adding all work done so far (lessons 1 - 5)

2019-07-10 19:58:53 +01:00
parent 8085149a49
commit b982957daf
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+Country,Life expectancy,BMI
+Afghanistan,52.8,20.62058
+Albania,76.8,26.44657
+Algeria,75.5,24.59620
+Andorra,84.6,27.63048
+Angola,56.7,22.25083
+Armenia,72.3,25.355420000000002
+Australia,81.6,27.56373
+Austria,80.4,26.467409999999997
+Azerbaijan,69.2,25.65117
+Bahamas,72.2,27.24594
+Bangladesh,68.3,20.39742
+Barbados,75.3,26.38439
+Belarus,70.0,26.16443
+Belgium,79.6,26.75915
+Belize,70.7,27.02255
+Benin,59.7,22.41835
+Bhutan,70.7,22.82180
+Bolivia,71.2,24.43335
+Bosnia and Herzegovina,77.5,26.61163
+Botswana,53.2,22.12984
+Brazil,73.2,25.78623
+Bulgaria,73.2,26.54286
+Burkina Faso,58.0,21.27157
+Burundi,59.1,21.50291
+Cambodia,66.1,20.80496
+Cameroon,56.6,23.68173
+Canada,80.8,27.45210
+Cape Verde,70.4,23.51522
+Chad,54.3,21.48569
+Chile,78.5,27.01542
+China,73.4,22.92176
+Colombia,76.2,24.94041
+Comoros,67.1,22.06131
+"Congo, Dem. Rep.",57.5,19.86692
+"Congo, Rep.",58.8,21.87134
+Costa Rica,79.8,26.47897
+Cote d'Ivoire,55.4,22.56469
+Croatia,76.2,26.59629
+Cuba,77.6,25.06867
+Cyprus,80.0,27.41899
+Denmark,78.9,26.13287
+Djibouti,61.8,23.38403
+Ecuador,74.7,25.58841
+Egypt,70.2,26.73243
+El Salvador,73.7,26.36751
+Eritrea,60.1,20.88509
+Estonia,74.2,26.26446
+Ethiopia,60.0,20.24700
+Fiji,64.9,26.53078
+Finland,79.6,26.73339
+France,81.1,25.85329
+French Polynesia,75.11,30.86752
+Gabon,61.7,24.07620
+Gambia,65.7,21.65029
+Georgia,71.8,25.54942
+Germany,80.0,27.16509
+Ghana,62.0,22.84247
+Greece,80.2,26.33786
+Greenland,70.3,26.01359
+Grenada,70.8,25.17988
+Guatemala,71.2,25.29947
+Guinea,57.1,22.52449
+Guinea-Bissau,53.6,21.64338
+Guyana,65.0,23.68465
+Haiti,61.0,23.66302
+Honduras,71.8,25.10872
+Hungary,73.9,27.11568
+Iceland,82.4,27.20687
+India,64.7,20.95956
+Indonesia,69.4,21.85576
+Iran,73.1,25.31003
+Iraq,66.6,26.71017
+Ireland,80.1,27.65325
+Israel,80.6,27.13151
+Jamaica,75.1,24.00421
+Japan,82.5,23.50004
+Jordan,76.9,27.47362
+Kazakhstan,67.1,26.29078
+Kenya,60.8,21.59258
+Kuwait,77.3,29.17211
+Latvia,72.4,26.45693
+Lesotho,44.5,21.90157
+Liberia,59.9,21.89537
+Libya,75.6,26.54164
+Lithuania,72.1,26.86102
+Luxembourg,81.0,27.43404
+"Macedonia, FYR",74.5,26.34473
+Madagascar,62.2,21.40347
+Malawi,52.4,22.03468
+Malaysia,74.5,24.73069
+Maldives,78.5,23.21991
+Mali,58.5,21.78881
+Malta,80.7,27.68361
+Marshall Islands,65.3,29.37337
+Mauritania,67.9,22.62295
+Mauritius,72.9,25.15669
+Mexico,75.4,27.42468
+Moldova,70.4,24.23690
+Mongolia,64.8,24.88385
+Montenegro,76.0,26.55412
+Morocco,73.3,25.63182
+Mozambique,54.0,21.93536
+Myanmar,59.4,21.44932
+Namibia,59.1,22.65008
+Nepal,68.4,20.76344
+Netherlands,80.3,26.01541
+Nicaragua,77.0,25.77291
+Niger,58.0,21.21958
+Nigeria,59.2,23.03322
+Norway,80.8,26.93424
+Oman,76.2,26.24109
+Pakistan,64.1,22.29914
+Panama,77.3,26.26959
+Papua New Guinea,58.6,25.01506
+Paraguay,74.0,25.54223
+Peru,78.2,24.77041
+Philippines,69.8,22.87263
+Poland,75.4,26.67380
+Portugal,79.4,26.68445
+Qatar,77.9,28.13138
+Romania,73.2,25.41069
+Russia,67.9,26.01131
+Rwanda,64.1,22.55453
+Samoa,72.3,30.42475
+Sao Tome and Principe,66.0,23.51233
+Senegal,63.5,21.92743
+Serbia,74.3,26.51495
+Sierra Leone,53.6,22.53139
+Singapore,80.6,23.83996
+Slovak Republic,74.9,26.92717
+Slovenia,78.7,27.43983
+Somalia,52.6,21.96917
+South Africa,53.4,26.85538
+Spain,81.1,27.49975
+Sri Lanka,74.0,21.96671
+Sudan,65.5,22.40484
+Suriname,70.2,25.49887
+Swaziland,45.1,23.16969
+Sweden,81.1,26.37629
+Switzerland,82.0,26.20195
+Syria,76.1,26.91969
+Tajikistan,69.6,23.77966
+Tanzania,60.4,22.47792
+Thailand,73.9,23.00803
+Timor-Leste,69.9,20.59082
+Togo,57.5,21.87875
+Tonga,70.3,30.99563
+Trinidad and Tobago,71.7,26.39669
+Tunisia,76.8,25.15699
+Turkey,77.8,26.70371
+Turkmenistan,67.2,25.24796
+Uganda,56.0,22.35833
+Ukraine,67.8,25.42379
+United Arab Emirates,75.6,28.05359
+United Kingdom,79.7,27.39249
+United States,78.3,28.45698
+Uruguay,76.0,26.39123
+Uzbekistan,69.6,25.32054
+Vanuatu,63.4,26.78926
+West Bank and Gaza,74.1,26.57750
+Vietnam,74.1,20.91630
+Zambia,51.1,20.68321
+Zimbabwe,47.3,22.02660
@@ -0,0 +1,19 @@
+from sklearn.linear_model import LinearRegression
+import pandas as pd
+
+df = pd.read_csv('data.csv')
+
+bmi_life_data = df
+
+bmi_life_model = LinearRegression()
+
+# print(bmi_life_data[['Life expectancy']], bmi_life_data[['BMI']])
+
+bmi_life_model.fit(bmi_life_data[['BMI']],
+                   bmi_life_data[['Life expectancy']])
+
+laos_life_exp = bmi_life_model.predict([[21.07931]])
+
+print(laos_life_exp)
+
+