diff --git a/Advanced Regrassion/Advanced Regression.ipynb b/Advanced Regrassion/Advanced Regression.ipynb
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+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "import seaborn as sns\n",
+ "import pandas as pd\n",
+ "import matplotlib.pyplot as plt\n",
+ "from scipy import stats\n",
+ "%matplotlib inline"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Load datasets\n",
+ "train = pd.read_csv('./train.csv')\n",
+ "test = pd.read_csv('./test.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \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",
+ "
\n",
+ " \n",
+ " \n",
+ " \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",
+ "
\n",
+ " \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",
+ "
\n",
+ " \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",
+ "
\n",
+ " \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",
+ "
\n",
+ " \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",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 81 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
+ "0 1 60 RL 65.0 8450 Pave NaN Reg \n",
+ "1 2 20 RL 80.0 9600 Pave NaN Reg \n",
+ "2 3 60 RL 68.0 11250 Pave NaN IR1 \n",
+ "3 4 70 RL 60.0 9550 Pave NaN IR1 \n",
+ "4 5 60 RL 84.0 14260 Pave NaN IR1 \n",
+ "\n",
+ " LandContour Utilities ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold \\\n",
+ "0 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n",
+ "1 Lvl AllPub ... 0 NaN NaN NaN 0 5 \n",
+ "2 Lvl AllPub ... 0 NaN NaN NaN 0 9 \n",
+ "3 Lvl AllPub ... 0 NaN NaN NaN 0 2 \n",
+ "4 Lvl AllPub ... 0 NaN NaN NaN 0 12 \n",
+ "\n",
+ " YrSold SaleType SaleCondition SalePrice \n",
+ "0 2008 WD Normal 208500 \n",
+ "1 2007 WD Normal 181500 \n",
+ "2 2008 WD Normal 223500 \n",
+ "3 2006 WD Abnorml 140000 \n",
+ "4 2008 WD Normal 250000 \n",
+ "\n",
+ "[5 rows x 81 columns]"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Id | \n",
+ " MSSubClass | \n",
+ " MSZoning | \n",
+ " LotFrontage | \n",
+ " LotArea | \n",
+ " Street | \n",
+ " Alley | \n",
+ " LotShape | \n",
+ " LandContour | \n",
+ " Utilities | \n",
+ " ... | \n",
+ " ScreenPorch | \n",
+ " PoolArea | \n",
+ " PoolQC | \n",
+ " Fence | \n",
+ " MiscFeature | \n",
+ " MiscVal | \n",
+ " MoSold | \n",
+ " YrSold | \n",
+ " SaleType | \n",
+ " SaleCondition | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 | \n",
+ " 1461 | \n",
+ " 20 | \n",
+ " RH | \n",
+ " 80.0 | \n",
+ " 11622 | \n",
+ " Pave | \n",
+ " NaN | \n",
+ " Reg | \n",
+ " Lvl | \n",
+ " AllPub | \n",
+ " ... | \n",
+ " 120 | \n",
+ " 0 | \n",
+ " NaN | \n",
+ " MnPrv | \n",
+ " NaN | \n",
+ " 0 | \n",
+ " 6 | \n",
+ " 2010 | \n",
+ " WD | \n",
+ " Normal | \n",
+ "
\n",
+ " \n",
+ " 1 | \n",
+ " 1462 | \n",
+ " 20 | \n",
+ " RL | \n",
+ " 81.0 | \n",
+ " 14267 | \n",
+ " Pave | \n",
+ " NaN | \n",
+ " IR1 | \n",
+ " Lvl | \n",
+ " AllPub | \n",
+ " ... | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " Gar2 | \n",
+ " 12500 | \n",
+ " 6 | \n",
+ " 2010 | \n",
+ " WD | \n",
+ " Normal | \n",
+ "
\n",
+ " \n",
+ " 2 | \n",
+ " 1463 | \n",
+ " 60 | \n",
+ " RL | \n",
+ " 74.0 | \n",
+ " 13830 | \n",
+ " Pave | \n",
+ " NaN | \n",
+ " IR1 | \n",
+ " Lvl | \n",
+ " AllPub | \n",
+ " ... | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " NaN | \n",
+ " MnPrv | \n",
+ " NaN | \n",
+ " 0 | \n",
+ " 3 | \n",
+ " 2010 | \n",
+ " WD | \n",
+ " Normal | \n",
+ "
\n",
+ " \n",
+ " 3 | \n",
+ " 1464 | \n",
+ " 60 | \n",
+ " RL | \n",
+ " 78.0 | \n",
+ " 9978 | \n",
+ " Pave | \n",
+ " NaN | \n",
+ " IR1 | \n",
+ " Lvl | \n",
+ " AllPub | \n",
+ " ... | \n",
+ " 0 | \n",
+ " 0 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 0 | \n",
+ " 6 | \n",
+ " 2010 | \n",
+ " WD | \n",
+ " Normal | \n",
+ "
\n",
+ " \n",
+ " 4 | \n",
+ " 1465 | \n",
+ " 120 | \n",
+ " RL | \n",
+ " 43.0 | \n",
+ " 5005 | \n",
+ " Pave | \n",
+ " NaN | \n",
+ " IR1 | \n",
+ " HLS | \n",
+ " AllPub | \n",
+ " ... | \n",
+ " 144 | \n",
+ " 0 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 0 | \n",
+ " 1 | \n",
+ " 2010 | \n",
+ " WD | \n",
+ " Normal | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
5 rows × 80 columns
\n",
+ "
"
+ ],
+ "text/plain": [
+ " Id MSSubClass MSZoning LotFrontage LotArea Street Alley LotShape \\\n",
+ "0 1461 20 RH 80.0 11622 Pave NaN Reg \n",
+ "1 1462 20 RL 81.0 14267 Pave NaN IR1 \n",
+ "2 1463 60 RL 74.0 13830 Pave NaN IR1 \n",
+ "3 1464 60 RL 78.0 9978 Pave NaN IR1 \n",
+ "4 1465 120 RL 43.0 5005 Pave NaN IR1 \n",
+ "\n",
+ " LandContour Utilities ... ScreenPorch PoolArea PoolQC Fence MiscFeature \\\n",
+ "0 Lvl AllPub ... 120 0 NaN MnPrv NaN \n",
+ "1 Lvl AllPub ... 0 0 NaN NaN Gar2 \n",
+ "2 Lvl AllPub ... 0 0 NaN MnPrv NaN \n",
+ "3 Lvl AllPub ... 0 0 NaN NaN NaN \n",
+ "4 HLS AllPub ... 144 0 NaN NaN NaN \n",
+ "\n",
+ " MiscVal MoSold YrSold SaleType SaleCondition \n",
+ "0 0 6 2010 WD Normal \n",
+ "1 12500 6 2010 WD Normal \n",
+ "2 0 3 2010 WD Normal \n",
+ "3 0 6 2010 WD Normal \n",
+ "4 0 1 2010 WD Normal \n",
+ "\n",
+ "[5 rows x 80 columns]"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.head()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Analys of Target values "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
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+ "