{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ArbitraryNumberImputer\n", "\n", "\n", "ArbitraryNumberImputer replaces NA by an arbitrary value. It works for numerical variables. The arbitrary value needs to be defined by the user.\n", "\n", "**For this demonstration, we use the Ames House Prices dataset produced by Professor Dean De Cock:**\n", "\n", "[Dean De Cock (2011) Ames, Iowa: Alternative to the Boston Housing\n", "Data as an End of Semester Regression Project, Journal of Statistics Education, Vol.19, No. 3](http://jse.amstat.org/v19n3/decock.pdf)\n", "\n", "The version of the dataset used in this notebook can be obtained from [Kaggle](https://www.kaggle.com/c/house-prices-advanced-regression-techniques/data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Version" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'1.2.0'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Make sure you are using this \n", "# Feature-engine version.\n", "\n", "import feature_engine\n", "\n", "feature_engine.__version__" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.model_selection import train_test_split\n", "\n", "from feature_engine.imputation import ArbitraryNumberImputer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load data" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", " | Id | \n", "MSSubClass | \n", "MSZoning | \n", "LotFrontage | \n", "LotArea | \n", "Street | \n", "Alley | \n", "LotShape | \n", "LandContour | \n", "Utilities | \n", "... | \n", "PoolArea | \n", "PoolQC | \n", "Fence | \n", "MiscFeature | \n", "MiscVal | \n", "MoSold | \n", "YrSold | \n", "SaleType | \n", "SaleCondition | \n", "SalePrice | \n", "
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | \n", "1 | \n", "60 | \n", "RL | \n", "65.0 | \n", "8450 | \n", "Pave | \n", "NaN | \n", "Reg | \n", "Lvl | \n", "AllPub | \n", "... | \n", "0 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0 | \n", "2 | \n", "2008 | \n", "WD | \n", "Normal | \n", "208500 | \n", "
1 | \n", "2 | \n", "20 | \n", "RL | \n", "80.0 | \n", "9600 | \n", "Pave | \n", "NaN | \n", "Reg | \n", "Lvl | \n", "AllPub | \n", "... | \n", "0 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0 | \n", "5 | \n", "2007 | \n", "WD | \n", "Normal | \n", "181500 | \n", "
2 | \n", "3 | \n", "60 | \n", "RL | \n", "68.0 | \n", "11250 | \n", "Pave | \n", "NaN | \n", "IR1 | \n", "Lvl | \n", "AllPub | \n", "... | \n", "0 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0 | \n", "9 | \n", "2008 | \n", "WD | \n", "Normal | \n", "223500 | \n", "
3 | \n", "4 | \n", "70 | \n", "RL | \n", "60.0 | \n", "9550 | \n", "Pave | \n", "NaN | \n", "IR1 | \n", "Lvl | \n", "AllPub | \n", "... | \n", "0 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0 | \n", "2 | \n", "2006 | \n", "WD | \n", "Abnorml | \n", "140000 | \n", "
4 | \n", "5 | \n", "60 | \n", "RL | \n", "84.0 | \n", "14260 | \n", "Pave | \n", "NaN | \n", "IR1 | \n", "Lvl | \n", "AllPub | \n", "... | \n", "0 | \n", "NaN | \n", "NaN | \n", "NaN | \n", "0 | \n", "12 | \n", "2008 | \n", "WD | \n", "Normal | \n", "250000 | \n", "
5 rows × 81 columns
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