reports/machine-learning-pipeline/report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "f1a30920",
   "metadata": {},
   "source": [
    "## Machine Learning Model Tracking\n",
    "\n",
    "This is part of a series of tutorials to help you learn how to use Datapane. In this example we use Datapane to produce automated reports of a long-running process, such as an ML pipeline.\n",
    "\n",
    "# Conducting the Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "45a6373a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Load the librarys\n",
    "import pandas as pd  # To work with dataset\n",
    "\n",
    "# from pandas_profiling import ProfileReport\n",
    "import numpy as np  # Math library\n",
    "import json\n",
    "import datapane as dp\n",
    "from datetime import datetime\n",
    "import seaborn as sns  # Graph library that use matplot in background\n",
    "import matplotlib.pyplot as plt  # to plot some parameters in seaborn\n",
    "import plotly.offline as py\n",
    "\n",
    "py.init_notebook_mode(connected=True)  # this code, allow us to work with offline plotly version\n",
    "import plotly.graph_objs as go  # it's like \"plt\" of matplot\n",
    "import plotly.tools as tls  # It's useful to get some tools of plotly\n",
    "import plotly.figure_factory as ff\n",
    "from plotly import tools\n",
    "import plotly.express as px\n",
    "from sklearn.model_selection import train_test_split, KFold, cross_val_score  # to split the data\n",
    "from sklearn.metrics import (\n",
    "    accuracy_score,\n",
    "    confusion_matrix,\n",
    "    classification_report,\n",
    "    roc_curve,\n",
    "    auc,\n",
    ")  # To evaluate our model\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "# Algorithmns models to be compared\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.discriminant_analysis import LinearDiscriminantAnalysis\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "# from xgboost import XGBClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c899a0b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the data\n",
    "df_credit = pd.read_csv(\n",
    "    \"https://raw.githubusercontent.com/shoumikgoswami/ML-pipeline/main/data/german_credit_data.csv\", index_col=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1bcce42b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Searching for Missings,type of data and also known the shape of data\n",
    "print(df_credit.info())\n",
    "\n",
    "# Looking unique values\n",
    "print(df_credit.nunique())\n",
    "# Looking the data\n",
    "print(df_credit.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "841db913",
   "metadata": {},
   "outputs": [],
   "source": [
    "# enable once ydata-profiling supports 3.11\n",
    "# profile = ProfileReport(df_credit, title=\"Data Profile Report\")\n",
    "# json_data = profile.to_json()\n",
    "# json_data = json.loads(json_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "297f077c",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(14, 12))\n",
    "corr_plot = sns.heatmap(df_credit.corr(), linewidths=0.1, vmax=1.0, square=True, linecolor=\"white\", annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "584db9c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_values = df_credit.isnull().sum() * 100 / len(df_credit)\n",
    "missing_values = missing_values[missing_values > 0]\n",
    "missing_values.sort_values(inplace=True)\n",
    "missing_values = missing_values.to_frame()\n",
    "missing_values.columns = [\"% of missing\"]\n",
    "missing_values.index.names = [\"Attribute\"]\n",
    "missing_values[\"Attribute\"] = missing_values.index\n",
    "sns.set(style=\"whitegrid\", color_codes=True)\n",
    "missing_plot = sns.barplot(x=\"Attribute\", y=\"% of missing\", data=missing_values)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3dd7130a",
   "metadata": {},
   "outputs": [],
   "source": [
    "trace0 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Risk\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Risk\"].value_counts().values,\n",
    "    name=\"Good credit\",\n",
    ")\n",
    "\n",
    "trace1 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Risk\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Risk\"].value_counts().values,\n",
    "    name=\"Bad credit\",\n",
    ")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout()\n",
    "\n",
    "layout = go.Layout(yaxis=dict(title=\"Count\"), xaxis=dict(title=\"Risk Variable\"), title=\"Target variable distribution\")\n",
    "\n",
    "plot1 = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(plot1, filename=\"grouped-bar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eb0d3b3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_good = df_credit.loc[df_credit[\"Risk\"] == \"good\"][\"Age\"].values.tolist()\n",
    "df_bad = df_credit.loc[df_credit[\"Risk\"] == \"bad\"][\"Age\"].values.tolist()\n",
    "df_age = df_credit[\"Age\"].values.tolist()\n",
    "\n",
    "# First plot\n",
    "trace0 = go.Histogram(x=df_good, histnorm=\"probability\", name=\"Good Credit\")\n",
    "# Second plot\n",
    "trace1 = go.Histogram(x=df_bad, histnorm=\"probability\", name=\"Bad Credit\")\n",
    "# Third plot\n",
    "trace2 = go.Histogram(x=df_age, histnorm=\"probability\", name=\"Overall Age\")\n",
    "\n",
    "# Creating the grid\n",
    "fig = tls.make_subplots(\n",
    "    rows=2, cols=2, specs=[[{}, {}], [{\"colspan\": 2}, None]], subplot_titles=(\"Good\", \"Bad\", \"General Distribuition\")\n",
    ")\n",
    "\n",
    "# setting the figs\n",
    "fig.append_trace(trace0, 1, 1)\n",
    "fig.append_trace(trace1, 1, 2)\n",
    "fig.append_trace(trace2, 2, 1)\n",
    "\n",
    "fig[\"layout\"].update(showlegend=True, title=\"Age Distribuition\", bargap=0.05)\n",
    "py.iplot(fig, filename=\"custom-sized-subplot-with-subplot-titles\")\n",
    "plot2 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e396a11e",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_good = df_credit[df_credit[\"Risk\"] == \"good\"]\n",
    "df_bad = df_credit[df_credit[\"Risk\"] == \"bad\"]\n",
    "\n",
    "fig, ax = plt.subplots(nrows=2, figsize=(12, 8))\n",
    "plt.subplots_adjust(hspace=0.4, top=0.8)\n",
    "\n",
    "g1 = sns.distplot(df_good[\"Age\"], ax=ax[0], color=\"g\")\n",
    "g1 = sns.distplot(df_bad[\"Age\"], ax=ax[0], color=\"r\")\n",
    "g1.set_title(\"Age Distribuition\", fontsize=15)\n",
    "g1.set_xlabel(\"Age\")\n",
    "g1.set_xlabel(\"Frequency\")\n",
    "\n",
    "g2 = sns.countplot(x=\"Age\", data=df_credit, palette=\"hls\", ax=ax[1], hue=\"Risk\")\n",
    "g2.set_title(\"Age Counting by Risk\", fontsize=15)\n",
    "g2.set_xlabel(\"Age\")\n",
    "g2.set_xlabel(\"Count\")\n",
    "plt.show()\n",
    "plot3 = g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eea923d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's look the Credit Amount column\n",
    "interval = (18, 25, 35, 60, 120)\n",
    "\n",
    "cats = [\"Student\", \"Young\", \"Adult\", \"Senior\"]\n",
    "df_credit[\"Age_cat\"] = pd.cut(df_credit.Age, interval, labels=cats)\n",
    "\n",
    "\n",
    "df_good = df_credit[df_credit[\"Risk\"] == \"good\"]\n",
    "df_bad = df_credit[df_credit[\"Risk\"] == \"bad\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d23dce05",
   "metadata": {},
   "outputs": [],
   "source": [
    "trace0 = go.Box(y=df_good[\"Credit amount\"], x=df_good[\"Age_cat\"], name=\"Good credit\", marker=dict(color=\"#3D9970\"))\n",
    "\n",
    "trace1 = go.Box(y=df_bad[\"Credit amount\"], x=df_bad[\"Age_cat\"], name=\"Bad credit\", marker=dict(color=\"#FF4136\"))\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(\n",
    "    yaxis=dict(title=\"Credit Amount (US Dollar)\", zeroline=False), xaxis=dict(title=\"Age Categorical\"), boxmode=\"group\"\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"box-age-cat\")\n",
    "plot4 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2c1b4f48",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First plot\n",
    "trace0 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Housing\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Housing\"].value_counts().values,\n",
    "    name=\"Good credit\",\n",
    ")\n",
    "\n",
    "# Second plot\n",
    "trace1 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Housing\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Housing\"].value_counts().values,\n",
    "    name=\"Bad Credit\",\n",
    ")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(title=\"Housing Distribuition\")\n",
    "\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"Housing-Grouped\")\n",
    "plot5 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "209c2632",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First plot\n",
    "trace0 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Sex\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Sex\"].value_counts().values,\n",
    "    name=\"Good credit\",\n",
    ")\n",
    "\n",
    "# First plot 2\n",
    "trace1 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Sex\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Sex\"].value_counts().values,\n",
    "    name=\"Bad Credit\",\n",
    ")\n",
    "\n",
    "# Second plot\n",
    "trace2 = go.Box(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Sex\"],\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Credit amount\"],\n",
    "    name=trace0.name,\n",
    ")\n",
    "\n",
    "# Second plot 2\n",
    "trace3 = go.Box(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Sex\"],\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Credit amount\"],\n",
    "    name=trace1.name,\n",
    ")\n",
    "\n",
    "data = [trace0, trace1, trace2, trace3]\n",
    "\n",
    "\n",
    "fig = tls.make_subplots(rows=1, cols=2, subplot_titles=(\"Sex Count\", \"Credit Amount by Sex\"))\n",
    "\n",
    "fig.append_trace(trace0, 1, 1)\n",
    "fig.append_trace(trace1, 1, 1)\n",
    "fig.append_trace(trace2, 1, 2)\n",
    "fig.append_trace(trace3, 1, 2)\n",
    "\n",
    "fig[\"layout\"].update(height=400, width=800, title=\"Sex Distribuition\", boxmode=\"group\")\n",
    "py.iplot(fig, filename=\"sex-subplot\")\n",
    "\n",
    "plot6 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3cfdd7fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First plot\n",
    "trace0 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Job\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Job\"].value_counts().values,\n",
    "    name=\"Good credit Distribuition\",\n",
    ")\n",
    "\n",
    "# Second plot\n",
    "trace1 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Job\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Job\"].value_counts().values,\n",
    "    name=\"Bad Credit Distribuition\",\n",
    ")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(title=\"Job Distribuition\")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"grouped-bar\")\n",
    "\n",
    "plot7 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1779a072",
   "metadata": {},
   "outputs": [],
   "source": [
    "trace0 = go.Box(x=df_good[\"Job\"], y=df_good[\"Credit amount\"], name=\"Good credit\")\n",
    "\n",
    "trace1 = go.Box(x=df_bad[\"Job\"], y=df_bad[\"Credit amount\"], name=\"Bad credit\")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(yaxis=dict(title=\"Credit Amount distribuition by Job\"), boxmode=\"group\")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"box-age-cat\")\n",
    "\n",
    "plot8 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a66a1a65",
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(figsize=(12, 12), nrows=2)\n",
    "\n",
    "g1 = sns.boxplot(x=\"Job\", y=\"Credit amount\", data=df_credit, palette=\"hls\", ax=ax[0], hue=\"Risk\")\n",
    "g1.set_title(\"Credit Amount by Job\", fontsize=15)\n",
    "g1.set_xlabel(\"Job Reference\", fontsize=12)\n",
    "g1.set_ylabel(\"Credit Amount\", fontsize=12)\n",
    "\n",
    "g2 = sns.violinplot(x=\"Job\", y=\"Age\", data=df_credit, ax=ax[1], hue=\"Risk\", split=True, palette=\"hls\")\n",
    "g2.set_title(\"Job Type reference x Age\", fontsize=15)\n",
    "g2.set_xlabel(\"Job Reference\", fontsize=12)\n",
    "g2.set_ylabel(\"Age\", fontsize=12)\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4, top=0.9)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plot9 = g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07ecab56",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Add histogram data\n",
    "x1 = np.log(df_good[\"Credit amount\"])\n",
    "x2 = np.log(df_bad[\"Credit amount\"])\n",
    "\n",
    "# Group data together\n",
    "hist_data = [x1, x2]\n",
    "\n",
    "group_labels = [\"Good Credit\", \"Bad Credit\"]\n",
    "\n",
    "# Create distplot with custom bin_size\n",
    "fig = ff.create_distplot(hist_data, group_labels, bin_size=0.2)\n",
    "\n",
    "# Plot!\n",
    "py.iplot(fig, filename=\"Distplot with Multiple Datasets\")\n",
    "\n",
    "plot10 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7ff14bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ploting the good and bad dataframes in distplot\n",
    "plt.figure(figsize=(8, 5))\n",
    "\n",
    "g = sns.distplot(df_good[\"Credit amount\"], color=\"r\")\n",
    "g = sns.distplot(df_bad[\"Credit amount\"], color=\"g\")\n",
    "g.set_title(\"Credit Amount Frequency distribuition\", fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b35b11b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "count_good = go.Bar(\n",
    "    x=df_good[\"Saving accounts\"].value_counts().index.values,\n",
    "    y=df_good[\"Saving accounts\"].value_counts().values,\n",
    "    name=\"Good credit\",\n",
    ")\n",
    "count_bad = go.Bar(\n",
    "    x=df_bad[\"Saving accounts\"].value_counts().index.values,\n",
    "    y=df_bad[\"Saving accounts\"].value_counts().values,\n",
    "    name=\"Bad credit\",\n",
    ")\n",
    "\n",
    "\n",
    "box_1 = go.Box(x=df_good[\"Saving accounts\"], y=df_good[\"Credit amount\"], name=\"Good credit\")\n",
    "box_2 = go.Box(x=df_bad[\"Saving accounts\"], y=df_bad[\"Credit amount\"], name=\"Bad credit\")\n",
    "\n",
    "scat_1 = go.Box(x=df_good[\"Saving accounts\"], y=df_good[\"Age\"], name=\"Good credit\")\n",
    "scat_2 = go.Box(x=df_bad[\"Saving accounts\"], y=df_bad[\"Age\"], name=\"Bad credit\")\n",
    "\n",
    "data = [scat_1, scat_2, box_1, box_2, count_good, count_bad]\n",
    "\n",
    "fig = tools.make_subplots(\n",
    "    rows=2,\n",
    "    cols=2,\n",
    "    specs=[[{}, {}], [{\"colspan\": 2}, None]],\n",
    "    subplot_titles=(\"Count Saving Accounts\", \"Credit Amount by Savings Acc\", \"Age by Saving accounts\"),\n",
    ")\n",
    "\n",
    "fig.append_trace(count_good, 1, 1)\n",
    "fig.append_trace(count_bad, 1, 1)\n",
    "\n",
    "fig.append_trace(box_2, 1, 2)\n",
    "fig.append_trace(box_1, 1, 2)\n",
    "\n",
    "fig.append_trace(scat_1, 2, 1)\n",
    "fig.append_trace(scat_2, 2, 1)\n",
    "\n",
    "\n",
    "fig[\"layout\"].update(height=700, width=800, title=\"Saving Accounts Exploration\", boxmode=\"group\")\n",
    "\n",
    "py.iplot(fig, filename=\"combined-savings\")\n",
    "\n",
    "plot11 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a485303",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Description of Distribuition Saving accounts by Risk:  \")\n",
    "print(pd.crosstab(df_credit[\"Saving accounts\"], df_credit.Risk))\n",
    "\n",
    "fig, ax = plt.subplots(3, 1, figsize=(12, 12))\n",
    "g = sns.countplot(x=\"Saving accounts\", data=df_credit, palette=\"hls\", ax=ax[0], hue=\"Risk\")\n",
    "g.set_title(\"Saving Accounts Count\", fontsize=15)\n",
    "g.set_xlabel(\"Saving Accounts type\", fontsize=12)\n",
    "g.set_ylabel(\"Count\", fontsize=12)\n",
    "\n",
    "g1 = sns.violinplot(x=\"Saving accounts\", y=\"Job\", data=df_credit, palette=\"hls\", hue=\"Risk\", ax=ax[1], split=True)\n",
    "g1.set_title(\"Saving Accounts by Job\", fontsize=15)\n",
    "g1.set_xlabel(\"Savings Accounts type\", fontsize=12)\n",
    "g1.set_ylabel(\"Job\", fontsize=12)\n",
    "\n",
    "g = sns.boxplot(x=\"Saving accounts\", y=\"Credit amount\", data=df_credit, ax=ax[2], hue=\"Risk\", palette=\"hls\")\n",
    "g2.set_title(\"Saving Accounts by Credit Amount\", fontsize=15)\n",
    "g2.set_xlabel(\"Savings Accounts type\", fontsize=12)\n",
    "g2.set_ylabel(\"Credit Amount(US)\", fontsize=12)\n",
    "\n",
    "plt.subplots_adjust(hspace=0.4, top=0.9)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plot12 = g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ab695668",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Values describe: \")\n",
    "print(pd.crosstab(df_credit.Purpose, df_credit.Risk))\n",
    "\n",
    "plt.figure(figsize=(14, 12))\n",
    "\n",
    "plt.subplot(221)\n",
    "g = sns.countplot(x=\"Purpose\", data=df_credit, palette=\"hls\", hue=\"Risk\")\n",
    "g.set_xticklabels(g.get_xticklabels(), rotation=45)\n",
    "g.set_xlabel(\"\", fontsize=12)\n",
    "g.set_ylabel(\"Count\", fontsize=12)\n",
    "g.set_title(\"Purposes Count\", fontsize=20)\n",
    "\n",
    "plt.subplot(222)\n",
    "g1 = sns.violinplot(x=\"Purpose\", y=\"Age\", data=df_credit, palette=\"hls\", hue=\"Risk\", split=True)\n",
    "g1.set_xticklabels(g1.get_xticklabels(), rotation=45)\n",
    "g1.set_xlabel(\"\", fontsize=12)\n",
    "g1.set_ylabel(\"Count\", fontsize=12)\n",
    "g1.set_title(\"Purposes by Age\", fontsize=20)\n",
    "\n",
    "plt.subplot(212)\n",
    "g2 = sns.boxplot(x=\"Purpose\", y=\"Credit amount\", data=df_credit, palette=\"hls\", hue=\"Risk\")\n",
    "g2.set_xlabel(\"Purposes\", fontsize=12)\n",
    "g2.set_ylabel(\"Credit Amount\", fontsize=12)\n",
    "g2.set_title(\"Credit Amount distribuition by Purposes\", fontsize=20)\n",
    "\n",
    "plt.subplots_adjust(hspace=0.6, top=0.8)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plot13 = g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18d9afe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(12, 14))\n",
    "\n",
    "g = plt.subplot(311)\n",
    "g = sns.countplot(x=\"Duration\", data=df_credit, palette=\"hls\", hue=\"Risk\")\n",
    "g.set_xlabel(\"Duration Distribuition\", fontsize=12)\n",
    "g.set_ylabel(\"Count\", fontsize=12)\n",
    "g.set_title(\"Duration Count\", fontsize=20)\n",
    "\n",
    "g1 = plt.subplot(312)\n",
    "g1 = sns.pointplot(x=\"Duration\", y=\"Credit amount\", data=df_credit, hue=\"Risk\", palette=\"hls\")\n",
    "g1.set_xlabel(\"Duration\", fontsize=12)\n",
    "g1.set_ylabel(\"Credit Amount(US)\", fontsize=12)\n",
    "g1.set_title(\"Credit Amount distribuition by Duration\", fontsize=20)\n",
    "\n",
    "g2 = plt.subplot(313)\n",
    "g2 = sns.distplot(df_good[\"Duration\"], color=\"g\")\n",
    "g2 = sns.distplot(df_bad[\"Duration\"], color=\"r\")\n",
    "g2.set_xlabel(\"Duration\", fontsize=12)\n",
    "g2.set_ylabel(\"Frequency\", fontsize=12)\n",
    "g2.set_title(\"Duration Frequency x good and bad Credit\", fontsize=20)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.4, hspace=0.4, top=0.9)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plot14 = g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "477c6966",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First plot\n",
    "trace0 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"good\"][\"Checking account\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"good\"][\"Checking account\"].value_counts().values,\n",
    "    name=\"Good credit Distribuition\",\n",
    ")\n",
    "\n",
    "# Second plot\n",
    "trace1 = go.Bar(\n",
    "    x=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Checking account\"].value_counts().index.values,\n",
    "    y=df_credit[df_credit[\"Risk\"] == \"bad\"][\"Checking account\"].value_counts().values,\n",
    "    name=\"Bad Credit Distribuition\",\n",
    ")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(\n",
    "    title=\"Checking accounts Distribuition\",\n",
    "    xaxis=dict(title=\"Checking accounts name\"),\n",
    "    yaxis=dict(title=\"Count\"),\n",
    "    barmode=\"group\",\n",
    ")\n",
    "\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"Age-ba\", validate=False)\n",
    "\n",
    "plot15 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e22ca5aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_good = df_credit[df_credit[\"Risk\"] == \"good\"]\n",
    "df_bad = df_credit[df_credit[\"Risk\"] == \"bad\"]\n",
    "\n",
    "trace0 = go.Box(\n",
    "    y=df_good[\"Credit amount\"], x=df_good[\"Checking account\"], name=\"Good credit\", marker=dict(color=\"#3D9970\")\n",
    ")\n",
    "\n",
    "trace1 = go.Box(\n",
    "    y=df_bad[\"Credit amount\"], x=df_bad[\"Checking account\"], name=\"Bad credit\", marker=dict(color=\"#FF4136\")\n",
    ")\n",
    "\n",
    "data = [trace0, trace1]\n",
    "\n",
    "layout = go.Layout(yaxis=dict(title=\"Cheking distribuition\"), boxmode=\"group\")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "\n",
    "py.iplot(fig, filename=\"box-age-cat\")\n",
    "\n",
    "plot16 = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bb99d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Total values of the most missing variable: \")\n",
    "print(df_credit.groupby(\"Checking account\")[\"Checking account\"].count())\n",
    "\n",
    "plt.figure(figsize=(12, 10))\n",
    "\n",
    "g = plt.subplot(221)\n",
    "g = sns.countplot(x=\"Checking account\", data=df_credit, palette=\"hls\", hue=\"Risk\")\n",
    "g.set_xlabel(\"Checking Account\", fontsize=12)\n",
    "g.set_ylabel(\"Count\", fontsize=12)\n",
    "g.set_title(\"Checking Account Counting by Risk\", fontsize=20)\n",
    "\n",
    "g1 = plt.subplot(222)\n",
    "g1 = sns.violinplot(x=\"Checking account\", y=\"Age\", data=df_credit, palette=\"hls\", hue=\"Risk\", split=True)\n",
    "g1.set_xlabel(\"Checking Account\", fontsize=12)\n",
    "g1.set_ylabel(\"Age\", fontsize=12)\n",
    "g1.set_title(\"Age by Checking Account\", fontsize=20)\n",
    "\n",
    "g2 = plt.subplot(212)\n",
    "g2 = sns.boxplot(x=\"Checking account\", y=\"Credit amount\", data=df_credit, hue=\"Risk\", palette=\"hls\")\n",
    "g2.set_xlabel(\"Checking Account\", fontsize=12)\n",
    "g2.set_ylabel(\"Credit Amount(US)\", fontsize=12)\n",
    "g2.set_title(\"Credit Amount by Cheking Account\", fontsize=20)\n",
    "\n",
    "plt.subplots_adjust(wspace=0.2, hspace=0.3, top=0.9)\n",
    "\n",
    "plt.show()\n",
    "plt.show()\n",
    "\n",
    "plot17 = g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "00441cb5",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(pd.crosstab(df_credit.Sex, df_credit.Job))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76cb48a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "g = sns.violinplot(x=\"Housing\", y=\"Job\", data=df_credit, hue=\"Risk\", palette=\"hls\", split=True)\n",
    "g.set_xlabel(\"Housing\", fontsize=12)\n",
    "g.set_ylabel(\"Job\", fontsize=12)\n",
    "g.set_title(\"Housing x Job - Dist\", fontsize=20)\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plot18 = g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7eacf52",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(pd.crosstab(df_credit[\"Checking account\"], df_credit.Sex))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44b3a39f",
   "metadata": {},
   "outputs": [],
   "source": [
    "date_int = [\"Purpose\", \"Sex\"]\n",
    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
    "pd.crosstab(df_credit[date_int[0]], df_credit[date_int[1]]).style.background_gradient(cmap=cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8af2818c",
   "metadata": {},
   "outputs": [],
   "source": [
    "date_int = [\"Purpose\", \"Sex\"]\n",
    "cm = sns.light_palette(\"green\", as_cmap=True)\n",
    "pd.crosstab(df_credit[date_int[0]], df_credit[date_int[1]]).style.background_gradient(cmap=cm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0f4689b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "print(\"Purpose : \", df_credit.Purpose.unique())\n",
    "print(\"Sex : \", df_credit.Sex.unique())\n",
    "print(\"Housing : \", df_credit.Housing.unique())\n",
    "print(\"Saving accounts : \", df_credit[\"Saving accounts\"].unique())\n",
    "print(\"Risk : \", df_credit[\"Risk\"].unique())\n",
    "print(\"Checking account : \", df_credit[\"Checking account\"].unique())\n",
    "print(\"Aget_cat : \", df_credit[\"Age_cat\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fc94057",
   "metadata": {},
   "outputs": [],
   "source": [
    "def one_hot_encoder(df, nan_as_category=False):\n",
    "    original_columns = list(df.columns)\n",
    "    categorical_columns = [col for col in df.columns if df[col].dtype == \"object\"]\n",
    "    df = pd.get_dummies(df, columns=categorical_columns, dummy_na=nan_as_category, drop_first=True)\n",
    "    new_columns = [c for c in df.columns if c not in original_columns]\n",
    "    return df, new_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb4b2fc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_credit[\"Saving accounts\"] = df_credit[\"Saving accounts\"].fillna(\"no_inf\")\n",
    "df_credit[\"Checking account\"] = df_credit[\"Checking account\"].fillna(\"no_inf\")\n",
    "\n",
    "# Purpose to Dummies Variable\n",
    "df_credit = df_credit.merge(\n",
    "    pd.get_dummies(df_credit.Purpose, drop_first=True, prefix=\"Purpose\"), left_index=True, right_index=True\n",
    ")\n",
    "# Sex feature in dummies\n",
    "df_credit = df_credit.merge(\n",
    "    pd.get_dummies(df_credit.Sex, drop_first=True, prefix=\"Sex\"), left_index=True, right_index=True\n",
    ")\n",
    "# Housing get dummies\n",
    "df_credit = df_credit.merge(\n",
    "    pd.get_dummies(df_credit.Housing, drop_first=True, prefix=\"Housing\"), left_index=True, right_index=True\n",
    ")\n",
    "# Housing get Saving Accounts\n",
    "df_credit = df_credit.merge(\n",
    "    pd.get_dummies(df_credit[\"Saving accounts\"], drop_first=True, prefix=\"Savings\"), left_index=True, right_index=True\n",
    ")\n",
    "# Housing get Risk\n",
    "df_credit = df_credit.merge(pd.get_dummies(df_credit.Risk, prefix=\"Risk\"), left_index=True, right_index=True)\n",
    "# Housing get Checking Account\n",
    "df_credit = df_credit.merge(\n",
    "    pd.get_dummies(df_credit[\"Checking account\"], drop_first=True, prefix=\"Check\"), left_index=True, right_index=True\n",
    ")\n",
    "# Housing get Age categorical\n",
    "df_credit = df_credit.merge(\n",
    "    pd.get_dummies(df_credit[\"Age_cat\"], drop_first=True, prefix=\"Age_cat\"), left_index=True, right_index=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5c63637",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Excluding the missing columns\n",
    "del df_credit[\"Saving accounts\"]\n",
    "del df_credit[\"Checking account\"]\n",
    "del df_credit[\"Purpose\"]\n",
    "del df_credit[\"Sex\"]\n",
    "del df_credit[\"Housing\"]\n",
    "del df_credit[\"Age_cat\"]\n",
    "del df_credit[\"Risk\"]\n",
    "del df_credit[\"Risk_good\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "50b3435c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_credit[\"Credit amount\"] = np.log(df_credit[\"Credit amount\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "60604996",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating the X and y variables\n",
    "X = df_credit.drop(\"Risk_bad\", 1).values\n",
    "y = df_credit[\"Risk_bad\"].values\n",
    "label_encoder = LabelEncoder()\n",
    "label_encoder = label_encoder.fit(y)\n",
    "label_encoded_y = label_encoder.transform(y)\n",
    "# Spliting X and y into train and test version\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, label_encoded_y, test_size=0.25, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f36485a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to feed the random state\n",
    "seed = 7\n",
    "\n",
    "# prepare models\n",
    "models = []\n",
    "models.append((\"LR\", LogisticRegression()))\n",
    "models.append((\"LDA\", LinearDiscriminantAnalysis()))\n",
    "models.append((\"KNN\", KNeighborsClassifier()))\n",
    "models.append((\"CART\", DecisionTreeClassifier()))\n",
    "models.append((\"NB\", GaussianNB()))\n",
    "models.append((\"RF\", RandomForestClassifier()))\n",
    "models.append((\"SVM\", SVC(gamma=\"auto\")))\n",
    "# models.append((\"XGB\", XGBClassifier()))\n",
    "\n",
    "# evaluate each model in turn\n",
    "results = []\n",
    "names = []\n",
    "scoring = \"recall\"\n",
    "\n",
    "for name, model in models:\n",
    "    kfold = KFold(n_splits=10, random_state=seed, shuffle=True)\n",
    "    cv_results = cross_val_score(model, X_train, y_train, cv=kfold, scoring=scoring)\n",
    "    results.append(cv_results)\n",
    "    names.append(name)\n",
    "    msg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n",
    "    print(msg)\n",
    "\n",
    "# boxplot algorithm comparison\n",
    "fig = plt.figure(figsize=(11, 6))\n",
    "fig.suptitle(\"Algorithm Comparison\")\n",
    "ax = fig.add_subplot(111)\n",
    "plt.boxplot(results)\n",
    "ax.set_xticklabels(names)\n",
    "plt.show()\n",
    "\n",
    "model_plot = fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c35cf5b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando o classificador logreg\n",
    "GNB = GaussianNB()\n",
    "\n",
    "# Fitting with train data\n",
    "model = GNB.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "931e400e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Printing the Training Score\n",
    "print(\"Training score data: \")\n",
    "model_score = model.score(X_train, y_train)\n",
    "print(model_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b73d28df",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = model.predict(X_test)\n",
    "\n",
    "print(accuracy_score(y_test, y_pred))\n",
    "print(\"\\n\")\n",
    "print(confusion_matrix(y_test, y_pred))\n",
    "print(\"\\n\")\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c64b459a",
   "metadata": {},
   "outputs": [],
   "source": [
    "report = classification_report(y_test, y_pred, output_dict=True)\n",
    "classification_plot = sns.heatmap(pd.DataFrame(report).iloc[:-1, :].T, annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f1e5dc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "confusion_plot = sns.heatmap(confusion_matrix(y_test, y_pred), annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2f1d18e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting proba\n",
    "y_pred_prob = model.predict_proba(X_test)[:, 1]\n",
    "\n",
    "fpr, tpr, thresholds = roc_curve(y_test, y_pred_prob)\n",
    "\n",
    "fig = px.area(\n",
    "    x=fpr,\n",
    "    y=tpr,\n",
    "    title=f\"ROC Curve (AUC={auc(fpr, tpr):.4f})\",\n",
    "    labels=dict(x=\"False Positive Rate\", y=\"True Positive Rate\"),\n",
    "    width=700,\n",
    "    height=500,\n",
    ")\n",
    "fig.add_shape(type=\"line\", line=dict(dash=\"dash\"), x0=0, x1=1, y0=0, y1=1)\n",
    "\n",
    "fig.update_yaxes(scaleanchor=\"x\", scaleratio=1)\n",
    "fig.update_xaxes(constrain=\"domain\")\n",
    "fig.show()\n",
    "\n",
    "roc_plot = fig"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce799c72",
   "metadata": {},
   "source": [
    "# Datapane"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18d8dd4e",
   "metadata": {},
   "source": [
    "### Blocks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "03e10d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Text blocks\n",
    "update_commentary = dp.Text(\n",
    "    \"\"\"\n",
    "## Update commentary\n",
    "This is a placeholder commentary section to display updates on the training and changes in the model. The section can provide a high level idea on what has changed and its impact on business.\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "context = dp.Text(\n",
    "    \"\"\"\n",
    "## Objective\n",
    "This project has been created as a demo to showcase how Datapane can be used to track, infer and share machine learning pipeline updates. The report has been created as an HTML dashboard which can be easily updated and shared across.\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "content = dp.Text(\n",
    "    \"\"\"\n",
    "## Content\n",
    "The original dataset contains 1000 entries with 20 categorial/symbolic attributes prepared by Prof. Hofmann. In this dataset, each entry represents a person who takes a credit by a bank. Each person is classified as good or bad credit risks according to the set of attributes.\n",
    "\n",
    "- Age (numeric)\n",
    "- Sex (text: male, female)\n",
    "- Job (numeric: 0 - unskilled and non-resident, 1 - unskilled and resident, 2 - skilled, 3 - highly skilled)\n",
    "- Housing (text: own, rent, or free)\n",
    "- Saving accounts (text - little, moderate, quite rich, rich)\n",
    "- Checking account (numeric, in DM - Deutsch Mark)\n",
    "- Credit amount (numeric, in DM)\n",
    "- Duration (numeric, in month)\n",
    "- Purpose (text: car, furniture/equipment, radio/TV, domestic appliances, repairs, education, business, vacation/others)\n",
    "\"\"\"\n",
    ")\n",
    "\n",
    "acknowledgement = dp.Text(\n",
    "    \"\"\"\n",
    "## Acknowledgements\n",
    "Source = https://www.kaggle.com/kabure/predicting-credit-risk-model-pipeline/data\n",
    "\"\"\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc14d7a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Big numbers\n",
    "report_update = dp.BigNumber(heading=\"Report last updated\", value=datetime.now().strftime(\"%Y-%m-%d %H:%M\"))\n",
    "\n",
    "accuracy_block = dp.BigNumber(heading=\"Model accuracy\", value=\"65%\", change=\"2%\", is_upward_change=True)\n",
    "\n",
    "fscore_block = dp.BigNumber(heading=\"F1-score\", value=\"66%\", change=\"1%\", is_upward_change=False)\n",
    "\n",
    "trainingtime_block = dp.BigNumber(heading=\"Model training time\", value=\"130ms\", change=\"3%\", is_upward_change=False)\n",
    "\n",
    "model_training_score = dp.BigNumber(\n",
    "    heading=\"Model training score (%)\", value=round(model_score * 100, 2), change=\"5%\", is_upward_change=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4be9778b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Groups - waiting on ydata-profiling support\n",
    "# overview_block = dp.Group(\n",
    "#     dp.BigNumber(heading=\"Number of variables\", value=json_data[\"table\"][\"n_var\"]),\n",
    "#     dp.BigNumber(heading=\"Number of observations\", value=json_data[\"table\"][\"n\"]),\n",
    "#     dp.BigNumber(heading=\"Missing cells\", value=json_data[\"table\"][\"n_cells_missing\"]),\n",
    "#     dp.BigNumber(heading=\"Missing cells (%)\", value=round(json_data[\"table\"][\"p_cells_missing\"] * 100, 2)),\n",
    "#     dp.BigNumber(heading=\"Duplicate rows\", value=json_data[\"table\"][\"n_duplicates\"]),\n",
    "#     dp.BigNumber(heading=\"Duplicate rows (%)\", value=round(json_data[\"table\"][\"p_duplicates\"] * 100, 2)),\n",
    "#     dp.BigNumber(heading=\"Total size in memory (KB)\", value=json_data[\"table\"][\"memory_size\"] / 1000),\n",
    "#     dp.BigNumber(heading=\"Numeric variables\", value=json_data[\"table\"][\"types\"][\"Numeric\"]),\n",
    "#     dp.BigNumber(heading=\"Categorical variables\", value=json_data[\"table\"][\"types\"][\"Categorical\"]),\n",
    "#     columns=3,\n",
    "#     label=\"Overview\",\n",
    "# )"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c79ff79",
   "metadata": {},
   "source": [
    "### Pages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "35409e6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "page1 = dp.Page(\n",
    "    title=\"Summary\",\n",
    "    blocks=[\n",
    "        dp.Group(\n",
    "            dp.Text(\"![](https://raw.githubusercontent.com/shoumikgoswami/ML-pipeline/main/assets/dp_credit.png)\"),\n",
    "            dp.Group(report_update, update_commentary, columns=1),\n",
    "            columns=2,\n",
    "        ),\n",
    "        dp.Group(accuracy_block, fscore_block, trainingtime_block, columns=3),\n",
    "    ],\n",
    ")\n",
    "\n",
    "page2 = dp.Page(title=\"Project details\", blocks=[context, content, acknowledgement])\n",
    "\n",
    "page3 = dp.Page(\n",
    "    title=\"Data profile\",\n",
    "    blocks=[\n",
    "        dp.Text(\"\"\"## Dataset details\"\"\"),\n",
    "        dp.Text(\n",
    "            \"\"\"This page can be used to display an overview of the dataset along with important elements of the data, features, attributes with null values and so on. \"\"\"\n",
    "        ),\n",
    "        dp.Select(\n",
    "            blocks=[\n",
    "                # overview_block,\n",
    "                dp.Plot(corr_plot, label=\"Correlation\"),\n",
    "                dp.Plot(missing_plot, label=\"Missing values\"),\n",
    "                dp.DataTable(df_credit, label=\"Dataset\"),\n",
    "            ]\n",
    "        ),\n",
    "    ],\n",
    ")\n",
    "\n",
    "page4 = dp.Page(\n",
    "    title=\"Exploration\",\n",
    "    blocks=[\n",
    "        dp.Text(\"\"\"## Data exploration\"\"\"),\n",
    "        dp.Text(\n",
    "            \"\"\"This page can be used to display plots and visualization elements used during the data exploration phase. \"\"\"\n",
    "        ),\n",
    "        dp.Plot(plot1),\n",
    "        dp.Plot(plot2),\n",
    "        dp.Plot(plot3),\n",
    "        dp.Plot(plot4),\n",
    "        dp.Plot(plot5),\n",
    "        dp.Plot(plot6),\n",
    "        dp.Plot(plot7),\n",
    "        dp.Plot(plot8),\n",
    "        dp.Plot(plot9),\n",
    "        dp.Plot(plot10),\n",
    "        dp.Plot(plot11),\n",
    "        dp.Plot(plot12),\n",
    "        dp.Plot(plot13),\n",
    "        dp.Plot(plot14),\n",
    "        dp.Plot(plot15),\n",
    "        dp.Plot(plot16),\n",
    "        dp.Plot(plot17),\n",
    "        dp.Plot(plot18),\n",
    "    ],\n",
    ")\n",
    "\n",
    "\n",
    "page5 = dp.Page(\n",
    "    title=\"Model tracking\",\n",
    "    blocks=[\n",
    "        dp.Text(\"\"\"## Model tracking and logging\"\"\"),\n",
    "        dp.Text(\"\"\"This page can be used to track models, changes in the scores and performance.\"\"\"),\n",
    "        dp.Text(\"\"\"### Comparing performance of different models\"\"\"),\n",
    "        dp.Plot(model_plot),\n",
    "        model_training_score,\n",
    "        dp.Text(\"\"\"### ROC curve based on the chosen model\"\"\"),\n",
    "        dp.Plot(roc_plot),\n",
    "        dp.Group(\n",
    "            dp.Group(dp.Text(\"\"\"### Classification report matrix\"\"\"), dp.Plot(classification_plot), columns=1),\n",
    "            dp.Group(dp.Text(\"\"\"### Confusion matrix\"\"\"), dp.Plot(confusion_plot), columns=1),\n",
    "            columns=2,\n",
    "        ),\n",
    "    ],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "230bb5f7",
   "metadata": {},
   "source": [
    "### Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4282052",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "report = dp.View(page1, page2, page3, page4, page5)\n",
    "dp.save_report(report, path=\"report.html\", open=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0de0880",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Run this cell to upload this report to Datapane Cloud\n",
    "dp.upload_report(report, name=\"Machine learning tracking and monitoring system\", publicly_visible=True)"
   ]
  }
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