credit-part2-data-preparation-1-preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b01e7898",
   "metadata": {
    "_cell_guid": "9a30a10e-c294-4aa0-846d-274fe0db043c",
    "_uuid": "01ef835a-c4f2-48fb-8049-a6731ee9faae",
    "papermill": {
     "duration": 0.056159,
     "end_time": "2022-01-10T20:52:34.620598",
     "exception": false,
     "start_time": "2022-01-10T20:52:34.564439",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Introduction: \n",
    "    \n",
    "Banks play a crucial role in market economies. They decide who can get finance and on what terms and can make or break investment decisions. For markets and society to function, individuals and companies need access to credit. \n",
    "\n",
    "Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted.\n",
    "\n",
    "<a id=0></a>\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style='color:white; background:#1777C4; border:0' role=\"tab\" aria-controls=\"home\">\n",
    "\n",
    "<center>CRISP-DM Methodology</center></h3>\n",
    "\n",
    "* [Buissness Understanding](#1)\n",
    "* [Data Understanding](#2)\n",
    "* [Data Preparation](#3)\n",
    "* [Data Modeling](#4)   \n",
    "* [Data Evaluation](#5)\n",
    "    \n",
    "\n",
    "In this section we overview our selected method for engineering our solution. CRISP-DM stands for Cross-Industry Standard Process for Data Mining. It is an open standard guide that describes common approaches that are used by data mining experts. CRISP-DM includes descriptions of the typical phases of a project, including tasks details and provides an overview of the data mining lifecycle. The lifecycle model consists of six phases with arrows indicating the most important and frequent dependencies between phases. The sequence of the phases is not strict. In fact, most projects move back and forth between phases as necessary. It starts with business understanding, and then moves to data understanding, data preparation, modelling, evaluation, and deployment. The CRISP-DM model is flexible and can be customized easily.\n",
    "## Buissness Understanding\n",
    "\n",
    "    Tasks:\n",
    "\n",
    "    1.Determine business objectives\n",
    "\n",
    "    2.Assess situation\n",
    "\n",
    "    3.Determine data mining goals\n",
    "\n",
    "    4.Produce project plan\n",
    "\n",
    "## Data Understanding\n",
    "     Tasks:\n",
    "\n",
    "    1.Collect data\n",
    "\n",
    "    2.Describe data\n",
    "\n",
    "    3.Explore data    \n",
    "\n",
    "## Data Preparation\n",
    "    \n",
    "    Tasks:\n",
    "    \n",
    "    1.Data selection\n",
    "\n",
    "    2.Data preprocessing\n",
    "\n",
    "    3.Feature engineering\n",
    "\n",
    "    4.Dimensionality reduction\n",
    "\n",
    "            Steps:\n",
    "\n",
    "            Data cleaning\n",
    "\n",
    "            Data integration\n",
    "\n",
    "            Data sampling\n",
    "\n",
    "            Data dimensionality reduction\n",
    "\n",
    "            Data formatting\n",
    "\n",
    "            Data transformation\n",
    "\n",
    "            Scaling\n",
    "\n",
    "            Aggregation\n",
    "\n",
    "            Decomposition\n",
    "\n",
    "## Data Modeling :\n",
    "\n",
    "Modeling is the part of the Cross-Industry Standard Process for Data Mining (CRISP-DM) process model that i like best. Our data is already in good shape, and now we can search for useful patterns in our data.\n",
    "\n",
    "   Tasks:\n",
    "    \n",
    "    1. Select modeling technique Select technique\n",
    "\n",
    "    2. Generate test design\n",
    "\n",
    "    3. Build model\n",
    "\n",
    "    4. Assess model\n",
    "\n",
    "## Data Evaluation :\n",
    "    \n",
    "    Tasks:\n",
    "\n",
    "    1.Evaluate Result\n",
    "\n",
    "    2.Review Process\n",
    "\n",
    "    3.Determine next steps\n",
    "\n",
    "<a id=1></a>\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style='color:white; background:#1777C4; border:0' role=\"tab\" aria-controls=\"home\">\n",
    "<center>Buissness Understanding</center></h3>\n",
    "\n",
    "\n",
    "There may be two types of questions:\n",
    "\n",
    "**A.Technical Questions:**\n",
    "  \n",
    "Can ML be a solution to the problem?\n",
    "\n",
    "    \n",
    "                Do we have THE data?\n",
    "                Do we have all necessary related data?\n",
    "                Is there enough amount of data to develop algorithm?\n",
    "                Is data collected in the right way?\n",
    "                Is data saved in the right format?\n",
    "                Is the access to information guaranteed?\n",
    "\n",
    "Can we satisfy all the Business Questions by means of ML?\n",
    "\n",
    "**B.Business Questions:**\n",
    "    \n",
    "What are the organization's business goals?\n",
    "    \n",
    "                To reduce cost and increase revenue? \n",
    "                To increase efficiencies?\n",
    "                To avoid risks? To improve quality?\n",
    "    \n",
    "Is it worth to develop ML?\n",
    "    \n",
    "                In short term? In long term?\n",
    "                What are the success metrics?\n",
    "                Can we handle the risk if the project is unsuccessful?\n",
    "    \n",
    "Do we have the resources?\n",
    "    \n",
    "                Do we have enough time to develop ML?\n",
    "                Do we have a right talented team?\n",
    "\n",
    "The goal of this project  is to build a model that borrowers can use to help make the best financial decisions.\n",
    "\n",
    "Historical data are provided on 250,000 borrowers.https://www.kaggle.com/c/GiveMeSomeCredit\n",
    "\n",
    "**Data Dictionary**\n",
    "\n",
    "**SeriousDlqin2yrs:** Person experienced 90 days past due delinquency or worse.\n",
    "\n",
    "**RevolvingUtilizationOfUnsecuredLines:** Total balance on credit cards and personal lines of credit except real estate and no installment debt like car loans divided by the sum of credit limits.\n",
    "\n",
    "**age:** Age of borrower in years.\n",
    "\n",
    "**NumberOfTime30-59DaysPastDueNotWorse:** Number of times borrower has been 30-59 days past due but no worse in the last 2 years.\n",
    "\n",
    "**DebtRatio:** Monthly debt payments, alimony,living costs divided by monthy gross income.\n",
    "\n",
    "**MonthlyIncome:** Monthly income.\n",
    "\n",
    "**NumberOfOpenCreditLinesAndLoans:** Number of Open loans (installment like car loan or mortgage) and Lines of credit (e.g. credit cards).\n",
    "\n",
    "**NumberOfTimes90DaysLate:** Number of times borrower has been 90 days or more past due.\n",
    "\n",
    "**NumberRealEstateLoansOrLines:** Number of mortgage and real estate loans including home equity lines of credit.\n",
    "\n",
    "**NumberOfTime60-89DaysPastDueNotWorse:** Number of times borrower has been 60-89 days past due but no worse in the last 2 years.\n",
    "\n",
    "**NumberOfDependents:** Number of dependents in family excluding themselves (spouse, children etc.).\n",
    "    \n",
    "    \n",
    " We are not looking for a winning solution but more for:\n",
    "\n",
    "- how you approach the problem\n",
    "\n",
    "- how do you look at the data\n",
    "\n",
    "- what do you look at\n",
    "\n",
    "- how do you structure your projects/prototypes.\n",
    "\n",
    "- If you chose to build a simple classifier which one did you chose, why etc.\n",
    "\n",
    "    \n",
    "**summary:**\n",
    "we are expecting the following:\n",
    "    \n",
    "- 1.Methodology: method for engineering our solution \n",
    "\n",
    "\n",
    "        a.CRISP_DM\n",
    "        \n",
    "    \n",
    "- 2. Model\n",
    "\n",
    "\n",
    "        a.We are expecting a machine learning model that can correctly classify financial decisions.\n",
    "\n",
    "\n",
    "**What is the objective of the machine learning model?**\n",
    "\n",
    "We aim to predict Credit scoring algorithms, which make a guess at the probability of default, are the method banks use to determine whether or not a loan should be granted. We will evaluate model performance with the:\n",
    "\n",
    "   - F beta score\n",
    "    \n",
    "   - ROC AUC score\n",
    "    \n",
    "   - PR AUC score | Average precision\n",
    "    \n",
    "    \n",
    "## Step 1: Import helpful libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d988d1f3",
   "metadata": {
    "_cell_guid": "ddedf45c-cdd2-4013-973f-6ccf6c78347a",
    "_uuid": "20c9b28e-4c93-447d-8210-cad89f404354",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:34.726247Z",
     "iopub.status.busy": "2022-01-10T20:52:34.725337Z",
     "iopub.status.idle": "2022-01-10T20:52:38.216635Z",
     "shell.execute_reply": "2022-01-10T20:52:38.215859Z",
     "shell.execute_reply.started": "2022-01-10T20:51:32.717440Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 3.546591,
     "end_time": "2022-01-10T20:52:38.216811",
     "exception": false,
     "start_time": "2022-01-10T20:52:34.670220",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type='text/css'>\n",
       ".datatable table.frame { margin-bottom: 0; }\n",
       ".datatable table.frame thead { border-bottom: none; }\n",
       ".datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n",
       ".datatable .bool    { background: #DDDD99; }\n",
       ".datatable .object  { background: #565656; }\n",
       ".datatable .int     { background: #5D9E5D; }\n",
       ".datatable .float   { background: #4040CC; }\n",
       ".datatable .str     { background: #CC4040; }\n",
       ".datatable .time    { background: #40CC40; }\n",
       ".datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n",
       ".datatable .frame tbody td { text-align: left; }\n",
       ".datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n",
       ".datatable th:nth-child(2) { padding-left: 12px; }\n",
       ".datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n",
       ".datatable .vellipsis {  background: var(--jp-layout-color0);  color: var(--jp-cell-editor-border-color);}\n",
       ".datatable .na {  color: var(--jp-cell-editor-border-color);  font-size: 80%;}\n",
       ".datatable .sp {  opacity: 0.25;}\n",
       ".datatable .footer { font-size: 9px; }\n",
       ".datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Load the librarys\n",
    "import pandas as pd #To work with dataset\n",
    "import numpy as np #Math library\n",
    "import matplotlib.gridspec as gridspec\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 warnings\n",
    "# Preparation  \n",
    "from sklearn.preprocessing import LabelEncoder, OrdinalEncoder\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import PowerTransformer, StandardScaler,Normalizer,RobustScaler,MaxAbsScaler,MinMaxScaler,QuantileTransformer\n",
    "from sklearn.preprocessing import FunctionTransformer\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.preprocessing import KBinsDiscretizer\n",
    "# Import StandardScaler from scikit-learn\n",
    "\n",
    "from sklearn.experimental import enable_iterative_imputer\n",
    "from sklearn.impute import SimpleImputer\n",
    "from sklearn.impute import KNNImputer,IterativeImputer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.compose import make_column_transformer,ColumnTransformer\n",
    "from sklearn.pipeline import make_pipeline, Pipeline,FeatureUnion\n",
    "from sklearn.manifold import TSNE\n",
    "# Import train_test_split()\n",
    "# Metrics\n",
    "from sklearn.metrics import roc_auc_score, average_precision_score\n",
    "from sklearn.metrics import make_scorer\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import roc_curve,confusion_matrix\n",
    "from datetime import datetime, date\n",
    "from sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\n",
    "from sklearn.linear_model import LinearRegression, RidgeCV\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#import tensorflow as tf \n",
    "#from tensorflow.keras import layers\n",
    "#from tensorflow.keras.callbacks import EarlyStopping\n",
    "#from tensorflow.keras.callbacks import LearningRateScheduler\n",
    "#import smogn\n",
    "from sklearn.experimental import enable_hist_gradient_boosting\n",
    "from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\n",
    "from sklearn.kernel_ridge import KernelRidge\n",
    "from sklearn.ensemble import GradientBoostingRegressor,RandomForestRegressor\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.ensemble import HistGradientBoostingClassifier\n",
    "# For training random forest model\n",
    "import lightgbm as lgb\n",
    "from scipy import sparse\n",
    "from sklearn.neighbors import KNeighborsRegressor \n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.cluster import KMeans \n",
    "# Model selection\n",
    "from sklearn.model_selection import StratifiedKFold\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_regression,f_classif,chi2\n",
    "from sklearn.feature_selection import mutual_info_regression\n",
    "from sklearn.feature_selection import SelectPercentile\n",
    "from sklearn.feature_selection import mutual_info_classif,VarianceThreshold\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from lightgbm import LGBMClassifier\n",
    "import lightgbm as lgbm\n",
    "#from catboost import CatBoostRegressor, CatBoostClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn import svm\n",
    "#from xgboost import XGBClassifier\n",
    "from sklearn import set_config\n",
    "from itertools import combinations\n",
    "# Cluster :\n",
    "from sklearn.cluster import MiniBatchKMeans\n",
    "#from yellowbrick.cluster import KElbowVisualizer\n",
    "#import smong \n",
    "import category_encoders as ce\n",
    "import warnings\n",
    "#import optuna \n",
    "from joblib import Parallel, delayed\n",
    "import joblib \n",
    "from sklearn import set_config\n",
    "from typing import List, Optional, Union\n",
    "set_config(display='diagram')\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d68b842",
   "metadata": {
    "_cell_guid": "38c6befb-892d-4ef3-9c63-13be5959b671",
    "_uuid": "2953f9f3-02f8-4fa9-a7fc-cb93565c2183",
    "papermill": {
     "duration": 0.036483,
     "end_time": "2022-01-10T20:52:38.291249",
     "exception": false,
     "start_time": "2022-01-10T20:52:38.254766",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "## Step 2: Load the data\n",
    "Complete guid to read data : \n",
    "Next, we'll load the training and test data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7c62ab8d",
   "metadata": {
    "_cell_guid": "98a5b4a6-90e9-449d-96cc-0d85c9ff1c77",
    "_uuid": "e086592f-9f6a-4592-80b5-c1e7ad227e02",
    "collapsed": false,
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:38.366701Z",
     "iopub.status.busy": "2022-01-10T20:52:38.366147Z",
     "iopub.status.idle": "2022-01-10T20:52:38.808386Z",
     "shell.execute_reply": "2022-01-10T20:52:38.807848Z",
     "shell.execute_reply.started": "2022-01-10T20:51:36.039904Z"
    },
    "jupyter": {
     "outputs_hidden": false
    },
    "papermill": {
     "duration": 0.481277,
     "end_time": "2022-01-10T20:52:38.808517",
     "exception": false,
     "start_time": "2022-01-10T20:52:38.327240",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 197 ms, sys: 58.4 ms, total: 256 ms\n",
      "Wall time: 421 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>SeriousDlqin2yrs</th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>age</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.766127</td>\n",
       "      <td>45</td>\n",
       "      <td>2</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.957151</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0.658180</td>\n",
       "      <td>38</td>\n",
       "      <td>1</td>\n",
       "      <td>0.085113</td>\n",
       "      <td>3042.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  SeriousDlqin2yrs  RevolvingUtilizationOfUnsecuredLines  age  \\\n",
       "0           1                 1                              0.766127   45   \n",
       "1           2                 0                              0.957151   40   \n",
       "2           3                 0                              0.658180   38   \n",
       "\n",
       "   NumberOfTime30-59DaysPastDueNotWorse  DebtRatio  MonthlyIncome  \\\n",
       "0                                     2   0.802982         9120.0   \n",
       "1                                     0   0.121876         2600.0   \n",
       "2                                     1   0.085113         3042.0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTimes90DaysLate  \\\n",
       "0                               13                        0   \n",
       "1                                4                        0   \n",
       "2                                2                        1   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                             6                                     0   \n",
       "1                             0                                     0   \n",
       "2                             0                                     0   \n",
       "\n",
       "   NumberOfDependents  \n",
       "0                 2.0  \n",
       "1                 1.0  \n",
       "2                 0.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time \n",
    "train = pd.read_csv('../input/GiveMeSomeCredit/cs-training.csv')\n",
    "test = pd.read_csv('../input/GiveMeSomeCredit/cs-test.csv')\n",
    "train.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01b7f1e0",
   "metadata": {
    "_cell_guid": "2d511862-f221-4721-8b68-a2d212ae8382",
    "_uuid": "77951bec-3a3b-4840-886e-83f318798859",
    "papermill": {
     "duration": 0.034755,
     "end_time": "2022-01-10T20:52:38.879826",
     "exception": false,
     "start_time": "2022-01-10T20:52:38.845071",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "<a id=2></a>\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style='color:white; background:#1777C4; border:0' role=\"tab\" aria-controls=\"home\">\n",
    "<center>Data Understanding</center></h3>\n",
    "\n",
    "\n",
    "### Explore the data/Analysis \n",
    "\n",
    "We will analyse the following:\n",
    "\n",
    "    The target variable\n",
    "    \n",
    "    Variable types (categorical and numerical)\n",
    "    \n",
    "    Numerical variables\n",
    "        Discrete\n",
    "        Continuous\n",
    "        Distributions\n",
    "        Transformations\n",
    "\n",
    "    Categorical variables\n",
    "        Cardinality\n",
    "        Rare Labels\n",
    "        Special mappings\n",
    "\n",
    "    Null Data\n",
    "\n",
    "    Text data \n",
    "    \n",
    "    wich columns will we use\n",
    "    \n",
    "    IS there outliers that can destory our algo\n",
    "    \n",
    "    IS there diffrent range of data\n",
    "    \n",
    "    Curse of dimm...\n",
    "    \n",
    "This Step is done on Part1\n",
    "\n",
    "\n",
    "<a id=3></a>\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style='color:white; background:#1777C4; border:0' role=\"tab\" aria-controls=\"home\">\n",
    "<center>Data Preparation</center></h3>\n",
    "\n",
    "\n",
    "## Data preprocessing\n",
    "\n",
    "\n",
    "\n",
    "Data preprocessing comes after you've cleaned up your data and after you've done some exploratory analysis to understand your dataset. Once you understand your dataset, you'll probably have some idea about how you want to model your data. Machine learning models in Python require numerical input, so if your dataset has categorical variables, you'll need to transform them. Think of data preprocessing as a prerequisite for modeling.\n",
    "\n",
    "\n",
    "### Missing Values  :\n",
    "\n",
    "- A Simple Option: Drop Columns with Missing Values\n",
    "\n",
    "-  Replacing missing values with constants        \n",
    "    \n",
    "-  A Better Option: Imputation\n",
    "\n",
    "Imputation fills in the missing value with some number. The imputed value won't be exactly right in most cases, but it usually gives more accurate models than dropping the column entirely.\n",
    "\n",
    "- An Extension To Imputation\n",
    "\n",
    "Imputation is the standard approach, and it usually works well. However, imputed values may by systematically above or below their actual values (which weren't collected in the dataset). Or rows with missing values may be unique in some other way. In that case, your model would make better predictions by considering which values were originally missing. \n",
    "                    \n",
    "A popular approach to missing data imputation is to use a model to predict the missing values. This requires a model to be created for each input variable that has missing values. Although any one among a range of different models can be used to predict the missing values, the k-nearest neighbor (KNN) algorithm has proven to be generally effective, often referred to as **“nearest neighbor imputation.”**\n",
    "\n",
    "- Iterative imputation\n",
    "\n",
    "One approach to imputing missing values is to use an iterative imputation model.\n",
    "Iterative imputation refers to a process where each feature is modeled as a function of the other features, e.g. a regression problem where missing values are predicted. Each feature is imputed sequentially, one after the other, allowing prior imputed values to be used as part of a model in predicting subsequent features.\n",
    "\n",
    "It is iterative because this process is repeated multiple times, allowing ever improved estimates of missing values to be calculated as missing values across all features are estimated.\n",
    "\n",
    "### Scaling \n",
    "\n",
    "While this assumption of similar scales is necessary, it is rarely true in real world data. For this reason you need to rescale your data to ensure that it is on the same scale. There are many different approaches to doing this but we will discuss the two most commonly used approaches here, Min-Max scaling (sometimes referred to as **normalization**), and **standardization**.\n",
    "you need to rescale your data to ensure that it is on the same scale. There are many different approaches to doing this:\n",
    "\n",
    "**Normalization:**\n",
    "In normalization you linearly scale the entire column between 0 and 1, with 0 corresponding with the lowest value in the column, and 1 with the largest. When using scikit-learn (the most commonly used machine learning library in Python) you can use a MinMaxScaler to apply normalization. (It is called this as it scales your values between a minimum and maximum value.)\n",
    "Normalization scales all points linearly between the upper and lower bound.\n",
    "\n",
    "**Standardization:**\n",
    "The other commonly used scaler is called standardization. As opposed to finding an outer boundary and squeezing everything within it, standardization instead finds the mean of your data and centers your distribution around it, calculating the number of standard deviations away from the mean each point is. These values (the number of standard deviations) are then used as your new values. This centers the data around 0 but technically has no limit to the maximum and minimum values as you can see here.\n",
    "\n",
    "**Log Transformer:**\n",
    "Both normalization and min-max scaling are types of scalers, in other words the data remained in the same shape but was squashed or scaled. A log transformation on the other hand can be used to make highly skewed distributions less skewed. Take for example one of the salary columns from the stack overflow dataset shown here where there is a very long right tail.\n",
    "\n",
    "Helps with skewness No predetermined range for scaled data Useful only on non-zero, non-negative data.\n",
    "\n",
    "The Log Transform is one of the most popular Transformation techniques out there. It is primarily used to convert a skewed distribution to a normal distribution/less-skewed distribution. In this transform, we take the log of the values in a column and use these values as the column instead.\n",
    "\n",
    "Why does it work? It is because the log function is equipped to deal with large numbers. Here is an example-\n",
    "\n",
    "log(10) = 1\n",
    "\n",
    "log(100) = 2, and\n",
    "\n",
    "log(10000) =4\n",
    "\n",
    "Thus, the log operation had a dual role:\n",
    "\n",
    "    Reducing the impact of too-low values\n",
    "    Reducing the impact of too-high values.\n",
    "\n",
    "A small caveat though – if our data has negative values or values ranging from 0 to 1, we cannot apply log transform directly – since the log of negative numbers and numbers between 0 and 1 is undefined, we would get error or NaN values in our data. In such cases, we can add a number to these values to make them all greater than 1. Then, we can apply the log transform.\n",
    "\n",
    "**Min-Max Scaler:**\n",
    "Rescales to predetermined range [0–1] Doesn’t change distribution’s center (doesn’t correct skewness) Sensitive to outliers\n",
    "\n",
    "**Max Abs Scaler:**\n",
    "Rescales to predetermined range [-1–1] Doesn’t change distribution’s center Sensitive to outliers\n",
    "\n",
    "In simplest terms, the MaxAbs scaler takes the absolute maximum value of each column and divides each value in the column by the maximum value.\n",
    "\n",
    "Thus, it first takes the absolute value of each value in the column and then takes the maximum value out of those. This operation scales the data between the range [-1, 1]\n",
    "\n",
    "**Standard Scaler:**\n",
    "Shifts distribution’s mean to 0 & unit variance No predetermined range Best to use on data that is approximately normally distributed\n",
    "For each feature, the Standard Scaler scales the values such that the mean is 0 and the standard deviation is 1(or the variance).\n",
    "x_scaled = x – mean/std_dev\n",
    "\n",
    "However, Standard Scaler assumes that the distribution of the variable is normal. Thus, in case, the variables are not normally distributed, we\n",
    "\n",
    "    either choose a different scaler\n",
    "    or first, convert the variables to a normal distribution and then apply this scaler\n",
    "\n",
    "\n",
    "**Robust Scaler:**\n",
    "0 mean & unit variance Use of quartile ranges makes this less sensitive to (a few) outliers No predetermined range\n",
    "If you have noticed in the scalers we used so far, each of them was using values like the mean, maximum and minimum values of the columns. All these values are sensitive to outliers. If there are too many outliers in the data, they will influence the mean and the max value or the min value. Thus, even if we scale this data using the above methods, we cannot guarantee a balanced data with a normal distribution.\n",
    "\n",
    "The Robust Scaler, as the name suggests is not sensitive to outliers. This scaler-\n",
    "\n",
    "    removes the median from the data\n",
    "    scales the data by the InterQuartile Range(IQR)\n",
    "\n",
    "Are you familiar with the Inter-Quartile Range? It is nothing but the difference between the first and third quartile of the variable. The interquartile range can be defined as-\n",
    "\n",
    "    IQR = Q3 – Q1\n",
    "\n",
    "Thus, the formula would be:\n",
    "\n",
    "x_scaled = (x – Q1)/(Q3 – Q1)\n",
    "\n",
    "\n",
    "**Power Transformer:**\n",
    "\n",
    "Helps correct skewness 0 mean & unit variance No predetermined range Yeo-Johnson or Box-Cox Box-Cox can only be used on non-negative data\n",
    "\n",
    "I often use this feature transformation technique when I am building a linear model. To be more specific, I use it when I am dealing with heteroskedasticity. Like some other scalers we studied above, the Power Transformer also changes the distribution of the variable, as in, it makes it more Gaussian(normal). We are familiar with similar power transforms such as square root, and cube root transforms, and log transforms.\n",
    "\n",
    "However, to use them, we need to first study the original distribution, and then make a choice. The Power Transformer actually automates this decision making by introducing a parameter called lambda. It decides on a generalized power transform by finding the best value of lambda using either the:\n",
    "\n",
    "1. Box-Cox transform\n",
    "\n",
    "2. The Yeo-Johnson transform\n",
    "\n",
    "While I will not get into too much detail of how each of the above transforms works, it is helpful to know that Box-Cox works with only positive values, while Yeo-Johnson works with both positive and negative values\n",
    "\n",
    "**Quantile Transformer Scaler:**\n",
    "One of the most interesting feature transformation techniques that I have used, the Quantile Transformer Scaler converts the variable distribution to a normal distribution. and scales it accordingly. Since it makes the variable normally distributed, it also deals with the outliers. Here are a few important points regarding the Quantile Transformer Scaler:\n",
    "\n",
    "1. It computes the cumulative distribution function of the variable\n",
    "\n",
    "2. It uses this cdf to map the values to a normal distribution\n",
    "\n",
    "3. Maps the obtained values to the desired output distribution using the associated quantile function\n",
    "\n",
    "A caveat to keep in mind though: Since this scaler changes the very distribution of the variables, linear relationships among variables may be destroyed by using this scaler. Thus, it is best to use this for non-linear data.\n",
    "\n",
    "**Unit Vector Scaler/Normalizer:**\n",
    "Normalization is the process of scaling individual samples to have unit norm. The most interesting part is that unlike the other scalers which work on the individual column values, the Normalizer works on the rows! Each row of the dataframe with at least one non-zero component is rescaled independently of other samples so that its norm (l1, l2, or inf) equals one.\n",
    "\n",
    "Just like MinMax Scaler, the Normalizer also converts the values between 0 and 1, and between -1 to 1 when there are negative values in our data.\n",
    "\n",
    "However, there is a difference in the way it does so.\n",
    "\n",
    "    If we are using L1 norm, the values in each column are converted so that the sum of their absolute values along the row = 1\n",
    "    If we are using L2 norm, the values in each column are first squared and added so that the sum of their absolute values along the row = 1\n",
    "    \n",
    "    \n",
    "**Custom Transformer:**\n",
    "Consider this situation – Suppose you have your own Python function to transform the data. Sklearn also provides the ability to apply this transform to our dataset using what is called a FunctionTransformer.\n",
    "\n",
    "Let us take a simple example. I have a feature transformation technique that involves taking (log to the base 2) of the values. In NumPy, there is a function called log2 which does that for us.\n",
    "\n",
    "Thus, we can now apply the FunctionTransformer.\n",
    "Credit : https://www.kaggle.com/yuyougnchan/numeric-variable-end-with-this\n",
    "\n",
    "**Binning:**\n",
    "While numeric values can often be used without any feature engineering, there will be cases when some form of manipulation can be useful. For example on some occasions, you might not care about the magnitude of a value but only care about its direction, or if it exists at all. In these situations, you will want to binarize a column. \n",
    "\n",
    "For many continuous values you will care less about the exact value of a numeric column, but instead care about the bucket it falls into. This can be useful when plotting values, or simplifying your machine learning models. It is mostly used on continuous variables where accuracy is not the biggest concern e.g. age, height, wages.\n",
    "\n",
    "Bins are created using pd.cut(df['column_name'], bins) where bins can be an integer specifying the number of evenly spaced bins, or a list of bin boundaries.\n",
    "\n",
    "\n",
    "**Cat Features:** \n",
    "\n",
    "    Label Encoding or Ordinal Encoding\n",
    "    One hot Encoding\n",
    "    Dummy Encoding\n",
    "    Effect Encoding\n",
    "    Binary Encoding\n",
    "    BaseN Encoding\n",
    "    Hash Encoding\n",
    "    Target Encoding\n",
    "\n",
    "## Data Umbalanced \n",
    "\n",
    "\n",
    "**Resampling**\n",
    "\n",
    "- Random Undersampling\n",
    "\n",
    "'not minority' = resample all classes but not the minority class\n",
    "\n",
    "- Random Oversampling\n",
    "\n",
    "\"not majority\" = resample all classes but not the majority class\n",
    "\n",
    "- Stratified Sampling\n",
    "\n",
    "**Applying SMOTE**\n",
    "\n",
    "**Adjusting your algorithm weights**\n",
    "\n",
    "- Model building with Class weight balancing\n",
    "\n",
    "        \n",
    "**Ensemble methods**\n",
    "\n",
    "- Are Robust \n",
    "\n",
    "- Avoid Overfitting \n",
    "\n",
    "- Improve Predictions Performance  \n",
    "         \n",
    "**Clustering methods to detect Minority** \n",
    "        \n",
    "**Other clustering fraud detection methods**\n",
    "Explore using a density based clustering method (DBSCAN) to detect fraud. The advantage of DBSCAN is that you do not need to define the number of clusters beforehand. Also, DBSCAN can handle weirdly shaped data (i.e. non-convex) much better than K-means can. This time, you are not going to take the outliers of the clusters and use that for Detect  the risk, but take the smallest clusters in the data and label those as Minority .\n",
    "\n",
    "   \n",
    "- you first need to figure out how big the clusters are, and filter out the smallest\n",
    "\n",
    "- then, you're going to take the smallest ones and flag those as fraud\n",
    "\n",
    "- last, you'll check with the original labels whether this does actually do a good job in detecting risk.\n",
    "\n",
    "        \n",
    "            \n",
    "**Using text data**   \n",
    "\n",
    "          \n",
    "**Using list of terms**  \n",
    "\n",
    "**Creating a flag**\n",
    "\n",
    "  \n",
    "\n",
    "**Topic modeling on Minority example fraud**  \n",
    "\n",
    "    \n",
    "**Flagging fraud based on topics**\n",
    "\n",
    "   \n",
    "    \n",
    "**Threshold**\n",
    "\n",
    "we can try using the original model (trained on the original “imbalanced” data set) and simply plot the trade-off between false positives and false negatives to choose a threshold that may produce a desirable business result.    \n",
    "    \n",
    "\n",
    "## Outlier Handling\n",
    "\n",
    "**Statistical outlier removal**\n",
    "\n",
    "While removing the top N% of your data is useful for ensuring that very spurious points are removed, it does have the disadvantage of always removing the same proportion of points, even if the data is correct. A commonly used alternative approach is to remove data that sits further than three standard deviations from the mean. You can implement this by first calculating the mean and standard deviation of the relevant column to find upper and lower bounds, and applying these bounds as a mask to the DataFrame. This method ensures that only data that is genuinely different from the rest is removed, and will remove fewer points if the data is close together. we can trim data like this :\n",
    "\n",
    "        #train_std = train['cont1'].mean()\n",
    "        #train_mean = train['cont1'].std()\n",
    "\n",
    "        #cut_off = train_std * 3\n",
    "        #train_lower, train_upper = train_mean - cut_off, train_mean + cut_off\n",
    "\n",
    "        # Trim the test DataFrame\n",
    "        #trimmed_df = so_test_numeric[(train['cont1'] < train_upper) \\\n",
    "                                    # & (train['cont1'] > train_lower)]\n",
    "\n",
    "    \n",
    "**Quantile outlier removal**\n",
    "\n",
    "\n",
    "# Feature Engineering\n",
    "\n",
    "Feature engineering is the act of taking raw data and extracting features from it that are suitable for tasks like machine learning. Most machine learning algorithms work with tabular data. When we talk about features, we are referring to the information stored in the columns of these tables \n",
    "\n",
    "**Binning**\n",
    "\n",
    "While working with numeric data we come across some features where distributions of variables are skewed in the sense that some sets of values will occur a lot and some will be very rare. Directly using this type of feature may cause issues or can give inaccurate results.\n",
    "\n",
    "Binning is a way to convert numerical continuous variables into discrete variables by categorizing them on the basis of the range of values of the column in which they fall. In this type of transformation, we create bins. Each bin allows a specific range of continuous numerical values. It prevents overfitting and increases the robustness of the model.\n",
    "\n",
    "Let’s understand this using an example. We have scores of 10 students as 35, 46, 89, 20, 58, 99, 74, 60, 18, 81. Our task is to make 3 teams. Team 1 will have students with scores between 1-40, Team 2 will have students with scores between 41-80, and Team 3 will have students with scores between 81-100.\n",
    "\n",
    "\n",
    "\n",
    "Binning can be done in different ways listed below.\n",
    "\n",
    "      Fixed – Width Binning\n",
    "      Quantile Binning\n",
    "      Binning by Instinct\n",
    "      \n",
    "the formula is:\n",
    "K = 1 + 3. 322*logN\n",
    "\n",
    "where:\n",
    "\n",
    "K = number of class intervals (bins).\n",
    "\n",
    "N = number of observations in the set.\n",
    "\n",
    "log = logarithm of the number.\n",
    "\n",
    "Also we have others techniques : \n",
    "\n",
    "\n",
    " **Sparse Interactions/Kmeans Features/Polynominal Features.....ex**\n",
    " \n",
    "\n",
    "\n",
    "\n",
    "# Build OOP Features Engineer Classes \n",
    "Feature generation creates new features using knowledge about the problem and data. In the real-world example, you saw that risk features could be derived from the history.\n",
    "\n",
    "    \n",
    " \n",
    "\n",
    "Here are some ways that feature columns can be analyzed, combined, and transformed to create new features: \n",
    "\n",
    "    Splitting or combining columns\n",
    "    Encoding target, frequency, and aggregation after binning some continous features .\n",
    "    \n",
    "\n",
    "**Splitting or combining columns:**\n",
    "\n",
    "If this correlates better with the target, combine columns or split one column into two.\n",
    "\n",
    "\n",
    "Encoding target, frequency, and aggregation\n",
    "\n",
    "To detect credit risk, you are looking for unusual credit . Target, frequency, and aggregation encodings add features that measure the rarity of features or combinations of features. \n",
    "\n",
    "With target encoding, features are replaced or added with the probability of the categorical value corresponding to the target. For example, if 3% of credit risk  are of card child cluster for example  ” then the value “age” is replaced with .03.  \n",
    "\n",
    "\n",
    "\n",
    "Aggregation encoding adds features based on feature aggregation statistics, such as mean or standard deviation for groups of features. This allows machine learning algorithms such as decision trees to determine if a value is common or rare for a particular group. \n",
    "\n",
    "You can calculate group statistics by providing pandas with three variables: group, variable of interest, and type of statistic\n",
    "        \n",
    "\n",
    "Classification identifies the class or category to which an item belongs, based on the training data of labels and features. Features are the interesting properties in the data that you can use to make predictions. To build a classifier model, you extract and test to find the features of interest that most contribute to the classification. For feature engineering for credit risk, the goal is to distinguish normal behavior  from unusual credit usage.\n",
    "Finally for this step we get the most import features by coding some transformer .  \n",
    "\n",
    "# Features selection : \n",
    "**Feature Selection**\n",
    "Feature selection is a method of selecting features from your feature set to be used for modeling. It draws from a set of existing features, so it's different than feature engineering because it doesn't create new features. The overarching goal of feature selection is to improve your model's performance. Perhaps your existing feature set is much too large, or some of the features you're working with are unnecessary. There are different ways you can perform feature selection. It's possible to do it in an automated way. Scikit-learn has several methods for automated feature selection, such as choosing a variance threshold and using univariate statistical tests\n",
    "\n",
    "**Why reduce dimensionality?**\n",
    "\n",
    "Your dataset will become simpler and thus easier to work with, require less disk space to store and computations will run faster. In addition, models are less likely to overfit on a dataset with fewer dimensions.\n",
    "\n",
    "**Selection vs extraction**\n",
    "\n",
    "When we apply feature selection, we completely remove a feature and the information it holds from the dataset. We try to minimize the information loss by only removing features that are irrelevant or hold little unique information, but this is not always possible.\n",
    "\n",
    "Compared to feature selection, feature extraction is a completely different approach but with the same goal of reducing dimensionality. Instead of selecting a subset of features from our initial dataset, we'll be calculating, or extracting, new features from the original ones. These new features have as little redundant information in them as possible and are therefore fewer in number. One downside is that the newly created features are often less intuitive to understand than the original ones. \n",
    "PCA Calculating.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "I will try  this in ohter notebook  , and try those methods in order to get better results .\n",
    "\n",
    "Credit : \n",
    "\n",
    "https://machinelearningmastery.com/feature-selection-with-real-and-categorical-data/\n",
    "\n",
    "https://dataaspirant.com/feature-selection-methods-machine-learning/#t-1609062922957\n",
    "\n",
    "https://www.kaggle.com/prashant111/comprehensive-guide-on-feature-selection\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53240666",
   "metadata": {
    "papermill": {
     "duration": 0.035327,
     "end_time": "2022-01-10T20:52:38.949780",
     "exception": false,
     "start_time": "2022-01-10T20:52:38.914453",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Convert Dtypes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bc1e64c5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:39.043565Z",
     "iopub.status.busy": "2022-01-10T20:52:39.041937Z",
     "iopub.status.idle": "2022-01-10T20:52:39.096561Z",
     "shell.execute_reply": "2022-01-10T20:52:39.096021Z",
     "shell.execute_reply.started": "2022-01-10T20:51:36.466408Z"
    },
    "papermill": {
     "duration": 0.112897,
     "end_time": "2022-01-10T20:52:39.096700",
     "exception": false,
     "start_time": "2022-01-10T20:52:38.983803",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Convert Dtypes :\n",
    "train[train.select_dtypes(['int64','int16','float16','float32','float64','int8']).columns] = train[train.select_dtypes(['int64','int16','float16','float32','float64','int8']).columns].apply(pd.to_numeric)\n",
    "train[train.select_dtypes(['object','category']).columns] = train.select_dtypes(['object','category']).apply(lambda x: x.astype('category'))\n",
    "# Convert Dtypes :\n",
    "test[test.select_dtypes(['int64','int16','float16','float32','float64','int8']).columns] = test[test.select_dtypes(['int64','int16','float16','float32','float64','int8']).columns].apply(pd.to_numeric)\n",
    "test[test.select_dtypes(['object','category']).columns] = test.select_dtypes(['object','category']).apply(lambda x: x.astype('category'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "308688ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:39.182742Z",
     "iopub.status.busy": "2022-01-10T20:52:39.181617Z",
     "iopub.status.idle": "2022-01-10T20:52:39.225596Z",
     "shell.execute_reply": "2022-01-10T20:52:39.226629Z",
     "shell.execute_reply.started": "2022-01-10T20:51:36.545361Z"
    },
    "papermill": {
     "duration": 0.094694,
     "end_time": "2022-01-10T20:52:39.226858",
     "exception": false,
     "start_time": "2022-01-10T20:52:39.132164",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Memory usage of dataframe is 13.73 MB\n",
      "Memory usage after optimization is: 3.29 MB\n",
      "Decreased by 76.0%\n",
      "Memory usage of dataframe is 9.29 MB\n",
      "Memory usage after optimization is: 2.90 MB\n",
      "Decreased by 68.7%\n"
     ]
    }
   ],
   "source": [
    "# Author : https://www.kaggle.com/gemartin/load-data-reduce-memory-usage\n",
    "def reduce_mem_usage(df):\n",
    "    \"\"\" iterate through all the columns of a dataframe and modify the data type\n",
    "        to reduce memory usage.        \n",
    "    \"\"\"\n",
    "    start_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage of dataframe is {:.2f} MB'.format(start_mem))\n",
    "    \n",
    "    for col in df.columns:\n",
    "        col_type = df[col].dtype\n",
    "        name =df[col].dtype.name \n",
    "        \n",
    "        if col_type != object and col_type.name != 'category':\n",
    "        #if name != \"category\":    \n",
    "            c_min = df[col].min()\n",
    "            c_max = df[col].max()\n",
    "            if str(col_type)[:3] == 'int':\n",
    "                if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max:\n",
    "                    df[col] = df[col].astype(np.int8)\n",
    "                elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max:\n",
    "                    df[col] = df[col].astype(np.int16)\n",
    "                elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max:\n",
    "                    df[col] = df[col].astype(np.int32)\n",
    "                elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max:\n",
    "                    df[col] = df[col].astype(np.int64)  \n",
    "            else:\n",
    "                if c_min > np.finfo(np.float16).min and c_max < np.finfo(np.float16).max:\n",
    "                    df[col] = df[col].astype(np.float16)\n",
    "                elif c_min > np.finfo(np.float32).min and c_max < np.finfo(np.float32).max:\n",
    "                    df[col] = df[col].astype(np.float32)\n",
    "                else:\n",
    "                    df[col] = df[col].astype(np.float64)\n",
    "        else:\n",
    "            df[col] = df[col].astype('category')\n",
    "\n",
    "    end_mem = df.memory_usage().sum() / 1024**2\n",
    "    print('Memory usage after optimization is: {:.2f} MB'.format(end_mem))\n",
    "    print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem))\n",
    "    \n",
    "    return df\n",
    "train= reduce_mem_usage(train)\n",
    "test= reduce_mem_usage(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5028aa2a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:39.301364Z",
     "iopub.status.busy": "2022-01-10T20:52:39.300687Z",
     "iopub.status.idle": "2022-01-10T20:52:39.316846Z",
     "shell.execute_reply": "2022-01-10T20:52:39.316264Z",
     "shell.execute_reply.started": "2022-01-10T20:51:36.602894Z"
    },
    "papermill": {
     "duration": 0.054343,
     "end_time": "2022-01-10T20:52:39.317005",
     "exception": false,
     "start_time": "2022-01-10T20:52:39.262662",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 12 columns):\n",
      " #   Column                                Non-Null Count   Dtype  \n",
      "---  ------                                --------------   -----  \n",
      " 0   Unnamed: 0                            150000 non-null  int32  \n",
      " 1   SeriousDlqin2yrs                      150000 non-null  int8   \n",
      " 2   RevolvingUtilizationOfUnsecuredLines  150000 non-null  float16\n",
      " 3   age                                   150000 non-null  int8   \n",
      " 4   NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  int8   \n",
      " 5   DebtRatio                             150000 non-null  float32\n",
      " 6   MonthlyIncome                         120269 non-null  float32\n",
      " 7   NumberOfOpenCreditLinesAndLoans       150000 non-null  int8   \n",
      " 8   NumberOfTimes90DaysLate               150000 non-null  int8   \n",
      " 9   NumberRealEstateLoansOrLines          150000 non-null  int8   \n",
      " 10  NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  int8   \n",
      " 11  NumberOfDependents                    146076 non-null  float16\n",
      "dtypes: float16(2), float32(2), int32(1), int8(7)\n",
      "memory usage: 3.3 MB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "08703d17",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:39.393338Z",
     "iopub.status.busy": "2022-01-10T20:52:39.391746Z",
     "iopub.status.idle": "2022-01-10T20:52:39.393980Z",
     "shell.execute_reply": "2022-01-10T20:52:39.394387Z",
     "shell.execute_reply.started": "2022-01-10T20:51:36.625771Z"
    },
    "papermill": {
     "duration": 0.042662,
     "end_time": "2022-01-10T20:52:39.394513",
     "exception": false,
     "start_time": "2022-01-10T20:52:39.351851",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Cardinality : \n",
    "# - RevolvingUtilizationOfUnsecuredLines :125728, high Outlier\n",
    "# - DebtRatio :114194 , high Outlier \n",
    "# deal with outlier + bin \n",
    "PERCENTAGE = [\"RevolvingUtilizationOfUnsecuredLines\", \"DebtRatio\"]\n",
    "# MonthlyIncome:13594 , high outlier +bin \n",
    "REAL= [\"MonthlyIncome\"]\n",
    "# Can be considred as cat \n",
    "NUMERIC_DISCRET_low = [\"NumberOfDependents\",\n",
    "                       \"NumberOfTime60-89DaysPastDueNotWorse\",\n",
    "                       \"NumberRealEstateLoansOrLines\",\n",
    "                       \"NumberOfTimes90DaysLate\",\n",
    "                       \"NumberOfOpenCreditLinesAndLoans\",\n",
    "                       \"NumberOfTime30-59DaysPastDueNotWorse\",\n",
    "                       \"age\"]\n",
    "Late_Pay_Cols = ['NumberOfTime30-59DaysPastDueNotWorse',\n",
    "                 'NumberOfTimes90DaysLate',\n",
    "                 'NumberOfTime60-89DaysPastDueNotWorse']\n",
    "TARGET = [\"SeriousDlqin2yrs\"]\n",
    "\n",
    "#also change the type for TARGET to categorical\n",
    "#df[TARGET] = df[TARGET].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "05996226",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:39.470122Z",
     "iopub.status.busy": "2022-01-10T20:52:39.468940Z",
     "iopub.status.idle": "2022-01-10T20:52:39.472427Z",
     "shell.execute_reply": "2022-01-10T20:52:39.472000Z",
     "shell.execute_reply.started": "2022-01-10T20:51:36.634040Z"
    },
    "papermill": {
     "duration": 0.043238,
     "end_time": "2022-01-10T20:52:39.472537",
     "exception": false,
     "start_time": "2022-01-10T20:52:39.429299",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "84.719833466606"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# n1=2(IQR)n−1/3\n",
    "n1=2* 4849*0.008735804647\n",
    "n1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0626ab68",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:39.547865Z",
     "iopub.status.busy": "2022-01-10T20:52:39.546937Z",
     "iopub.status.idle": "2022-01-10T20:52:39.550619Z",
     "shell.execute_reply": "2022-01-10T20:52:39.551068Z",
     "shell.execute_reply.started": "2022-01-10T20:51:36.648093Z"
    },
    "papermill": {
     "duration": 0.043818,
     "end_time": "2022-01-10T20:52:39.551195",
     "exception": false,
     "start_time": "2022-01-10T20:52:39.507377",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.59289348376642"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_bin = 1 + 3.322 * np.log(train.shape[0])\n",
    "n_bin"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "285f8945",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:39.638407Z",
     "iopub.status.busy": "2022-01-10T20:52:39.636865Z",
     "iopub.status.idle": "2022-01-10T20:52:40.216079Z",
     "shell.execute_reply": "2022-01-10T20:52:40.215109Z",
     "shell.execute_reply.started": "2022-01-10T20:51:36.658772Z"
    },
    "papermill": {
     "duration": 0.629858,
     "end_time": "2022-01-10T20:52:40.216254",
     "exception": false,
     "start_time": "2022-01-10T20:52:39.586396",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Add bin data \n",
    "# initializing append_str\n",
    "append_str = 'cat_'\n",
    "# Append suffix / prefix to strings in list\n",
    "num_features1=[\"RevolvingUtilizationOfUnsecuredLines\", \"DebtRatio\",\"MonthlyIncome\"]\n",
    "num_features2=[\"NumberOfDependents\",\n",
    "                       \"NumberOfTime60-89DaysPastDueNotWorse\",\n",
    "                       \"NumberRealEstateLoansOrLines\",\n",
    "                       \"NumberOfTimes90DaysLate\",\n",
    "                       \"NumberOfOpenCreditLinesAndLoans\",\n",
    "                       \"NumberOfTime30-59DaysPastDueNotWorse\",\n",
    "                       \"age\"]\n",
    "cat_features1 = [append_str + sub for sub in num_features1]\n",
    "cat_features2 = [append_str + sub for sub in num_features2]\n",
    "\n",
    "# create the discretizer object with strategy quantile and 1000 bins\n",
    "discretizer1 = KBinsDiscretizer(n_bins=40, encode='ordinal',strategy='quantile')\n",
    "discretizer2 = KBinsDiscretizer(n_bins=4, encode='ordinal',strategy='quantile')\n",
    "\n",
    "pipeline1 = Pipeline([\n",
    "        ('imputer', SimpleImputer( strategy='median')),\n",
    "        ('bin', discretizer1)\n",
    "    ])\n",
    "# fit the discretizer to the train set\n",
    "pipeline1.fit(train.loc[:,num_features1])\n",
    "# apply the discretisation\n",
    "train_cat1 = pipeline1.transform(train.loc[:,num_features1])\n",
    "test_cat1 = pipeline1.transform(test.loc[:,num_features1])\n",
    "train_df1=pd.DataFrame(train_cat1,columns=cat_features1).astype('category')\n",
    "test_df1=pd.DataFrame(test_cat1,columns=cat_features1).astype('category')\n",
    "train_final1= pd.concat( [train.loc[:,num_features1], train_df1], axis=1) \n",
    "test_final1= pd.concat( [test.loc[:,num_features1], test_df1], axis=1) \n",
    "\n",
    "pipeline2 = Pipeline([\n",
    "        ('imputer', SimpleImputer( strategy='median')),\n",
    "        ('bin', discretizer2)\n",
    "    ])\n",
    "# fit the discretizer to the train set\n",
    "pipeline2.fit(train.loc[:,num_features2])\n",
    "# apply the discretisation\n",
    "train_cat2 = pipeline2.transform(train.loc[:,num_features2])\n",
    "test_cat2 = pipeline2.transform(test.loc[:,num_features2])\n",
    "train_df2=pd.DataFrame(train_cat2,columns=cat_features2).astype('category')\n",
    "test_df2=pd.DataFrame(test_cat2,columns=cat_features2).astype('category')\n",
    "train_final2= pd.concat( [train.loc[:,num_features2], train_df2], axis=1) \n",
    "test_final2= pd.concat( [test.loc[:,num_features2], test_df2], axis=1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bafddcdd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:40.297171Z",
     "iopub.status.busy": "2022-01-10T20:52:40.295934Z",
     "iopub.status.idle": "2022-01-10T20:52:40.321700Z",
     "shell.execute_reply": "2022-01-10T20:52:40.322286Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.260960Z"
    },
    "papermill": {
     "duration": 0.069324,
     "end_time": "2022-01-10T20:52:40.322456",
     "exception": false,
     "start_time": "2022-01-10T20:52:40.253132",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>DebtRatio</th>\n",
       "      <th>MonthlyIncome</th>\n",
       "      <th>cat_RevolvingUtilizationOfUnsecuredLines</th>\n",
       "      <th>cat_DebtRatio</th>\n",
       "      <th>cat_MonthlyIncome</th>\n",
       "      <th>NumberOfDependents</th>\n",
       "      <th>NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>NumberRealEstateLoansOrLines</th>\n",
       "      <th>NumberOfTimes90DaysLate</th>\n",
       "      <th>NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>age</th>\n",
       "      <th>cat_NumberOfDependents</th>\n",
       "      <th>cat_NumberOfTime60-89DaysPastDueNotWorse</th>\n",
       "      <th>cat_NumberRealEstateLoansOrLines</th>\n",
       "      <th>cat_NumberOfTimes90DaysLate</th>\n",
       "      <th>cat_NumberOfOpenCreditLinesAndLoans</th>\n",
       "      <th>cat_NumberOfTime30-59DaysPastDueNotWorse</th>\n",
       "      <th>cat_age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.766113</td>\n",
       "      <td>0.802982</td>\n",
       "      <td>9120.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>45</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.957031</td>\n",
       "      <td>0.121876</td>\n",
       "      <td>2600.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   RevolvingUtilizationOfUnsecuredLines  DebtRatio  MonthlyIncome  \\\n",
       "0                              0.766113   0.802982         9120.0   \n",
       "1                              0.957031   0.121876         2600.0   \n",
       "\n",
       "  cat_RevolvingUtilizationOfUnsecuredLines cat_DebtRatio cat_MonthlyIncome  \\\n",
       "0                                     30.0          28.0              25.0   \n",
       "1                                     33.0           6.0               5.0   \n",
       "\n",
       "   NumberOfDependents  NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                 2.0                                     0   \n",
       "1                 1.0                                     0   \n",
       "\n",
       "   NumberRealEstateLoansOrLines  NumberOfTimes90DaysLate  \\\n",
       "0                             6                        0   \n",
       "1                             0                        0   \n",
       "\n",
       "   NumberOfOpenCreditLinesAndLoans  NumberOfTime30-59DaysPastDueNotWorse  age  \\\n",
       "0                               13                                     2   45   \n",
       "1                                4                                     0   40   \n",
       "\n",
       "  cat_NumberOfDependents cat_NumberOfTime60-89DaysPastDueNotWorse  \\\n",
       "0                    1.0                                      0.0   \n",
       "1                    1.0                                      0.0   \n",
       "\n",
       "  cat_NumberRealEstateLoansOrLines cat_NumberOfTimes90DaysLate  \\\n",
       "0                              2.0                         0.0   \n",
       "1                              0.0                         0.0   \n",
       "\n",
       "  cat_NumberOfOpenCreditLinesAndLoans  \\\n",
       "0                                 3.0   \n",
       "1                                 0.0   \n",
       "\n",
       "  cat_NumberOfTime30-59DaysPastDueNotWorse cat_age  \n",
       "0                                      0.0     1.0  \n",
       "1                                      0.0     0.0  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final= pd.concat( [train_final1, train_final2], axis=1) \n",
    "test_final= pd.concat( [test_final2, test_final2], axis=1) \n",
    "train_final.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "983194b2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:40.414291Z",
     "iopub.status.busy": "2022-01-10T20:52:40.413108Z",
     "iopub.status.idle": "2022-01-10T20:52:40.416331Z",
     "shell.execute_reply": "2022-01-10T20:52:40.416792Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.289953Z"
    },
    "papermill": {
     "duration": 0.049435,
     "end_time": "2022-01-10T20:52:40.416959",
     "exception": false,
     "start_time": "2022-01-10T20:52:40.367524",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(150000, 20)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_final.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "341964b9",
   "metadata": {
    "papermill": {
     "duration": 0.038189,
     "end_time": "2022-01-10T20:52:40.495119",
     "exception": false,
     "start_time": "2022-01-10T20:52:40.456930",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "## Define the model features and target\n",
    "\n",
    "### Extract X and y "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "db954bcc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:40.577212Z",
     "iopub.status.busy": "2022-01-10T20:52:40.575577Z",
     "iopub.status.idle": "2022-01-10T20:52:40.577843Z",
     "shell.execute_reply": "2022-01-10T20:52:40.578277Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.298199Z"
    },
    "papermill": {
     "duration": 0.045099,
     "end_time": "2022-01-10T20:52:40.578400",
     "exception": false,
     "start_time": "2022-01-10T20:52:40.533301",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Pour le train test\n",
    "target= \"SeriousDlqin2yrs\"\n",
    "X = train_final# axis=1\n",
    "X_test_final =test_final# axis=1\n",
    "y = train[target]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "406741df",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:40.654939Z",
     "iopub.status.busy": "2022-01-10T20:52:40.654416Z",
     "iopub.status.idle": "2022-01-10T20:52:40.658109Z",
     "shell.execute_reply": "2022-01-10T20:52:40.657644Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.306150Z"
    },
    "papermill": {
     "duration": 0.043632,
     "end_time": "2022-01-10T20:52:40.658217",
     "exception": false,
     "start_time": "2022-01-10T20:52:40.614585",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "del train\n",
    "del test \n",
    "del train_final\n",
    "del test_final"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07bb5f3a",
   "metadata": {
    "papermill": {
     "duration": 0.036226,
     "end_time": "2022-01-10T20:52:40.730433",
     "exception": false,
     "start_time": "2022-01-10T20:52:40.694207",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "\n",
    "# Create test and train groups\n",
    "\n",
    "Now we’ve got our dataframe ready we can split it up into the train and test datasets for our model to use. We’ll use the Scikit-Learn train_test_split() function for this. By passing in the X dataframe of raw features, the y series containing the target, and the ************size of the test group (i.e. 0.2 for 20%), we get back the X_train, X_test, y_train and y_test data to use in the model.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9b9e3ee3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:40.808417Z",
     "iopub.status.busy": "2022-01-10T20:52:40.807645Z",
     "iopub.status.idle": "2022-01-10T20:52:40.909982Z",
     "shell.execute_reply": "2022-01-10T20:52:40.909527Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.315989Z"
    },
    "papermill": {
     "duration": 0.143943,
     "end_time": "2022-01-10T20:52:40.910106",
     "exception": false,
     "start_time": "2022-01-10T20:52:40.766163",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30000 rows in test set vs. 120000 in training set. 20 Features.\n"
     ]
    }
   ],
   "source": [
    "# Split the dataset and labels into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=0,stratify=y )\n",
    "print(\"{} rows in test set vs. {} in training set. {} Features.\".format(X_test.shape[0], X_train.shape[0], X_test.shape[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b87e3dab",
   "metadata": {
    "papermill": {
     "duration": 0.036533,
     "end_time": "2022-01-10T20:52:40.983131",
     "exception": false,
     "start_time": "2022-01-10T20:52:40.946598",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# What should we do for each colmun\n",
    "\n",
    "**Separate features by dtype**\n",
    "\n",
    "Next we’ll separate the features in the dataframe by their datatype. There are a few different ways to achieve this. I’ve used the select_dtypes() function to obtain specific data types by passing in np.number to obtain the numeric data and exclude=['np.number'] to return the categorical data. Appending .columns to the end returns an Index list containing the column names. For the categorical features, we don’t want to include the target income column, so I’ve dropped that.\n",
    "\n",
    "**Cat Features**\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "24fba8c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:41.066477Z",
     "iopub.status.busy": "2022-01-10T20:52:41.065750Z",
     "iopub.status.idle": "2022-01-10T20:52:41.068484Z",
     "shell.execute_reply": "2022-01-10T20:52:41.068901Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.429422Z"
    },
    "papermill": {
     "duration": 0.049819,
     "end_time": "2022-01-10T20:52:41.069026",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.019207",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cat_RevolvingUtilizationOfUnsecuredLines', 'cat_DebtRatio',\n",
       "       'cat_MonthlyIncome', 'cat_NumberOfDependents',\n",
       "       'cat_NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'cat_NumberRealEstateLoansOrLines', 'cat_NumberOfTimes90DaysLate',\n",
       "       'cat_NumberOfOpenCreditLinesAndLoans',\n",
       "       'cat_NumberOfTime30-59DaysPastDueNotWorse', 'cat_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select non-numeric columns\n",
    "cat_columns = X.select_dtypes(exclude=['int64','int16','float16','float32','float64','int8']).columns\n",
    "cat_columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e2c1547",
   "metadata": {
    "papermill": {
     "duration": 0.036635,
     "end_time": "2022-01-10T20:52:41.142398",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.105763",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "**Num Features**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "36426fb7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:41.223593Z",
     "iopub.status.busy": "2022-01-10T20:52:41.222331Z",
     "iopub.status.idle": "2022-01-10T20:52:41.229806Z",
     "shell.execute_reply": "2022-01-10T20:52:41.229378Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.442820Z"
    },
    "papermill": {
     "duration": 0.051238,
     "end_time": "2022-01-10T20:52:41.229938",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.178700",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select the float columns\n",
    "num_columns = X.select_dtypes(include=['int64','int16','float16','float32','float64','int8']).columns\n",
    "num_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f1db90bd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:41.308425Z",
     "iopub.status.busy": "2022-01-10T20:52:41.307917Z",
     "iopub.status.idle": "2022-01-10T20:52:41.313236Z",
     "shell.execute_reply": "2022-01-10T20:52:41.312773Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.457541Z"
    },
    "papermill": {
     "duration": 0.046806,
     "end_time": "2022-01-10T20:52:41.313338",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.266532",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['cat_RevolvingUtilizationOfUnsecuredLines', 'cat_DebtRatio',\n",
      "       'cat_MonthlyIncome', 'cat_NumberOfDependents',\n",
      "       'cat_NumberOfTime60-89DaysPastDueNotWorse',\n",
      "       'cat_NumberRealEstateLoansOrLines', 'cat_NumberOfTimes90DaysLate',\n",
      "       'cat_NumberOfOpenCreditLinesAndLoans',\n",
      "       'cat_NumberOfTime30-59DaysPastDueNotWorse', 'cat_age'],\n",
      "      dtype='object')\n",
      "Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
      "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
      "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
      "       'NumberOfOpenCreditLinesAndLoans',\n",
      "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
      "      dtype='object')\n",
      "Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
      "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
      "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
      "       'NumberOfOpenCreditLinesAndLoans',\n",
      "       'NumberOfTime30-59DaysPastDueNotWorse', 'age',\n",
      "       'cat_RevolvingUtilizationOfUnsecuredLines', 'cat_DebtRatio',\n",
      "       'cat_MonthlyIncome', 'cat_NumberOfDependents',\n",
      "       'cat_NumberOfTime60-89DaysPastDueNotWorse',\n",
      "       'cat_NumberRealEstateLoansOrLines', 'cat_NumberOfTimes90DaysLate',\n",
      "       'cat_NumberOfOpenCreditLinesAndLoans',\n",
      "       'cat_NumberOfTime30-59DaysPastDueNotWorse', 'cat_age'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "all_columns = (num_columns.append(cat_columns))\n",
    "print(cat_columns)\n",
    "print(num_columns)\n",
    "print(all_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bee333c1",
   "metadata": {
    "papermill": {
     "duration": 0.036992,
     "end_time": "2022-01-10T20:52:41.387912",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.350920",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# check that we have all column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1ddab5a9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:41.467306Z",
     "iopub.status.busy": "2022-01-10T20:52:41.464178Z",
     "iopub.status.idle": "2022-01-10T20:52:41.470002Z",
     "shell.execute_reply": "2022-01-10T20:52:41.470403Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.465529Z"
    },
    "papermill": {
     "duration": 0.04539,
     "end_time": "2022-01-10T20:52:41.470519",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.425129",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ok\n"
     ]
    }
   ],
   "source": [
    "if set(all_columns) == set(X.columns):\n",
    "    print('Ok')\n",
    "else:\n",
    "    # Let's see the difference \n",
    "    print('in all_columns but not in  train  :', set(all_columns) - set(X.columns))\n",
    "    print('in X.columns   but not all_columns :', set(X.columns) - set(all_columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "021789cd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:41.571268Z",
     "iopub.status.busy": "2022-01-10T20:52:41.570463Z",
     "iopub.status.idle": "2022-01-10T20:52:41.572451Z",
     "shell.execute_reply": "2022-01-10T20:52:41.572856Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.474245Z"
    },
    "papermill": {
     "duration": 0.065515,
     "end_time": "2022-01-10T20:52:41.573003",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.507488",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "class ColumnsSelector(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, positions):\n",
    "        self.positions = positions\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        #return np.array(X)[:, self.positions]\n",
    "        return X.loc[:, self.positions] \n",
    "########################################################################\n",
    "class CustomLogTransformer(BaseEstimator, TransformerMixin):\n",
    "    # https://towardsdatascience.com/how-to-write-powerful-code-others-admire-with-custom-sklearn-transformers-34bc9087fdd\n",
    "    def __init__(self):\n",
    "        self._estimator = PowerTransformer()\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        X_copy = np.copy(X) + 1\n",
    "        self._estimator.fit(X_copy)\n",
    "\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        X_copy = np.copy(X) + 1\n",
    "\n",
    "        return self._estimator.transform(X_copy)\n",
    "\n",
    "    def inverse_transform(self, X):\n",
    "        X_reversed = self._estimator.inverse_transform(np.copy(X))\n",
    "\n",
    "        return X_reversed - 1  \n",
    "\n",
    "class TemporalVariableTransformer(BaseEstimator, TransformerMixin):\n",
    "    # Temporal elapsed time transformer\n",
    "\n",
    "    def __init__(self, variables, reference_variable):\n",
    "        \n",
    "        if not isinstance(variables, list):\n",
    "            raise ValueError('variables should be a list')\n",
    "        \n",
    "        self.variables = variables\n",
    "        self.reference_variable = reference_variable\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        # we need this step to fit the sklearn pipeline\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "\n",
    "       # so that we do not over-write the original dataframe\n",
    "        X = X.copy()\n",
    "        \n",
    "        for feature in self.variables:\n",
    "            X[feature] = X[self.reference_variable] - X[feature]\n",
    "\n",
    "        return X\n",
    "    \n",
    "class CustomImputer(BaseEstimator, TransformerMixin) : \n",
    "     def __init__(self, variable, by) : \n",
    "            #self.something enables you to include the passed parameters\n",
    "            #as object attributes and use it in other methods of the class\n",
    "            self.variable = variable\n",
    "            self.by = by\n",
    "\n",
    "     def fit(self, X, y=None) : \n",
    "          self.map = X.groupby(self.by)[variable].mean()\n",
    "          #self.map become an attribute that is, the map of values to\n",
    "          #impute in function of index (corresponding table, like a dict)\n",
    "          return self\n",
    "\n",
    "     def transform(self, X, y=None) : \n",
    "          X[variable] = X[variable].fillna(value = X[by].map(self.map))\n",
    "          #Change the variable column. If the value is missing, value should \n",
    "          #be replaced by the mapping of column \"by\" according to the map you\n",
    "          #created in fit method (self.map)\n",
    "          return X\n",
    "    \n",
    "# categorical missing value imputer\n",
    "class Mapper(BaseEstimator, TransformerMixin):\n",
    "\n",
    "    def __init__(self, variables, mappings):\n",
    "\n",
    "        if not isinstance(variables, list):\n",
    "            raise ValueError('variables should be a list')\n",
    "\n",
    "        self.variables = variables\n",
    "        self.mappings = mappings\n",
    "\n",
    "    def fit(self, X, y=None):\n",
    "        # we need the fit statement to accomodate the sklearn pipeline\n",
    "        return self\n",
    "\n",
    "    def transform(self, X):\n",
    "        X = X.copy()\n",
    "        for feature in self.variables:\n",
    "            X[feature] = X[feature].map(self.mappings)\n",
    "\n",
    "        return X  \n",
    "    \n",
    "class SparseInteractions(BaseEstimator, TransformerMixin):\n",
    "    def __init__(self, degree=2, feature_name_separator=\"_\"):\n",
    "        self.degree = degree\n",
    "        self.feature_name_separator = feature_name_separator\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "        \n",
    "    def transform(self, X):\n",
    "        if not sparse.isspmatrix_csc(X):\n",
    "            X = sparse.csc_matrix(X)\n",
    "            \n",
    "        if hasattr(X, \"columns\"):\n",
    "            self.orig_col_names = X.columns\n",
    "        else:\n",
    "            self.orig_col_names = np.array([str(i) for i in range(X.shape[1])])\n",
    "            \n",
    "        spi = self._create_sparse_interactions(X)\n",
    "        return spi\n",
    "    \n",
    "    \n",
    "    def get_feature_names(self):\n",
    "        return self.feature_names\n",
    "    \n",
    "    def _create_sparse_interactions(self, X):\n",
    "        out_mat = []\n",
    "        self.feature_names = self.orig_col_names.tolist()\n",
    "        \n",
    "        for sub_degree in range(2, self.degree + 1):\n",
    "            for col_ixs in combinations(range(X.shape[1]), sub_degree):\n",
    "                # add name for new column\n",
    "                name = self.feature_name_separator.join(self.orig_col_names[list(col_ixs)])\n",
    "                self.feature_names.append(name)\n",
    "                \n",
    "                # get column multiplications value\n",
    "                out = X[:, col_ixs[0]]    \n",
    "                for j in col_ixs[1:]:\n",
    "                    out = out.multiply(X[:, j])\n",
    "\n",
    "                out_mat.append(out)\n",
    "\n",
    "        return sparse.hstack([X] + out_mat)\n",
    "# Outlier Handle \n",
    "class OutlierReplace(BaseEstimator,TransformerMixin):\n",
    "    def __init__(self,factor=1.5):\n",
    "        self.factor = factor\n",
    "\n",
    "    def outlier_removal(self,X,y=None):\n",
    "        X = pd.Series(X).copy()\n",
    "        qmin=X.quantile(0.05)\n",
    "        qmax=X.quantile(0.95)\n",
    "        q1 = X.quantile(0.25)\n",
    "        q3 = X.quantile(0.75)\n",
    "        iqr = q3 - q1\n",
    "        lower_bound = q1 - (self.factor * iqr)\n",
    "        upper_bound = q3 + (self.factor * iqr)\n",
    "        #X.loc[((X < lower_bound) | (X > upper_bound))] = np.nan \n",
    "        X.loc[X < lower_bound] = qmin\n",
    "        X.loc[X > upper_bound] = qmax\n",
    "        return pd.Series(X)\n",
    "\n",
    "    def fit(self,X,y=None):\n",
    "        return self\n",
    "\n",
    "    def transform(self,X,y=None):\n",
    "        return X.apply(self.outlier_removal)    \n",
    "    "
   ]
  },
  {
   "attachments": {
    "c079f007-c7fe-4d24-bbf1-d6f82c05453b.png": {
     "image/png": "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"
    },
    "c07dc0db-a761-401f-8ad9-b81619e226e7.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "deda71b1",
   "metadata": {
    "papermill": {
     "duration": 0.037318,
     "end_time": "2022-01-10T20:52:41.647936",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.610618",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# cross_validation_design\n",
    "## A quick explanation as follows:\n",
    "\n",
    "**Cross Validation:** Splits the data into k \"random\" folds\n",
    "\n",
    "**Stratified Cross Valiadtion:** Splits the data into k folds, making sure each fold is an appropriate representative of the original data. (class distribution, mean, variance, etc)\n",
    "\n",
    "![image.png](attachment:c079f007-c7fe-4d24-bbf1-d6f82c05453b.png)\n",
    "\n",
    "Example of 5 fold Cross Validation:\n",
    "\n",
    "![image.png](attachment:c07dc0db-a761-401f-8ad9-b81619e226e7.png)\n",
    "\n",
    "Cross Validation design will be in One Notebook as it s a very important step no only for chosing best models but for general evalaution ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "fcf06162",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:41.727193Z",
     "iopub.status.busy": "2022-01-10T20:52:41.726633Z",
     "iopub.status.idle": "2022-01-10T20:52:41.731976Z",
     "shell.execute_reply": "2022-01-10T20:52:41.731527Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.505535Z"
    },
    "papermill": {
     "duration": 0.046893,
     "end_time": "2022-01-10T20:52:41.732081",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.685188",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "StratifiedKFold(n_splits=2, random_state=1, shuffle=True)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cross_validation_design = StratifiedKFold( n_splits=2,\n",
    "                                           shuffle=True\n",
    "                                           ,random_state=1)\n",
    "cross_validation_design"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40c9b26f",
   "metadata": {
    "papermill": {
     "duration": 0.037369,
     "end_time": "2022-01-10T20:52:41.806810",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.769441",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "# Let's test some Basic Scaler/transformer/Features Enginner : \n",
    "I usulally try this if the data set is small , this task is really  time consuming .\n",
    "This step will give me the best start for chosing ghe best preprocess steps .\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8a2ef279",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:41.889462Z",
     "iopub.status.busy": "2022-01-10T20:52:41.888505Z",
     "iopub.status.idle": "2022-01-10T20:52:41.907268Z",
     "shell.execute_reply": "2022-01-10T20:52:41.907813Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.514736Z"
    },
    "papermill": {
     "duration": 0.062578,
     "end_time": "2022-01-10T20:52:41.908000",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.845422",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150000 entries, 0 to 149999\n",
      "Data columns (total 20 columns):\n",
      " #   Column                                    Non-Null Count   Dtype   \n",
      "---  ------                                    --------------   -----   \n",
      " 0   RevolvingUtilizationOfUnsecuredLines      150000 non-null  float16 \n",
      " 1   DebtRatio                                 150000 non-null  float32 \n",
      " 2   MonthlyIncome                             120269 non-null  float32 \n",
      " 3   cat_RevolvingUtilizationOfUnsecuredLines  150000 non-null  category\n",
      " 4   cat_DebtRatio                             150000 non-null  category\n",
      " 5   cat_MonthlyIncome                         150000 non-null  category\n",
      " 6   NumberOfDependents                        146076 non-null  float16 \n",
      " 7   NumberOfTime60-89DaysPastDueNotWorse      150000 non-null  int8    \n",
      " 8   NumberRealEstateLoansOrLines              150000 non-null  int8    \n",
      " 9   NumberOfTimes90DaysLate                   150000 non-null  int8    \n",
      " 10  NumberOfOpenCreditLinesAndLoans           150000 non-null  int8    \n",
      " 11  NumberOfTime30-59DaysPastDueNotWorse      150000 non-null  int8    \n",
      " 12  age                                       150000 non-null  int8    \n",
      " 13  cat_NumberOfDependents                    150000 non-null  category\n",
      " 14  cat_NumberOfTime60-89DaysPastDueNotWorse  150000 non-null  category\n",
      " 15  cat_NumberRealEstateLoansOrLines          150000 non-null  category\n",
      " 16  cat_NumberOfTimes90DaysLate               150000 non-null  category\n",
      " 17  cat_NumberOfOpenCreditLinesAndLoans       150000 non-null  category\n",
      " 18  cat_NumberOfTime30-59DaysPastDueNotWorse  150000 non-null  category\n",
      " 19  cat_age                                   150000 non-null  category\n",
      "dtypes: category(10), float16(2), float32(2), int8(6)\n",
      "memory usage: 4.0 MB\n"
     ]
    }
   ],
   "source": [
    "X.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "927a95db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:41.987585Z",
     "iopub.status.busy": "2022-01-10T20:52:41.986807Z",
     "iopub.status.idle": "2022-01-10T20:52:41.992426Z",
     "shell.execute_reply": "2022-01-10T20:52:41.991874Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.544521Z"
    },
    "papermill": {
     "duration": 0.045685,
     "end_time": "2022-01-10T20:52:41.992549",
     "exception": false,
     "start_time": "2022-01-10T20:52:41.946864",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['cat_RevolvingUtilizationOfUnsecuredLines', 'cat_DebtRatio',\n",
       "       'cat_MonthlyIncome', 'cat_NumberOfDependents',\n",
       "       'cat_NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'cat_NumberRealEstateLoansOrLines', 'cat_NumberOfTimes90DaysLate',\n",
       "       'cat_NumberOfOpenCreditLinesAndLoans',\n",
       "       'cat_NumberOfTime30-59DaysPastDueNotWorse', 'cat_age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "c44cf287",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:42.073947Z",
     "iopub.status.busy": "2022-01-10T20:52:42.073271Z",
     "iopub.status.idle": "2022-01-10T20:52:42.075880Z",
     "shell.execute_reply": "2022-01-10T20:52:42.076317Z",
     "shell.execute_reply.started": "2022-01-10T20:51:37.552868Z"
    },
    "papermill": {
     "duration": 0.0457,
     "end_time": "2022-01-10T20:52:42.076439",
     "exception": false,
     "start_time": "2022-01-10T20:52:42.030739",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['RevolvingUtilizationOfUnsecuredLines', 'DebtRatio', 'MonthlyIncome',\n",
       "       'NumberOfDependents', 'NumberOfTime60-89DaysPastDueNotWorse',\n",
       "       'NumberRealEstateLoansOrLines', 'NumberOfTimes90DaysLate',\n",
       "       'NumberOfOpenCreditLinesAndLoans',\n",
       "       'NumberOfTime30-59DaysPastDueNotWorse', 'age'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fe631233",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T20:52:42.172784Z",
     "iopub.status.busy": "2022-01-10T20:52:42.171997Z",
     "iopub.status.idle": "2022-01-10T21:01:10.299937Z",
     "shell.execute_reply": "2022-01-10T21:01:10.300599Z"
    },
    "papermill": {
     "duration": 508.184851,
     "end_time": "2022-01-10T21:01:10.300796",
     "exception": false,
     "start_time": "2022-01-10T20:52:42.115945",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "{'encoder': 'OrdinalEncoder', 'scaler': 'RobustScaler', 'Percentnumoffeatures': '100features', 'auc': 0.863797793133227}\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "{'encoder': 'TargetEncoder', 'scaler': 'RobustScaler', 'Percentnumoffeatures': '100features', 'auc': 0.8637929398481006}\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "{'encoder': 'OrdinalEncoder', 'scaler': 'MinMaxScaler', 'Percentnumoffeatures': '100features', 'auc': 0.8635400125076946}\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "[LightGBM] [Warning] min_data_in_leaf is set with min_child_samples=20, will be overridden by min_samples_leaf=47. Current value: min_data_in_leaf=47\n",
      "[LightGBM] [Warning] Unknown parameter: tree_method\n",
      "{'encoder': 'TargetEncoder', 'scaler': 'MinMaxScaler', 'Percentnumoffeatures': '100features', 'auc': 0.863586479630934}\n"
     ]
    }
   ],
   "source": [
    "# Different Encoders \n",
    "encoders = {\n",
    "    #'BackwardDifferenceEncoder': ce.backward_difference.BackwardDifferenceEncoder,\n",
    "    #'BaseNEncoder': ce.basen.BaseNEncoder,\n",
    "    #'BinaryEncoder': ce.binary.BinaryEncoder,\n",
    "   # 'CatBoostEncoder': ce.cat_boost.CatBoostEncoder,\n",
    "    #'HashingEncoder': ce.hashing.HashingEncoder,\n",
    "   # 'HelmertEncoder': ce.helmert.HelmertEncoder,\n",
    "   # 'JamesSteinEncoder': ce.james_stein.JamesSteinEncoder,\n",
    "    #'OneHotEncoder': ce.one_hot.OneHotEncoder,\n",
    "    #'LeaveOneOutEncoder': ce.leave_one_out.LeaveOneOutEncoder,\n",
    "    #'MEstimateEncoder': ce.m_estimate.MEstimateEncoder,\n",
    "    'OrdinalEncoder': ce.ordinal.OrdinalEncoder,\n",
    "    #'PolynomialEncoder': ce.polynomial.PolynomialEncoder,\n",
    "    #'SumEncoder': ce.sum_coding.SumEncoder,\n",
    "    'TargetEncoder': ce.target_encoder.TargetEncoder,\n",
    "    #'WOEEncoder': ce.woe.WOEEncoder\n",
    "}\n",
    "# Differents Scaler\n",
    "Scalers={\n",
    "    #'StandardScaler': StandardScaler,\n",
    "    'RobustScaler': RobustScaler,\n",
    "    'MinMaxScaler': MinMaxScaler,\n",
    "    #'PowerTransformer': PowerTransformer,\n",
    "    #'QuantileTransformer': QuantileTransformer,\n",
    "    #'Normalizer': Normalizer,\n",
    "    #'MaxAbsScaler': MaxAbsScaler\n",
    "}\n",
    "# SelectBest features \n",
    "BestfeaturesPercentile={#'50features': 50,\n",
    "                        #'75features': 75,\n",
    "                       '100features': 100}\n",
    "#X1=X.iloc[0:10,:].copy()\n",
    "#y1=y[0:10].copy()\n",
    "df_resultsLGBM = pd.DataFrame(columns=['encoder', 'scaler', 'Percentnumoffeatures', 'auc'])\n",
    "for num in BestfeaturesPercentile:\n",
    "    for scaler in Scalers:\n",
    "        for key in encoders:\n",
    "            try :\n",
    "                # Cat pipeline\n",
    "                categorical_transformer = Pipeline(\n",
    "                    steps=[\n",
    "                        ('imputer', SimpleImputer(strategy='most_frequent',\n",
    "                                                  fill_value='missing',\n",
    "                                                  add_indicator=True)),\n",
    "                        ('encoder', encoders[key]()),#(Numerical Input, Categorical Output)\n",
    "                         ('reducedim',  SelectPercentile( mutual_info_classif,\n",
    "                                                         percentile=BestfeaturesPercentile[num]))\n",
    "\n",
    "                    ]\n",
    "                ) \n",
    "                # Num pipeline  \n",
    "                numeric_transformer = Pipeline(\n",
    "                    steps=[\n",
    "                        ('imputer', SimpleImputer(strategy='median',add_indicator=True)),\n",
    "                        ('scaler', Scalers[scaler]()),#(Numerical Input, Numerical Output)\n",
    "                        # Create an SelectKBest object to select features with two best ANOVA F-Values\n",
    "                        #The F-value scores examine if, when we group the numerical feature by the target vector, the means for each group are significantly different\n",
    "                        ('reducedim',  SelectPercentile(f_classif,\n",
    "                                                        percentile=BestfeaturesPercentile[num]))\n",
    "\n",
    "                    ]\n",
    "                )\n",
    "                # Features union cat + num \n",
    "                preprocessor = ColumnTransformer(\n",
    "                    transformers=[\n",
    "                        ('numerical', numeric_transformer, num_columns),\n",
    "                        ('categorical', categorical_transformer, cat_columns)\n",
    "                    ]\n",
    "                )\n",
    "                lgbm_param={'learning_rate': 0.0018069834369607075,\n",
    "                             'max_depth': 8,\n",
    "                             #'max_features': 4,\n",
    "                             'min_samples_leaf': 47,\n",
    "                             #'min_samples_split': 389,\n",
    "                             'subsample': 0.8573598985000007,\n",
    "                             #'n_iter_no_change': 300,\n",
    "                             'n_estimators': 5000,\n",
    "                            # 'verbose': 0,\n",
    "                             'random_state': 144,\n",
    "                             'metric': 'auc',\n",
    "                             \"device_type\" : \"gpu\",\n",
    "                            'boosting_type': 'gbdt',\n",
    "                            'tree_method': \"gpu_hist\"\n",
    "                           }\n",
    "                pipe_LGBM = Pipeline(\n",
    "                    steps=[\n",
    "                        ('preprocessor', preprocessor),\n",
    "                        ('classifier', lgbm.LGBMClassifier( n_jobs=-1,\n",
    "                                                           verbose=-1,\n",
    "                                                           **lgbm_param))\n",
    "                    ]\n",
    "                )\n",
    "                auc =cross_val_score(pipe_LGBM, X, y, cv=cross_validation_design,scoring='roc_auc').mean()\n",
    "                #pipe_LGBM.fit(X1, y1)\n",
    "                # We need to use the probability to be in the class '1'.\n",
    "                #y_pred = pipe_LGBM.predict_proba(X1)[:, 1]\n",
    "                #auc =roc_auc_score(y1, y_pred)\n",
    "                row = {\n",
    "                    'encoder': key,\n",
    "                    'scaler': scaler,\n",
    "                    'Percentnumoffeatures': num,\n",
    "                    'auc': auc\n",
    "                }\n",
    "                df_resultsLGBM = df_resultsLGBM.append(row, ignore_index=True)\n",
    "                df_resultsLGBM.to_csv('firstmodellgbm3.csv',index=False)               \n",
    "                print(row)\n",
    "            except :\n",
    "                row={\n",
    "                    'encoder': key,\n",
    "                    'scaler': scaler,\n",
    "                    'Percentnumoffeatures': num,\n",
    "                    'auc': np.nan\n",
    "                }\n",
    "                df_resultsLGBM = df_resultsLGBM.append(row, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "94196d6f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-10T21:01:10.392094Z",
     "iopub.status.busy": "2022-01-10T21:01:10.391273Z",
     "iopub.status.idle": "2022-01-10T21:01:10.399351Z",
     "shell.execute_reply": "2022-01-10T21:01:10.398898Z"
    },
    "papermill": {
     "duration": 0.055758,
     "end_time": "2022-01-10T21:01:10.399477",
     "exception": false,
     "start_time": "2022-01-10T21:01:10.343719",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>encoder</th>\n",
       "      <th>scaler</th>\n",
       "      <th>Percentnumoffeatures</th>\n",
       "      <th>auc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OrdinalEncoder</td>\n",
       "      <td>RobustScaler</td>\n",
       "      <td>100features</td>\n",
       "      <td>0.863798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>TargetEncoder</td>\n",
       "      <td>RobustScaler</td>\n",
       "      <td>100features</td>\n",
       "      <td>0.863793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>TargetEncoder</td>\n",
       "      <td>MinMaxScaler</td>\n",
       "      <td>100features</td>\n",
       "      <td>0.863586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OrdinalEncoder</td>\n",
       "      <td>MinMaxScaler</td>\n",
       "      <td>100features</td>\n",
       "      <td>0.863540</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          encoder        scaler Percentnumoffeatures       auc\n",
       "0  OrdinalEncoder  RobustScaler          100features  0.863798\n",
       "1   TargetEncoder  RobustScaler          100features  0.863793\n",
       "3   TargetEncoder  MinMaxScaler          100features  0.863586\n",
       "2  OrdinalEncoder  MinMaxScaler          100features  0.863540"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_resultsLGBM.sort_values(by='auc',ascending=False).head(50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9928c694",
   "metadata": {
    "papermill": {
     "duration": 0.042439,
     "end_time": "2022-01-10T21:01:10.485301",
     "exception": false,
     "start_time": "2022-01-10T21:01:10.442862",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=4></a>\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style='color:white; background:#1777C4; border:0' role=\"tab\" aria-controls=\"home\">\n",
    "<center>Modeling</center></h3>\n",
    "\n",
    "\n",
    "Modeling is the part of the Cross-Industry Standard Process for Data Mining (CRISP-DM) process model that i like best. Our data is already in good shape, and now we can search for useful patterns in our data.\n",
    "\n",
    "\n",
    "Tasks\n",
    "\n",
    "1. Select modeling technique Select technique\n",
    "\n",
    "2. Generate test design\n",
    "\n",
    "3. Build model\n",
    "\n",
    "4. Assess model\n",
    "\n",
    "This Step is done here : \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b674468c",
   "metadata": {
    "papermill": {
     "duration": 0.042013,
     "end_time": "2022-01-10T21:01:10.569378",
     "exception": false,
     "start_time": "2022-01-10T21:01:10.527365",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=5></a>\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style='color:white; background:#1777C4; border:0' role=\"tab\" aria-controls=\"home\">\n",
    "<center>Evaluation</center></h3>\n",
    "\n",
    "# Model accuracy scoring\n",
    "\n",
    "The easiest way to analyze performance is with accuracy. \n",
    "It measures how many observations, both positive and negative, were correctly classified.\n",
    "\n",
    "\n",
    "You shouldn’t use accuracy on imbalanced problems. Then, it is easy to get a high accuracy score by simply classifying all observations as the majority class. For example in our case, by classifying all transactions as non-fraudulent we can get an accuracy of over 0.9.\n",
    "\n",
    "**When to use it:**\n",
    "\n",
    "    When your problem is balanced using accuracy is usually a good start. An additional benefit is that it is really easy to explain it to non-technical stakeholders in your project,\n",
    "    When every class is equally important to you.\n",
    "\n",
    "# Confusion Matrix\n",
    "\n",
    "**How to compute:**\n",
    "\n",
    "It is a common way of presenting true positive (tp), true negative (tn), false positive (fp) and false negative (fn) predictions. Those values are presented in the form of a matrix where the Y-axis shows the true classes while the X-axis shows the predicted classes.\n",
    "\n",
    "It is calculated on class predictions, which means the outputs from your model need to be thresholded first.\n",
    "\n",
    "**When to use it:**\n",
    "\n",
    "    Pretty much always. I like to see the nominal values rather than normalized to get a feeling on how the model is doing on different, often imbalanced, classes.\n",
    "\n",
    "\n",
    "\n",
    "# ROC Curve\n",
    "\n",
    "\n",
    "It is a chart that visualizes the tradeoff between true positive rate (TPR) and false positive rate (FPR). Basically, for every threshold, we calculate TPR and FPR and plot it on one chart.\n",
    "\n",
    "Of course, the higher TPR and the lower FPR is for each threshold the better and so classifiers that have curves that are more top-left side are better.\n",
    "\n",
    "Since we have an imbalanced data set, Receiver Operating Characteristic Curves are not that useful although it's an expected output of most binary classifiers.\n",
    "Because you can generate a pretty good-looking curve by just simply guessing each one is the non-fraud case.\n",
    "\n",
    "**When to use it:**\n",
    "\n",
    "    You should use it when you ultimately care about ranking predictions and not necessarily about outputting well-calibrated probabilities (read this article by Jason Brownlee if you want to learn about probability calibration).\n",
    "    You should not use it when your data is heavily imbalanced. It was discussed extensively in this article by Takaya Saito and Marc Rehmsmeier. The intuition is the following: false positive rate for highly imbalanced datasets is pulled down due to a large number of true negatives.\n",
    "    You should use it when you care equally about positive and negative classes.. If we care about true negatives as much as we care about true positives then it totally makes sense to use ROC AUC.\n",
    "    \n",
    "# ROC AUC score   \n",
    "AUC - ROC curve is a performance measurement for the classification problems at various threshold settings. ROC is a probability curve and AUC represents the degree or measure of separability. It tells how much the model is capable of distinguishing between classes. Higher the AUC, the better the model is at predicting 0 classes as 0 and 1 classes as 1. By analogy, the Higher the AUC, the better the model is at distinguishing between patients with the disease and no disease. The ROC curve is plotted with TPR against the FPR where TPR is on the y-axis and FPR is on the x-axis.\n",
    "\n",
    "**When to use it:**\n",
    "\n",
    "    You should use it when you ultimately care about ranking predictions and not necessarily about outputting well-calibrated probabilities (read this article by Jason Brownlee if you want to learn about probability calibration).\n",
    "    You should not use it when your data is heavily imbalanced. It was discussed extensively in this article by Takaya Saito and Marc Rehmsmeier. The intuition is the following: false positive rate for highly imbalanced datasets is pulled down due to a large number of true negatives.\n",
    "    You should use it when you care equally about positive and negative classes. It naturally extends the imbalanced data discussion from the last section. If we care about true negatives as much as we care about true positives then it totally makes sense to use ROC AUC.\n",
    "\n",
    "# Recall    \n",
    "It measures how many observations out of all positive observations have we classified as positive. It tells us how many fraudulent transactions we recalled from all fraudulent transactions.\n",
    "true positive rate\n",
    "\n",
    "When you are optimizing recall you want to put all guilty in prison.\n",
    "**When to use it:**\n",
    "\n",
    "    Usually, you will not use it alone but rather coupled with other metrics like precision.\n",
    "    That being said, recall is a go-to metric, when you really care about catching all fraudulent transactions even at a cost of false alerts. Potentially it is cheap for you to process those alerts and very expensive when the transaction goes unseen.\n",
    "    \n",
    "# Precision\n",
    "\n",
    "It measures how many observations predicted as positive are in fact positive. Taking our fraud detection example, it tells us what is the ratio of transactions correctly classified as fraudulent.\n",
    "positive predictive value\n",
    "\n",
    "When you are optimizing precision you want to make sure that people that you put in prison are guilty. \n",
    "\n",
    "**When to use it:**\n",
    "\n",
    "    Again, it usually doesn’t make sense to use it alone but rather coupled with other metrics like recall.\n",
    "    When raising false alerts is costly, when you want all the positive predictions to be worth looking at you should optimize for precision.\n",
    "    \n",
    "\n",
    "\n",
    "**Precision vs. Recall for Imbalanced Classification:**\n",
    "\n",
    "You may decide to use precision or recall on your imbalanced classification problem.\n",
    "\n",
    "Maximizing precision will minimize the number false positives, whereas maximizing the recall will minimize the number of false negatives.\n",
    "\n",
    "    Precision: Appropriate when minimizing false positives is the focus.\n",
    "    Recall: Appropriate when minimizing false negatives is the focus.\n",
    "\n",
    "Sometimes, we want excellent predictions of the positive class. We want high precision and high recall.\n",
    "\n",
    "This can be challenging, as often increases in recall often come at the expense of decreases in precision.\n",
    "\n",
    "    In imbalanced datasets, the goal is to improve recall without hurting precision. These goals, however, are often conflicting, since in order to increase the TP for the minority class, the number of FP is also often increased, resulting in reduced precision.\n",
    "    \n",
    "    \n",
    "# PR AUC score | Average precision\n",
    "\n",
    "Similarly to ROC AUC score you can calculate the Area Under the Precision-Recall Curve to get one number that describes model performance.\n",
    "\n",
    "You can also think about PR AUC as the average of precision scores calculated for each recall threshold [0.0, 1.0]. You can also adjust this definition to suit your business needs by choosing/clipping recall thresholds if needed.\n",
    "\n",
    "**When to use it:**\n",
    "\n",
    "    when you want to communicate precision/recall decision to other stakeholders\n",
    "    when you want to choose the threshold that fits the business problem.\n",
    "    when your data is heavily imbalanced. As mentioned before, it was discussed extensively in this article by Takaya Saito and Marc Rehmsmeier. The intuition is the following: since PR AUC focuses mainly on the positive class (PPV and TPR) it cares less about the frequent negative class.\n",
    "    when you care more about positive than negative class. If you care more about the positive class and hence PPV and TPR you should go with Precision-Recall curve and PR AUC (average precision).\n",
    "    \n",
    "# F beta score\n",
    "\n",
    "Simply put, it combines precision and recall into one metric. The higher the score the better our model is. You can calculate it in the following way:\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "When choosing beta in your F-beta score the more you care about recall over precision the higher beta you should choose. For example, with F1 score we care equally about recall and precision with F2 score, recall is twice as important to us.\n",
    "F beta by beta\n",
    "\n",
    "With 0<beta<1 we care more about precision and so the higher the threshold the higher the F beta score. When beta>1 our optimal threshold moves toward lower thresholds and with beta=1 it is somewhere in the middle.  \n",
    "\n",
    "**When to use it:**\n",
    "\n",
    "    Pretty much in every binary classification problem. It is my go-to metric when working on those problems. It can be easily explained to business stakeholders.\n",
    "    \n",
    " for more details see this article:\n",
    " [https://neptune.ai/blog/evaluation-metrics-binary-classification](http://)  \n",
    " \n",
    " https://neptune.ai/blog/f1-score-accuracy-roc-auc-pr-auc\n",
    " \n",
    "==>Complete evaluation will be done when we train the model on all data that we have and with the best tuned model."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f822e98",
   "metadata": {
    "papermill": {
     "duration": 0.041879,
     "end_time": "2022-01-10T21:01:10.653839",
     "exception": false,
     "start_time": "2022-01-10T21:01:10.611960",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id=6></a>\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style='color:white; background:#1777C4; border:0' role=\"tab\" aria-controls=\"home\">\n",
    "<center>Deploy</center></h3>\n",
    "\n",
    "The deployment of machine learning models is the process for making models available in production environments, where they can provide predictions to other software systems.\n",
    "\n",
    "●One of the last stages in the Machine Learning Lifecycle.\n",
    "\n",
    "●Potentially the most challenging stage.\n",
    "\n",
    "●Challenges of traditional software\n",
    "\n",
    "oReliability\n",
    "oReusability\n",
    "oMaintainability\n",
    "oFlexibility\n",
    "\n",
    "●Additional challenges specific to Machine Learning\n",
    "\n",
    "oReproducibility\n",
    "\n",
    "Needs coordination of data scientists, IT teams, software developers and business professionals:\n",
    "\n",
    "oEnsure model works reliably\n",
    "oEnsure model delivers the intended result.\n",
    "\n",
    "●Potential discrepancy between programming language in which the model is developed and the production system language.\n",
    "\n",
    "oRe-coding the model extends the project timeline and risks lack of reproducibility\n",
    "\n",
    "Why is Model Deployment important?\n",
    "\n",
    "●To start using a Machine Learning Model, it needs to be effectively deployed into production, so that they can provide predictions to other software systems.\n",
    "\n",
    "●To maximize the value of the Machine Learning Model, we need to be able to reliably extract the predictions and share them with other systems.\n",
    "\n",
    "\n",
    "**Research Environment**\n",
    "\n",
    "●The Research Environment is a setting with tools, programs and software suitable for data analysis and the development of machine learning models.\n",
    "\n",
    "●Here, we develop the Machine Learning Models and identify their value.\n",
    "Its done by a data scientist : i prefer work on jupyter for this phase .\n",
    "\n",
    "**Production Environment**\n",
    "\n",
    "●The Production Environment is a real-time setting with running programs and hardware setups that allow the organization’s daily operations.\n",
    "\n",
    "●It’s the place where the machine learning models is actually available for business use.\n",
    "\n",
    "●It allows organisations to show clients a “live” service.\n",
    "This job is done by solid sofware+ml engineer+ devops team\n",
    "\n",
    "\n",
    "\n",
    "we have 4 ways to deploy models .\n",
    "ML System Architectures:\n",
    "1. Model embedded in application\n",
    "\n",
    "2. Served via a dedicated service\n",
    "\n",
    "3. Model published as data(streaming)\n",
    "\n",
    "4. Batch prediction (offline process)\n",
    "\n",
    "\n",
    "I developed  a baseline how to deploy model using Fastapi+docker on herokou :\n",
    "\n",
    "https://github.com/DeepSparkChaker/FraudDetection_Fastapi\n",
    "\n",
    "\n",
    "Complete deployment of our model is done here : \n",
    "<a id=7></a>\n",
    "<h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" style='color:white; background:#1777C4; border:0' role=\"tab\" aria-controls=\"home\">\n",
    "<center>Summary</center></h3> \n",
    "\n",
    "We had developed end-to-end machine learning using the CRISP_DM methodology. Work still in progress. Always keep in mind that the data science / ML project must be done as a team and iteratively in order to properly exploit our data and add value to our business. Also keep in mind that AI helps you make the decision by using the added value extracted from the data but not the accountability. So we have to keep in mind to always use a composite AI in order to make the final decision.\n",
    "\n",
    "References :\n",
    "\n",
    "https://developer.nvidia.com/blog/leveraging-machine-learning-to-detect-fraud-tips-to-developing-a-winning-kaggle-solution/\n",
    "\n",
    "python guidline : \n",
    "\n",
    "https://gist.github.com/sloria/7001839\n",
    "\n",
    "features  selections :\n",
    "\n",
    "https://www.kaggle.com/sz8416/6-ways-for-feature-selection\n",
    "\n",
    "https://pub.towardsai.net/feature-selection-and-removing-in-machine-learning-dd3726f5865c\n",
    "\n",
    "https://www.kaggle.com/bannourchaker/1-featuresengineer-selectionpart1?scriptVersionId=72906910\n",
    "\n",
    "Cripspdm :\n",
    "https://www.kaggle.com/bannourchaker/4-featureengineer-featuresselectionpart4?scriptVersionId=73374083\n",
    "\n",
    "Quanrile transformer : \n",
    "\n",
    "https://machinelearningmastery.com/quantile-transforms-for-machine-learning/\n",
    "\n",
    "Best link for all : \n",
    "\n",
    "https://neptune.ai/blog/tabular-data-binary-classification-tips-and-tricks-from-5-kaggle-competitions\n",
    "\n",
    "complete guide Stacking :\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2021/08/ensemble-stacking-for-machine-learning-and-deep-learning/\n",
    "\n",
    "https://neptune.ai/blog/ensemble-learning-guide\n",
    "\n",
    "https://www.kaggle.com/prashant111/adaboost-classifier-tutorial\n",
    "\n",
    "\n",
    "Missing : \n",
    "\n",
    "https://www.kaggle.com/dansbecker/handling-missing-values\n",
    "\n",
    "Binning : \n",
    "\n",
    "https://heartbeat.fritz.ai/hands-on-with-feature-engineering-techniques-variable-discretization-7deb6a5c6e27\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2020/10/getting-started-with-feature-engineering/\n",
    "\n",
    "Cat :\n",
    "\n",
    "https://innovation.alteryx.com/encode-smarter/\n",
    "\n",
    "https://github.com/alteryx/categorical_encoding/blob/main/guides/notebooks/categorical-encoding-guide.ipynb\n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2020/08/types-of-categorical-data-encoding/\n",
    "\n",
    "https://maxhalford.github.io/blog/target-encoding/\n",
    "\n",
    "\n",
    "Choice of kmeans : \n",
    "\n",
    "https://www.analyticsvidhya.com/blog/2021/05/k-mean-getting-the-optimal-number-of-clusters/\n",
    "\n",
    "Imputation : \n",
    "\n",
    "https://machinelearningmastery.com/knn-imputation-for-missing-values-in-machine-learning/\n",
    "\n",
    "https://machinelearningmastery.com/iterative-imputation-for-missing-values-in-machine-learning/\n",
    "\n",
    "Choice of  roc vs precssion_recall : \n",
    "\n",
    "https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/\n",
    "\n",
    "\n",
    "https://machinelearningmastery.com/roc-curves-and-precision-recall-curves-for-classification-in-python/\n",
    "\n",
    "\n",
    "How to tune for he futur work : \n",
    "\n",
    "https://www.kaggle.com/hamidrezabakhtaki/xgboost-catboost-lighgbm-optuna-final-submission\n",
    "\n",
    "https://www.kaggle.com/bextuychiev/lgbm-optuna-hyperparameter-tuning-w-understanding\n",
    "\n",
    "\n",
    "\n",
    "Deploy:\n",
    "\n",
    "https://towardsdatascience.com/from-jupyter-notebook-to-deployment-a-straightforward-example-1838c203a437\n",
    "\n",
    " https://github.com/DeepSparkChaker/Titanic_Deep_Spark/blob/main/app.py\n",
    "https://github.com/Kunal-Varma/Deployment-of-ML-model-using-FASTAPI/tree/2cc0319abbec469010a5139f460004f2a75a7482\n",
    "https://realpython.com/fastapi-python-web-apis/\n",
    " https://github.com/tiangolo/fastapi/issues/3373\n",
    " https://www.freecodecamp.org/news/data-science-and-machine-learning-project-house-prices/\n",
    "https://github.com/tiangolo/fastapi/issues/1616\n",
    "https://stackoverflow.com/questions/68244582/display-dataframe-as-fastapi-output\n",
    "https://www.kaggle.com/sakshigoyal7/credit-card-customers\n",
    "https://github.com/renanmouraf/data-science-house-prices    \n",
    "https://towardsdatascience.com/data-science-quick-tips-012-creating-a-machine-learning-inference-api-with-fastapi-bb6bcd0e6b01\n",
    "https://towardsdatascience.com/how-to-build-and-deploy-a-machine-learning-model-with-fastapi-64c505213857\n",
    "https://analyticsindiamag.com/complete-hands-on-guide-to-fastapi-with-machine-learning-deployment/\n",
    "\n",
    "https://github.com/shaz13/katana/blob/develop/Dockerfile\n",
    "\n",
    "\n",
    "https://github.com/TripathiAshutosh/FastAPI/blob/main/main.py\n",
    "\n",
    "Best practices : \n",
    "    \n",
    "https://theaisummer.com/best-practices-deep-learning-code/    \n",
    "https://github.com/The-AI-Summer/Deep-Learning-In-Production/tree/master/2.%20Writing%20Deep%20Learning%20code:%20Best%20Practises\n",
    "\n",
    " Docker :\n",
    " \n",
    " https://towardsdatascience.com/docker-in-pieces-353525ec39b0?fbclid=IwAR102sks2L0vRTde2qz1g4I4NhqXxnoqfV4IFzmZke4DvGcuiuYhj25eVSY\n",
    " \n",
    "https://github.com/dkhundley/ds-quick-tips/blob/master/012_dockerizing_fastapi/Dockerfile\n",
    "\n",
    "\n",
    " Deploy + scaling :\n",
    "https://towardsdatascience.com/deploying-ml-models-in-production-with-fastapi-and-celery-7063e539a5db\n",
    "https://github.com/jonathanreadshaw/ServingMLFastCelery\n",
    "\n",
    "https://github.com/trainindata/deploying-machine-learning-models/blob/aaeb3e65d0a58ad583289aaa39b089f11d06a4eb/section-04-research-and-development/07-feature-engineering-pipeline.ipynb\n",
    "\n",
    "Ml OPS : \n",
    "https://www.linkedin.com/posts/vipulppatel_getting-started-with-mlops-21-page-tutorial-activity-6863895411837415424-dWMh/?fbclid=IwAR3Y4clbzujS_s2FFWg3tTYMKaGhh3vo25NUyoVdKHAJ7zynmCTNtzlHQ4M\n",
    "\n",
    "https://towardsai.net/p/machine-learning/mlops-demystified?utm_source=twitter&utm_medium=social&utm_campaign=rop-content-recycle&fbclid=IwAR3MimsSXCFq3GqiLKoaQqXbeb3bkSwKhSkfQSKT_c1gsHDMGSBAv63s7Po\n",
    "https://www.youtube.com/watch?v=9I8X-3HIErc\n",
    "\n",
    "https://pub.towardsai.net/deployment-ml-ops-guide-series-2-69d4a13b0dcf\n",
    "\n",
    "Publish to medium : \n",
    "\n",
    "https://towardsai.net/p/data-science/how-to-publish-a-jupyter-notebook-as-a-medium-blogpost?utm_source=twitter&utm_medium=social&utm_campaign=rop-content-recycle&fbclid=IwAR2-an7kknO3bsI5xjRdjL3jiwuPy7MBN5lVBc6fzx15mGY2iLS5KndCYWc\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.12"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 525.12699,
   "end_time": "2022-01-10T21:01:11.605904",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2022-01-10T20:52:26.478914",
   "version": "2.3.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}