{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Machine Learning to explain and predict the life expectancy of different countries" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The project tries to create a model based on data provided by the World Health Organization (WHO) to evaluate the life\n", "expectancy for different countries in years. The data offers a timeframe from 2000 to 2015. The data originates from here:\n", "https://www.kaggle.com/kumarajarshi/life-expectancy-who/data\n", "The output algorithms have been used to test if they can maintain their accuracy in predicting the life expectancy for data they haven't been trained. Four algorithms have been used:\n", "
    \n", "
  1. Linear Regression
    2. \n", "
  2. Ridge Regression
    3. \n", "
  3. Lasso Regression
    4. \n", "
  4. ElasticNet Regression
    5. \n", "
  5. Linear Regression with Polynomic features
    6. \n", "
  6. Decision Tree Regression
    7. \n", "
  7. Random Forest Regression
    8. \n", "
" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 78, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "from nose.tools import *\n", "from sklearn.model_selection import train_test_split\n", "from sklearn.linear_model import LinearRegression\n", "from sklearn.model_selection import GridSearchCV\n", "from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n", "from sklearn.tree import DecisionTreeRegressor\n", "from sklearn.ensemble import RandomForestRegressor\n", "from sklearn.preprocessing import MinMaxScaler\n", "from sklearn.preprocessing import PolynomialFeatures\n", "from sklearn.model_selection import KFold\n", "from sklearn.model_selection import cross_val_score\n", "from sklearn.linear_model import Ridge\n", "from sklearn.linear_model import Lasso\n", "from sklearn.linear_model import ElasticNet\n", "from sklearn.metrics import make_scorer\n", "from scipy import stats\n", "import seaborn as sns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 1. Loading packages\n", "\n", "The following packages have been imported NymPy, Pandas, Matplotlib, Scipy, Seaborn. Sklearn is the most widely used package for the machine learning process. The fallowing subpackages have been used:\n", "
    \n", "
  1. train_test_split
    2. \n", "
  2. linear_model
    3. \n", "
  3. model_selection
    4. \n", "
  4. metrics
    5. \n", "
  5. tree
    6. \n", "
  6. ensemble
    7. \n", "
  7. preprocessing
    8. \n", "
" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": true }, "outputs": [], "source": [ "life_data = pd.read_csv('data/lifeExpectancy.csv', sep = ',')\n", "life_data = life_data.drop('Year', axis = 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 2. Reading the data\n", "\n", "The data is saved as a csv file as LifeExpectancy.csv and it is read and stored in the life_data variable.The Year column is dropped as it will not be used in the analysis. Below the first 5 rows are shown. The data contains 21 columns and 2938 rows with the header row. The table contains data about:\n", "
    \n", "
  1. Country
    2. \n", "
  2. Status
    3. \n", "
  3. Life Expectancy
    4. \n", "
  4. Adult Mortality
    5. \n", "
  5. Alcohol
    6. \n", "
  6. percentage expenditure
    7. \n", "
  7. Hepatitis B
    8. \n", "
  8. Measles
    9. \n", "
  9. BMI
    10. \n", "
  10. under-five deaths
    11. \n", "
  11. Polio
    12. \n", "
  12. Total expenditure
    13. \n", "
  13. Diphtheria
    14. \n", "
  14. HIV/AIDS
    15. \n", "
  15. GDP
    16. \n", "
  16. Population
    17. \n", "
  17. thinness 1-19 years
    18. \n", "
  18. thinness 5-9 years
    19. \n", "
  19. Income composition of resources
    20. \n", "
  20. Schooling\n", "
\n", "With the exclution of Country name and Status(either developed or developing) all of the data is numeric. The values are either in years, precentages, millions or dollars in the case of Gross Domestic Product (GDP)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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CountryStatusLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMI...PolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling
0AfghanistanDeveloping65.0263.0620.0171.27962465.0115419.1...6.08.1665.00.1584.25921033736494.017.217.30.47910.1
1AfghanistanDeveloping59.9271.0640.0173.52358262.049218.6...58.08.1862.00.1612.696514327582.017.517.50.47610.0
2AfghanistanDeveloping59.9268.0660.0173.21924364.043018.1...62.08.1364.00.1631.74497631731688.017.717.70.4709.9
3AfghanistanDeveloping59.5272.0690.0178.18421567.0278717.6...67.08.5267.00.1669.9590003696958.017.918.00.4639.8
4AfghanistanDeveloping59.2275.0710.017.09710968.0301317.2...68.07.8768.00.163.5372312978599.018.218.20.4549.5
\n", "

5 rows × 21 columns

\n", "
" ], "text/plain": [ " Country Status Life expectancy Adult Mortality infant deaths \\\n", "0 Afghanistan Developing 65.0 263.0 62 \n", "1 Afghanistan Developing 59.9 271.0 64 \n", "2 Afghanistan Developing 59.9 268.0 66 \n", "3 Afghanistan Developing 59.5 272.0 69 \n", "4 Afghanistan Developing 59.2 275.0 71 \n", "\n", " Alcohol percentage expenditure Hepatitis B Measles BMI ... \\\n", "0 0.01 71.279624 65.0 1154 19.1 ... \n", "1 0.01 73.523582 62.0 492 18.6 ... \n", "2 0.01 73.219243 64.0 430 18.1 ... \n", "3 0.01 78.184215 67.0 2787 17.6 ... \n", "4 0.01 7.097109 68.0 3013 17.2 ... \n", "\n", " Polio Total expenditure Diphtheria HIV/AIDS GDP Population \\\n", "0 6.0 8.16 65.0 0.1 584.259210 33736494.0 \n", "1 58.0 8.18 62.0 0.1 612.696514 327582.0 \n", "2 62.0 8.13 64.0 0.1 631.744976 31731688.0 \n", "3 67.0 8.52 67.0 0.1 669.959000 3696958.0 \n", "4 68.0 7.87 68.0 0.1 63.537231 2978599.0 \n", "\n", " thinness 1-19 years thinness 5-9 years \\\n", "0 17.2 17.3 \n", "1 17.5 17.5 \n", "2 17.7 17.7 \n", "3 17.9 18.0 \n", "4 18.2 18.2 \n", "\n", " Income composition of resources Schooling \n", "0 0.479 10.1 \n", "1 0.476 10.0 \n", "2 0.470 9.9 \n", "3 0.463 9.8 \n", "4 0.454 9.5 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "life_data.head()" ] }, { "cell_type": "code", "execution_count": 60, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CountryLife expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deaths...DiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchooling1Developing
0Afghanistan65.0263.0620.0171.27962465.0115419.183...65.00.1584.25921033736494.017.217.30.47910.101
1Afghanistan59.9271.0640.0173.52358262.049218.686...62.00.1612.696514327582.017.517.50.47610.001
2Afghanistan59.9268.0660.0173.21924364.043018.189...64.00.1631.74497631731688.017.717.70.4709.901
3Afghanistan59.5272.0690.0178.18421567.0278717.693...67.00.1669.9590003696958.017.918.00.4639.801
4Afghanistan59.2275.0710.017.09710968.0301317.297...68.00.163.5372312978599.018.218.20.4549.501
5Afghanistan58.8279.0740.0179.67936766.0198916.7102...66.00.1553.3289402883167.018.418.40.4489.201
6Afghanistan58.6281.0770.0156.76221763.0286116.2106...63.00.1445.893298284331.018.618.70.4348.901
7Afghanistan58.1287.0800.0325.87392564.0159915.7110...64.00.1373.3611162729431.018.818.90.4338.701
8Afghanistan57.5295.0820.0210.91015663.0114115.2113...63.00.1369.83579626616792.019.019.10.4158.401
9Afghanistan57.3295.0840.0317.17151864.0199014.7116...58.00.1272.5637702589345.019.219.30.4058.101
10Afghanistan57.3291.0850.021.38864866.0129614.2118...58.00.125.294130257798.019.319.50.3967.901
11Afghanistan57.0293.0870.0215.29606667.046613.8120...5.00.1219.14135324118979.019.519.70.3816.801
12Afghanistan56.7295.0870.0111.08905365.079813.4122...41.00.1198.7285442364851.019.719.90.3736.501
13Afghanistan56.23.0880.0116.88735164.0248613.0122...36.00.1187.84595021979923.019.92.20.3416.201
14Afghanistan55.3316.0880.0110.57472863.0876212.6122...33.00.1117.4969802966463.02.12.40.3405.901
15Afghanistan54.8321.0880.0110.42496062.0653212.2122...24.00.1114.560000293756.02.32.50.3385.501
16Albania77.874.004.60364.97522999.0058.00...99.00.13954.22783028873.01.21.30.76214.201
17Albania77.58.004.51428.74906798.0057.21...98.00.14575.763787288914.01.21.30.76114.201
18Albania77.284.004.76430.87697999.0056.51...99.00.14414.723140289592.01.31.40.75914.201
19Albania76.986.005.14412.44335699.0955.81...99.00.14247.6143802941.01.31.40.75214.201
20Albania76.688.005.37437.06210099.02855.11...99.00.14437.178680295195.01.41.50.73813.301
21Albania76.291.015.2841.82275799.01054.31...99.00.1494.358832291321.01.41.50.72512.501
22Albania76.191.015.79348.05595298.0053.51...98.00.14114.1365452927519.01.51.60.72112.201
23Albania75.31.015.6136.62206899.0052.61...99.00.1437.5396472947314.01.61.60.71312.001
24Albania75.99.015.5832.24655298.02251.71...98.00.1363.13685029717.01.61.70.70311.601
25Albania74.299.015.313.30215498.0685.81...97.00.135.1293002992547.01.71.80.69611.401
26Albania73.515.015.1626.99312198.0649.91...98.00.1279.142931311487.01.81.80.68510.801
27Albania73.017.014.54221.84280099.0748.91...97.00.12416.588235326939.01.81.90.68110.901
28Albania72.818.014.2914.71928997.0847.91...97.00.1189.681557339616.01.92.00.67410.701
29Albania73.315.013.73104.51691696.01646.91...98.00.11453.6427773511.02.02.10.67010.701
..................................................................
2908Zambia63.0328.0292.4120.62306379.03522.342...79.04.8185.7933591515321.06.46.20.56512.501
2909Zambia59.2349.0292.59196.91525078.089621.743...78.05.61734.93612014699937.06.56.30.55412.301
2910Zambia58.2366.0292.57183.04617081.01323421.244...81.06.31644.61967214264756.06.66.40.54312.001
2911Zambia58.0363.0302.47184.36491083.0157542.745...83.06.81463.213573138533.06.76.50.53311.801
2912Zambia57.4368.0302.30143.86988794.0262.247...94.09.11139.11233013456417.06.76.60.51811.601
2913Zambia55.745.0312.12153.67837587.014019.749...87.011.91369.6824901382517.06.86.70.50411.401
2914Zambia52.6487.0322.0810.8514828.053519.251...8.013.6114.58798512725974.06.96.80.49211.101
2915Zambia58.0526.0332.251.86000481.045918.852...81.015.913.15419912383446.07.06.90.47910.901
2916Zambia49.3554.0342.33121.87933182.04518.455...82.017.0691.3178161252156.07.17.00.46710.701
2917Zambia47.9578.0362.468.369852NaN3518.059...83.017.653.27722211731746.07.27.10.45610.501
2918Zambia46.464.0392.3365.789974NaN88117.662...83.018.2429.15834311421984.07.37.20.44310.201
2919Zambia45.569.0412.4454.043480NaN2503617.366...84.018.4377.135244111249.07.47.30.43310.001
2920Zambia44.6611.0432.6146.830275NaN1699717.170...85.018.6378.2736241824125.07.47.40.4249.801
2921Zambia43.8614.0442.6245.616880NaN3093016.872...85.018.7341.9556251531221.07.57.50.4189.601
2922Zimbabwe67.0336.022NaN0.00000087.0031.832...87.06.2118.69383015777451.05.65.50.50710.301
2923Zimbabwe59.2371.0236.5010.82259591.0031.334...91.06.3127.47462015411675.05.95.70.49810.301
2924Zimbabwe58.0399.0256.3910.66670795.003.836...95.06.8111.227396155456.06.26.00.48810.401
2925Zimbabwe56.6429.0266.0992.60233697.003.339...95.08.8955.6484661471826.06.56.40.4649.801
2926Zimbabwe54.9464.0286.0063.75053094.0029.942...93.013.3839.92793614386649.06.86.70.45210.101
2927Zimbabwe52.4527.0295.2153.3085819.0969629.444...89.015.7713.6356201486317.07.17.00.43610.001
2928Zimbabwe50.0587.0304.641.04002173.085329.045...73.018.165.8241211381599.07.57.40.4199.901
2929Zimbabwe48.2632.0303.5620.84342975.0028.646...75.020.5325.67857313558469.07.87.80.4219.701
2930Zimbabwe46.667.0293.8829.81456672.024228.246...73.023.7396.9982171332999.08.28.20.4149.601
2931Zimbabwe45.47.0284.5734.26216968.021227.945...7.026.8414.79623213124267.08.68.60.4089.501
2932Zimbabwe44.6717.0284.148.71740965.042027.543...68.030.3444.765750129432.09.09.00.4069.301
2933Zimbabwe44.3723.0274.360.00000068.03127.142...65.033.6454.36665412777511.09.49.40.4079.201
2934Zimbabwe44.5715.0264.060.0000007.099826.741...68.036.7453.35115512633897.09.89.90.4189.501
2935Zimbabwe44.873.0254.430.00000073.030426.340...71.039.857.348340125525.01.21.30.42710.001
2936Zimbabwe45.3686.0251.720.00000076.052925.939...75.042.1548.58731212366165.01.61.70.4279.801
2937Zimbabwe46.0665.0241.680.00000079.0148325.539...78.043.5547.35887912222251.011.011.20.4349.801
\n", "

2938 rows × 22 columns

\n", "
" ], "text/plain": [ " Country Life expectancy Adult Mortality infant deaths Alcohol \\\n", "0 Afghanistan 65.0 263.0 62 0.01 \n", "1 Afghanistan 59.9 271.0 64 0.01 \n", "2 Afghanistan 59.9 268.0 66 0.01 \n", "3 Afghanistan 59.5 272.0 69 0.01 \n", "4 Afghanistan 59.2 275.0 71 0.01 \n", "5 Afghanistan 58.8 279.0 74 0.01 \n", "6 Afghanistan 58.6 281.0 77 0.01 \n", "7 Afghanistan 58.1 287.0 80 0.03 \n", "8 Afghanistan 57.5 295.0 82 0.02 \n", "9 Afghanistan 57.3 295.0 84 0.03 \n", "10 Afghanistan 57.3 291.0 85 0.02 \n", "11 Afghanistan 57.0 293.0 87 0.02 \n", "12 Afghanistan 56.7 295.0 87 0.01 \n", "13 Afghanistan 56.2 3.0 88 0.01 \n", "14 Afghanistan 55.3 316.0 88 0.01 \n", "15 Afghanistan 54.8 321.0 88 0.01 \n", "16 Albania 77.8 74.0 0 4.60 \n", "17 Albania 77.5 8.0 0 4.51 \n", "18 Albania 77.2 84.0 0 4.76 \n", "19 Albania 76.9 86.0 0 5.14 \n", "20 Albania 76.6 88.0 0 5.37 \n", "21 Albania 76.2 91.0 1 5.28 \n", "22 Albania 76.1 91.0 1 5.79 \n", "23 Albania 75.3 1.0 1 5.61 \n", "24 Albania 75.9 9.0 1 5.58 \n", "25 Albania 74.2 99.0 1 5.31 \n", "26 Albania 73.5 15.0 1 5.16 \n", "27 Albania 73.0 17.0 1 4.54 \n", "28 Albania 72.8 18.0 1 4.29 \n", "29 Albania 73.3 15.0 1 3.73 \n", "... ... ... ... ... ... \n", "2908 Zambia 63.0 328.0 29 2.41 \n", "2909 Zambia 59.2 349.0 29 2.59 \n", "2910 Zambia 58.2 366.0 29 2.57 \n", "2911 Zambia 58.0 363.0 30 2.47 \n", "2912 Zambia 57.4 368.0 30 2.30 \n", "2913 Zambia 55.7 45.0 31 2.12 \n", "2914 Zambia 52.6 487.0 32 2.08 \n", "2915 Zambia 58.0 526.0 33 2.25 \n", "2916 Zambia 49.3 554.0 34 2.33 \n", "2917 Zambia 47.9 578.0 36 2.46 \n", "2918 Zambia 46.4 64.0 39 2.33 \n", "2919 Zambia 45.5 69.0 41 2.44 \n", "2920 Zambia 44.6 611.0 43 2.61 \n", "2921 Zambia 43.8 614.0 44 2.62 \n", "2922 Zimbabwe 67.0 336.0 22 NaN \n", "2923 Zimbabwe 59.2 371.0 23 6.50 \n", "2924 Zimbabwe 58.0 399.0 25 6.39 \n", "2925 Zimbabwe 56.6 429.0 26 6.09 \n", "2926 Zimbabwe 54.9 464.0 28 6.00 \n", "2927 Zimbabwe 52.4 527.0 29 5.21 \n", "2928 Zimbabwe 50.0 587.0 30 4.64 \n", "2929 Zimbabwe 48.2 632.0 30 3.56 \n", "2930 Zimbabwe 46.6 67.0 29 3.88 \n", "2931 Zimbabwe 45.4 7.0 28 4.57 \n", "2932 Zimbabwe 44.6 717.0 28 4.14 \n", "2933 Zimbabwe 44.3 723.0 27 4.36 \n", "2934 Zimbabwe 44.5 715.0 26 4.06 \n", "2935 Zimbabwe 44.8 73.0 25 4.43 \n", "2936 Zimbabwe 45.3 686.0 25 1.72 \n", "2937 Zimbabwe 46.0 665.0 24 1.68 \n", "\n", " percentage expenditure Hepatitis B Measles BMI \\\n", "0 71.279624 65.0 1154 19.1 \n", "1 73.523582 62.0 492 18.6 \n", "2 73.219243 64.0 430 18.1 \n", "3 78.184215 67.0 2787 17.6 \n", "4 7.097109 68.0 3013 17.2 \n", "5 79.679367 66.0 1989 16.7 \n", "6 56.762217 63.0 2861 16.2 \n", "7 25.873925 64.0 1599 15.7 \n", "8 10.910156 63.0 1141 15.2 \n", "9 17.171518 64.0 1990 14.7 \n", "10 1.388648 66.0 1296 14.2 \n", "11 15.296066 67.0 466 13.8 \n", "12 11.089053 65.0 798 13.4 \n", "13 16.887351 64.0 2486 13.0 \n", "14 10.574728 63.0 8762 12.6 \n", "15 10.424960 62.0 6532 12.2 \n", "16 364.975229 99.0 0 58.0 \n", "17 428.749067 98.0 0 57.2 \n", "18 430.876979 99.0 0 56.5 \n", "19 412.443356 99.0 9 55.8 \n", "20 437.062100 99.0 28 55.1 \n", "21 41.822757 99.0 10 54.3 \n", "22 348.055952 98.0 0 53.5 \n", "23 36.622068 99.0 0 52.6 \n", "24 32.246552 98.0 22 51.7 \n", "25 3.302154 98.0 68 5.8 \n", "26 26.993121 98.0 6 49.9 \n", "27 221.842800 99.0 7 48.9 \n", "28 14.719289 97.0 8 47.9 \n", "29 104.516916 96.0 16 46.9 \n", "... ... ... ... ... \n", "2908 20.623063 79.0 35 22.3 \n", "2909 196.915250 78.0 896 21.7 \n", "2910 183.046170 81.0 13234 21.2 \n", "2911 184.364910 83.0 15754 2.7 \n", "2912 143.869887 94.0 26 2.2 \n", "2913 153.678375 87.0 140 19.7 \n", "2914 10.851482 8.0 535 19.2 \n", "2915 1.860004 81.0 459 18.8 \n", "2916 121.879331 82.0 45 18.4 \n", "2917 8.369852 NaN 35 18.0 \n", "2918 65.789974 NaN 881 17.6 \n", "2919 54.043480 NaN 25036 17.3 \n", "2920 46.830275 NaN 16997 17.1 \n", "2921 45.616880 NaN 30930 16.8 \n", "2922 0.000000 87.0 0 31.8 \n", "2923 10.822595 91.0 0 31.3 \n", "2924 10.666707 95.0 0 3.8 \n", "2925 92.602336 97.0 0 3.3 \n", "2926 63.750530 94.0 0 29.9 \n", "2927 53.308581 9.0 9696 29.4 \n", "2928 1.040021 73.0 853 29.0 \n", "2929 20.843429 75.0 0 28.6 \n", "2930 29.814566 72.0 242 28.2 \n", "2931 34.262169 68.0 212 27.9 \n", "2932 8.717409 65.0 420 27.5 \n", "2933 0.000000 68.0 31 27.1 \n", "2934 0.000000 7.0 998 26.7 \n", "2935 0.000000 73.0 304 26.3 \n", "2936 0.000000 76.0 529 25.9 \n", "2937 0.000000 79.0 1483 25.5 \n", "\n", " under-five deaths ... Diphtheria HIV/AIDS GDP \\\n", "0 83 ... 65.0 0.1 584.259210 \n", "1 86 ... 62.0 0.1 612.696514 \n", "2 89 ... 64.0 0.1 631.744976 \n", "3 93 ... 67.0 0.1 669.959000 \n", "4 97 ... 68.0 0.1 63.537231 \n", "5 102 ... 66.0 0.1 553.328940 \n", "6 106 ... 63.0 0.1 445.893298 \n", "7 110 ... 64.0 0.1 373.361116 \n", "8 113 ... 63.0 0.1 369.835796 \n", "9 116 ... 58.0 0.1 272.563770 \n", "10 118 ... 58.0 0.1 25.294130 \n", "11 120 ... 5.0 0.1 219.141353 \n", "12 122 ... 41.0 0.1 198.728544 \n", "13 122 ... 36.0 0.1 187.845950 \n", "14 122 ... 33.0 0.1 117.496980 \n", "15 122 ... 24.0 0.1 114.560000 \n", "16 0 ... 99.0 0.1 3954.227830 \n", "17 1 ... 98.0 0.1 4575.763787 \n", "18 1 ... 99.0 0.1 4414.723140 \n", "19 1 ... 99.0 0.1 4247.614380 \n", "20 1 ... 99.0 0.1 4437.178680 \n", "21 1 ... 99.0 0.1 494.358832 \n", "22 1 ... 98.0 0.1 4114.136545 \n", "23 1 ... 99.0 0.1 437.539647 \n", "24 1 ... 98.0 0.1 363.136850 \n", "25 1 ... 97.0 0.1 35.129300 \n", "26 1 ... 98.0 0.1 279.142931 \n", "27 1 ... 97.0 0.1 2416.588235 \n", "28 1 ... 97.0 0.1 189.681557 \n", "29 1 ... 98.0 0.1 1453.642777 \n", "... ... ... ... ... ... \n", "2908 42 ... 79.0 4.8 185.793359 \n", "2909 43 ... 78.0 5.6 1734.936120 \n", "2910 44 ... 81.0 6.3 1644.619672 \n", "2911 45 ... 83.0 6.8 1463.213573 \n", "2912 47 ... 94.0 9.1 1139.112330 \n", "2913 49 ... 87.0 11.9 1369.682490 \n", "2914 51 ... 8.0 13.6 114.587985 \n", "2915 52 ... 81.0 15.9 13.154199 \n", "2916 55 ... 82.0 17.0 691.317816 \n", "2917 59 ... 83.0 17.6 53.277222 \n", "2918 62 ... 83.0 18.2 429.158343 \n", "2919 66 ... 84.0 18.4 377.135244 \n", "2920 70 ... 85.0 18.6 378.273624 \n", "2921 72 ... 85.0 18.7 341.955625 \n", "2922 32 ... 87.0 6.2 118.693830 \n", "2923 34 ... 91.0 6.3 127.474620 \n", "2924 36 ... 95.0 6.8 111.227396 \n", "2925 39 ... 95.0 8.8 955.648466 \n", "2926 42 ... 93.0 13.3 839.927936 \n", "2927 44 ... 89.0 15.7 713.635620 \n", "2928 45 ... 73.0 18.1 65.824121 \n", "2929 46 ... 75.0 20.5 325.678573 \n", "2930 46 ... 73.0 23.7 396.998217 \n", "2931 45 ... 7.0 26.8 414.796232 \n", "2932 43 ... 68.0 30.3 444.765750 \n", "2933 42 ... 65.0 33.6 454.366654 \n", "2934 41 ... 68.0 36.7 453.351155 \n", "2935 40 ... 71.0 39.8 57.348340 \n", "2936 39 ... 75.0 42.1 548.587312 \n", "2937 39 ... 78.0 43.5 547.358879 \n", "\n", " Population thinness 1-19 years thinness 5-9 years \\\n", "0 33736494.0 17.2 17.3 \n", "1 327582.0 17.5 17.5 \n", "2 31731688.0 17.7 17.7 \n", "3 3696958.0 17.9 18.0 \n", "4 2978599.0 18.2 18.2 \n", "5 2883167.0 18.4 18.4 \n", "6 284331.0 18.6 18.7 \n", "7 2729431.0 18.8 18.9 \n", "8 26616792.0 19.0 19.1 \n", "9 2589345.0 19.2 19.3 \n", "10 257798.0 19.3 19.5 \n", "11 24118979.0 19.5 19.7 \n", "12 2364851.0 19.7 19.9 \n", "13 21979923.0 19.9 2.2 \n", "14 2966463.0 2.1 2.4 \n", "15 293756.0 2.3 2.5 \n", "16 28873.0 1.2 1.3 \n", "17 288914.0 1.2 1.3 \n", "18 289592.0 1.3 1.4 \n", "19 2941.0 1.3 1.4 \n", "20 295195.0 1.4 1.5 \n", "21 291321.0 1.4 1.5 \n", "22 2927519.0 1.5 1.6 \n", "23 2947314.0 1.6 1.6 \n", "24 29717.0 1.6 1.7 \n", "25 2992547.0 1.7 1.8 \n", "26 311487.0 1.8 1.8 \n", "27 326939.0 1.8 1.9 \n", "28 339616.0 1.9 2.0 \n", "29 3511.0 2.0 2.1 \n", "... ... ... ... \n", "2908 1515321.0 6.4 6.2 \n", "2909 14699937.0 6.5 6.3 \n", "2910 14264756.0 6.6 6.4 \n", "2911 138533.0 6.7 6.5 \n", "2912 13456417.0 6.7 6.6 \n", "2913 1382517.0 6.8 6.7 \n", "2914 12725974.0 6.9 6.8 \n", "2915 12383446.0 7.0 6.9 \n", "2916 1252156.0 7.1 7.0 \n", "2917 11731746.0 7.2 7.1 \n", "2918 11421984.0 7.3 7.2 \n", "2919 111249.0 7.4 7.3 \n", "2920 1824125.0 7.4 7.4 \n", "2921 1531221.0 7.5 7.5 \n", "2922 15777451.0 5.6 5.5 \n", "2923 15411675.0 5.9 5.7 \n", "2924 155456.0 6.2 6.0 \n", "2925 1471826.0 6.5 6.4 \n", "2926 14386649.0 6.8 6.7 \n", "2927 1486317.0 7.1 7.0 \n", "2928 1381599.0 7.5 7.4 \n", "2929 13558469.0 7.8 7.8 \n", "2930 1332999.0 8.2 8.2 \n", "2931 13124267.0 8.6 8.6 \n", "2932 129432.0 9.0 9.0 \n", "2933 12777511.0 9.4 9.4 \n", "2934 12633897.0 9.8 9.9 \n", "2935 125525.0 1.2 1.3 \n", "2936 12366165.0 1.6 1.7 \n", "2937 12222251.0 11.0 11.2 \n", "\n", " Income composition of resources Schooling 1 Developing \n", "0 0.479 10.1 0 1 \n", "1 0.476 10.0 0 1 \n", "2 0.470 9.9 0 1 \n", "3 0.463 9.8 0 1 \n", "4 0.454 9.5 0 1 \n", "5 0.448 9.2 0 1 \n", "6 0.434 8.9 0 1 \n", "7 0.433 8.7 0 1 \n", "8 0.415 8.4 0 1 \n", "9 0.405 8.1 0 1 \n", "10 0.396 7.9 0 1 \n", "11 0.381 6.8 0 1 \n", "12 0.373 6.5 0 1 \n", "13 0.341 6.2 0 1 \n", "14 0.340 5.9 0 1 \n", "15 0.338 5.5 0 1 \n", "16 0.762 14.2 0 1 \n", "17 0.761 14.2 0 1 \n", "18 0.759 14.2 0 1 \n", "19 0.752 14.2 0 1 \n", "20 0.738 13.3 0 1 \n", "21 0.725 12.5 0 1 \n", "22 0.721 12.2 0 1 \n", "23 0.713 12.0 0 1 \n", "24 0.703 11.6 0 1 \n", "25 0.696 11.4 0 1 \n", "26 0.685 10.8 0 1 \n", "27 0.681 10.9 0 1 \n", "28 0.674 10.7 0 1 \n", "29 0.670 10.7 0 1 \n", "... ... ... .. ... \n", "2908 0.565 12.5 0 1 \n", "2909 0.554 12.3 0 1 \n", "2910 0.543 12.0 0 1 \n", "2911 0.533 11.8 0 1 \n", "2912 0.518 11.6 0 1 \n", "2913 0.504 11.4 0 1 \n", "2914 0.492 11.1 0 1 \n", "2915 0.479 10.9 0 1 \n", "2916 0.467 10.7 0 1 \n", "2917 0.456 10.5 0 1 \n", "2918 0.443 10.2 0 1 \n", "2919 0.433 10.0 0 1 \n", "2920 0.424 9.8 0 1 \n", "2921 0.418 9.6 0 1 \n", "2922 0.507 10.3 0 1 \n", "2923 0.498 10.3 0 1 \n", "2924 0.488 10.4 0 1 \n", "2925 0.464 9.8 0 1 \n", "2926 0.452 10.1 0 1 \n", "2927 0.436 10.0 0 1 \n", "2928 0.419 9.9 0 1 \n", "2929 0.421 9.7 0 1 \n", "2930 0.414 9.6 0 1 \n", "2931 0.408 9.5 0 1 \n", "2932 0.406 9.3 0 1 \n", "2933 0.407 9.2 0 1 \n", "2934 0.418 9.5 0 1 \n", "2935 0.427 10.0 0 1 \n", "2936 0.427 9.8 0 1 \n", "2937 0.434 9.8 0 1 \n", "\n", "[2938 rows x 22 columns]" ] }, "execution_count": 60, "metadata": {}, "output_type": "execute_result" } ], "source": [ "status = pd.get_dummies(life_data.Status)\n", "life_data = pd.concat([life_data, status], axis = 1)\n", "life_data = life_data.drop(['Status'], axis=1)\n", "life_data.rename(columns = {'Deloping' : '0', 'Developed' : 1})" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Life expectancyAdult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolio...DiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchoolingDevelopedDeveloping
Country
Afghanistan58.19375269.062578.25000.01437534.96011064.5625002362.250015.51875107.562548.3750...52.31250.10000340.0154259.972260e+0616.5812515.581250.4153758.2125001
Albania75.1562545.06250.68754.848750193.25909198.00000053.375049.068750.937598.1250...98.06250.100002119.7266796.969116e+051.618751.700000.70987512.1375001
Algeria73.61875108.187520.31250.406667236.18524178.0000001943.875048.7437523.500091.7500...91.87500.100002847.8533922.164983e+076.093755.975000.69487512.7125001
Angola49.01875328.562583.75005.740667102.10026870.2222223561.312518.01875132.625046.1250...47.68752.368751975.1430451.014710e+076.193756.668750.4583758.0437501
Antigua and Barbuda75.05625127.50000.00007.9493331001.58522698.2666670.000038.425000.000096.9375...98.31250.125009759.305728NaN3.425003.375000.4886258.8437501
\n", "

5 rows × 21 columns

\n", "
" ], "text/plain": [ " Life expectancy Adult Mortality infant deaths \\\n", "Country \n", "Afghanistan 58.19375 269.0625 78.2500 \n", "Albania 75.15625 45.0625 0.6875 \n", "Algeria 73.61875 108.1875 20.3125 \n", "Angola 49.01875 328.5625 83.7500 \n", "Antigua and Barbuda 75.05625 127.5000 0.0000 \n", "\n", " Alcohol percentage expenditure Hepatitis B Measles \\\n", "Country \n", "Afghanistan 0.014375 34.960110 64.562500 2362.2500 \n", "Albania 4.848750 193.259091 98.000000 53.3750 \n", "Algeria 0.406667 236.185241 78.000000 1943.8750 \n", "Angola 5.740667 102.100268 70.222222 3561.3125 \n", "Antigua and Barbuda 7.949333 1001.585226 98.266667 0.0000 \n", "\n", " BMI under-five deaths Polio ... \\\n", "Country ... \n", "Afghanistan 15.51875 107.5625 48.3750 ... \n", "Albania 49.06875 0.9375 98.1250 ... \n", "Algeria 48.74375 23.5000 91.7500 ... \n", "Angola 18.01875 132.6250 46.1250 ... \n", "Antigua and Barbuda 38.42500 0.0000 96.9375 ... \n", "\n", " Diphtheria HIV/AIDS GDP Population \\\n", "Country \n", "Afghanistan 52.3125 0.10000 340.015425 9.972260e+06 \n", "Albania 98.0625 0.10000 2119.726679 6.969116e+05 \n", "Algeria 91.8750 0.10000 2847.853392 2.164983e+07 \n", "Angola 47.6875 2.36875 1975.143045 1.014710e+07 \n", "Antigua and Barbuda 98.3125 0.12500 9759.305728 NaN \n", "\n", " thinness 1-19 years thinness 5-9 years \\\n", "Country \n", "Afghanistan 16.58125 15.58125 \n", "Albania 1.61875 1.70000 \n", "Algeria 6.09375 5.97500 \n", "Angola 6.19375 6.66875 \n", "Antigua and Barbuda 3.42500 3.37500 \n", "\n", " Income composition of resources Schooling Developed \\\n", "Country \n", "Afghanistan 0.415375 8.21250 0 \n", "Albania 0.709875 12.13750 0 \n", "Algeria 0.694875 12.71250 0 \n", "Angola 0.458375 8.04375 0 \n", "Antigua and Barbuda 0.488625 8.84375 0 \n", "\n", " Developing \n", "Country \n", "Afghanistan 1 \n", "Albania 1 \n", "Algeria 1 \n", "Angola 1 \n", "Antigua and Barbuda 1 \n", "\n", "[5 rows x 21 columns]" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "life_data = life_data.groupby('Country').mean()\n", "life_data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 3. EDA" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Life expectancy ', 'Adult Mortality', 'infant deaths', 'Alcohol',\n", " 'percentage expenditure', 'Hepatitis B', 'Measles ', ' BMI ',\n", " 'under-five deaths ', 'Polio', 'Total expenditure', 'Diphtheria ',\n", " ' HIV/AIDS', 'GDP', 'Population', ' thinness 1-19 years',\n", " ' thinness 5-9 years', 'Income composition of resources', 'Schooling',\n", " 'Developed', 'Developing'],\n", " dtype='object')" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "life_data.columns" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Life expectancy')" ] }, "execution_count": 94, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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ET4DflWw+GbgieXwFhT/4tlIh7raX1Om6J3n8HIV1PgZp82NeJe62l9RFez55\n2pv8BHAsMLGmStsd83p1ayIYBJ4sev4UHfI/HoX/yX4saY2kpa0OZhr2iYhNUDgBAHu3OJ56nJ2U\nR7+83ZpXSklaABwG3E0HHfOSuKEDjnmyyuI6YDNwG/ArYEtEbE126aTzS1ndmghUZluntIEtiojD\ngROBjybNGJa9y4ADgYXAJuBLrQ2nMkkvA64HPhkRz7Y6nrTKxN0RxzwitkXEQmBfCq0Nf1Zut+ZG\nNbO6NRE8Bbyq6Pm+wMYWxVKXiNiY/N4M3EiFMt1t7OmiCrPzKFxFtb2IeDr5g98OfI02Pe5JO/X1\nwFURcUOyue2Pebm4O+WYT4iILcC/AkcD/ZIminZ2zPmlkm5NBD8DXpP07O9CoQz2zS2OqSZJuyed\naUjaHTiezivTfTNwVvL4LOCmFsaS2sSJNHEKbXjck47LbwAPRsSXi15q62NeKe4OOeYDkvqTx33A\nX1Do41gNnJbs1nbHvF5dOWoIIBmK9ndAD3B5RHyhxSHVJOkACncBUCgR/t12jlvSSuAtFKoxPg2c\nD6wCrgH2A54ATo+ItuqYrRD3Wyg0UQTwGPDhiXb3diHpzcC/AevZsfzr31Bob2/bY14l7jNp/2N+\nCIXO4B4KF87XRMSFyd/q9yissrgWeG+y2FZH6tpEYGZm6XRr05CZmaXkRGBmlnNOBGZmOedEYGaW\nc04EZmY550RguSHp+ZLn75P01eTxBZI+nWxbWbLfXpJGJe2aPD9T0meLXr9J0n+UvOcCSZ9OHn9L\n0qNJBctfSPq2pMGifT+gQsXZ+yTdL8l1saypnAjMproBeKukOUXbTgNuLhon/jbgFoBkstHhFGaa\n7l/lc8+JiEOBgyiMO18taRdJ+wKfBd4cEYdQmLV634z+i8xqcCIwK5LUwPkJ8M6izWcAK2FyluxC\n4J7ktVOB71OYXHRGis+PiLgU+DWFelJ7A89RKItNRDwfEY/OyD/GLCUnAsuTvqJFUNYBF1bYbyXJ\nSV3SfOBPKZQUgELlzHtjx0zMM5P9VyaP07oHOBi4l8Ls5kclfVPSO6u/zWzmORFYnoxFxMKJH+Bv\nK+z3A+DNkvagsHjKdRGxLXntbcA/A0jaB/hPwL9HxC+ArZJelzIWQaGyZfKZp1FYTOnSZJEcs6Zx\nIjArERFjFPoATqGoWShxPPDj5PG7gbkUruYfAxaQonkocRiF4mUTzUU/jYiLkvef2uA/wawuTgRm\n5a2ksBzhPsBdAJJeAcyOiN8m+5wJvC0iFkTEAuAIaiQCFXwcmAfcImm+pMOLdlkIPD6j/xKzGpwI\nzMr7MTAfuLqoP+CtwL/A5Eq7FzgVAAAAb0lEQVRb+5EkCYCkk/dZSUeV+byLJd1LofnnDcDiZBnV\nXuASSRuSfot3U1gT16xpXH3ULCVJXwe+HhF31dzZrIM4EZiZ5ZybhszMcs6JwMws55wIzMxyzonA\nzCznnAjMzHLOicDMLOecCMzMcu7/A5xS0zt45Ce7AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(life_data[' HIV/AIDS'], life_data['Life expectancy '])\n", "plt.xlabel('HIV/AIDS')\n", "plt.ylabel('Life expectancy')" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Life expectancy')" ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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c7u7Puvukux8A/gE4PeK7N7r7oLsP9vX1VX3sa4eWRE7ka4a7+aGBfq67ZAn9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\n/wO/tG5ZeYNhywAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(life_data.GDP, life_data['Life expectancy '])\n", "plt.xlabel('GDP')\n", "plt.ylabel('Life expectancy')" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Life expectancy')" ] }, "execution_count": 74, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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anczNB0WYnpll0zU7gewq5TRGKA1CHWYTa+aw1EGSPoK5cIeyxoSyFYSzjNtx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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(life_data[' BMI '], life_data['Life expectancy '])\n", "plt.xlabel('BMI')\n", "plt.ylabel('Life expectancy')" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Life expectancy')" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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GSJaqzDpq6APAxZJ2Ax4E/hz4kqQFQAAbgb+YYMyZfPpN81l26R2M1ukwGNm6nUUrbuTx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\nSUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(life_data['under-five deaths '], life_data['Life expectancy '])\n", "plt.xlabel('under-five deaths')\n", "plt.ylabel('Life expectancy')" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Life expectancy')" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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DUsKUeTPvp1Ow15alTE5asyhrzmYYveGyeL0A7wKOVNXPishc4Hmq+h8ddj0S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\nAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(life_data['Alcohol'], life_data['Life expectancy '])\n", "plt.xlabel('Alcohol')\n", "plt.ylabel('Life expectancy')" ] }, { "cell_type": "code", "execution_count": 88, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Life expectancy')" ] }, "execution_count": 88, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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QVMdvJZlcVDNL1DyFSzdsn2q/r9bfZ7zw0r6mOnYbBZK4SSuawCbSOkkCwR8AbyXo7H0C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\n0uMUCEREetz/B5zNzHOufPg1AAAAAElFTkSuQmCC\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(life_data['Adult Mortality'], life_data['Life expectancy '])\n", "plt.xlabel('Adult Mortality')\n", "plt.ylabel('Life expectancy')" ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Life expectancy')" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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pbGsPRDrV8YIyM/t1d/8q8Boz+5+Ntys1JFkrOk+vef5SVc1SQ4eFXw+PuE0L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\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(life_data['Schooling'], life_data['Life expectancy '])\n", "plt.xlabel('Schooling')\n", "plt.ylabel('Life expectancy')" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'Life expectancy')" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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B0r3A48AWYHNELJS0O7ASmEs2R/NJEfFoI/fb09tXMSBkedq+Itmtj8zMthmP\nO4WjIuJ3Za+XAtdFxLmSlqbXn2jkDmtVFj/61LZhtT0UhpnZ9iaiTuH1wEXp+UVAd6N34MpiM7PR\nKTooBPAjSWskLU5pz4+IjQDp7/MavdNalcVq9M7MzJpI0UHhiIg4GDgOeL+kI/OuKGmxpNWSVm/a\ntKmundaqLB5hpk4zs5ZWaFCIiIfS30eAq4BDgYclzQFIfx+psu6FEbEwIhbOnj27rv12L+iq2uS0\ny01OzcyqKiwoSNpF0m6l58CxwG3A1cBpabHTgO8Wsf+zXnfADrOvATz5zGZ6evuK2KWZ2ZRXZOuj\n5wNXSSrt59sRca2km4FLJb0LuB94UxE7L7UqOvt7t2/X4qh/YJAll63bbhkzM8sUdqcQEXdHxEHp\ncUBEfDql/z4ijomIF6e/fygqD90LuogKlQiDW4PlV99e1G7NzKasph/mon9gsOJ71dLNzFpZ0waF\n0jwKZmaWX9MGhUrzKJSr1jrJzKyVNW1QqNWrub1NnPW6A8YxN2ZmU0PTBoVqvZrbJFaceJBbHpmZ\nVdC0QWHJonk79FPoaG/jvJMcEMzMqmna+RRKJ34PjW1mll/TBgXw0NhmZvVq2uIjMzOrn4OCmZkN\ncVAwM7MhDgpmZjbEQcHMzIY0deujnt4+N0k1M6tD0waF0oB4pfGP+voHhgbIc2AwM6usaYuPKg2I\nNzC4hRWrNkxQjszMJr+mDQrVBsSrNVCemVmra9qgUG1AvGrpZmY2DkFBUpukXknXpNffkHSPpLXp\nMb+I/VYbEG/JonlF7M7MrCmMR0Xzh4E7geeUpS2JiMuL3KkHxDMzq1+hQUHSC4DXAp8GPlLkvirx\ngHhmZvUpuvjo88DHga3D0j8t6VZJ50vaqeA8mJlZToUFBUl/ATwSEWuGvbUM2B94ObA78Ikq6y+W\ntFrS6k2bNhWVTTMzK1PkncIRwPGS7gW+Axwt6VsRsTEyzwBfBw6ttHJEXBgRCyNi4ezZswvMppmZ\nlRQWFCJiWUS8ICLmAicDP4mIUyXNAZAkoBu4rag8mJlZfSZimIuLJc0GBKwF3jcBeTAzswoUEROd\nhxFJ2gTcN8rV9wR+18DsTBU+7tbSqscNrXvseY77RRFRV/n7lAgKYyFpdUQsnOh8jDcfd2tp1eOG\n1j32oo67aYe5MDOz+jkomJnZkFYIChdOdAYmiI+7tbTqcUPrHnshx930dQpmZpZfK9wpmJlZTk0d\nFCT9uaQNkn4taelE52csJL1Q0vWS7pR0u6QPp/TdJf1Y0q/S31kpXZK+kI79VkkHl23rtLT8rySd\nNlHHVI8KQ7DvI+mmdAwrJc3XT5FQAAAKbUlEQVRI6Tul179O788t28aylL5B0qKJOZL6SOqUdLmk\nu9J3f3grfOeSzki/89skXSJp52b8ziV9TdIjkm4rS2vY9yvpEEnr0zpfSJ2Ga4uIpnwAbcBvgH2B\nGcA64CUTna8xHM8c4OD0fDfgv4GXAP8ILE3pS4HPpOevAX5I1knwMOCmlL47cHf6Oys9nzXRx5fj\n+D8CfBu4Jr2+FDg5Pf8S8Ffp+V8DX0rPTwZWpucvSb+BnYB90m+jbaKPK8dxXwS8Oz2fAXQ2+3cO\ndAH3AB1l3/Xbm/E7B44EDgZuK0tr2PcL/BI4PK3zQ+C4EfM00R9KgR/24cCqstfLgGUTna8GHt93\ngT8DNgBzUtocYEN6/mXglLLlN6T3TwG+XJa+3XKT8QG8ALgOOBq4Jv3AfwdMH/5dA6uAw9Pz6Wk5\nDf/+y5ebrA+yOUjuIdX9Df8um/U7T0HhgXSSm56+80XN+p0Dc4cFhYZ8v+m9u8rSt1uu2qOZi49K\nP6ySB1PalJdujxcANwHPj4iNAOnv89Ji1Y5/Kn4uw4dg3wPoj4jN6XX5MQwdX3r/sbT8VDzufYFN\nwNdT0dlXJO1Ck3/nEdEHfBa4H9hI9h2uoTW+c2jc99uVng9Pr6mZg0KlsrMp39RK0q7AFcDpEfE/\ntRatkBY10iclVR6CvdYxNMVxJ9PJihYuiIgFwJNkxQnVNMWxpzL015MV+ewF7AIcV2HRZvzOa6n3\nOEd1/M0cFB4EXlj2+gXAQxOUl4aQ1E4WEC6OiCtT8sPaNvLsHOCRlF7t+Kfa57LDEOxkdw6dkkoD\nOpYfw9DxpfefC/yBqXfckOX5wYi4Kb2+nCxINPt3/mrgnojYFBGDwJXAK2mN7xwa9/0+mJ4PT6+p\nmYPCzcCLU4uFGWQVUFdPcJ5GLbUa+CpwZ0R8ruytq4FSa4PTyOoaSulvSy0WDgMeS7eiq4BjJc1K\nV2THprRJKSoPwf4W4HrgxLTY8OMufR4npuUjpZ+cWqrsA7yYrBJu0oqI3wIPSJqXko4B7qDJv3Oy\nYqPDJM1Mv/vScTf9d5405PtN7z0u6bD0Ob6tbFvVTXQlS8EVOK8ha6XzG+BvJzo/YzyWPyW79buV\nbMjxten49iCrhP1V+rt7Wl7Av6RjXw8sLNvWO4Ffp8c7JvrY6vgMXsW21kf7kv2D/xq4DNgppe+c\nXv86vb9v2fp/mz6PDeRohTEZHsB8YHX63nvIWpc0/XcOnA3cRTbfyjfJWhA13XcOXEJWbzJIdmX/\nrkZ+v8DC9Bn+BvgiwxotVHq4R7OZmQ1p5uIjMzOrk4OCmZkNcVAwM7MhDgpmZjbEQcHMzIY4KLQI\nSVskrU2jTl4maeYE5eP0ovctaW75qJMpbbmkj41ye2+X9MX0/BuSThxpnWYn6V5Je6bnP09/50r6\ny4nNmY2Vg0LrGIiI+RHxUuBZ4H15V5TU1sB8nA5MSECarMp66U5JEfHK9HQuUHdQaPDvy8bIQaE1\n/SfwRwCSTpX0y3QX8eXSP6ikJyR9UtJNwOGSXi7p55LWpeV3UzbHwQpJN6fx3d+b1n2VpJ9q2zwA\nF6demB8iG8vmeknXp2UvkLRa2dj5Z5cyKOk1ad3/SuPAl+ZR2EXZGPQ3p0HiXl/vwUvaT9K1ktZI\n+k9J+6f01ykbj79X0r9Len6VTRyZPou7y+8aJH1c2dj16ySdm9Lek/K6TtIVpbukdMfxufQ5fCbv\ncUlaUvZ5n53SXp5e75y2c7ukl6bv4WeSrpJ0h6QvSZqW1jlW0i8k3ZLuHHdN6fdKOjulry/7bPaQ\n9KOUty9TNq6OpCfS03OB/51+S2eU32Gl5a6R9KrSOsN+X4dI+o/0naxSGubBJsBE9+jzY3wewBPp\n73Syru5/BfwJ8D2gPb33r8Db0vMATkrPZ5CN0f7y9Po5aTuLgTNT2k5kPW/3Iet5/BjZWCvTgF8A\nf5qWuxfYsyxfpd6abcBPgZeR9VB9ANgnvXcJ23oy/wNwanreSdZjfZdhxzoXGGBbz++1wG+Bj6X3\nrwNenJ6/gmxYBMh6C5c6dL4bOC89fzvwxfT8G2S9Z6eRjdf/65R+HPBzYOaw49qjLF+fAj5Ytp1r\nSOP75zyuY8nm5VXa/zXAkWXb/ixZj9dlKe1VwNNkPYHbgB+TDQOxJ/Cz0vaBTwB/V/b9lPL418BX\n0vMvlC3zWrLfx57DfluvKn1Pwz+39Poa4FUVfl/t6bObnV6/GfjaRP/PtOpjSt+2Wl06JK1Nz/+T\nbBylxcAhwM3KJmTqYNvgW1vIBt8DmAdsjIibASKNzirpWOBlZVfLzyUbX+ZZ4JcR8WBabi3Zifq/\nKuTrJEmLyYLMHLIT7TTg7oi4Jy1zScorZCfG47WtfmBnYG/gzmHb/U1EzC+9kLQ8/d2VbHC1y7Rt\nEqqd0t8XACvTVeoMsrkMKumJiK3AHWV3E68Gvh4RT6XP6A8p/aWSPkV2ot+V7cccuiwittRxXMem\nR296vSvZ5/0z4JNk4309DXyobJ1fRsTd6dgvIRsu5Wmyz/mG9BnMIAvcJaXBFtcAJ6TnR5aeR8T3\nJT1a5bPJa/jv66XAj1N+2siGfrAJ4KDQOgbKT5IwNMjeRRGxrMLyT5edsETlIXdFdlW53eBqqYjg\nmbKkLVT4rSkbpOxjZHcgj0r6BtnJsNaUgQLeGBEbaixTyzSycfnnV3jvn4HPRcTV6RiWV9lG+bGp\n7G+lz+gbQHdErJP0drKr6ZInh21npOMScE5EfLnCe7uTBYl2ss+wtO3heSoNqfzjiDilyn5Kxzf8\ne6t3TJzNbF9EvXPZ8+G/r9sj4vA6t28FcJ1Ca7sOOFHS82BobtgXVVjuLmAvSS9Py+2mrHJ0FfBX\nyob0RtIfK5sEppbHyaYThawY6kngsXTFXRoz/y5gX22ba/fNZeuvAj6YAhqSFuQ9WBi6y7lH0pvS\n+pJ0UHr7uUBfen5apfVr+BHwzrI6g91T+m7AxvQZvaXG+nmOa1XaR6n8v6v03ZEVK/1f4GLgM2Xr\nHKpspOBpZJ/jfwE3AkdIKtUrzZT0xyMc389K+Zd0HFlR23Dl3y1kRVHzJU2T9ELg0Crb3gDMlnR4\n2n67pANGyI8VxHcKLSwi7pB0JvCjdNIYBN4P3DdsuWclvRn4Z0kdZOX1rwa+QlYsdEs6mW0CukfY\n7YXADyVtjIijJPUCt5PVWdyQ9jcg6a+BayX9ju2HO/57svkUbk37vBf4izoP/S3ABenY28nmaVhH\ndmdwmaQ+shPnPnk3GBHXSpoPrJb0LPAD4G/ITtQ3kX2m69n+pFluxOOKiB9J+hPgFyl2PAGcKunP\ngc0R8W1lDQV+LulospnqfkFWAXwg2Yn9qojYmu5aLpFUKjo7k6weo5qz0/K3AP9BNrz1cLcCmyWt\nI7tD+jxZEdx6spE6b6m04fT7OhH4gqTnkp2XPk/2u7Bx5lFSbVKStGtEPJFOkP8C/Coizp/ofE0l\nqQjsYxFRb9C0FubiI5us3pM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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.scatter(life_data['percentage expenditure'], life_data['Life expectancy '])\n", "plt.xlabel('Percentage Healhcare expenditure')\n", "plt.ylabel('Life expectancy')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using the scatter plot we plot the Life Expectancy against some other varyables to see if there is any correlation between them.\n", "
\n", "There seem to be a positive correlation between The Percentage of Healthcare Expenditure, Schooling, GDP and BMI and Life Expectancy, while there is a negative one between Adult Mortality, AIDS and Life Expectancy,there does not seem to have any correlation between Alcohol, under 5 years - old deaths and Life Expectancy." ] }, { "cell_type": "code", "execution_count": 86, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 86, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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gibFLqr2iKKnAb8AswBLoVd6DVl4mzPiE46cjSU5OpX23txgxqA+vvvT4E82U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56I3sMEr+P2Df+Xje/eMwRqPCY20aMKrbA6VsNp+8yhfbYgBoGOzDosEPAvDhxqPs+fsGigLt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wYzskO9sr6eu8XKveeUdxpfNxb1a0Nseg7TNh7EYJTRG40MblmTAS1qWqV5l3HzX6Vl15bk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\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5Uz2wCo6ODvTqEMH2QyctbISAzGzjwENGVg7+pvbEvuNnqVerKvVrG2fZeHu4odXa1zzS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\n9juUxlFVbrpMG0l3AaMajwlYx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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize = (14, 10))\n", "sns.heatmap(life_data.corr(), annot = True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will plot the correlation matrix visulizing it with a heatmap. The legend tells that the warmer colors show higher and positive correlation, while the colder low or negative.\n", "
\n", "There is a very high correlation between thinness of 5-9 year-old and that of 1-19 year-old. Also between population and infant deaths, under 5 deaths, another is between schooling and income composition of resources. On the other hand Life expectancy and Adult Mortality are very highly negatively correlated." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 4. Preprocessing the data\n", "\n", "The raw data is not suitable for us to start building a model so some preprocessing will be done. First the Status of the country is turned into numberical with the get_dummies function, so we get 2 new columns. The original column is being dropped. Second the data is being grouped by the country and we find the mean values during the 2000 - 2015 year period.\n", "
\n", "Then the Life expectancy column is removed to form the life_labels variable or the output, and the rest is stored as the life_features variable. Now we consider that we have some null values in the table, the isnull function has been used to find the with the boolean True. Below that the number of null values are displayed in each separate column. It is mostly situated in the Population and GDP columns. \n", "
\n", "Now the missing values are filled with the mean of its respective column. This will create some distortions, but the other option in removing parts of the table will shrink the data so it will be avoided here because the number of rows is not that high. The final shape for the life_features is 193 rows to 20 columns.\n", "Finally considering the large differences in the values of the columns, there will be some scaling with the MinMaxScaler function.\n", "
\n", "Now we will split the data into a training part of 70% and a testing of 30%. Cross validation will be initialized with the creation of 5 fold split." ] }, { "cell_type": "code", "execution_count": 95, "metadata": { "collapsed": true }, "outputs": [], "source": [ "life_labels = life_data['Life expectancy ']\n", "life_features = life_data.drop('Life expectancy ', axis = 1)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Adult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchoolingDevelopedDeveloping
Country
AfghanistanFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
AlbaniaFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
AlgeriaFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
AngolaFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalse
Antigua and BarbudaFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseFalseTrueFalseFalseFalseFalseFalseFalse
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" ], "text/plain": [ " Adult Mortality infant deaths Alcohol \\\n", "Country \n", "Afghanistan False False False \n", "Albania False False False \n", "Algeria False False False \n", "Angola False False False \n", "Antigua and Barbuda False False False \n", "\n", " percentage expenditure Hepatitis B Measles BMI \\\n", "Country \n", "Afghanistan False False False False \n", "Albania False False False False \n", "Algeria False False False False \n", "Angola False False False False \n", "Antigua and Barbuda False False False False \n", "\n", " under-five deaths Polio Total expenditure \\\n", "Country \n", "Afghanistan False False False \n", "Albania False False False \n", "Algeria False False False \n", "Angola False False False \n", "Antigua and Barbuda False False False \n", "\n", " Diphtheria HIV/AIDS GDP Population \\\n", "Country \n", "Afghanistan False False False False \n", "Albania False False False False \n", "Algeria False False False False \n", "Angola False False False False \n", "Antigua and Barbuda False False False True \n", "\n", " thinness 1-19 years thinness 5-9 years \\\n", "Country \n", "Afghanistan False False \n", "Albania False False \n", "Algeria False False \n", "Angola False False \n", "Antigua and Barbuda False False \n", "\n", " Income composition of resources Schooling Developed \\\n", "Country \n", "Afghanistan False False False \n", "Albania False False False \n", "Algeria False False False \n", "Angola False False False \n", "Antigua and Barbuda False False False \n", "\n", " Developing \n", "Country \n", "Afghanistan False \n", "Albania False \n", "Algeria False \n", "Angola False \n", "Antigua and Barbuda False " ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "life_features.isnull().head()" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Adult Mortality 10\n", "infant deaths 0\n", "Alcohol 2\n", "percentage expenditure 0\n", "Hepatitis B 9\n", "Measles 0\n", " BMI 4\n", "under-five deaths 0\n", "Polio 0\n", "Total expenditure 2\n", "Diphtheria 0\n", " HIV/AIDS 0\n", "GDP 30\n", "Population 48\n", " thinness 1-19 years 4\n", " thinness 5-9 years 4\n", "Income composition of resources 17\n", "Schooling 13\n", "Developed 0\n", "Developing 0\n", "dtype: int64" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "life_features.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "10" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "life_labels.isnull().sum()" ] }, { "cell_type": "code", "execution_count": 98, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Adult Mortalityinfant deathsAlcoholpercentage expenditureHepatitis BMeaslesBMIunder-five deathsPolioTotal expenditureDiphtheriaHIV/AIDSGDPPopulationthinness 1-19 yearsthinness 5-9 yearsIncome composition of resourcesSchoolingDevelopedDeveloping
Country
Afghanistan269.06250078.25000.01437534.96011064.5625002362.250015.51875107.562548.3750008.25250052.3125000.10000340.0154259.972260e+0616.5812515.581250.4153758.21250001
Albania45.0625000.68754.848750193.25909198.00000053.375049.068750.937598.1250005.94562598.0625000.100002119.7266796.969116e+051.618751.700000.70987512.13750001
Algeria108.18750020.31250.406667236.18524178.0000001943.875048.7437523.500091.7500004.60400091.8750000.100002847.8533922.164983e+076.093755.975000.69487512.71250001
Angola328.56250083.75005.740667102.10026870.2222223561.312518.01875132.625046.1250003.91933347.6875002.368751975.1430451.014710e+076.193756.668750.4583758.04375001
Antigua and Barbuda127.5000000.00007.9493331001.58522698.2666670.000038.425000.000096.9375004.79133398.3125000.125009759.3057281.257006e+073.425003.375000.4886258.84375001
Argentina106.00000010.12507.966667773.03898181.2857142.000054.9812511.375093.3750006.91266792.3750000.100006998.5751032.012120e+071.075000.950000.79412516.50625001
Armenia117.3750001.00003.702667131.00741987.562500274.250044.706251.000089.1250004.86133387.3750000.100001999.9863181.015165e+062.037502.112500.69837511.78750001
Australia63.1875001.000010.1553335332.22647393.400000103.937555.862501.375086.7500008.83666786.8750000.1000034637.5650474.587010e+060.668750.625000.91812520.03750010
Austria65.7500000.000012.2360004928.43918881.06250077.250048.287500.000086.0000004.71533386.7500000.1000033827.4763096.474880e+061.731251.937500.86237515.38750010
Azerbaijan115.1875006.06250.992000131.14874759.214286598.187543.843757.187575.8125005.67933382.3125000.100003302.9478072.157370e+062.900002.943750.70268711.35000001
Bahamas152.0625000.000010.2713330.00000083.6000000.000052.881250.000095.0000006.70600095.7500000.118757223.4520501.257006e+072.550002.518750.73793812.42500001
Bahrain65.3750000.00001.934667602.08714098.1250006.687551.656250.000098.0000003.80733398.0625000.1000011191.2474211.257006e+076.106255.950000.80793714.11250001
Bangladesh141.375000153.62500.01000032.32097277.6153854649.937512.87500201.562587.5000002.85466792.7500000.10000443.5614814.298516e+0713.7750014.331250.5178758.69375001
Barbados77.6250000.00008.206667389.07699584.0666670.000045.975000.000080.3125006.12200089.9375000.2562512017.0985291.257006e+073.956253.918750.77043714.89375001
Belarus218.7500000.562513.497333329.04645588.93750024.812554.743750.875089.8750006.19200092.1875000.100003811.5253486.371867e+062.212502.337500.74325014.75000001
Belgium70.1875000.250011.0426672392.43265774.50000081.562550.893751.000097.7500005.96200097.3125000.1000016915.3060002.884043e+060.862500.856250.87775016.78750010
Belize155.5000000.00006.252667365.84349094.7500000.000037.675000.000095.5000005.03933395.1875000.431253933.0121741.703929e+053.556253.487500.69393712.45625001
Benin269.37500025.00001.00000043.99195662.5714291116.875019.6125039.250067.6875004.63600067.9375001.70625561.1679683.942979e+068.250008.137500.4380628.85000001
Bhutan230.2500000.68750.278667134.76407094.12500080.250017.581250.937589.3750005.34600093.8750000.356251353.9869464.925849e+0517.2187517.925000.18387510.22500001
Bolivia (Plurinational State of)177.50000010.75003.2120000.00000082.8750007.625044.8187514.000081.6875005.62600087.3750000.156257223.4520501.257006e+071.300001.187500.63375013.96250001
Bosnia and Herzegovina64.9375000.00004.405333215.45745971.666667319.187548.962500.000078.3125008.74200078.5000000.100002245.0260241.986993e+062.781252.756250.45037512.22500001
Botswana448.1250002.00004.670000334.26697187.312500237.875032.243753.125096.1875005.51066795.81250016.525004498.2854311.063867e+067.437507.812500.63037512.13750001
Brazil150.68750068.25007.213333390.69559596.18750095.375047.0625077.250098.3125008.01933397.9375000.100006143.1617948.812807e+073.050003.012500.71018714.16250001
Brunei Darussalam67.0625000.00000.3786671276.87948597.8750007.312529.718750.000097.5000002.82000096.8125000.1000019744.8081021.257006e+076.200005.675000.83937514.10625001
Bulgaria125.5000000.937510.865333350.74520494.5000001530.312554.500001.000094.2500007.23600094.0000000.100004938.9818215.290924e+062.131252.156250.75462513.72500010
Burkina Faso252.50000044.75004.24133352.04558181.1000005091.750015.5000079.937569.0000006.03800077.3125001.83750410.3720346.143695e+067.418756.981250.2296885.40625001
Burundi291.56250023.25004.10333315.33549893.3333331349.500015.3125035.562586.6250007.08933385.7500003.08750137.8153213.915447e+068.018758.012500.3278757.48125001
Cabo Verde116.1875000.00003.448000188.63298780.6428570.125024.375000.000084.3750004.75533379.0000000.531252023.5410392.926002e+058.012507.975000.57000012.20000001
Cambodia196.37500016.75001.48600033.79656179.6000001880.812515.3625020.500074.1875005.74066772.0625001.02500466.1968787.145967e+0610.1437510.881250.4919379.87500001
Cameroon294.87500054.93754.14133344.46230076.8181822979.250023.6187586.187572.5000004.71933369.4375006.21250781.0160049.967292e+066.550006.606250.4700008.90625001
...............................................................
Suriname166.0000000.00004.984000508.49403385.2727270.000050.156250.000078.2500006.66266782.5625000.762504781.2168221.909166e+053.512503.456250.48106211.82500001
Swaziland339.0000002.62504.463333278.09971376.00000049.125025.500003.687589.0000007.30866783.75000032.943752165.0908384.573031e+056.475006.631250.51568810.35000001
Sweden59.1875000.00006.9266674438.16315459.20000018.937556.250000.000098.3125009.93266798.3125000.1000029334.9906395.514868e+061.350001.306250.89312515.86875010
Switzerland55.7500000.000010.3380009801.81037778.518282397.500051.437500.000095.3750006.08733394.5625000.1000057362.8746015.913242e+060.537500.393750.91106215.39375010
Syrian Arab Republic139.6250008.00000.80466739.29948664.125000295.687547.312509.562564.8125003.94666748.4375000.100001087.4357746.741445e+066.450006.256250.61806210.98125001
Tajikistan177.56250010.68750.33066717.27719284.428571208.187533.1125012.750088.3125005.64800089.2500000.25625335.8417254.751355e+063.806253.850000.58381210.68125001
Thailand160.37500012.12506.131333401.25996397.0000004311.937521.5937514.125098.3125003.75733398.1875000.387503494.7817023.117051e+078.543758.700000.69468812.55000001
The former Yugoslav republic of Macedonia60.8125000.00001.8586670.00000083.54545574.750052.906250.000095.5000007.53600094.8750000.100007223.4520501.257006e+072.431252.456250.45575012.38125001
Timor-Leste170.3750002.37500.23533321.18752376.00000085.062514.550003.000063.2142861.64666764.2142860.10000551.7106494.601956e+0510.5250011.693750.51762510.70000001
Togo311.31250014.18750.94733323.70560476.625000532.500017.9937521.437567.1875005.24333374.3125003.86250317.2040923.722046e+067.868757.737500.44593710.65000001
Tonga129.6250000.00001.167333323.80934375.5000000.250062.943750.000076.8750005.20600070.3125000.100001981.5551862.658981e+040.100000.100000.69881314.21875001
Trinidad and Tobago163.3750000.00006.044667559.31487254.9285710.000037.731250.250076.4375005.19933366.5000000.625007741.7480907.761331e+056.137506.506250.75337512.33125001
Tunisia18.7500003.25001.310667366.74126096.75000033.187548.256253.687597.6875006.05266797.6875000.100003044.0814883.274493e+066.406256.337500.69262514.05625001
Turkey98.37500026.93751.421333253.41717887.0625005272.937556.4125032.250080.8125005.61800080.7500000.100003983.9177223.350135e+075.012504.850000.70331312.67500001
Turkmenistan214.8125006.56252.654667187.78198897.28571415.125038.118758.062595.5625002.82866796.3750000.100002511.6115402.635550e+063.337503.362500.2116259.83125001
Tuvalu164.7964480.00000.01000078.2812039.0000000.000079.300000.00009.00000016.6100009.0000000.100003542.1358901.819000e+030.200000.100000.6295020.00000001
Uganda300.18750089.68758.05066747.69210863.42857112394.625015.52500138.875065.4375008.54200064.8750007.65000421.0484961.554059e+076.262506.256250.44531210.59375001
Ukraine205.7500005.06257.369333169.42733054.6250005395.187550.893755.937581.4375006.77733370.0000000.537501577.2933291.000493e+072.575002.675000.71656314.60625001
United Arab Emirates67.0625001.00001.7500001886.73159093.87500075.625053.806251.000095.0625003.03666794.6250000.1000022110.3669861.257006e+075.181254.968750.81956312.81250001
United Kingdom of Great Britain and Northern Ireland70.3750003.625011.1312500.00000078.518282715.750055.387504.062592.8750008.53400092.8750000.100007223.4520501.257006e+070.750000.506250.62950211.89409710
United Republic of Tanzania304.43750095.25003.5826670.00000073.5000003348.562517.31875143.187583.3750004.70133374.6875007.275007223.4520501.257006e+077.425007.356250.62950211.89409701
United States of America58.18750026.18758.5793330.00000081.375000130.625058.4500030.750082.12500015.86333395.1250000.100007223.4520501.257006e+070.731250.606250.62950211.89409710
Uruguay119.9375000.56256.172667621.83891994.3125000.000052.925000.750094.2500008.75000089.1250000.100007192.5848752.396771e+061.600001.543750.76562515.23125001
Uzbekistan184.81250021.93751.60866744.37345095.642857208.437534.8062525.687598.5625005.63800098.4375000.20625651.0923599.036317e+053.143753.175000.60300011.64375001
Vanuatu137.8750000.00000.806667282.32574656.12500020.875044.256250.000066.1875003.92866759.0625000.100002000.2455181.230962e+051.568751.493750.36750010.56875001
Venezuela (Bolivarian Republic of)163.0000009.37507.4200000.00000066.250000165.000054.4875010.750074.6875004.99866768.5000000.100007223.4520501.257006e+071.650001.562500.72681212.78750001
Viet Nam126.56250029.18753.0873330.00000087.5384624232.937511.1875036.500094.9375005.97733391.7500000.143757223.4520501.257006e+0714.9250015.625000.62706311.51250001
Yemen211.81250039.37500.0473330.00000055.6875002761.187533.4875051.625067.1250005.00533372.6250000.100007223.4520501.257006e+0713.8312513.750000.4755008.50625001
Zambia354.31250033.43752.23933389.65040769.8181826563.812517.4500052.375064.3750005.82400074.25000011.93125811.8118416.260246e+066.881256.762500.49843711.21250001
Zimbabwe462.37500026.56254.48200020.36427170.562500923.000025.1375040.812575.6250006.15866775.18750023.26250410.9801948.021343e+067.012506.987500.4391259.82500001
\n", "

193 rows × 20 columns

\n", "
" ], "text/plain": [ " Adult Mortality \\\n", "Country \n", "Afghanistan 269.062500 \n", "Albania 45.062500 \n", "Algeria 108.187500 \n", "Angola 328.562500 \n", "Antigua and Barbuda 127.500000 \n", "Argentina 106.000000 \n", "Armenia 117.375000 \n", "Australia 63.187500 \n", "Austria 65.750000 \n", "Azerbaijan 115.187500 \n", "Bahamas 152.062500 \n", "Bahrain 65.375000 \n", "Bangladesh 141.375000 \n", "Barbados 77.625000 \n", "Belarus 218.750000 \n", "Belgium 70.187500 \n", "Belize 155.500000 \n", "Benin 269.375000 \n", "Bhutan 230.250000 \n", "Bolivia (Plurinational State of) 177.500000 \n", "Bosnia and Herzegovina 64.937500 \n", "Botswana 448.125000 \n", "Brazil 150.687500 \n", "Brunei Darussalam 67.062500 \n", "Bulgaria 125.500000 \n", "Burkina Faso 252.500000 \n", "Burundi 291.562500 \n", "Cabo Verde 116.187500 \n", "Cambodia 196.375000 \n", "Cameroon 294.875000 \n", "... ... \n", "Suriname 166.000000 \n", "Swaziland 339.000000 \n", "Sweden 59.187500 \n", "Switzerland 55.750000 \n", "Syrian Arab Republic 139.625000 \n", "Tajikistan 177.562500 \n", "Thailand 160.375000 \n", "The former Yugoslav republic of Macedonia 60.812500 \n", "Timor-Leste 170.375000 \n", "Togo 311.312500 \n", "Tonga 129.625000 \n", "Trinidad and Tobago 163.375000 \n", "Tunisia 18.750000 \n", "Turkey 98.375000 \n", "Turkmenistan 214.812500 \n", "Tuvalu 164.796448 \n", "Uganda 300.187500 \n", "Ukraine 205.750000 \n", "United Arab Emirates 67.062500 \n", "United Kingdom of Great Britain and Northern Ir... 70.375000 \n", "United Republic of Tanzania 304.437500 \n", "United States of America 58.187500 \n", "Uruguay 119.937500 \n", "Uzbekistan 184.812500 \n", "Vanuatu 137.875000 \n", "Venezuela (Bolivarian Republic of) 163.000000 \n", "Viet Nam 126.562500 \n", "Yemen 211.812500 \n", "Zambia 354.312500 \n", "Zimbabwe 462.375000 \n", "\n", " infant deaths Alcohol \\\n", "Country \n", "Afghanistan 78.2500 0.014375 \n", "Albania 0.6875 4.848750 \n", "Algeria 20.3125 0.406667 \n", "Angola 83.7500 5.740667 \n", "Antigua and Barbuda 0.0000 7.949333 \n", "Argentina 10.1250 7.966667 \n", "Armenia 1.0000 3.702667 \n", "Australia 1.0000 10.155333 \n", "Austria 0.0000 12.236000 \n", "Azerbaijan 6.0625 0.992000 \n", "Bahamas 0.0000 10.271333 \n", "Bahrain 0.0000 1.934667 \n", "Bangladesh 153.6250 0.010000 \n", "Barbados 0.0000 8.206667 \n", "Belarus 0.5625 13.497333 \n", "Belgium 0.2500 11.042667 \n", "Belize 0.0000 6.252667 \n", "Benin 25.0000 1.000000 \n", "Bhutan 0.6875 0.278667 \n", "Bolivia (Plurinational State of) 10.7500 3.212000 \n", "Bosnia and Herzegovina 0.0000 4.405333 \n", "Botswana 2.0000 4.670000 \n", "Brazil 68.2500 7.213333 \n", "Brunei Darussalam 0.0000 0.378667 \n", "Bulgaria 0.9375 10.865333 \n", "Burkina Faso 44.7500 4.241333 \n", "Burundi 23.2500 4.103333 \n", "Cabo Verde 0.0000 3.448000 \n", "Cambodia 16.7500 1.486000 \n", "Cameroon 54.9375 4.141333 \n", "... ... ... \n", "Suriname 0.0000 4.984000 \n", "Swaziland 2.6250 4.463333 \n", "Sweden 0.0000 6.926667 \n", "Switzerland 0.0000 10.338000 \n", "Syrian Arab Republic 8.0000 0.804667 \n", "Tajikistan 10.6875 0.330667 \n", "Thailand 12.1250 6.131333 \n", "The former Yugoslav republic of Macedonia 0.0000 1.858667 \n", "Timor-Leste 2.3750 0.235333 \n", "Togo 14.1875 0.947333 \n", "Tonga 0.0000 1.167333 \n", "Trinidad and Tobago 0.0000 6.044667 \n", "Tunisia 3.2500 1.310667 \n", "Turkey 26.9375 1.421333 \n", "Turkmenistan 6.5625 2.654667 \n", "Tuvalu 0.0000 0.010000 \n", "Uganda 89.6875 8.050667 \n", "Ukraine 5.0625 7.369333 \n", "United Arab Emirates 1.0000 1.750000 \n", "United Kingdom of Great Britain and Northern Ir... 3.6250 11.131250 \n", "United Republic of Tanzania 95.2500 3.582667 \n", "United States of America 26.1875 8.579333 \n", "Uruguay 0.5625 6.172667 \n", "Uzbekistan 21.9375 1.608667 \n", "Vanuatu 0.0000 0.806667 \n", "Venezuela (Bolivarian Republic of) 9.3750 7.420000 \n", "Viet Nam 29.1875 3.087333 \n", "Yemen 39.3750 0.047333 \n", "Zambia 33.4375 2.239333 \n", "Zimbabwe 26.5625 4.482000 \n", "\n", " percentage expenditure \\\n", "Country \n", "Afghanistan 34.960110 \n", "Albania 193.259091 \n", "Algeria 236.185241 \n", "Angola 102.100268 \n", "Antigua and Barbuda 1001.585226 \n", "Argentina 773.038981 \n", "Armenia 131.007419 \n", "Australia 5332.226473 \n", "Austria 4928.439188 \n", "Azerbaijan 131.148747 \n", "Bahamas 0.000000 \n", "Bahrain 602.087140 \n", "Bangladesh 32.320972 \n", "Barbados 389.076995 \n", "Belarus 329.046455 \n", "Belgium 2392.432657 \n", "Belize 365.843490 \n", "Benin 43.991956 \n", "Bhutan 134.764070 \n", "Bolivia (Plurinational State of) 0.000000 \n", "Bosnia and Herzegovina 215.457459 \n", "Botswana 334.266971 \n", "Brazil 390.695595 \n", "Brunei Darussalam 1276.879485 \n", "Bulgaria 350.745204 \n", "Burkina Faso 52.045581 \n", "Burundi 15.335498 \n", "Cabo Verde 188.632987 \n", "Cambodia 33.796561 \n", "Cameroon 44.462300 \n", "... ... \n", "Suriname 508.494033 \n", "Swaziland 278.099713 \n", "Sweden 4438.163154 \n", "Switzerland 9801.810377 \n", "Syrian Arab Republic 39.299486 \n", "Tajikistan 17.277192 \n", "Thailand 401.259963 \n", "The former Yugoslav republic of Macedonia 0.000000 \n", "Timor-Leste 21.187523 \n", "Togo 23.705604 \n", "Tonga 323.809343 \n", "Trinidad and Tobago 559.314872 \n", "Tunisia 366.741260 \n", "Turkey 253.417178 \n", "Turkmenistan 187.781988 \n", "Tuvalu 78.281203 \n", "Uganda 47.692108 \n", "Ukraine 169.427330 \n", "United Arab Emirates 1886.731590 \n", "United Kingdom of Great Britain and Northern Ir... 0.000000 \n", "United Republic of Tanzania 0.000000 \n", "United States of America 0.000000 \n", "Uruguay 621.838919 \n", "Uzbekistan 44.373450 \n", "Vanuatu 282.325746 \n", "Venezuela (Bolivarian Republic of) 0.000000 \n", "Viet Nam 0.000000 \n", "Yemen 0.000000 \n", "Zambia 89.650407 \n", "Zimbabwe 20.364271 \n", "\n", " Hepatitis B Measles \\\n", "Country \n", "Afghanistan 64.562500 2362.2500 \n", "Albania 98.000000 53.3750 \n", "Algeria 78.000000 1943.8750 \n", "Angola 70.222222 3561.3125 \n", "Antigua and Barbuda 98.266667 0.0000 \n", "Argentina 81.285714 2.0000 \n", "Armenia 87.562500 274.2500 \n", "Australia 93.400000 103.9375 \n", "Austria 81.062500 77.2500 \n", "Azerbaijan 59.214286 598.1875 \n", "Bahamas 83.600000 0.0000 \n", "Bahrain 98.125000 6.6875 \n", "Bangladesh 77.615385 4649.9375 \n", "Barbados 84.066667 0.0000 \n", "Belarus 88.937500 24.8125 \n", "Belgium 74.500000 81.5625 \n", "Belize 94.750000 0.0000 \n", "Benin 62.571429 1116.8750 \n", "Bhutan 94.125000 80.2500 \n", "Bolivia (Plurinational State of) 82.875000 7.6250 \n", "Bosnia and Herzegovina 71.666667 319.1875 \n", "Botswana 87.312500 237.8750 \n", "Brazil 96.187500 95.3750 \n", "Brunei Darussalam 97.875000 7.3125 \n", "Bulgaria 94.500000 1530.3125 \n", "Burkina Faso 81.100000 5091.7500 \n", "Burundi 93.333333 1349.5000 \n", "Cabo Verde 80.642857 0.1250 \n", "Cambodia 79.600000 1880.8125 \n", "Cameroon 76.818182 2979.2500 \n", "... ... ... \n", "Suriname 85.272727 0.0000 \n", "Swaziland 76.000000 49.1250 \n", "Sweden 59.200000 18.9375 \n", "Switzerland 78.518282 397.5000 \n", "Syrian Arab Republic 64.125000 295.6875 \n", "Tajikistan 84.428571 208.1875 \n", "Thailand 97.000000 4311.9375 \n", "The former Yugoslav republic of Macedonia 83.545455 74.7500 \n", "Timor-Leste 76.000000 85.0625 \n", "Togo 76.625000 532.5000 \n", "Tonga 75.500000 0.2500 \n", "Trinidad and Tobago 54.928571 0.0000 \n", "Tunisia 96.750000 33.1875 \n", "Turkey 87.062500 5272.9375 \n", "Turkmenistan 97.285714 15.1250 \n", "Tuvalu 9.000000 0.0000 \n", "Uganda 63.428571 12394.6250 \n", "Ukraine 54.625000 5395.1875 \n", "United Arab Emirates 93.875000 75.6250 \n", "United Kingdom of Great Britain and Northern Ir... 78.518282 715.7500 \n", "United Republic of Tanzania 73.500000 3348.5625 \n", "United States of America 81.375000 130.6250 \n", "Uruguay 94.312500 0.0000 \n", "Uzbekistan 95.642857 208.4375 \n", "Vanuatu 56.125000 20.8750 \n", "Venezuela (Bolivarian Republic of) 66.250000 165.0000 \n", "Viet Nam 87.538462 4232.9375 \n", "Yemen 55.687500 2761.1875 \n", "Zambia 69.818182 6563.8125 \n", "Zimbabwe 70.562500 923.0000 \n", "\n", " BMI \\\n", "Country \n", "Afghanistan 15.51875 \n", "Albania 49.06875 \n", "Algeria 48.74375 \n", "Angola 18.01875 \n", "Antigua and Barbuda 38.42500 \n", "Argentina 54.98125 \n", "Armenia 44.70625 \n", "Australia 55.86250 \n", "Austria 48.28750 \n", "Azerbaijan 43.84375 \n", "Bahamas 52.88125 \n", "Bahrain 51.65625 \n", "Bangladesh 12.87500 \n", "Barbados 45.97500 \n", "Belarus 54.74375 \n", "Belgium 50.89375 \n", "Belize 37.67500 \n", "Benin 19.61250 \n", "Bhutan 17.58125 \n", "Bolivia (Plurinational State of) 44.81875 \n", "Bosnia and Herzegovina 48.96250 \n", "Botswana 32.24375 \n", "Brazil 47.06250 \n", "Brunei Darussalam 29.71875 \n", "Bulgaria 54.50000 \n", "Burkina Faso 15.50000 \n", "Burundi 15.31250 \n", "Cabo Verde 24.37500 \n", "Cambodia 15.36250 \n", "Cameroon 23.61875 \n", "... ... \n", "Suriname 50.15625 \n", "Swaziland 25.50000 \n", "Sweden 56.25000 \n", "Switzerland 51.43750 \n", "Syrian Arab Republic 47.31250 \n", "Tajikistan 33.11250 \n", "Thailand 21.59375 \n", "The former Yugoslav republic of Macedonia 52.90625 \n", "Timor-Leste 14.55000 \n", "Togo 17.99375 \n", "Tonga 62.94375 \n", "Trinidad and Tobago 37.73125 \n", "Tunisia 48.25625 \n", "Turkey 56.41250 \n", "Turkmenistan 38.11875 \n", "Tuvalu 79.30000 \n", "Uganda 15.52500 \n", "Ukraine 50.89375 \n", "United Arab Emirates 53.80625 \n", "United Kingdom of Great Britain and Northern Ir... 55.38750 \n", "United Republic of Tanzania 17.31875 \n", "United States of America 58.45000 \n", "Uruguay 52.92500 \n", "Uzbekistan 34.80625 \n", "Vanuatu 44.25625 \n", "Venezuela (Bolivarian Republic of) 54.48750 \n", "Viet Nam 11.18750 \n", "Yemen 33.48750 \n", "Zambia 17.45000 \n", "Zimbabwe 25.13750 \n", "\n", " under-five deaths \\\n", "Country \n", "Afghanistan 107.5625 \n", "Albania 0.9375 \n", "Algeria 23.5000 \n", "Angola 132.6250 \n", "Antigua and Barbuda 0.0000 \n", "Argentina 11.3750 \n", "Armenia 1.0000 \n", "Australia 1.3750 \n", "Austria 0.0000 \n", "Azerbaijan 7.1875 \n", "Bahamas 0.0000 \n", "Bahrain 0.0000 \n", "Bangladesh 201.5625 \n", "Barbados 0.0000 \n", "Belarus 0.8750 \n", "Belgium 1.0000 \n", "Belize 0.0000 \n", "Benin 39.2500 \n", "Bhutan 0.9375 \n", "Bolivia (Plurinational State of) 14.0000 \n", "Bosnia and Herzegovina 0.0000 \n", "Botswana 3.1250 \n", "Brazil 77.2500 \n", "Brunei Darussalam 0.0000 \n", "Bulgaria 1.0000 \n", "Burkina Faso 79.9375 \n", "Burundi 35.5625 \n", "Cabo Verde 0.0000 \n", "Cambodia 20.5000 \n", "Cameroon 86.1875 \n", "... ... \n", "Suriname 0.0000 \n", "Swaziland 3.6875 \n", "Sweden 0.0000 \n", "Switzerland 0.0000 \n", "Syrian Arab Republic 9.5625 \n", "Tajikistan 12.7500 \n", "Thailand 14.1250 \n", "The former Yugoslav republic of Macedonia 0.0000 \n", "Timor-Leste 3.0000 \n", "Togo 21.4375 \n", "Tonga 0.0000 \n", "Trinidad and Tobago 0.2500 \n", "Tunisia 3.6875 \n", "Turkey 32.2500 \n", "Turkmenistan 8.0625 \n", "Tuvalu 0.0000 \n", "Uganda 138.8750 \n", "Ukraine 5.9375 \n", "United Arab Emirates 1.0000 \n", "United Kingdom of Great Britain and Northern Ir... 4.0625 \n", "United Republic of Tanzania 143.1875 \n", "United States of America 30.7500 \n", "Uruguay 0.7500 \n", "Uzbekistan 25.6875 \n", "Vanuatu 0.0000 \n", "Venezuela (Bolivarian Republic of) 10.7500 \n", "Viet Nam 36.5000 \n", "Yemen 51.6250 \n", "Zambia 52.3750 \n", "Zimbabwe 40.8125 \n", "\n", " Polio \\\n", "Country \n", "Afghanistan 48.375000 \n", "Albania 98.125000 \n", "Algeria 91.750000 \n", "Angola 46.125000 \n", "Antigua and Barbuda 96.937500 \n", "Argentina 93.375000 \n", "Armenia 89.125000 \n", "Australia 86.750000 \n", "Austria 86.000000 \n", "Azerbaijan 75.812500 \n", "Bahamas 95.000000 \n", "Bahrain 98.000000 \n", "Bangladesh 87.500000 \n", "Barbados 80.312500 \n", "Belarus 89.875000 \n", "Belgium 97.750000 \n", "Belize 95.500000 \n", "Benin 67.687500 \n", "Bhutan 89.375000 \n", "Bolivia (Plurinational State of) 81.687500 \n", "Bosnia and Herzegovina 78.312500 \n", "Botswana 96.187500 \n", "Brazil 98.312500 \n", "Brunei Darussalam 97.500000 \n", "Bulgaria 94.250000 \n", "Burkina Faso 69.000000 \n", "Burundi 86.625000 \n", "Cabo Verde 84.375000 \n", "Cambodia 74.187500 \n", "Cameroon 72.500000 \n", "... ... \n", "Suriname 78.250000 \n", "Swaziland 89.000000 \n", "Sweden 98.312500 \n", "Switzerland 95.375000 \n", "Syrian Arab Republic 64.812500 \n", "Tajikistan 88.312500 \n", "Thailand 98.312500 \n", "The former Yugoslav republic of Macedonia 95.500000 \n", "Timor-Leste 63.214286 \n", "Togo 67.187500 \n", "Tonga 76.875000 \n", "Trinidad and Tobago 76.437500 \n", "Tunisia 97.687500 \n", "Turkey 80.812500 \n", "Turkmenistan 95.562500 \n", "Tuvalu 9.000000 \n", "Uganda 65.437500 \n", "Ukraine 81.437500 \n", "United Arab Emirates 95.062500 \n", "United Kingdom of Great Britain and Northern Ir... 92.875000 \n", "United Republic of Tanzania 83.375000 \n", "United States of America 82.125000 \n", "Uruguay 94.250000 \n", "Uzbekistan 98.562500 \n", "Vanuatu 66.187500 \n", "Venezuela (Bolivarian Republic of) 74.687500 \n", "Viet Nam 94.937500 \n", "Yemen 67.125000 \n", "Zambia 64.375000 \n", "Zimbabwe 75.625000 \n", "\n", " Total expenditure \\\n", "Country \n", "Afghanistan 8.252500 \n", "Albania 5.945625 \n", "Algeria 4.604000 \n", "Angola 3.919333 \n", "Antigua and Barbuda 4.791333 \n", "Argentina 6.912667 \n", "Armenia 4.861333 \n", "Australia 8.836667 \n", "Austria 4.715333 \n", "Azerbaijan 5.679333 \n", "Bahamas 6.706000 \n", "Bahrain 3.807333 \n", "Bangladesh 2.854667 \n", "Barbados 6.122000 \n", "Belarus 6.192000 \n", "Belgium 5.962000 \n", "Belize 5.039333 \n", "Benin 4.636000 \n", "Bhutan 5.346000 \n", "Bolivia (Plurinational State of) 5.626000 \n", "Bosnia and Herzegovina 8.742000 \n", "Botswana 5.510667 \n", "Brazil 8.019333 \n", "Brunei Darussalam 2.820000 \n", "Bulgaria 7.236000 \n", "Burkina Faso 6.038000 \n", "Burundi 7.089333 \n", "Cabo Verde 4.755333 \n", "Cambodia 5.740667 \n", "Cameroon 4.719333 \n", "... ... \n", "Suriname 6.662667 \n", "Swaziland 7.308667 \n", "Sweden 9.932667 \n", "Switzerland 6.087333 \n", "Syrian Arab Republic 3.946667 \n", "Tajikistan 5.648000 \n", "Thailand 3.757333 \n", "The former Yugoslav republic of Macedonia 7.536000 \n", "Timor-Leste 1.646667 \n", "Togo 5.243333 \n", "Tonga 5.206000 \n", "Trinidad and Tobago 5.199333 \n", "Tunisia 6.052667 \n", "Turkey 5.618000 \n", "Turkmenistan 2.828667 \n", "Tuvalu 16.610000 \n", "Uganda 8.542000 \n", "Ukraine 6.777333 \n", "United Arab Emirates 3.036667 \n", "United Kingdom of Great Britain and Northern Ir... 8.534000 \n", "United Republic of Tanzania 4.701333 \n", "United States of America 15.863333 \n", "Uruguay 8.750000 \n", "Uzbekistan 5.638000 \n", "Vanuatu 3.928667 \n", "Venezuela (Bolivarian Republic of) 4.998667 \n", "Viet Nam 5.977333 \n", "Yemen 5.005333 \n", "Zambia 5.824000 \n", "Zimbabwe 6.158667 \n", "\n", " Diphtheria HIV/AIDS \\\n", "Country \n", "Afghanistan 52.312500 0.10000 \n", "Albania 98.062500 0.10000 \n", "Algeria 91.875000 0.10000 \n", "Angola 47.687500 2.36875 \n", "Antigua and Barbuda 98.312500 0.12500 \n", "Argentina 92.375000 0.10000 \n", "Armenia 87.375000 0.10000 \n", "Australia 86.875000 0.10000 \n", "Austria 86.750000 0.10000 \n", "Azerbaijan 82.312500 0.10000 \n", "Bahamas 95.750000 0.11875 \n", "Bahrain 98.062500 0.10000 \n", "Bangladesh 92.750000 0.10000 \n", "Barbados 89.937500 0.25625 \n", "Belarus 92.187500 0.10000 \n", "Belgium 97.312500 0.10000 \n", "Belize 95.187500 0.43125 \n", "Benin 67.937500 1.70625 \n", "Bhutan 93.875000 0.35625 \n", "Bolivia (Plurinational State of) 87.375000 0.15625 \n", "Bosnia and Herzegovina 78.500000 0.10000 \n", "Botswana 95.812500 16.52500 \n", "Brazil 97.937500 0.10000 \n", "Brunei Darussalam 96.812500 0.10000 \n", "Bulgaria 94.000000 0.10000 \n", "Burkina Faso 77.312500 1.83750 \n", "Burundi 85.750000 3.08750 \n", "Cabo Verde 79.000000 0.53125 \n", "Cambodia 72.062500 1.02500 \n", "Cameroon 69.437500 6.21250 \n", "... ... ... \n", "Suriname 82.562500 0.76250 \n", "Swaziland 83.750000 32.94375 \n", "Sweden 98.312500 0.10000 \n", "Switzerland 94.562500 0.10000 \n", "Syrian Arab Republic 48.437500 0.10000 \n", "Tajikistan 89.250000 0.25625 \n", "Thailand 98.187500 0.38750 \n", "The former Yugoslav republic of Macedonia 94.875000 0.10000 \n", "Timor-Leste 64.214286 0.10000 \n", "Togo 74.312500 3.86250 \n", "Tonga 70.312500 0.10000 \n", "Trinidad and Tobago 66.500000 0.62500 \n", "Tunisia 97.687500 0.10000 \n", "Turkey 80.750000 0.10000 \n", "Turkmenistan 96.375000 0.10000 \n", "Tuvalu 9.000000 0.10000 \n", "Uganda 64.875000 7.65000 \n", "Ukraine 70.000000 0.53750 \n", "United Arab Emirates 94.625000 0.10000 \n", "United Kingdom of Great Britain and Northern Ir... 92.875000 0.10000 \n", "United Republic of Tanzania 74.687500 7.27500 \n", "United States of America 95.125000 0.10000 \n", "Uruguay 89.125000 0.10000 \n", "Uzbekistan 98.437500 0.20625 \n", "Vanuatu 59.062500 0.10000 \n", "Venezuela (Bolivarian Republic of) 68.500000 0.10000 \n", "Viet Nam 91.750000 0.14375 \n", "Yemen 72.625000 0.10000 \n", "Zambia 74.250000 11.93125 \n", "Zimbabwe 75.187500 23.26250 \n", "\n", " GDP \\\n", "Country \n", "Afghanistan 340.015425 \n", "Albania 2119.726679 \n", "Algeria 2847.853392 \n", "Angola 1975.143045 \n", "Antigua and Barbuda 9759.305728 \n", "Argentina 6998.575103 \n", "Armenia 1999.986318 \n", "Australia 34637.565047 \n", "Austria 33827.476309 \n", "Azerbaijan 3302.947807 \n", "Bahamas 7223.452050 \n", "Bahrain 11191.247421 \n", "Bangladesh 443.561481 \n", "Barbados 12017.098529 \n", "Belarus 3811.525348 \n", "Belgium 16915.306000 \n", "Belize 3933.012174 \n", "Benin 561.167968 \n", "Bhutan 1353.986946 \n", "Bolivia (Plurinational State of) 7223.452050 \n", "Bosnia and Herzegovina 2245.026024 \n", "Botswana 4498.285431 \n", "Brazil 6143.161794 \n", "Brunei Darussalam 19744.808102 \n", "Bulgaria 4938.981821 \n", "Burkina Faso 410.372034 \n", "Burundi 137.815321 \n", "Cabo Verde 2023.541039 \n", "Cambodia 466.196878 \n", "Cameroon 781.016004 \n", "... ... \n", "Suriname 4781.216822 \n", "Swaziland 2165.090838 \n", "Sweden 29334.990639 \n", "Switzerland 57362.874601 \n", "Syrian Arab Republic 1087.435774 \n", "Tajikistan 335.841725 \n", "Thailand 3494.781702 \n", "The former Yugoslav republic of Macedonia 7223.452050 \n", "Timor-Leste 551.710649 \n", "Togo 317.204092 \n", "Tonga 1981.555186 \n", "Trinidad and Tobago 7741.748090 \n", "Tunisia 3044.081488 \n", "Turkey 3983.917722 \n", "Turkmenistan 2511.611540 \n", "Tuvalu 3542.135890 \n", "Uganda 421.048496 \n", "Ukraine 1577.293329 \n", "United Arab Emirates 22110.366986 \n", "United Kingdom of Great Britain and Northern Ir... 7223.452050 \n", "United Republic of Tanzania 7223.452050 \n", "United States of America 7223.452050 \n", "Uruguay 7192.584875 \n", "Uzbekistan 651.092359 \n", "Vanuatu 2000.245518 \n", "Venezuela (Bolivarian Republic of) 7223.452050 \n", "Viet Nam 7223.452050 \n", "Yemen 7223.452050 \n", "Zambia 811.811841 \n", "Zimbabwe 410.980194 \n", "\n", " Population \\\n", "Country \n", "Afghanistan 9.972260e+06 \n", "Albania 6.969116e+05 \n", "Algeria 2.164983e+07 \n", "Angola 1.014710e+07 \n", "Antigua and Barbuda 1.257006e+07 \n", "Argentina 2.012120e+07 \n", "Armenia 1.015165e+06 \n", "Australia 4.587010e+06 \n", "Austria 6.474880e+06 \n", "Azerbaijan 2.157370e+06 \n", "Bahamas 1.257006e+07 \n", "Bahrain 1.257006e+07 \n", "Bangladesh 4.298516e+07 \n", "Barbados 1.257006e+07 \n", "Belarus 6.371867e+06 \n", "Belgium 2.884043e+06 \n", "Belize 1.703929e+05 \n", "Benin 3.942979e+06 \n", "Bhutan 4.925849e+05 \n", "Bolivia (Plurinational State of) 1.257006e+07 \n", "Bosnia and Herzegovina 1.986993e+06 \n", "Botswana 1.063867e+06 \n", "Brazil 8.812807e+07 \n", "Brunei Darussalam 1.257006e+07 \n", "Bulgaria 5.290924e+06 \n", "Burkina Faso 6.143695e+06 \n", "Burundi 3.915447e+06 \n", "Cabo Verde 2.926002e+05 \n", "Cambodia 7.145967e+06 \n", "Cameroon 9.967292e+06 \n", "... ... \n", "Suriname 1.909166e+05 \n", "Swaziland 4.573031e+05 \n", "Sweden 5.514868e+06 \n", "Switzerland 5.913242e+06 \n", "Syrian Arab Republic 6.741445e+06 \n", "Tajikistan 4.751355e+06 \n", "Thailand 3.117051e+07 \n", "The former Yugoslav republic of Macedonia 1.257006e+07 \n", "Timor-Leste 4.601956e+05 \n", "Togo 3.722046e+06 \n", "Tonga 2.658981e+04 \n", "Trinidad and Tobago 7.761331e+05 \n", "Tunisia 3.274493e+06 \n", "Turkey 3.350135e+07 \n", "Turkmenistan 2.635550e+06 \n", "Tuvalu 1.819000e+03 \n", "Uganda 1.554059e+07 \n", "Ukraine 1.000493e+07 \n", "United Arab Emirates 1.257006e+07 \n", "United Kingdom of Great Britain and Northern Ir... 1.257006e+07 \n", "United Republic of Tanzania 1.257006e+07 \n", "United States of America 1.257006e+07 \n", "Uruguay 2.396771e+06 \n", "Uzbekistan 9.036317e+05 \n", "Vanuatu 1.230962e+05 \n", "Venezuela (Bolivarian Republic of) 1.257006e+07 \n", "Viet Nam 1.257006e+07 \n", "Yemen 1.257006e+07 \n", "Zambia 6.260246e+06 \n", "Zimbabwe 8.021343e+06 \n", "\n", " thinness 1-19 years \\\n", "Country \n", "Afghanistan 16.58125 \n", "Albania 1.61875 \n", "Algeria 6.09375 \n", "Angola 6.19375 \n", "Antigua and Barbuda 3.42500 \n", "Argentina 1.07500 \n", "Armenia 2.03750 \n", "Australia 0.66875 \n", "Austria 1.73125 \n", "Azerbaijan 2.90000 \n", "Bahamas 2.55000 \n", "Bahrain 6.10625 \n", "Bangladesh 13.77500 \n", "Barbados 3.95625 \n", "Belarus 2.21250 \n", "Belgium 0.86250 \n", "Belize 3.55625 \n", "Benin 8.25000 \n", "Bhutan 17.21875 \n", "Bolivia (Plurinational State of) 1.30000 \n", "Bosnia and Herzegovina 2.78125 \n", "Botswana 7.43750 \n", "Brazil 3.05000 \n", "Brunei Darussalam 6.20000 \n", "Bulgaria 2.13125 \n", "Burkina Faso 7.41875 \n", "Burundi 8.01875 \n", "Cabo Verde 8.01250 \n", "Cambodia 10.14375 \n", "Cameroon 6.55000 \n", "... ... \n", "Suriname 3.51250 \n", "Swaziland 6.47500 \n", "Sweden 1.35000 \n", "Switzerland 0.53750 \n", "Syrian Arab Republic 6.45000 \n", "Tajikistan 3.80625 \n", "Thailand 8.54375 \n", "The former Yugoslav republic of Macedonia 2.43125 \n", "Timor-Leste 10.52500 \n", "Togo 7.86875 \n", "Tonga 0.10000 \n", "Trinidad and Tobago 6.13750 \n", "Tunisia 6.40625 \n", "Turkey 5.01250 \n", "Turkmenistan 3.33750 \n", "Tuvalu 0.20000 \n", "Uganda 6.26250 \n", "Ukraine 2.57500 \n", "United Arab Emirates 5.18125 \n", "United Kingdom of Great Britain and Northern Ir... 0.75000 \n", "United Republic of Tanzania 7.42500 \n", "United States of America 0.73125 \n", "Uruguay 1.60000 \n", "Uzbekistan 3.14375 \n", "Vanuatu 1.56875 \n", "Venezuela (Bolivarian Republic of) 1.65000 \n", "Viet Nam 14.92500 \n", "Yemen 13.83125 \n", "Zambia 6.88125 \n", "Zimbabwe 7.01250 \n", "\n", " thinness 5-9 years \\\n", "Country \n", "Afghanistan 15.58125 \n", "Albania 1.70000 \n", "Algeria 5.97500 \n", "Angola 6.66875 \n", "Antigua and Barbuda 3.37500 \n", "Argentina 0.95000 \n", "Armenia 2.11250 \n", "Australia 0.62500 \n", "Austria 1.93750 \n", "Azerbaijan 2.94375 \n", "Bahamas 2.51875 \n", "Bahrain 5.95000 \n", "Bangladesh 14.33125 \n", "Barbados 3.91875 \n", "Belarus 2.33750 \n", "Belgium 0.85625 \n", "Belize 3.48750 \n", "Benin 8.13750 \n", "Bhutan 17.92500 \n", "Bolivia (Plurinational State of) 1.18750 \n", "Bosnia and Herzegovina 2.75625 \n", "Botswana 7.81250 \n", "Brazil 3.01250 \n", "Brunei Darussalam 5.67500 \n", "Bulgaria 2.15625 \n", "Burkina Faso 6.98125 \n", "Burundi 8.01250 \n", "Cabo Verde 7.97500 \n", "Cambodia 10.88125 \n", "Cameroon 6.60625 \n", "... ... \n", "Suriname 3.45625 \n", "Swaziland 6.63125 \n", "Sweden 1.30625 \n", "Switzerland 0.39375 \n", "Syrian Arab Republic 6.25625 \n", "Tajikistan 3.85000 \n", "Thailand 8.70000 \n", "The former Yugoslav republic of Macedonia 2.45625 \n", "Timor-Leste 11.69375 \n", "Togo 7.73750 \n", "Tonga 0.10000 \n", "Trinidad and Tobago 6.50625 \n", "Tunisia 6.33750 \n", "Turkey 4.85000 \n", "Turkmenistan 3.36250 \n", "Tuvalu 0.10000 \n", "Uganda 6.25625 \n", "Ukraine 2.67500 \n", "United Arab Emirates 4.96875 \n", "United Kingdom of Great Britain and Northern Ir... 0.50625 \n", "United Republic of Tanzania 7.35625 \n", "United States of America 0.60625 \n", "Uruguay 1.54375 \n", "Uzbekistan 3.17500 \n", "Vanuatu 1.49375 \n", "Venezuela (Bolivarian Republic of) 1.56250 \n", "Viet Nam 15.62500 \n", "Yemen 13.75000 \n", "Zambia 6.76250 \n", "Zimbabwe 6.98750 \n", "\n", " Income composition of resources \\\n", "Country \n", "Afghanistan 0.415375 \n", "Albania 0.709875 \n", "Algeria 0.694875 \n", "Angola 0.458375 \n", "Antigua and Barbuda 0.488625 \n", "Argentina 0.794125 \n", "Armenia 0.698375 \n", "Australia 0.918125 \n", "Austria 0.862375 \n", "Azerbaijan 0.702687 \n", "Bahamas 0.737938 \n", "Bahrain 0.807937 \n", "Bangladesh 0.517875 \n", "Barbados 0.770437 \n", "Belarus 0.743250 \n", "Belgium 0.877750 \n", "Belize 0.693937 \n", "Benin 0.438062 \n", "Bhutan 0.183875 \n", "Bolivia (Plurinational State of) 0.633750 \n", "Bosnia and Herzegovina 0.450375 \n", "Botswana 0.630375 \n", "Brazil 0.710187 \n", "Brunei Darussalam 0.839375 \n", "Bulgaria 0.754625 \n", "Burkina Faso 0.229688 \n", "Burundi 0.327875 \n", "Cabo Verde 0.570000 \n", "Cambodia 0.491937 \n", "Cameroon 0.470000 \n", "... ... \n", "Suriname 0.481062 \n", "Swaziland 0.515688 \n", "Sweden 0.893125 \n", "Switzerland 0.911062 \n", "Syrian Arab Republic 0.618062 \n", "Tajikistan 0.583812 \n", "Thailand 0.694688 \n", "The former Yugoslav republic of Macedonia 0.455750 \n", "Timor-Leste 0.517625 \n", "Togo 0.445937 \n", "Tonga 0.698813 \n", "Trinidad and Tobago 0.753375 \n", "Tunisia 0.692625 \n", "Turkey 0.703313 \n", "Turkmenistan 0.211625 \n", "Tuvalu 0.629502 \n", "Uganda 0.445312 \n", "Ukraine 0.716563 \n", "United Arab Emirates 0.819563 \n", "United Kingdom of Great Britain and Northern Ir... 0.629502 \n", "United Republic of Tanzania 0.629502 \n", "United States of America 0.629502 \n", "Uruguay 0.765625 \n", "Uzbekistan 0.603000 \n", "Vanuatu 0.367500 \n", "Venezuela (Bolivarian Republic of) 0.726812 \n", "Viet Nam 0.627063 \n", "Yemen 0.475500 \n", "Zambia 0.498437 \n", "Zimbabwe 0.439125 \n", "\n", " Schooling Developed \\\n", "Country \n", "Afghanistan 8.212500 0 \n", "Albania 12.137500 0 \n", "Algeria 12.712500 0 \n", "Angola 8.043750 0 \n", "Antigua and Barbuda 8.843750 0 \n", "Argentina 16.506250 0 \n", "Armenia 11.787500 0 \n", "Australia 20.037500 1 \n", "Austria 15.387500 1 \n", "Azerbaijan 11.350000 0 \n", "Bahamas 12.425000 0 \n", "Bahrain 14.112500 0 \n", "Bangladesh 8.693750 0 \n", "Barbados 14.893750 0 \n", "Belarus 14.750000 0 \n", "Belgium 16.787500 1 \n", "Belize 12.456250 0 \n", "Benin 8.850000 0 \n", "Bhutan 10.225000 0 \n", "Bolivia (Plurinational State of) 13.962500 0 \n", "Bosnia and Herzegovina 12.225000 0 \n", "Botswana 12.137500 0 \n", "Brazil 14.162500 0 \n", "Brunei Darussalam 14.106250 0 \n", "Bulgaria 13.725000 1 \n", "Burkina Faso 5.406250 0 \n", "Burundi 7.481250 0 \n", "Cabo Verde 12.200000 0 \n", "Cambodia 9.875000 0 \n", "Cameroon 8.906250 0 \n", "... ... ... \n", "Suriname 11.825000 0 \n", "Swaziland 10.350000 0 \n", "Sweden 15.868750 1 \n", "Switzerland 15.393750 1 \n", "Syrian Arab Republic 10.981250 0 \n", "Tajikistan 10.681250 0 \n", "Thailand 12.550000 0 \n", "The former Yugoslav republic of Macedonia 12.381250 0 \n", "Timor-Leste 10.700000 0 \n", "Togo 10.650000 0 \n", "Tonga 14.218750 0 \n", "Trinidad and Tobago 12.331250 0 \n", "Tunisia 14.056250 0 \n", "Turkey 12.675000 0 \n", "Turkmenistan 9.831250 0 \n", "Tuvalu 0.000000 0 \n", "Uganda 10.593750 0 \n", "Ukraine 14.606250 0 \n", "United Arab Emirates 12.812500 0 \n", "United Kingdom of Great Britain and Northern Ir... 11.894097 1 \n", "United Republic of Tanzania 11.894097 0 \n", "United States of America 11.894097 1 \n", "Uruguay 15.231250 0 \n", "Uzbekistan 11.643750 0 \n", "Vanuatu 10.568750 0 \n", "Venezuela (Bolivarian Republic of) 12.787500 0 \n", "Viet Nam 11.512500 0 \n", "Yemen 8.506250 0 \n", "Zambia 11.212500 0 \n", "Zimbabwe 9.825000 0 \n", "\n", " Developing \n", "Country \n", "Afghanistan 1 \n", "Albania 1 \n", "Algeria 1 \n", "Angola 1 \n", "Antigua and Barbuda 1 \n", "Argentina 1 \n", "Armenia 1 \n", "Australia 0 \n", "Austria 0 \n", "Azerbaijan 1 \n", "Bahamas 1 \n", "Bahrain 1 \n", "Bangladesh 1 \n", "Barbados 1 \n", "Belarus 1 \n", "Belgium 0 \n", "Belize 1 \n", "Benin 1 \n", "Bhutan 1 \n", "Bolivia (Plurinational State of) 1 \n", "Bosnia and Herzegovina 1 \n", "Botswana 1 \n", "Brazil 1 \n", "Brunei Darussalam 1 \n", "Bulgaria 0 \n", "Burkina Faso 1 \n", "Burundi 1 \n", "Cabo Verde 1 \n", "Cambodia 1 \n", "Cameroon 1 \n", "... ... \n", "Suriname 1 \n", "Swaziland 1 \n", "Sweden 0 \n", "Switzerland 0 \n", "Syrian Arab Republic 1 \n", "Tajikistan 1 \n", "Thailand 1 \n", "The former Yugoslav republic of Macedonia 1 \n", "Timor-Leste 1 \n", "Togo 1 \n", "Tonga 1 \n", "Trinidad and Tobago 1 \n", "Tunisia 1 \n", "Turkey 1 \n", "Turkmenistan 1 \n", "Tuvalu 1 \n", "Uganda 1 \n", "Ukraine 1 \n", "United Arab Emirates 1 \n", "United Kingdom of Great Britain and Northern Ir... 0 \n", "United Republic of Tanzania 1 \n", "United States of America 0 \n", "Uruguay 1 \n", "Uzbekistan 1 \n", "Vanuatu 1 \n", "Venezuela (Bolivarian Republic of) 1 \n", "Viet Nam 1 \n", "Yemen 1 \n", "Zambia 1 \n", "Zimbabwe 1 \n", "\n", "[193 rows x 20 columns]" ] }, "execution_count": 98, "metadata": {}, "output_type": "execute_result" } ], "source": [ "life_features.fillna(value = life_features.mean(), inplace = True)" ] }, { "cell_type": "code", "execution_count": 97, "metadata": { "collapsed": true }, "outputs": [], "source": [ "life_labels.fillna(value = life_labels.mean(), inplace = True)" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "DescribeResult(nobs=192, minmax=(array([ 1.87500000e+01, 0.00000000e+00, 1.00000000e-02,\n", " 0.00000000e+00, 8.00000000e+00, 0.00000000e+00,\n", " 5.20000000e+00, 0.00000000e+00, 9.00000000e+00,\n", " 1.64666667e+00, 9.00000000e+00, 1.00000000e-01,\n", " 1.36183210e+02, 2.92000000e+02, 1.00000000e-01,\n", " 1.00000000e-01, 1.31687500e-01, 0.00000000e+00,\n", " 0.00000000e+00, 0.00000000e+00]), array([ 5.50062500e+02, 1.36668750e+03, 1.34973333e+01,\n", " 9.80181038e+03, 9.90000000e+01, 6.58579375e+04,\n", " 8.73000000e+01, 1.81250000e+03, 9.90000000e+01,\n", " 1.72400000e+01, 9.90000000e+01, 3.29437500e+01,\n", " 5.73628746e+04, 4.21467691e+08, 2.71000000e+01,\n", " 2.79437500e+01, 9.31437500e-01, 2.00375000e+01,\n", " 1.00000000e+00, 1.00000000e+00])), mean=array([ 1.64253396e+02, 2.85745443e+01, 4.46296480e+00,\n", " 7.12321318e+02, 7.85909681e+01, 2.30174674e+03,\n", " 3.96797168e+01, 3.96419271e+01, 8.26078218e+01,\n", " 6.01668050e+00, 8.23842541e+01, 1.67047526e+00,\n", " 7.25930328e+03, 1.25835883e+07, 4.62089964e+00,\n", " 4.65416339e+00, 6.30617733e-01, 1.19132722e+01,\n", " 1.66666667e-01, 8.33333333e-01]), variance=array([ 8.75843142e+03, 1.28540496e+04, 1.48700520e+01,\n", " 2.10950227e+06, 3.38370018e+02, 6.25178778e+07,\n", " 3.01949462e+02, 2.36800172e+04, 2.74638648e+02,\n", " 4.83699092e+00, 2.92218598e+02, 1.89712358e+01,\n", " 9.65744006e+07, 1.09787565e+15, 1.57725109e+01,\n", " 1.65888768e+01, 3.03882176e-02, 1.03601660e+01,\n", " 1.39616056e-01, 1.39616056e-01]), skewness=array([ 1.19225145, 9.3630654 , 0.5920435 , 3.27460365,\n", " -1.36320782, 6.05254594, 0.09453142, 9.01254987,\n", " -1.3261097 , 1.66458352, -1.43066862, 4.23443451,\n", " 2.46889639, 10.16055662, 1.72955815, 1.8023565 ,\n", " -0.48106083, -0.71599891, 1.78885438, -1.78885438]), kurtosis=array([ 1.604733 , 101.90959278, -0.86402143, 12.36028988,\n", " 2.20027664, 38.55513895, -0.66005587, 94.30411285,\n", " 1.85630801, 6.59116886, 2.06805175, 20.55716829,\n", " 6.62435538, 119.83739182, 5.27091348, 5.62745981,\n", " -0.34978933, 1.50234464, 1.2 , 1.2 ]))" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stats.describe(life_features[1:])" ] }, { "cell_type": "code", "execution_count": 99, "metadata": {}, "outputs": [], "source": [ "min_max_scaler = MinMaxScaler()\n", "life_features = min_max_scaler.fit_transform(life_features)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[ 4.71121045e-01, 5.72552248e-02, 3.24378429e-04, ...,\n", " 4.09856519e-01, 0.00000000e+00, 1.00000000e+00],\n", " [ 4.95235855e-02, 5.03041112e-04, 3.58762543e-01, ...,\n", " 6.05739239e-01, 0.00000000e+00, 1.00000000e+00],\n", " [ 1.68333137e-01, 1.48625783e-02, 2.94103109e-02, ...,\n", " 6.34435434e-01, 0.00000000e+00, 1.00000000e+00],\n", " ..., \n", " [ 3.63369015e-01, 2.88105364e-02, 2.76802926e-03, ...,\n", " 4.24516532e-01, 0.00000000e+00, 1.00000000e+00],\n", " [ 6.31572756e-01, 2.44660905e-02, 1.65290890e-01, ...,\n", " 5.59575795e-01, 0.00000000e+00, 1.00000000e+00],\n", " [ 8.34960593e-01, 1.94356793e-02, 3.31570362e-01, ...,\n", " 4.90330630e-01, 0.00000000e+00, 1.00000000e+00]])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "life_features" ] }, { "cell_type": "code", "execution_count": 100, "metadata": { "collapsed": true }, "outputs": [], "source": [ "life_features_train, life_features_test, life_labels_train, life_labels_test = train_test_split(\n", " life_features, life_labels, train_size = 0.7, test_size = 0.3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 5 Linear Regression and additions" ] }, { "cell_type": "code", "execution_count": 101, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)" ] }, "execution_count": 101, "metadata": {}, "output_type": "execute_result" } ], "source": [ "linear_model = LinearRegression()\n", "linear_model.fit(life_features_train, life_labels_train)" ] }, { "cell_type": "code", "execution_count": 102, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "R_square score on the training: 0.92\n" ] } ], "source": [ "print('R_square score on the training: %.2f' % linear_model.score(life_features_train, life_labels_train))" ] }, { "cell_type": "code", "execution_count": 103, "metadata": { "collapsed": true }, "outputs": [], "source": [ "linear_model_predict = linear_model.predict(life_features_test)" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Coefficients: \n", " [ -26.81746051 160.65752164 3.37662659 5.17479785 -1.62130018\n", " 0.26189121 3.75961357 -180.19924226 1.39153602 1.99816095\n", " 5.43624984 -4.59596039 -1.35215782 12.43058537 20.18640175\n", " -18.82270723 5.73376054 2.86398351 -0.28926014 0.28926014]\n", "Mean squared error: 9.80\n", "Mean absolute error: 2.32\n", "R_square score: 0.87\n" ] } ], "source": [ "print('Coefficients: \\n', linear_model.coef_)\n", "print(\"Mean squared error: %.2f\"\n", " % mean_squared_error(life_labels_test, linear_model_predict))\n", "print(\"Mean absolute error: %.2f\"\n", " % mean_absolute_error(life_labels_test, linear_model_predict))\n", "print('R_square score: %.2f' % r2_score(life_labels_test, linear_model_predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Several algorithms will be tried out. First the classical linear regression. \n", "
\n", "The model is fitted first on the trained data the R square is 0.92 on the training data.Later its R square is checked on the testing data. The score is 87 % in the iteration of writing. We also calculate the MAE (the modulus between the predicted and the real value) at 2.32 and the MSE (the same only put to the power of 2) at 9.8. Now we will try to have some changes to the initial model. We will use both the properties of the Ridge regression and the Lasso and eventually the ElasticNet to see if the score can be improved." ] }, { "cell_type": "code", "execution_count": 104, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 2.0868024066249365e-18\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 7.372725493506869e-18\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 9.43416281984731e-19\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 1.3449932991350745e-18\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 2.0868024066249365e-18\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 7.372725493506869e-18\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 9.43416281984731e-19\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 1.3449932991350745e-18\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 2.0868024066249365e-18\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 7.372725493506869e-18\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 9.43416281984731e-19\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\scipy\\linalg\\basic.py:223: RuntimeWarning: scipy.linalg.solve\n", "Ill-conditioned matrix detected. Result is not guaranteed to be accurate.\n", "Reciprocal condition number: 1.3449932991350745e-18\n", " ' condition number: {}'.format(rcond), RuntimeWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'alpha': 1, 'max_iter': 10}\n", "R^2 score on training data: 0.90\n", "R^2 score: 0.91\n", "Mean squared error: 6.05\n", "Mean absolute error: 1.83\n" ] } ], "source": [ "scoring = make_scorer(r2_score)\n", "grid_cv = GridSearchCV(Ridge(),\n", " param_grid={'alpha': range(0, 10), 'max_iter' : [10, 100, 1000]},\n", " scoring=scoring, cv=5, refit=True)\n", "\n", "\n", "grid_cv.fit(life_features_train, life_labels_train)\n", "print(\"Best Parameters: \" + str(grid_cv.best_params_))\n", "\n", "result = grid_cv.cv_results_\n", "print(\"R^2 score on training data: %.2f\" %grid_cv.score(life_features_train, life_labels_train))\n", "print(\"R^2 score: %.2f\" \n", " % r2_score(life_labels_test, grid_cv.best_estimator_.predict(life_features_test)))\n", "print(\"Mean squared error: %.2f\"\n", " % mean_squared_error(life_labels_test, linear_model_predict))\n", "print(\"Mean absolute error: %.2f\"\n", " % mean_absolute_error(life_labels_test, linear_model_predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Ridge Regression uses L2 regularization to minimize the weights of the coefficients, this is controlled with the\n", "hyperparameter alpha. Increasing the value of alpha decreases the weights of the coefficients.\n", "
\n", "We performed a grid search with cross-validation on the grid regression with alpha varying between 0 and 10 and having 3 max iterations of 10, 100 and 1000. Finally the best parameters here are alpha = 1, and max iterations = 10. The R square on the training data is 90% compared to 92 % on the standard linear model\n", "There is some improvement of the R square on the test data statistic 91% vs 87% the errors stayed the same.\n", "The MAE is 1.83 better and MSE is 6.05 again lower the classic linear regression" ] }, { "cell_type": "code", "execution_count": 107, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'alpha': 0, 'max_iter': 10}\n", "R^2 score on training data: 0.92\n", "R^2 score: 0.92\n", "Mean squared error: 6.05\n", "Mean absolute error: 1.83\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:739: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " self.best_estimator_.fit(X, y, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n" ] } ], "source": [ "scoring = make_scorer(r2_score)\n", "grid_cv = GridSearchCV(Lasso(),\n", " param_grid={'alpha': range(0, 10), 'max_iter' : [10, 100, 1000]},\n", " scoring=scoring, cv=5, refit=True)\n", "\n", "grid_cv.fit(life_features_train, life_labels_train)\n", "print(\"Best Parameters: \" + str(grid_cv.best_params_))\n", "\n", "result = grid_cv.cv_results_\n", "print(\"R^2 score on training data: %.2f\" % grid_cv.score(life_features_train, life_labels_train))\n", "print(\"R^2 score: %.2f\" \n", " % r2_score(life_labels_test, grid_cv.best_estimator_.predict(life_features_test)))\n", "print(\"Mean squared error: %.2f\"\n", " % mean_squared_error(life_labels_test, linear_model_predict))\n", "print(\"Mean absolute error: %.2f\"\n", " % mean_absolute_error(life_labels_test, linear_model_predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Lasso Regression uses the L1 regularization which reduces some coefficent weights to zero. Again the regularization is\n", "controlled with aplha, decreasing it reduces the weights, also at the same time the max iterations hyperparameter \n", "needs to be increased.\n", "
\n", "The best parameters for the Lasso Regression are alpha = 0, and max iterations set to 100. The R square is a little higher at 92 % on the training data and 92 % on the test data the errors continue to stay the same as the Ridge regression." ] }, { "cell_type": "code", "execution_count": 108, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_validation.py:458: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " estimator.fit(X_train, y_train, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'alpha': 0, 'l1_ratio': 0.1, 'max_iter': 10}\n", "R^2 score on training data: 0.92\n", "R^2 score: 0.92\n", "Mean squared error: 6.05\n", "Mean absolute error: 1.83\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\model_selection\\_search.py:739: UserWarning: With alpha=0, this algorithm does not converge well. You are advised to use the LinearRegression estimator\n", " self.best_estimator_.fit(X, y, **fit_params)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:477: UserWarning: Coordinate descent with no regularization may lead to unexpected results and is discouraged.\n", " positive)\n", "C:\\Users\\Stefan\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:491: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations. Fitting data with very small alpha may cause precision problems.\n", " ConvergenceWarning)\n" ] } ], "source": [ "scoring = make_scorer(r2_score)\n", "grid_cv = GridSearchCV(ElasticNet(),\n", " param_grid={'alpha': range(0, 10), 'max_iter' : [10, 100, 1000], 'l1_ratio' : [0.1, 0.4, 0.8]},\n", " scoring=scoring, cv=5, refit=True)\n", "\n", "grid_cv.fit(life_features_train, life_labels_train)\n", "print(\"Best Parameters: \" + str(grid_cv.best_params_))\n", "\n", "result = grid_cv.cv_results_\n", "print(\"R^2 score on training data: %.2f\" % grid_cv.score(life_features_train, life_labels_train))\n", "print(\"R^2 score: %.2f\" \n", " % r2_score(life_labels_test, grid_cv.best_estimator_.predict(life_features_test)))\n", "print(\"Mean squared error: %.2f\"\n", " % mean_squared_error(life_labels_test, linear_model_predict))\n", "print(\"Mean absolute error: %.2f\"\n", " % mean_absolute_error(life_labels_test, linear_model_predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The ElasticNet combines both L1 and L2 regularization of the previous regression types.\n", "
\n", "Finally the grid search concerning ElasticNet shows that it performs as well as the Lasso regression so far with alpha = 0, \n", "max iterations = 100 and the L1 ration set at 0.1. The R square and the errors remain the same as the Lasso Regression\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 5 Linear Regression with Polynomial Features\n", "\n", "The Polynomial Features function has been used to get the interactions of the input variables only to the power of 2" ] }, { "cell_type": "code", "execution_count": 109, "metadata": { "collapsed": true }, "outputs": [], "source": [ "quad_feature_transformer = PolynomialFeatures(2, interaction_only = True)\n", "quad_feature_transformer.fit(life_features_train)\n", "life_features_train_quad = quad_feature_transformer.transform(life_features_train)\n", "life_features_test_quad = quad_feature_transformer.transform(life_features_test)" ] }, { "cell_type": "code", "execution_count": 110, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1.0\n" ] } ], "source": [ "poly_model_quad = LinearRegression()\n", "poly_model_quad.fit(life_features_train_quad, life_labels_train)\n", "accuracy_score_quad = poly_model_quad.score(life_features_train_quad, life_labels_train)\n", "print(accuracy_score_quad)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Linear Regression is being tested on the training data with the new Polynomial Features, the accuracy is 1, meaning the \n", "model has learned all the data. An prediction for the life_features_test_quad variable has been made." ] }, { "cell_type": "code", "execution_count": 111, "metadata": { "collapsed": true }, "outputs": [], "source": [ "poly_model_quad_predict = poly_model_quad.predict(life_features_test_quad)" ] }, { "cell_type": "code", "execution_count": 112, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Mean squared error: 1620.72\n", "Mean absolute error: 14.84\n", "R_square score: -18.45\n" ] } ], "source": [ "print(\"Mean squared error: %.2f\"\n", " % mean_squared_error(life_labels_test, poly_model_quad_predict))\n", "print(\"Mean absolute error: %.2f\"\n", " % mean_absolute_error(life_labels_test, poly_model_quad_predict))\n", "print('R_square score: %.2f' % r2_score(life_labels_test, poly_model_quad_predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All the errors are significantly higher then the privious models, and the R square is in this case negative \n", "(this happens only in sklearn). This is the worst performing model for now." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 5 Decision Tree Regression" ] }, { "cell_type": "code", "execution_count": 113, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean cross validation score: 0.77\n", "score without cv: 1.00\n", "R^2 score on the test data 0.80\n" ] } ], "source": [ "decision_tree_model = DecisionTreeRegressor()\n", "decision_tree_fit = decision_tree_model.fit(life_features_train, life_labels_train)\n", "\n", "decision_tree_score = cross_val_score(decision_tree_fit, life_features_train, life_labels_train, cv = 5)\n", "print(\"mean cross validation score: %.2f\" % np.mean(decision_tree_score))\n", "print(\"score without cv: %.2f\" % decision_tree_fit.score(life_features_train, life_labels_train))\n", "print(\"R^2 score on the test data %.2f\"% r2_score(life_labels_test, decision_tree_fit.predict(life_features_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will try the Decision Tree Regression. Cross Validation has been performed\n", "
\n", "The R square on the training data is 1 meaning that the algorithm has learned the data by heart, with the cross validation the figure declines to 77% and using the test date we get 80%\n", "
\n", "Now we use the algorithm to predict the values of the life_features_test." ] }, { "cell_type": "code", "execution_count": 114, "metadata": { "collapsed": true }, "outputs": [], "source": [ "decision_tree_model_predict = decision_tree_model.predict(life_features_test)" ] }, { "cell_type": "code", "execution_count": 118, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'min_samples_split': 9}\n", "R^2 score on training data: 0.98\n", "R^2 score: 0.81\n", "Mean squared error: 16.59\n", "Mean absolute error: 2.71\n" ] } ], "source": [ "scoring = make_scorer(r2_score)\n", "grid_cv = GridSearchCV(DecisionTreeRegressor(),\n", " param_grid={'min_samples_split': range(2, 10)},\n", " scoring=scoring, cv=5, refit=True)\n", "\n", "grid_cv.fit(life_features_train, life_labels_train)\n", "grid_cv.best_params_\n", "\n", "print(\"Best Parameters: \" + str(grid_cv.best_params_))\n", "result = grid_cv.cv_results_\n", "print(\"R^2 score on training data: %.2f\" % grid_cv.best_estimator_.score(life_features_train, life_labels_train))\n", "print(\"R^2 score: %.2f\" \n", " % r2_score(life_labels_test, grid_cv.best_estimator_.predict(life_features_test)))\n", "print(\"Mean squared error: %.2f\"\n", " % mean_squared_error(life_labels_test, decision_tree_model_predict))\n", "print(\"Mean absolute error: %.2f\"\n", " % mean_absolute_error(life_labels_test, decision_tree_model_predict))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "After performing grid search with minimun samples split in the range between 2 and 10 we get the best split of 3. \n", "
\n", "The R square on the training data is 98%, the algorithms has nearly leaned the data by hearth. On the test data we get R square of 81 %, the MAE is 2.71 and MSE is 16.59. This method is worse than the Elastic Net Regression" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 6 Random Forest Regression" ] }, { "cell_type": "code", "execution_count": 119, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "mean cross validation score: 0.88\n", "score without cv: 0.98\n", "R^2 score on the test data 0.92\n" ] } ], "source": [ "random_forest_model = RandomForestRegressor()\n", "random_forest_fit = random_forest_model.fit(life_features_train, life_labels_train)\n", "\n", "random_forest_score = cross_val_score(random_forest_fit, life_features_train, life_labels_train, cv = 5)\n", "print(\"mean cross validation score: %.2f\" \n", " % np.mean(random_forest_score))\n", "print(\"score without cv: %.2f\"\n", " % random_forest_fit.score(life_features_train, life_labels_train))\n", "print(\"R^2 score on the test data %.2f\"\n", " %r2_score(life_labels_test, random_forest_fit.predict(life_features_test)))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will use the Random Forest Regression\n", "
\n", "The alogorithm has learned 98% on the training data without cross validation and 88% with, the value is 92 % on the test data." ] }, { "cell_type": "code", "execution_count": 120, "metadata": {}, "outputs": [], "source": [ "random_forest_model_predict = random_forest_model.predict(life_features_test)" ] }, { "cell_type": "code", "execution_count": 125, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Best Parameters: {'min_samples_split': 2}\n", "R^2 score on training data: 0.98\n", "R^2 score: 0.91\n", "Mean squared error: 6.71\n", "Mean absolute error: 1.89\n" ] } ], "source": [ "scoring = make_scorer(r2_score)\n", "grid_cv = GridSearchCV(RandomForestRegressor(),\n", " param_grid={'min_samples_split': range(2, 10)},\n", " scoring=scoring, cv=5, refit=True)\n", "\n", "grid_cv.fit(life_features_train, life_labels_train)\n", "grid_cv.best_params_\n", "\n", "result = grid_cv.cv_results_\n", "print(\"Best Parameters: \" + str(grid_cv.best_params_))\n", "result = grid_cv.cv_results_\n", "print(\"R^2 score on training data: %.2f\" % grid_cv.best_estimator_.score(life_features_train, life_labels_train))\n", "print(\"R^2 score: %.2f\" \n", " % r2_score(life_labels_test, grid_cv.best_estimator_.predict(life_features_test)))\n", "print(\"Mean squared error: %.2f\"\n", " % mean_squared_error(life_labels_test, random_forest_model_predict))\n", "print(\"Mean absolute error: %.2f\"\n", " % mean_absolute_error(life_labels_test, random_forest_model_predict))" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "After performing grid search in the range 2 to 10 for the minimun samples split we get 2 for the split. The R square on the training is 98 % while on the test it is 91% and a MAE of 1.89 and MSE of 6.71." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 7 Conclution\n", "After comparing all the algorithms we can conclude the Lasso and the Elastic Net Regression offer which are the same:\n", "
\n", "
    \n", "
  1. Best Parameters: {'alpha': 0, 'max_iter': 10}
    2. \n", "
  2. R square on the test data of 92%
    3. \n", "
  3. MAE of 1.83
    4. \n", "
  4. MSE of 6.05\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 8 Sources\n", "The following sources have been used:\n", "
\n", "
    \n", "
  1. https://www.kaggle.com/kumarajarshi/life-expectancy-who/data
    2. \n", "
  2. Introduction to Machine Learning with Python by Andreas C. Müller & Sarah Guido
    3. \n", "
  3. Labs of the couse
    4. \n", "
  4. stack overflow
    5. \n", "
  5. Lectures of the course\n", "
" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "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": "3.6.3" } }, "nbformat": 4, "nbformat_minor": 2 }