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New example of regression
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Andreaierardi
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Diamonds price regression\n",
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"\n",
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"## Using Machine Lerning regressor\n",
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"\n",
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"The dataset used is [here](https://www.kaggle.com/shivam2503/diamonds/data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 54,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>carat</th>\n",
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" <th>cut</th>\n",
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" <th>color</th>\n",
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" <th>clarity</th>\n",
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" <th>depth</th>\n",
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" <th>table</th>\n",
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" <th>price</th>\n",
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" <th>x</th>\n",
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" <th>y</th>\n",
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" <th>z</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>1</td>\n",
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" <td>0.23</td>\n",
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" <td>Ideal</td>\n",
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" <td>E</td>\n",
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" <td>SI2</td>\n",
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" <td>61.5</td>\n",
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" <td>55.0</td>\n",
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" <td>326</td>\n",
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" <td>3.95</td>\n",
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" <td>3.98</td>\n",
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" <td>2.43</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>2</td>\n",
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" <td>0.21</td>\n",
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" <td>Premium</td>\n",
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" <td>E</td>\n",
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" <td>SI1</td>\n",
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" <td>59.8</td>\n",
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" <td>61.0</td>\n",
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" <td>326</td>\n",
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" <td>3.89</td>\n",
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" <td>3.84</td>\n",
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" <td>2.31</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>3</td>\n",
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" <td>0.23</td>\n",
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" <td>Good</td>\n",
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" <td>E</td>\n",
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" <td>VS1</td>\n",
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" <td>56.9</td>\n",
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" <td>65.0</td>\n",
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" <td>327</td>\n",
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" <td>4.05</td>\n",
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" <td>4.07</td>\n",
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" <td>2.31</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>4</td>\n",
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" <td>0.29</td>\n",
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" <td>Premium</td>\n",
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" <td>I</td>\n",
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" <td>VS2</td>\n",
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" <td>62.4</td>\n",
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" <td>58.0</td>\n",
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" <td>334</td>\n",
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" <td>4.20</td>\n",
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" <td>4.23</td>\n",
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" <td>2.63</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>5</td>\n",
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" <td>0.31</td>\n",
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" <td>Good</td>\n",
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" <td>J</td>\n",
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" <td>SI2</td>\n",
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" <td>63.3</td>\n",
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" <td>58.0</td>\n",
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" <td>335</td>\n",
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" <td>4.34</td>\n",
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" <td>4.35</td>\n",
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" <td>2.75</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" carat cut color clarity depth table price x y z\n",
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"1 0.23 Ideal E SI2 61.5 55.0 326 3.95 3.98 2.43\n",
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"2 0.21 Premium E SI1 59.8 61.0 326 3.89 3.84 2.31\n",
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"3 0.23 Good E VS1 56.9 65.0 327 4.05 4.07 2.31\n",
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"4 0.29 Premium I VS2 62.4 58.0 334 4.20 4.23 2.63\n",
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"5 0.31 Good J SI2 63.3 58.0 335 4.34 4.35 2.75"
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]
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},
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"execution_count": 54,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import pandas as pd\n",
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"import numpy as np\n",
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"\n",
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"df = pd.read_csv(\"Datasets/Diamonds/diamonds.csv\", index_col=0)\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 55,
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"metadata": {},
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"outputs": [],
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"source": [
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"df['cut'].unique()\n",
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"cut_class_dict = {\"Fair\": 1, \"Good\": 2, \"Very Good\": 3, \"Premium\": 4, \"Ideal\": 5}\n",
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"clarity_dict = {\"I3\": 1, \"I2\": 2, \"I1\": 3, \"SI2\": 4, \"SI1\": 5, \"VS2\": 6, \"VS1\": 7, \"VVS2\": 8, \"VVS1\": 9, \"IF\": 10, \"FL\": 11}\n",
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"color_dict = {\"J\": 1,\"I\": 2,\"H\": 3,\"G\": 4,\"F\": 5,\"E\": 6,\"D\": 7}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 56,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>carat</th>\n",
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" <th>cut</th>\n",
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" <th>color</th>\n",
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" <th>clarity</th>\n",
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" <th>depth</th>\n",
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" <th>table</th>\n",
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" <th>price</th>\n",
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" <th>x</th>\n",
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" <th>y</th>\n",
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" <th>z</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <td>1</td>\n",
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" <td>0.23</td>\n",
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" <td>5</td>\n",
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" <td>6</td>\n",
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" <td>4</td>\n",
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" <td>61.5</td>\n",
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" <td>55.0</td>\n",
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" <td>326</td>\n",
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" <td>3.95</td>\n",
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" <td>3.98</td>\n",
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" <td>2.43</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>2</td>\n",
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" <td>0.21</td>\n",
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" <td>4</td>\n",
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" <td>6</td>\n",
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" <td>5</td>\n",
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" <td>59.8</td>\n",
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" <td>61.0</td>\n",
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" <td>326</td>\n",
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" <td>3.89</td>\n",
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" <td>3.84</td>\n",
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" <td>2.31</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>3</td>\n",
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" <td>0.23</td>\n",
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" <td>2</td>\n",
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" <td>6</td>\n",
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" <td>7</td>\n",
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" <td>56.9</td>\n",
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" <td>65.0</td>\n",
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" <td>327</td>\n",
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" <td>4.05</td>\n",
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" <td>4.07</td>\n",
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" <td>2.31</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>4</td>\n",
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" <td>0.29</td>\n",
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" <td>4</td>\n",
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" <td>2</td>\n",
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" <td>6</td>\n",
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" <td>62.4</td>\n",
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" <td>58.0</td>\n",
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" <td>334</td>\n",
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" <td>4.20</td>\n",
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" <td>4.23</td>\n",
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" <td>2.63</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <td>5</td>\n",
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" <td>0.31</td>\n",
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" <td>2</td>\n",
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" <td>1</td>\n",
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" <td>4</td>\n",
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" <td>63.3</td>\n",
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" <td>58.0</td>\n",
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" <td>335</td>\n",
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" <td>4.34</td>\n",
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" <td>4.35</td>\n",
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" <td>2.75</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" carat cut color clarity depth table price x y z\n",
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"1 0.23 5 6 4 61.5 55.0 326 3.95 3.98 2.43\n",
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"2 0.21 4 6 5 59.8 61.0 326 3.89 3.84 2.31\n",
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"3 0.23 2 6 7 56.9 65.0 327 4.05 4.07 2.31\n",
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"4 0.29 4 2 6 62.4 58.0 334 4.20 4.23 2.63\n",
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"5 0.31 2 1 4 63.3 58.0 335 4.34 4.35 2.75"
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]
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},
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"execution_count": 56,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df['cut'] = df['cut'].map(cut_class_dict)\n",
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"df['clarity'] = df['clarity'].map(clarity_dict)\n",
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"df['color'] = df['color'].map(color_dict)\n",
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"df.head()\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 59,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sklearn\n",
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"from sklearn import svm, preprocessing\n",
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"from sklearn.linear_model import SGDRegressor\n",
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"\n",
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"df = sklearn.utils.shuffle(df) # always shuffle your data to avoid any biases that may emerge b/c of some order.\n",
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"\n",
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"X = df.drop(\"price\", axis=1).values\n",
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"X = preprocessing.scale(X)\n",
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"y = df[\"price\"].values"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 67,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Data len: 53940 \n",
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"As test we used 20%: 10788.0\n"
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]
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}
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],
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"source": [
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"len(y)\n",
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"print(\"Data len: \",len(y),\"\\nAs test we used 20%: \",20/100*len(y))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train, X_test, y_train, y_test = sklearn.model_selection.train_test_split(X, y, test_size=0.2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## SGD Regressor"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 69,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.9040861346309637\n"
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]
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}
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],
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"source": [
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"clf = SGDRegressor(max_iter=1000)\n",
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"clf.fit(X_train, y_train)\n",
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"\n",
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"print(clf.score(X_test, y_test))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"15298.319296019743 13919\n",
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"12283.622023001368 14386\n",
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"5396.202925107901 3951\n",
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"4034.4365255612984 2855\n",
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"215.1080120323627 645\n",
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"3533.2049908575455 2978\n",
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"-624.3585716217572 654\n",
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"3935.1728997587816 3170\n",
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"-1127.3151816200148 450\n",
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"4022.1708282842237 2956\n"
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]
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}
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],
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"source": [
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"for X,y in list(zip(X_test, y_test))[:10]:\n",
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" print(clf.predict([X])[0], y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## SVR Regressor"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 71,
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:193: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
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" \"avoid this warning.\", FutureWarning)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"0.5413237370675921\n"
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]
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}
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],
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"source": [
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"from sklearn import svm\n",
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"\n",
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"clf = svm.SVR()\n",
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"\n",
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"clf.fit(X_train, y_train)\n",
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"print(clf.score(X_test, y_test))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 72,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"5122.681145918745 13919\n",
|
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"6632.724385241532 14386\n",
|
||||
"4567.411354034963 3951\n",
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"3261.084788402066 2855\n",
|
||||
"529.2786025656524 645\n",
|
||||
"3219.2301461725656 2978\n",
|
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"1002.5617023863538 654\n",
|
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"3440.406994396222 3170\n",
|
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"685.2569483457883 450\n",
|
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"3101.373161450196 2956\n"
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]
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}
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],
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"source": [
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"for X,y in list(zip(X_test, y_test))[:10]:\n",
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" print(clf.predict([X])[0], y)"
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||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Support Vector Regression (SVR) with linear kernel"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"clf = svm.SVR(kernel=\"linear\")\n",
|
||||
"\n",
|
||||
"clf.fit(X_train, y_train)\n",
|
||||
"print(clf.score(X_test, y_test))\n",
|
||||
"\n",
|
||||
"predictions_lin = clf.predict(X_test, y_test) # make predictions\n",
|
||||
"\n",
|
||||
"acc = clf.accuracy_score(y_test, predictions_lin)\n",
|
||||
"\n",
|
||||
"print(\"Accuracy: \",acc)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Support Vector Regression (SVR) with rbf kernel"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"clf = svm.SVR(kernel=\"rbf\")\n",
|
||||
"\n",
|
||||
"clf.fit(X_train, y_train)\n",
|
||||
"print(clf.score(X_test, y_test))\n",
|
||||
"\n",
|
||||
"predictions = clf.predict(X_test, y_test) # make predictions\n",
|
||||
"\n",
|
||||
"acc = clf.accuracy_score(y_test, predictions)\n",
|
||||
"\n",
|
||||
"print(\"Accuracy: \",acc)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Random Forest regression"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from sklearn.ensemble import RandomForestRegressor\n",
|
||||
"rf = RandomForestRegressor(n_estimators=10, random_state=0)\n",
|
||||
"rf.fit(X_train,y_train)\n",
|
||||
"print(\"SCORE: \",rf.score(X_test, y_test))\n",
|
||||
"\n",
|
||||
"for X,y in list(zip(X_test, y_test))[:10]:\n",
|
||||
" print(rf.predict([X])[0], y)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Linear Regression"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 80,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"0.905111184064965\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn import linear_model\n",
|
||||
"\n",
|
||||
"linear = linear_model.LinearRegression()\n",
|
||||
"\n",
|
||||
"linear.fit(X_train, y_train)\n",
|
||||
"print(linear.score(X_test, y_test))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Logistic regression"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
|
||||
" FutureWarning)\n",
|
||||
"E:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:469: FutureWarning: Default multi_class will be changed to 'auto' in 0.22. Specify the multi_class option to silence this warning.\n",
|
||||
" \"this warning.\", FutureWarning)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from sklearn.linear_model import LogisticRegression\n",
|
||||
"logistic = LogisticRegression(random_state=0) # create object for the class\n",
|
||||
"logistic.fit(X_train, y_train) # perform logistic regression\n",
|
||||
"ac = logistic.score(X_test, y_test)\n",
|
||||
"Y_pred = logistic.predict(X_test, y_test) # make predictions\n",
|
||||
"\n",
|
||||
"print(\"Accuracy: \",ac)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"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": "3.7.4"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
Loading…
Reference in New Issue
Block a user