forked from tdwojak/Python2018
486 lines
25 KiB
Plaintext
486 lines
25 KiB
Plaintext
{
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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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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"# Analiza danych w Pythonie: sklearn\n",
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"\n",
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"### Tomasz Dwojak\n",
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"\n",
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"### 3 czerwca 2018"
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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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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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" * Pierwsza część: pandas\n",
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" * Druga część: sklearn"
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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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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"### Przypomnienie z UMZ\n",
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" * przygotowanie i czyszczenie danych\n",
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" * wybór i trening modelu\n",
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" * tuning\n",
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" * ewaluacja"
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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": 17,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [],
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"source": [
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"import sklearn\n",
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"import pandas as pd\n",
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"import numpy as np"
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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": 4,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [],
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"source": [
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"data = pd.read_csv(\"./gapminder.csv\", index_col=0)"
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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": 6,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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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>female_BMI</th>\n",
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" <th>male_BMI</th>\n",
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" <th>gdp</th>\n",
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" <th>population</th>\n",
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" <th>under5mortality</th>\n",
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" <th>life_expectancy</th>\n",
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" <th>fertility</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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" <th>Afghanistan</th>\n",
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" <td>21.07402</td>\n",
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" <td>20.62058</td>\n",
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" <td>1311.0</td>\n",
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" <td>26528741.0</td>\n",
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" <td>110.4</td>\n",
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" <td>52.8</td>\n",
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" <td>6.20</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Albania</th>\n",
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" <td>25.65726</td>\n",
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" <td>26.44657</td>\n",
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" <td>8644.0</td>\n",
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" <td>2968026.0</td>\n",
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" <td>17.9</td>\n",
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" <td>76.8</td>\n",
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" <td>1.76</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Algeria</th>\n",
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" <td>26.36841</td>\n",
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" <td>24.59620</td>\n",
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" <td>12314.0</td>\n",
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" <td>34811059.0</td>\n",
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" <td>29.5</td>\n",
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" <td>75.5</td>\n",
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" <td>2.73</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Angola</th>\n",
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" <td>23.48431</td>\n",
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" <td>22.25083</td>\n",
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" <td>7103.0</td>\n",
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" <td>19842251.0</td>\n",
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" <td>192.0</td>\n",
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" <td>56.7</td>\n",
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" <td>6.43</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Antigua and Barbuda</th>\n",
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" <td>27.50545</td>\n",
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" <td>25.76602</td>\n",
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" <td>25736.0</td>\n",
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" <td>85350.0</td>\n",
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" <td>10.9</td>\n",
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" <td>75.5</td>\n",
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" <td>2.16</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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" female_BMI male_BMI gdp population \\\n",
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"Afghanistan 21.07402 20.62058 1311.0 26528741.0 \n",
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"Albania 25.65726 26.44657 8644.0 2968026.0 \n",
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"Algeria 26.36841 24.59620 12314.0 34811059.0 \n",
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"Angola 23.48431 22.25083 7103.0 19842251.0 \n",
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"Antigua and Barbuda 27.50545 25.76602 25736.0 85350.0 \n",
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"\n",
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" under5mortality life_expectancy fertility \n",
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"Afghanistan 110.4 52.8 6.20 \n",
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"Albania 17.9 76.8 1.76 \n",
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"Algeria 29.5 75.5 2.73 \n",
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"Angola 192.0 56.7 6.43 \n",
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"Antigua and Barbuda 10.9 75.5 2.16 "
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]
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},
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"execution_count": 6,
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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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"data.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": 7,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [],
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"source": [
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"y = data['life_expectancy']\n",
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"X = data.drop('life_expectancy', axis=1)"
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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": 13,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [],
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"source": [
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"from sklearn.model_selection import train_test_split\n",
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"train_X, test_X, train_y, test_y = \\\n",
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" train_test_split(X, y, test_size=0.2, random_state=123, shuffle=True)"
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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": 14,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
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]
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},
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"execution_count": 14,
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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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"from sklearn.linear_model import LinearRegression\n",
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"model = LinearRegression()\n",
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"model.fit(X,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": 16,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"array([67.56279809, 76.25840076, 50.21126326, 59.21303855, 72.06348723])"
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]
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},
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"execution_count": 16,
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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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"predicted = model.predict(test_X)\n",
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"predicted[:5]"
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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": 18,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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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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"RMSE: 3.5179543848147863\n"
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]
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}
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],
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"source": [
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"from sklearn.metrics import mean_squared_error\n",
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"rmse = np.sqrt(mean_squared_error(predicted, test_y))\n",
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"print(\"RMSE:\", rmse)"
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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": 20,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.795295000468209"
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]
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},
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"execution_count": 20,
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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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" r2 = model.score(test_X, test_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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"slideshow": {
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"slide_type": "slide"
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}
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},
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"source": [
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"#### API\n",
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" * model\n",
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" * `fix`\n",
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" * `predict`"
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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": 30,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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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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"female_BMI: -1.18\n",
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"male_BMI: 1.46\n",
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"gdp: 5.11e-05\n",
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"population: 7.21e-10\n",
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"under5mortality: -0.159\n",
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"fertility: 0.421\n"
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]
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}
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],
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"source": [
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"for p in zip(train_X.columns, model.coef_):\n",
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" print(\"{}: {:.3}\".format(p[0], p[1]))"
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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": 36,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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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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"/usr/lib/python3.6/site-packages/ipykernel_launcher.py:2: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
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" \n"
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]
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},
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{
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"data": {
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"text/plain": [
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"LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
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]
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},
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"execution_count": 36,
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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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"model2 = LinearRegression()\n",
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"model2.fit(train_X['male_BMI'].reshape(-1, 1), train_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": 37,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"0.5852413468462743"
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]
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},
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"execution_count": 37,
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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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"model2.intercept_"
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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": 41,
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"metadata": {
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"slideshow": {
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"slide_type": "slide"
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}
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},
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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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"/usr/lib/python3.6/site-packages/ipykernel_launcher.py:5: FutureWarning: reshape is deprecated and will raise in a subsequent release. Please use .values.reshape(...) instead\n",
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" \"\"\"\n"
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]
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},
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{
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"data": {
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"image/png": 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\n",
|
|
"text/plain": [
|
|
"<matplotlib.figure.Figure at 0x7fa465870198>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"from matplotlib import pyplot as plt\n",
|
|
"%matplotlib inline\n",
|
|
"\n",
|
|
"plt.scatter(train_X['male_BMI'], train_y,color='g')\n",
|
|
"plt.plot(train_X['male_BMI'], model2.predict(train_X['male_BMI'].reshape(-1, 1)),color='k')\n",
|
|
"\n",
|
|
"plt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
}
|
|
],
|
|
"metadata": {
|
|
"celltoolbar": "Slideshow",
|
|
"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": "3.6.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 2
|
|
}
|