From 92a81b1aa109ff1312f080c25abc15c0b7813631 Mon Sep 17 00:00:00 2001
From: vladislavovich-d <48733961+vladislavovich-d@users.noreply.github.com>
Date: Tue, 28 May 2019 17:31:57 +0300
Subject: [PATCH 1/5] Add files via upload
---
Advanced Regrassion/Advanced Regression.ipynb | 1438 ++++++++++++++++
Advanced Regrassion/data_description.txt | 523 ++++++
Advanced Regrassion/sample_submission.csv | 1460 ++++++++++++++++
Advanced Regrassion/test.csv | 1460 ++++++++++++++++
Advanced Regrassion/train.csv | 1461 +++++++++++++++++
5 files changed, 6342 insertions(+)
create mode 100644 Advanced Regrassion/Advanced Regression.ipynb
create mode 100644 Advanced Regrassion/data_description.txt
create mode 100644 Advanced Regrassion/sample_submission.csv
create mode 100644 Advanced Regrassion/test.csv
create mode 100644 Advanced Regrassion/train.csv
diff --git a/Advanced Regrassion/Advanced Regression.ipynb b/Advanced Regrassion/Advanced Regression.ipynb
new file mode 100644
index 0000000..210b5be
--- /dev/null
+++ b/Advanced Regrassion/Advanced Regression.ipynb
@@ -0,0 +1,1438 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Importing libraries\n",
+ "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": 4,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Load data\n",
+ "train = pd.read_csv('./train.csv')\n",
+ "test = pd.read_csv('./test.csv')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "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": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "train.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "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": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "test.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1460, 81)"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "#Shape of train dataset\n",
+ "train.shape"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Target variable\n",
+ "Some analysis on target variable"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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\n",
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+ "