell_type":"code","source":"fig = make_subplots(rows=4, cols=3, \n                    subplot_titles=cat_cols[:-1], \n                    vertical_spacing=0.1)\nrow=0\nc=[1,2,3]*5\nplot_df=train[cat_cols]\nfor i,col in enumerate(cat_cols[:-1]):\n    if i%3==0:\n        row+=1\n    plot_df[col]=plot_df[col].astype(object)\n    df=plot_df.groupby(col)['Target'].value_counts().rename('count').reset_index().replace('',np.nan)\n    \n    fig.add_trace(go.Bar(x=df[df.Target==1][col], y=df[df.Target==1]['count'],\n                         marker_color=rgb[1], marker_line=dict(color=pal[1],width=2), \n                         hovertemplate='Value %{x} Frequency = %{y}',\n                         name='Default', showlegend=(True if i==0 else False)),\n                  row=row, col=c[i])\n    fig.add_trace(go.Bar(x=df[df.Target==0][col], y=df[df.Target==0]['count'],\n                         marker_color=rgb[0], marker_line=dict(color=pal[0],width=2),\n                         hovertemplate='Value %{x} Frequency = %{y}',\n                         name='Paid', showlegend=(True if i==0 else False)),\n                  row=row, col=c[i])\n    if i%3==0:\n        fig.update_yaxes(title='Frequency',row=row,col=c[i])\nfig.update_layout(template=temp,title=\"Distribution of Categorical Variables\",\n                  legend=dict(orientation=\"h\",yanchor=\"bottom\",y=1.03,xanchor=\"right\",x=0.2),\n                  barmode='group',height=1500,width=900)\nfig.show()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr = train.corr()\ncorr = corr['Target'].sort_values(ascending=False)[1:-1]\npal = sns.color_palette('Reds_r',135).as_hex()\nrgb=['rgba'+str(matplotlib.colors.to_rgba(i, 0.7)) for i in pal]\nfig = go.Figure()\nfig.add_trace(go.Bar(x=corr[corr >= 0], y = corr[corr>=0].index,\n                    marker_color = rgb, orientation='h',\n                    marker_line = dict(color=pal,width = 2), name='',\n                    hovertemplate = '%{y} correlation with target: %{x:.3f}',\n                    showlegend=False))\n\npal = sns.color_palette(\"Blues\",100).as_hex()\nrgb = ['rgba'+ str(matplotlib.colors.to_rgba(i,0.7)) for i in pal]\nfig.add_trace(go.Bar(x=corr[corr<0], y = corr[corr<0].index,\n                    marker_color=rgb[25:], orientation='h',\n                    marker_line=dict(color=pal[25:],width=2), name='',\n                    hovertemplate='%{y} correlation with target: %{x:.3f}',\n                    showlegend=False))\nfig.update_layout(template=temp, title = 'Feature Correlations with Target',\n                 xaxis_title=\"Correlation\", margin=dict(l=150),\n                 height = 3000, width = 700, hovermode = 'closest')\n\nfig.show()","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"There are several strong correlations with the target variable. `Payment 2` is the most negatively correlated with the probability of defaulting with a correlation of -0.67, while `Delinquency 48` is the most positively correlated overall at 0.61 `Delinquency 87` is also missing from the correlations above due to the proportion of null values. In fact, 24 of the top 30 features with missing values are in `Delinquency` variables.","metadata":{}},{"cell_type":"code","source":"null = round((train.isna().sum()/train.shape[0]*100),2).sort_values(ascending=False).astype(str)+('%')\nnull = null.to_frame().rename(columns={0:'Missing %'})\nnull.head()\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Default Prediction","metadata":{}},{"cell_type":"code","source":"def 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').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        \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').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        \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)\n\ndef plot_roc(y_val, y_prob):\n    colors = px.colors.qualitative.Prism\n    fig = go.Figure()\n    fig.add_trace(go.Scatter(x=np.linspace(0,1,11), y=np.linspace(0,1,11),\n                            name = 'Random Chance', mode='lines', showlegend=False,\n                            line=dict(color='Black', width = 1, dash='dot')))\n    for i in range(len(y_val)):\n        y = y_val[i]\n        prob = y_prob[i]\n        fpr, tpr, _ = roc_curve(y, prob)\n        roc_auc = auc(fpr,tpr)\n        fig.add_trace(go.Scatter(x=fpr, y = tpr, line = dict(color=colors[::-1][i+1], width=3),\n                                hovertemplate='True positive rate = %{y:.3f}<br>False positive rate = {x:.3f}%',\n                                name = 'Fold {}: Gini = {:.3f}, AUC = {:.3f}'.format(i+1,gini[i],roc_auc)))\n        fig.update_layout(template=temp, title = 'Cross-Validation ROC Curves',\n                         hovermode = 'x unified', width = 700, height = 600,\n                         xaxis_title = 'False Positive Rate (1 - Specificity)',\n                         yaxis_title = 'True Positive Rate (Sensitivity)',\n                         legend = dict(orientation='v', y=.07, x=1, xanchor='right',\n                                      bordercolor=\"black\", borderwidth = .5))\n        fig.show()\n        \n    \n\n    \n    \n    \n    \n    \n    \n    ","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# LightGBM","metadata":{}},{"cell_type":"code","source":"enc = LabelEncoder()\nfor col in cat_cols[:-1]:\n    train[col] = enc.fit_transform(train[col])\n    test[col] = enc.transform(test[col])\nX = train.drop(['Target'], axis = 1)\ny = train['Target']\n\ny_valid, gbm_val_probs, gbm_test_preds, gini = [], [], [], []\nft_importance = pd.DataFrame(index=X.columns)\nsk_fold = StratifiedKFold(n_splits=10, shuffle = True, random_state = 21)\n\nfor fold, (train_idx, val_idx) in enumerate(sk_fold.split(X,y)):\n    print(\"\\nFold {}\".format(fold + 1))\n    X_train, y_train = X.iloc[train_idx,:], y[train_idx]\n    X_val, y_val = X.iloc[val_idx,:], y[val_idx]\n    print(\"Train shape: {}, {}, Valid shape: {}, {}\\n\".format(X_train.shape, \n                                                              y_train.shape,\n                                                             X_val.shape,\n                                                             y_val.shape))\n    params = {\"boosting_type\":\"gbdt\",\n             \"n_estimators\":1000,\n             \"num_leaves\":50,\n             \"learning_rate\":0.05,\n             \"colsample_bytree\":0.9,\n             \"min_child_samples\":2000,\n             \"max_bins\": 500,\n             \"reg_alpha\":2,\n             \"objective\":\"binary\",\n             \"random_state\": 21\n             }\n    gbm = LGBMClassifier(**params).fit(X_train, y_train, eval_set=[(X_train, y_train), (X_val, y_val)],\n                                      callbacks=[early_stopping(200), log_evaluation(500)],\n                                      eval_metric=['auc','binary_logloss'])\n    gbm_prob = gbm.predict_proba(X_val)[:,1]\n    gbm_val_probs.append(gbm_prob)\n    y_valid.append(y_val)\n    \n    y_pred = pd.DataFrame(data={'prediction':gbm_prob})\n    y_true = pd.DataFrame(data={'target': y_val.reset_index(drop=True)})\n    \n    gini_score = amex_metric(y_true = y_true, y_pred = y_pred)\n    gini.append(gini_score)\n    \n    auc_score = roc_auc_score(y_val,gbm_prob)\n    gbm_test_preds.append(gbm.predict_proba(test)[:,1])\n    ft_importance[\"Importance_Fold\"+str(fold)] = gbm.feature_importances_\n    \n    print(\"Validation Gini: {:.5f}, AUC: {:.4f}\".format(gini_score, auc_score))\n    \n    del X_train, y_train, X_val, y_val\n    _=gc.collect()\n\ndel X, y\nplot_roc(y_valid, gbm_val_probs)\n    \n    ","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ft_importance['avg'] = ft_importance.mean(axis=1)\nft_importance= pd.DataFrame(ft_importance)\nft_importance = ft_importance.avg.nlargest(50).sort_values(ascending = True)\n\npal=sns.color_palette(\"YlGnBu\", 65).as_hex()\nfig = go.Figure()\n\nfor i in range(len(ft_importance.index)):\n    fig.add_shape(dict(type=\"line\", y0=i, y1=i, x0=0, x1=ft_importance[i],\n                      line_color = pal[::-1][i],opacity=0.8, line_width=4))\n    \nfig.add_trace(go.Scatter(x=ft_importance, y=ft_importance.index, mode='markers',\n                        marker_color = pal[::-1], marker_size=8,\n                        hovertemplate='%{y} Importance = %{x:.0f}<extra></extra>'))\nfig.update_layout(template = temp, title = 'LGBM Feature Importance<br>Top 50',\n                 margin = dict(l=150, t = 80),\n                 xaxis = dict(title='Importance', zeroline = False),\n                 yaxis_showgrid=False, height = 1000, width=800)\nfig.show()\n\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Submission\n","metadata":{}},{"cell_type":"code","source":"sub = pd.read_csv('./input/submission.csv')\nsub['prediction'] = np.mean(gbm_test_preds, axis = 0)\n\ndf=pd.DataFrame(data={'Target':sub['prediction'].apply(lambda x: 1 if x>0.5 else 0)})\ndf = df.Target.value_counts(normalize=True)\ndf.rename(index={1:'Default', 0:'Paid'}, inplace = True)\npal,color = ['#016CC9','#DEB078'], ['#8DBAE2','#EDD3B3']\nfig = go.Figure()\nfig.add_trace(go.Pie(labels=df.index, values = df*100, hole = .45,\n                    showlegend=True, sort = False,\n                    marker = dict(colors=color, line = dict(color=pal, width = 2.5)),\n                    hovertemplate = '%{label} Accounts: %{value:.2f}%<extra></extra>'\n                    ))\nfig.update_layout(template=temp, title = 'Prediction Target Distribution',\n                 legend = dict(traceorder='reversed', y=1.05, x = 0),\n                 uniformtext_minsize=15, uniformtext_mode='hide', width = 700)\nfig.show()\n","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sub.to_csv('submissionLightGBM.csv', index=False)\ndisplay(sub.head())","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}