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Predicting Car Prices DQ Project
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predict_car_prices.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Predicting Car Prices"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<style>\n",
" div#notebook-container { width: 85%; }\n",
" div#menubar-container { width: 65%; }\n",
" div#maintoolbar-container { width: 99%; }\n",
"</style>\n"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from IPython.display import display, HTML\n",
"display(HTML(data=\"\"\"\n",
"<style>\n",
" div#notebook-container { width: 85%; }\n",
" div#menubar-container { width: 65%; }\n",
" div#maintoolbar-container { width: 99%; }\n",
"</style>\n",
"\"\"\"))\n",
"import datetime as dt\n",
"from itertools import permutations \n",
"from collections import Counter\n",
"\n",
"import numpy as np\n",
"from numpy import arange\n",
"from numpy.random import randint, seed, random\n",
"import pandas as pd\n",
"import pandas_profiling\n",
"\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import matplotlib.style as style\n",
"import seaborn as sns\n",
"\n",
"from scipy.stats import percentileofscore, chisquare, chi2_contingency\n",
"from scipy import stats\n",
"from scipy.spatial import distance\n",
"\n",
"from sklearn.neighbors import KNeighborsRegressor\n",
"from sklearn.metrics import mean_squared_error\n",
"from sklearn.model_selection import KFold, cross_val_score"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to the Data Set"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"columns = ['symboling', 'normalized-losses', 'make', 'fuel-type', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels', 'engine-location', \n",
" 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type', 'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', \n",
" 'stroke', 'compression-rate', 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n",
"cars = pd.read_csv('imports-85.data', names=columns)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"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>symboling</th>\n",
" <th>normalized-losses</th>\n",
" <th>make</th>\n",
" <th>fuel-type</th>\n",
" <th>aspiration</th>\n",
" <th>num-of-doors</th>\n",
" <th>body-style</th>\n",
" <th>drive-wheels</th>\n",
" <th>engine-location</th>\n",
" <th>wheel-base</th>\n",
" <th>...</th>\n",
" <th>engine-size</th>\n",
" <th>fuel-system</th>\n",
" <th>bore</th>\n",
" <th>stroke</th>\n",
" <th>compression-rate</th>\n",
" <th>horsepower</th>\n",
" <th>peak-rpm</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>?</td>\n",
" <td>alfa-romero</td>\n",
" <td>gas</td>\n",
" <td>std</td>\n",
" <td>two</td>\n",
" <td>convertible</td>\n",
" <td>rwd</td>\n",
" <td>front</td>\n",
" <td>88.6</td>\n",
" <td>...</td>\n",
" <td>130</td>\n",
" <td>mpfi</td>\n",
" <td>3.47</td>\n",
" <td>2.68</td>\n",
" <td>9.0</td>\n",
" <td>111</td>\n",
" <td>5000</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>13495</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>?</td>\n",
" <td>alfa-romero</td>\n",
" <td>gas</td>\n",
" <td>std</td>\n",
" <td>two</td>\n",
" <td>convertible</td>\n",
" <td>rwd</td>\n",
" <td>front</td>\n",
" <td>88.6</td>\n",
" <td>...</td>\n",
" <td>130</td>\n",
" <td>mpfi</td>\n",
" <td>3.47</td>\n",
" <td>2.68</td>\n",
" <td>9.0</td>\n",
" <td>111</td>\n",
" <td>5000</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>16500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>?</td>\n",
" <td>alfa-romero</td>\n",
" <td>gas</td>\n",
" <td>std</td>\n",
" <td>two</td>\n",
" <td>hatchback</td>\n",
" <td>rwd</td>\n",
" <td>front</td>\n",
" <td>94.5</td>\n",
" <td>...</td>\n",
" <td>152</td>\n",
" <td>mpfi</td>\n",
" <td>2.68</td>\n",
" <td>3.47</td>\n",
" <td>9.0</td>\n",
" <td>154</td>\n",
" <td>5000</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>16500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 26 columns</p>\n",
"</div>"
],
"text/plain": [
" symboling normalized-losses make fuel-type aspiration num-of-doors \\\n",
"0 3 ? alfa-romero gas std two \n",
"1 3 ? alfa-romero gas std two \n",
"2 1 ? alfa-romero gas std two \n",
"\n",
" body-style drive-wheels engine-location wheel-base ... engine-size \\\n",
"0 convertible rwd front 88.6 ... 130 \n",
"1 convertible rwd front 88.6 ... 130 \n",
"2 hatchback rwd front 94.5 ... 152 \n",
"\n",
" fuel-system bore stroke compression-rate horsepower peak-rpm city-mpg \\\n",
"0 mpfi 3.47 2.68 9.0 111 5000 21 \n",
"1 mpfi 3.47 2.68 9.0 111 5000 21 \n",
"2 mpfi 2.68 3.47 9.0 154 5000 19 \n",
"\n",
" highway-mpg price \n",
"0 27 13495 \n",
"1 27 16500 \n",
"2 26 16500 \n",
"\n",
"[3 rows x 26 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"numerical_values = ['normalized-losses', 'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-size', 'bore', 'stroke', 'compression-rate', \n",
" 'horsepower', 'peak-rpm', 'city-mpg', 'highway-mpg', 'price']\n",
"cars_numerical_values = cars[numerical_values]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"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>normalized-losses</th>\n",
" <th>wheel-base</th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>height</th>\n",
" <th>curb-weight</th>\n",
" <th>engine-size</th>\n",
" <th>bore</th>\n",
" <th>stroke</th>\n",
" <th>compression-rate</th>\n",
" <th>horsepower</th>\n",
" <th>peak-rpm</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>?</td>\n",
" <td>88.6</td>\n",
" <td>168.8</td>\n",
" <td>64.1</td>\n",
" <td>48.8</td>\n",
" <td>2548</td>\n",
" <td>130</td>\n",
" <td>3.47</td>\n",
" <td>2.68</td>\n",
" <td>9.0</td>\n",
" <td>111</td>\n",
" <td>5000</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>13495</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>?</td>\n",
" <td>88.6</td>\n",
" <td>168.8</td>\n",
" <td>64.1</td>\n",
" <td>48.8</td>\n",
" <td>2548</td>\n",
" <td>130</td>\n",
" <td>3.47</td>\n",
" <td>2.68</td>\n",
" <td>9.0</td>\n",
" <td>111</td>\n",
" <td>5000</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>16500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>?</td>\n",
" <td>94.5</td>\n",
" <td>171.2</td>\n",
" <td>65.5</td>\n",
" <td>52.4</td>\n",
" <td>2823</td>\n",
" <td>152</td>\n",
" <td>2.68</td>\n",
" <td>3.47</td>\n",
" <td>9.0</td>\n",
" <td>154</td>\n",
" <td>5000</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>16500</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" normalized-losses wheel-base length width height curb-weight \\\n",
"0 ? 88.6 168.8 64.1 48.8 2548 \n",
"1 ? 88.6 168.8 64.1 48.8 2548 \n",
"2 ? 94.5 171.2 65.5 52.4 2823 \n",
"\n",
" engine-size bore stroke compression-rate horsepower peak-rpm city-mpg \\\n",
"0 130 3.47 2.68 9.0 111 5000 21 \n",
"1 130 3.47 2.68 9.0 111 5000 21 \n",
"2 152 2.68 3.47 9.0 154 5000 19 \n",
"\n",
" highway-mpg price \n",
"0 27 13495 \n",
"1 27 16500 \n",
"2 26 16500 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars_numerical_values.head(3)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Cleaning"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 205 entries, 0 to 204\n",
"Data columns (total 15 columns):\n",
"normalized-losses 205 non-null object\n",
"wheel-base 205 non-null float64\n",
"length 205 non-null float64\n",
"width 205 non-null float64\n",
"height 205 non-null float64\n",
"curb-weight 205 non-null int64\n",
"engine-size 205 non-null int64\n",
"bore 205 non-null object\n",
"stroke 205 non-null object\n",
"compression-rate 205 non-null float64\n",
"horsepower 205 non-null object\n",
"peak-rpm 205 non-null object\n",
"city-mpg 205 non-null int64\n",
"highway-mpg 205 non-null int64\n",
"price 205 non-null object\n",
"dtypes: float64(5), int64(4), object(6)\n",
"memory usage: 24.1+ KB\n"
]
}
],
"source": [
"cars_numerical_values.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\Users\\Leo\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:4263: SettingWithCopyWarning: \n",
"A value is trying to be set on a copy of a slice from a DataFrame\n",
"\n",
"See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
" method=method,\n"
]
}
],
"source": [
"cars_numerical_values.replace('?', np.nan, inplace=True)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"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>normalized-losses</th>\n",
" <th>wheel-base</th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>height</th>\n",
" <th>curb-weight</th>\n",
" <th>engine-size</th>\n",
" <th>bore</th>\n",
" <th>stroke</th>\n",
" <th>compression-rate</th>\n",
" <th>horsepower</th>\n",
" <th>peak-rpm</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>NaN</td>\n",
" <td>88.6</td>\n",
" <td>168.8</td>\n",
" <td>64.1</td>\n",
" <td>48.8</td>\n",
" <td>2548</td>\n",
" <td>130</td>\n",
" <td>3.47</td>\n",
" <td>2.68</td>\n",
" <td>9.0</td>\n",
" <td>111</td>\n",
" <td>5000</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>13495</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>NaN</td>\n",
" <td>88.6</td>\n",
" <td>168.8</td>\n",
" <td>64.1</td>\n",
" <td>48.8</td>\n",
" <td>2548</td>\n",
" <td>130</td>\n",
" <td>3.47</td>\n",
" <td>2.68</td>\n",
" <td>9.0</td>\n",
" <td>111</td>\n",
" <td>5000</td>\n",
" <td>21</td>\n",
" <td>27</td>\n",
" <td>16500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>NaN</td>\n",
" <td>94.5</td>\n",
" <td>171.2</td>\n",
" <td>65.5</td>\n",
" <td>52.4</td>\n",
" <td>2823</td>\n",
" <td>152</td>\n",
" <td>2.68</td>\n",
" <td>3.47</td>\n",
" <td>9.0</td>\n",
" <td>154</td>\n",
" <td>5000</td>\n",
" <td>19</td>\n",
" <td>26</td>\n",
" <td>16500</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>164</td>\n",
" <td>99.8</td>\n",
" <td>176.6</td>\n",
" <td>66.2</td>\n",
" <td>54.3</td>\n",
" <td>2337</td>\n",
" <td>109</td>\n",
" <td>3.19</td>\n",
" <td>3.40</td>\n",
" <td>10.0</td>\n",
" <td>102</td>\n",
" <td>5500</td>\n",
" <td>24</td>\n",
" <td>30</td>\n",
" <td>13950</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>164</td>\n",
" <td>99.4</td>\n",
" <td>176.6</td>\n",
" <td>66.4</td>\n",
" <td>54.3</td>\n",
" <td>2824</td>\n",
" <td>136</td>\n",
" <td>3.19</td>\n",
" <td>3.40</td>\n",
" <td>8.0</td>\n",
" <td>115</td>\n",
" <td>5500</td>\n",
" <td>18</td>\n",
" <td>22</td>\n",
" <td>17450</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>200</td>\n",
" <td>95</td>\n",
" <td>109.1</td>\n",
" <td>188.8</td>\n",
" <td>68.9</td>\n",
" <td>55.5</td>\n",
" <td>2952</td>\n",
" <td>141</td>\n",
" <td>3.78</td>\n",
" <td>3.15</td>\n",
" <td>9.5</td>\n",
" <td>114</td>\n",
" <td>5400</td>\n",
" <td>23</td>\n",
" <td>28</td>\n",
" <td>16845</td>\n",
" </tr>\n",
" <tr>\n",
" <td>201</td>\n",
" <td>95</td>\n",
" <td>109.1</td>\n",
" <td>188.8</td>\n",
" <td>68.8</td>\n",
" <td>55.5</td>\n",
" <td>3049</td>\n",
" <td>141</td>\n",
" <td>3.78</td>\n",
" <td>3.15</td>\n",
" <td>8.7</td>\n",
" <td>160</td>\n",
" <td>5300</td>\n",
" <td>19</td>\n",
" <td>25</td>\n",
" <td>19045</td>\n",
" </tr>\n",
" <tr>\n",
" <td>202</td>\n",
" <td>95</td>\n",
" <td>109.1</td>\n",
" <td>188.8</td>\n",
" <td>68.9</td>\n",
" <td>55.5</td>\n",
" <td>3012</td>\n",
" <td>173</td>\n",
" <td>3.58</td>\n",
" <td>2.87</td>\n",
" <td>8.8</td>\n",
" <td>134</td>\n",
" <td>5500</td>\n",
" <td>18</td>\n",
" <td>23</td>\n",
" <td>21485</td>\n",
" </tr>\n",
" <tr>\n",
" <td>203</td>\n",
" <td>95</td>\n",
" <td>109.1</td>\n",
" <td>188.8</td>\n",
" <td>68.9</td>\n",
" <td>55.5</td>\n",
" <td>3217</td>\n",
" <td>145</td>\n",
" <td>3.01</td>\n",
" <td>3.40</td>\n",
" <td>23.0</td>\n",
" <td>106</td>\n",
" <td>4800</td>\n",
" <td>26</td>\n",
" <td>27</td>\n",
" <td>22470</td>\n",
" </tr>\n",
" <tr>\n",
" <td>204</td>\n",
" <td>95</td>\n",
" <td>109.1</td>\n",
" <td>188.8</td>\n",
" <td>68.9</td>\n",
" <td>55.5</td>\n",
" <td>3062</td>\n",
" <td>141</td>\n",
" <td>3.78</td>\n",
" <td>3.15</td>\n",
" <td>9.5</td>\n",
" <td>114</td>\n",
" <td>5400</td>\n",
" <td>19</td>\n",
" <td>25</td>\n",
" <td>22625</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>205 rows × 15 columns</p>\n",
"</div>"
],
"text/plain": [
" normalized-losses wheel-base length width height curb-weight \\\n",
"0 NaN 88.6 168.8 64.1 48.8 2548 \n",
"1 NaN 88.6 168.8 64.1 48.8 2548 \n",
"2 NaN 94.5 171.2 65.5 52.4 2823 \n",
"3 164 99.8 176.6 66.2 54.3 2337 \n",
"4 164 99.4 176.6 66.4 54.3 2824 \n",
".. ... ... ... ... ... ... \n",
"200 95 109.1 188.8 68.9 55.5 2952 \n",
"201 95 109.1 188.8 68.8 55.5 3049 \n",
"202 95 109.1 188.8 68.9 55.5 3012 \n",
"203 95 109.1 188.8 68.9 55.5 3217 \n",
"204 95 109.1 188.8 68.9 55.5 3062 \n",
"\n",
" engine-size bore stroke compression-rate horsepower peak-rpm city-mpg \\\n",
"0 130 3.47 2.68 9.0 111 5000 21 \n",
"1 130 3.47 2.68 9.0 111 5000 21 \n",
"2 152 2.68 3.47 9.0 154 5000 19 \n",
"3 109 3.19 3.40 10.0 102 5500 24 \n",
"4 136 3.19 3.40 8.0 115 5500 18 \n",
".. ... ... ... ... ... ... ... \n",
"200 141 3.78 3.15 9.5 114 5400 23 \n",
"201 141 3.78 3.15 8.7 160 5300 19 \n",
"202 173 3.58 2.87 8.8 134 5500 18 \n",
"203 145 3.01 3.40 23.0 106 4800 26 \n",
"204 141 3.78 3.15 9.5 114 5400 19 \n",
"\n",
" highway-mpg price \n",
"0 27 13495 \n",
"1 27 16500 \n",
"2 26 16500 \n",
"3 30 13950 \n",
"4 22 17450 \n",
".. ... ... \n",
"200 28 16845 \n",
"201 25 19045 \n",
"202 23 21485 \n",
"203 27 22470 \n",
"204 25 22625 \n",
"\n",
"[205 rows x 15 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars_numerical_values"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>wheel-base</th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>height</th>\n",
" <th>curb-weight</th>\n",
" <th>engine-size</th>\n",
" <th>compression-rate</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>205.000000</td>\n",
" <td>205.000000</td>\n",
" <td>205.000000</td>\n",
" <td>205.000000</td>\n",
" <td>205.000000</td>\n",
" <td>205.000000</td>\n",
" <td>205.000000</td>\n",
" <td>205.000000</td>\n",
" <td>205.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>98.756585</td>\n",
" <td>174.049268</td>\n",
" <td>65.907805</td>\n",
" <td>53.724878</td>\n",
" <td>2555.565854</td>\n",
" <td>126.907317</td>\n",
" <td>10.142537</td>\n",
" <td>25.219512</td>\n",
" <td>30.751220</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>6.021776</td>\n",
" <td>12.337289</td>\n",
" <td>2.145204</td>\n",
" <td>2.443522</td>\n",
" <td>520.680204</td>\n",
" <td>41.642693</td>\n",
" <td>3.972040</td>\n",
" <td>6.542142</td>\n",
" <td>6.886443</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>86.600000</td>\n",
" <td>141.100000</td>\n",
" <td>60.300000</td>\n",
" <td>47.800000</td>\n",
" <td>1488.000000</td>\n",
" <td>61.000000</td>\n",
" <td>7.000000</td>\n",
" <td>13.000000</td>\n",
" <td>16.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>94.500000</td>\n",
" <td>166.300000</td>\n",
" <td>64.100000</td>\n",
" <td>52.000000</td>\n",
" <td>2145.000000</td>\n",
" <td>97.000000</td>\n",
" <td>8.600000</td>\n",
" <td>19.000000</td>\n",
" <td>25.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>97.000000</td>\n",
" <td>173.200000</td>\n",
" <td>65.500000</td>\n",
" <td>54.100000</td>\n",
" <td>2414.000000</td>\n",
" <td>120.000000</td>\n",
" <td>9.000000</td>\n",
" <td>24.000000</td>\n",
" <td>30.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>102.400000</td>\n",
" <td>183.100000</td>\n",
" <td>66.900000</td>\n",
" <td>55.500000</td>\n",
" <td>2935.000000</td>\n",
" <td>141.000000</td>\n",
" <td>9.400000</td>\n",
" <td>30.000000</td>\n",
" <td>34.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>120.900000</td>\n",
" <td>208.100000</td>\n",
" <td>72.300000</td>\n",
" <td>59.800000</td>\n",
" <td>4066.000000</td>\n",
" <td>326.000000</td>\n",
" <td>23.000000</td>\n",
" <td>49.000000</td>\n",
" <td>54.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" wheel-base length width height curb-weight \\\n",
"count 205.000000 205.000000 205.000000 205.000000 205.000000 \n",
"mean 98.756585 174.049268 65.907805 53.724878 2555.565854 \n",
"std 6.021776 12.337289 2.145204 2.443522 520.680204 \n",
"min 86.600000 141.100000 60.300000 47.800000 1488.000000 \n",
"25% 94.500000 166.300000 64.100000 52.000000 2145.000000 \n",
"50% 97.000000 173.200000 65.500000 54.100000 2414.000000 \n",
"75% 102.400000 183.100000 66.900000 55.500000 2935.000000 \n",
"max 120.900000 208.100000 72.300000 59.800000 4066.000000 \n",
"\n",
" engine-size compression-rate city-mpg highway-mpg \n",
"count 205.000000 205.000000 205.000000 205.000000 \n",
"mean 126.907317 10.142537 25.219512 30.751220 \n",
"std 41.642693 3.972040 6.542142 6.886443 \n",
"min 61.000000 7.000000 13.000000 16.000000 \n",
"25% 97.000000 8.600000 19.000000 25.000000 \n",
"50% 120.000000 9.000000 24.000000 30.000000 \n",
"75% 141.000000 9.400000 30.000000 34.000000 \n",
"max 326.000000 23.000000 49.000000 54.000000 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars_numerical_values.describe()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"# convert all fields to float data type.\n",
"cars_numerical_values = cars_numerical_values.astype(float)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"normalized-losses 37\n",
"wheel-base 0\n",
"length 0\n",
"width 0\n",
"height 0\n",
"curb-weight 0\n",
"engine-size 0\n",
"bore 4\n",
"stroke 4\n",
"compression-rate 0\n",
"horsepower 2\n",
"peak-rpm 2\n",
"city-mpg 0\n",
"highway-mpg 0\n",
"price 0\n",
"dtype: int64"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# remove all price data points that are missing because this is what we're trying to predict\n",
"cars_numerical_values = cars_numerical_values.dropna(subset=['price'])\n",
"cars_numerical_values.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"normalized-losses 0\n",
"wheel-base 0\n",
"length 0\n",
"width 0\n",
"height 0\n",
"curb-weight 0\n",
"engine-size 0\n",
"bore 0\n",
"stroke 0\n",
"compression-rate 0\n",
"horsepower 0\n",
"peak-rpm 0\n",
"city-mpg 0\n",
"highway-mpg 0\n",
"price 0\n",
"dtype: int64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# replace null values with the means of its attribute\n",
"cars_numerical_values = cars_numerical_values.fillna(cars_numerical_values.mean())\n",
"cars_numerical_values.isnull().sum()"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>normalized-losses</th>\n",
" <th>wheel-base</th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>height</th>\n",
" <th>curb-weight</th>\n",
" <th>engine-size</th>\n",
" <th>bore</th>\n",
" <th>stroke</th>\n",
" <th>compression-rate</th>\n",
" <th>horsepower</th>\n",
" <th>peak-rpm</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>0.298429</td>\n",
" <td>0.058309</td>\n",
" <td>0.413433</td>\n",
" <td>0.324786</td>\n",
" <td>0.083333</td>\n",
" <td>0.411171</td>\n",
" <td>0.260377</td>\n",
" <td>0.664286</td>\n",
" <td>0.290476</td>\n",
" <td>0.125</td>\n",
" <td>0.294393</td>\n",
" <td>0.346939</td>\n",
" <td>0.222222</td>\n",
" <td>0.289474</td>\n",
" <td>13495.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>0.298429</td>\n",
" <td>0.058309</td>\n",
" <td>0.413433</td>\n",
" <td>0.324786</td>\n",
" <td>0.083333</td>\n",
" <td>0.411171</td>\n",
" <td>0.260377</td>\n",
" <td>0.664286</td>\n",
" <td>0.290476</td>\n",
" <td>0.125</td>\n",
" <td>0.294393</td>\n",
" <td>0.346939</td>\n",
" <td>0.222222</td>\n",
" <td>0.289474</td>\n",
" <td>16500.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>0.298429</td>\n",
" <td>0.230321</td>\n",
" <td>0.449254</td>\n",
" <td>0.444444</td>\n",
" <td>0.383333</td>\n",
" <td>0.517843</td>\n",
" <td>0.343396</td>\n",
" <td>0.100000</td>\n",
" <td>0.666667</td>\n",
" <td>0.125</td>\n",
" <td>0.495327</td>\n",
" <td>0.346939</td>\n",
" <td>0.166667</td>\n",
" <td>0.263158</td>\n",
" <td>16500.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" normalized-losses wheel-base length width height curb-weight \\\n",
"0 0.298429 0.058309 0.413433 0.324786 0.083333 0.411171 \n",
"1 0.298429 0.058309 0.413433 0.324786 0.083333 0.411171 \n",
"2 0.298429 0.230321 0.449254 0.444444 0.383333 0.517843 \n",
"\n",
" engine-size bore stroke compression-rate horsepower peak-rpm \\\n",
"0 0.260377 0.664286 0.290476 0.125 0.294393 0.346939 \n",
"1 0.260377 0.664286 0.290476 0.125 0.294393 0.346939 \n",
"2 0.343396 0.100000 0.666667 0.125 0.495327 0.346939 \n",
"\n",
" city-mpg highway-mpg price \n",
"0 0.222222 0.289474 13495.0 \n",
"1 0.222222 0.289474 16500.0 \n",
"2 0.166667 0.263158 16500.0 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# normalize the feature by using min-max scaling\n",
"price_column = cars_numerical_values['price']\n",
"cars_numerical_values = (cars_numerical_values - cars_numerical_values.min())/(cars_numerical_values.max() - cars_numerical_values.min())\n",
"cars_numerical_values['price'] = price_column\n",
"cars_numerical_values.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>normalized-losses</th>\n",
" <th>wheel-base</th>\n",
" <th>length</th>\n",
" <th>width</th>\n",
" <th>height</th>\n",
" <th>curb-weight</th>\n",
" <th>engine-size</th>\n",
" <th>bore</th>\n",
" <th>stroke</th>\n",
" <th>compression-rate</th>\n",
" <th>horsepower</th>\n",
" <th>peak-rpm</th>\n",
" <th>city-mpg</th>\n",
" <th>highway-mpg</th>\n",
" <th>price</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>count</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" <td>201.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>mean</td>\n",
" <td>0.298429</td>\n",
" <td>0.355598</td>\n",
" <td>0.494045</td>\n",
" <td>0.477697</td>\n",
" <td>0.497222</td>\n",
" <td>0.414145</td>\n",
" <td>0.248587</td>\n",
" <td>0.564793</td>\n",
" <td>0.565192</td>\n",
" <td>0.197767</td>\n",
" <td>0.258864</td>\n",
" <td>0.394934</td>\n",
" <td>0.338308</td>\n",
" <td>0.386489</td>\n",
" <td>13207.129353</td>\n",
" </tr>\n",
" <tr>\n",
" <td>std</td>\n",
" <td>0.167520</td>\n",
" <td>0.176862</td>\n",
" <td>0.183913</td>\n",
" <td>0.179613</td>\n",
" <td>0.203985</td>\n",
" <td>0.200658</td>\n",
" <td>0.156781</td>\n",
" <td>0.191480</td>\n",
" <td>0.150499</td>\n",
" <td>0.250310</td>\n",
" <td>0.174606</td>\n",
" <td>0.195148</td>\n",
" <td>0.178423</td>\n",
" <td>0.179346</td>\n",
" <td>7947.066342</td>\n",
" </tr>\n",
" <tr>\n",
" <td>min</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5118.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25%</td>\n",
" <td>0.188482</td>\n",
" <td>0.230321</td>\n",
" <td>0.383582</td>\n",
" <td>0.324786</td>\n",
" <td>0.350000</td>\n",
" <td>0.264158</td>\n",
" <td>0.139623</td>\n",
" <td>0.435714</td>\n",
" <td>0.495238</td>\n",
" <td>0.100000</td>\n",
" <td>0.102804</td>\n",
" <td>0.265306</td>\n",
" <td>0.166667</td>\n",
" <td>0.236842</td>\n",
" <td>7775.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50%</td>\n",
" <td>0.298429</td>\n",
" <td>0.303207</td>\n",
" <td>0.479104</td>\n",
" <td>0.444444</td>\n",
" <td>0.525000</td>\n",
" <td>0.359193</td>\n",
" <td>0.222642</td>\n",
" <td>0.550000</td>\n",
" <td>0.580952</td>\n",
" <td>0.125000</td>\n",
" <td>0.219626</td>\n",
" <td>0.394934</td>\n",
" <td>0.305556</td>\n",
" <td>0.368421</td>\n",
" <td>10295.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75%</td>\n",
" <td>0.376963</td>\n",
" <td>0.460641</td>\n",
" <td>0.632836</td>\n",
" <td>0.538462</td>\n",
" <td>0.641667</td>\n",
" <td>0.557797</td>\n",
" <td>0.301887</td>\n",
" <td>0.742857</td>\n",
" <td>0.638095</td>\n",
" <td>0.150000</td>\n",
" <td>0.317757</td>\n",
" <td>0.551020</td>\n",
" <td>0.472222</td>\n",
" <td>0.473684</td>\n",
" <td>16500.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>max</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>45400.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" normalized-losses wheel-base length width height \\\n",
"count 201.000000 201.000000 201.000000 201.000000 201.000000 \n",
"mean 0.298429 0.355598 0.494045 0.477697 0.497222 \n",
"std 0.167520 0.176862 0.183913 0.179613 0.203985 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.188482 0.230321 0.383582 0.324786 0.350000 \n",
"50% 0.298429 0.303207 0.479104 0.444444 0.525000 \n",
"75% 0.376963 0.460641 0.632836 0.538462 0.641667 \n",
"max 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
"\n",
" curb-weight engine-size bore stroke compression-rate \\\n",
"count 201.000000 201.000000 201.000000 201.000000 201.000000 \n",
"mean 0.414145 0.248587 0.564793 0.565192 0.197767 \n",
"std 0.200658 0.156781 0.191480 0.150499 0.250310 \n",
"min 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 0.264158 0.139623 0.435714 0.495238 0.100000 \n",
"50% 0.359193 0.222642 0.550000 0.580952 0.125000 \n",
"75% 0.557797 0.301887 0.742857 0.638095 0.150000 \n",
"max 1.000000 1.000000 1.000000 1.000000 1.000000 \n",
"\n",
" horsepower peak-rpm city-mpg highway-mpg price \n",
"count 201.000000 201.000000 201.000000 201.000000 201.000000 \n",
"mean 0.258864 0.394934 0.338308 0.386489 13207.129353 \n",
"std 0.174606 0.195148 0.178423 0.179346 7947.066342 \n",
"min 0.000000 0.000000 0.000000 0.000000 5118.000000 \n",
"25% 0.102804 0.265306 0.166667 0.236842 7775.000000 \n",
"50% 0.219626 0.394934 0.305556 0.368421 10295.000000 \n",
"75% 0.317757 0.551020 0.472222 0.473684 16500.000000 \n",
"max 1.000000 1.000000 1.000000 1.000000 45400.000000 "
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cars_numerical_values.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Univariate Model\n",
"A univariate model involves the analysis of a single variable (feature)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"def knn_train_test(train, target, k):\n",
" kf = KFold(n_splits=2, shuffle=True, random_state=1)\n",
" knn = KNeighborsRegressor(n_neighbors=k)\n",
" mses = cross_val_score(knn, train, target, scoring=\"neg_mean_squared_error\", cv=kf)\n",
" rmse = np.mean(np.sqrt(np.abs(mses)))\n",
" return rmse"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"engine-size 3364\n",
"horsepower 3983\n",
"curb-weight 4130\n",
"highway-mpg 4336\n",
"width 4480\n",
"city-mpg 4788\n",
"length 5645\n",
"wheel-base 5709\n",
"bore 6561\n",
"compression-rate 6875\n",
"normalized-losses 7482\n",
"peak-rpm 7721\n",
"height 7735\n",
"stroke 7768\n",
"dtype: int64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# loop through all features to see which performs best.\n",
"features = cars_numerical_values.columns.drop('price').to_list()\n",
"train = cars_numerical_values\n",
"target = cars_numerical_values['price']\n",
"k = 5\n",
"features_rmse = {}\n",
"\n",
"for feature in features:\n",
" rmse = knn_train_test(cars_numerical_values[[feature]], cars_numerical_values['price'], k)\n",
" features_rmse[feature] = int(rmse)\n",
"\n",
"# create a Series object from the dictionary so we can easily view the results and work better with the results\n",
"features_rmse = pd.Series(features_rmse)\n",
"features_rmse.sort_values()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"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>k</th>\n",
" <th>feature</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>normalized-losses</td>\n",
" <td>10243</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>wheel-base</td>\n",
" <td>5676</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>length</td>\n",
" <td>5289</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>width</td>\n",
" <td>4657</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>height</td>\n",
" <td>10146</td>\n",
" </tr>\n",
" <tr>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <td>415</td>\n",
" <td>30</td>\n",
" <td>compression-rate</td>\n",
" <td>7634</td>\n",
" </tr>\n",
" <tr>\n",
" <td>416</td>\n",
" <td>30</td>\n",
" <td>horsepower</td>\n",
" <td>5428</td>\n",
" </tr>\n",
" <tr>\n",
" <td>417</td>\n",
" <td>30</td>\n",
" <td>peak-rpm</td>\n",
" <td>7832</td>\n",
" </tr>\n",
" <tr>\n",
" <td>418</td>\n",
" <td>30</td>\n",
" <td>city-mpg</td>\n",
" <td>5596</td>\n",
" </tr>\n",
" <tr>\n",
" <td>419</td>\n",
" <td>30</td>\n",
" <td>highway-mpg</td>\n",
" <td>5359</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>420 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" k feature score\n",
"0 1 normalized-losses 10243\n",
"1 1 wheel-base 5676\n",
"2 1 length 5289\n",
"3 1 width 4657\n",
"4 1 height 10146\n",
".. .. ... ...\n",
"415 30 compression-rate 7634\n",
"416 30 horsepower 5428\n",
"417 30 peak-rpm 7832\n",
"418 30 city-mpg 5596\n",
"419 30 highway-mpg 5359\n",
"\n",
"[420 rows x 3 columns]"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k_range = range(1,31)\n",
"i_list = []\n",
"\n",
"features = cars_numerical_values.columns.drop('price').to_list()\n",
"train = cars_numerical_values\n",
"target = cars_numerical_values['price']\n",
"columns=['k', 'feature', 'score']\n",
"\n",
"for k in k_range:\n",
" for feature in features:\n",
" rmse = knn_train_test(cars_numerical_values[[feature]], cars_numerical_values['price'], k)\n",
" i_list.append([k, feature, int(rmse)])\n",
"\n",
"k_features_scores = pd.DataFrame(i_list, columns=columns)\n",
"k_features_scores"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"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>k</th>\n",
" <th>feature</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>3</td>\n",
" <td>engine-size</td>\n",
" <td>3282</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>4</td>\n",
" <td>engine-size</td>\n",
" <td>3327</td>\n",
" </tr>\n",
" <tr>\n",
" <td>62</td>\n",
" <td>5</td>\n",
" <td>engine-size</td>\n",
" <td>3364</td>\n",
" </tr>\n",
" <tr>\n",
" <td>76</td>\n",
" <td>6</td>\n",
" <td>engine-size</td>\n",
" <td>3487</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>2</td>\n",
" <td>engine-size</td>\n",
" <td>3519</td>\n",
" </tr>\n",
" <tr>\n",
" <td>90</td>\n",
" <td>7</td>\n",
" <td>engine-size</td>\n",
" <td>3650</td>\n",
" </tr>\n",
" <tr>\n",
" <td>104</td>\n",
" <td>8</td>\n",
" <td>engine-size</td>\n",
" <td>3697</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>2</td>\n",
" <td>horsepower</td>\n",
" <td>3802</td>\n",
" </tr>\n",
" <tr>\n",
" <td>118</td>\n",
" <td>9</td>\n",
" <td>engine-size</td>\n",
" <td>3841</td>\n",
" </tr>\n",
" <tr>\n",
" <td>66</td>\n",
" <td>5</td>\n",
" <td>horsepower</td>\n",
" <td>3983</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" k feature score\n",
"34 3 engine-size 3282\n",
"48 4 engine-size 3327\n",
"62 5 engine-size 3364\n",
"76 6 engine-size 3487\n",
"20 2 engine-size 3519\n",
"90 7 engine-size 3650\n",
"104 8 engine-size 3697\n",
"24 2 horsepower 3802\n",
"118 9 engine-size 3841\n",
"66 5 horsepower 3983"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"k_features_scores.sort_values(by=['score']).head(10)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Cross Validation Accuracy')"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
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"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(20, 10))\n",
"plt.plot(k_features_scores['k'], k_features_scores['score'])\n",
"plt.xlabel('K Value for KNN')\n",
"plt.ylabel('Cross Validation Accuracy')"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"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>score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>k</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>5699.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>5745.785714</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>5755.500000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score\n",
"k \n",
"4 5699.000000\n",
"3 5745.785714\n",
"5 5755.500000"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# assessing which K values is the best\n",
"k_means = k_features_scores.groupby('k').mean()\n",
"k_means.sort_values('score').head(3)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"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>score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>engine-size</td>\n",
" <td>4404.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>curb-weight</td>\n",
" <td>4698.633333</td>\n",
" </tr>\n",
" <tr>\n",
" <td>horsepower</td>\n",
" <td>4771.966667</td>\n",
" </tr>\n",
" <tr>\n",
" <td>highway-mpg</td>\n",
" <td>5064.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <td>city-mpg</td>\n",
" <td>5095.133333</td>\n",
" </tr>\n",
" <tr>\n",
" <td>width</td>\n",
" <td>5140.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>length</td>\n",
" <td>5718.933333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score\n",
"feature \n",
"engine-size 4404.800000\n",
"curb-weight 4698.633333\n",
"horsepower 4771.966667\n",
"highway-mpg 5064.666667\n",
"city-mpg 5095.133333\n",
"width 5140.200000\n",
"length 5718.933333"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# assessing which feature produces the best score\n",
"feature_means = k_features_scores.drop(columns='k').groupby('feature').mean()\n",
"feature_means.sort_values('score').head(7)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Multivariate Model\n",
"A multivariate model analysis examines two or more variables (features)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"def knn_train_test(train, target, k):\n",
" kf = KFold(n_splits=2, shuffle=True, random_state=1)\n",
" knn = KNeighborsRegressor(n_neighbors=k)\n",
" mses = cross_val_score(knn, train, target, scoring=\"neg_mean_squared_error\", cv=kf)\n",
" rmse = np.mean(np.sqrt(np.abs(mses)))\n",
" return rmse"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"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>k</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>5</td>\n",
" <td>4009</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" k score\n",
"0 5 4009"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# running model on all features with k-value = 5\n",
"k_range = range(1,31)\n",
"i_list = []\n",
"\n",
"features = cars_numerical_values.columns.drop('price').to_list()\n",
"train = cars_numerical_values[features]\n",
"target = cars_numerical_values['price']\n",
"columns=['k', 'score']\n",
"k = 5\n",
"\n",
"rmse = knn_train_test(train, target, k)\n",
"i_list.append([5, int(rmse)])\n",
"\n",
"k_scores = pd.DataFrame(i_list, columns=columns)\n",
"k_scores.sort_values(by='score').head()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"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>score</th>\n",
" </tr>\n",
" <tr>\n",
" <th>feature</th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>engine-size</td>\n",
" <td>4404.800000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>curb-weight</td>\n",
" <td>4698.633333</td>\n",
" </tr>\n",
" <tr>\n",
" <td>horsepower</td>\n",
" <td>4771.966667</td>\n",
" </tr>\n",
" <tr>\n",
" <td>highway-mpg</td>\n",
" <td>5064.666667</td>\n",
" </tr>\n",
" <tr>\n",
" <td>city-mpg</td>\n",
" <td>5095.133333</td>\n",
" </tr>\n",
" <tr>\n",
" <td>width</td>\n",
" <td>5140.200000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>length</td>\n",
" <td>5718.933333</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" score\n",
"feature \n",
"engine-size 4404.800000\n",
"curb-weight 4698.633333\n",
"horsepower 4771.966667\n",
"highway-mpg 5064.666667\n",
"city-mpg 5095.133333\n",
"width 5140.200000\n",
"length 5718.933333"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# assessing which feature produces the best score\n",
"feature_means = k_features_scores.drop(columns='k').groupby('feature').mean()\n",
"feature_means.sort_values('score').head(7)"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['engine-size', 'curb-weight', 'horsepower', 'highway-mpg', 'city-mpg']"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert resorted groupby object indexes into a list for model\n",
"best_features = feature_means.sort_values('score').head(5)\n",
"best_features = best_features.index.to_list()\n",
"best_features"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"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>n features</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>3299</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>3353</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>3422</td>\n",
" </tr>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>3427</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n features score\n",
"2 4 3299\n",
"3 5 3353\n",
"1 3 3422\n",
"0 2 3427"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# running model on best n features\n",
"i_list = []\n",
"n_feature = []\n",
"\n",
"target = cars_numerical_values['price']\n",
"columns=['n features', 'score']\n",
"k = 5\n",
"\n",
"for feature in best_features:\n",
" n_feature.append(feature)\n",
" if feature != 'engine-size':\n",
" rmse = knn_train_test(cars_numerical_values[n_feature], target, k)\n",
" i_list.append([len(n_feature), int(rmse)])\n",
"\n",
"k_scores = pd.DataFrame(i_list, columns=columns)\n",
"k_scores.sort_values(by='score').head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"It looks like the **Multivariate model** preforms best with **four features**."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Hyperparameter Tuning"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['engine-size', 'curb-weight', 'horsepower', 'highway-mpg', 'city-mpg']"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# convert resorted groupby object indexes into a list for model\n",
"best_features = feature_means.sort_values('score').head(5)\n",
"best_features = best_features.index.to_list()\n",
"best_features"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"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>n features</th>\n",
" <th>k</th>\n",
" <th>score</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>91</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>2882</td>\n",
" </tr>\n",
" <tr>\n",
" <td>90</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>2949</td>\n",
" </tr>\n",
" <tr>\n",
" <td>61</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>2980</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>3030</td>\n",
" </tr>\n",
" <tr>\n",
" <td>92</td>\n",
" <td>5</td>\n",
" <td>3</td>\n",
" <td>3063</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" n features k score\n",
"91 5 2 2882\n",
"90 5 1 2949\n",
"61 4 2 2980\n",
"60 4 1 3030\n",
"92 5 3 3063"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# running model on best n features\n",
"i_list = []\n",
"n_feature = []\n",
"k_range = range(1,31)\n",
"\n",
"target = cars_numerical_values['price']\n",
"columns= ['n features', 'k', 'score']\n",
"\n",
"for feature in best_features:\n",
" n_feature.append(feature)\n",
" if feature != 'engine-size':\n",
" for k in k_range:\n",
" rmse = knn_train_test(cars_numerical_values[n_feature], target, k)\n",
" i_list.append([len(n_feature), k, int(rmse)])\n",
"\n",
"best_feature_k_scores = pd.DataFrame(i_list, columns=columns)\n",
"best_feature_k_scores.sort_values(by='score').head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"According to the knn model, in order **to predict the most accurate price per car**, it's best to use to the **5 best features** with only **2 nearest neighbors** (k-value)."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5, 1.0, 'Cross Validation of K values ')"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<Figure size 1440x720 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.style.use('fivethirtyeight')\n",
"plt.figure(figsize=(20, 10))\n",
"plt.plot(best_feature_k_scores['k'], best_feature_k_scores['score'])\n",
"plt.xlabel('K Value for KNN')\n",
"plt.ylabel('RMSE')\n",
"plt.title(\"Cross Validation of K values \", fontdict={'fontsize': '40'})"
]
}
],
"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": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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