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Step 7: Try the model on testing data

Workshop Resources

Pre-requisite: none

Difficulty: Intermediate

Now we are using the trained model to estimate players in test_data. Similar to what we do to the train_data, we create x_test and y_test.

model.predict() will generate a list of predicted results.

# we would like to sort test data on target value ("Overall")
test_data = test_data.sort_values([target], ascending=False)

x_test = test_data[features]
y_test = test_data[target]

y_pred = model.predict(x_test)

Let’s compare with the actual overall ratings

# add a new column of predicted overall to test_data
test_data['Predicted Overall'] = y_pred.copy()

# add a new column of prediction difference ratio to test_data
difference = (y_pred - y_test) / y_test * 100
test_data['Difference (%)'] = difference

# print the results
test_data[["Name", "Nationality", "Club", "Overall", "Predicted Overall", "Difference (%)"]]

	Name	Nationality	Club	Overall	Predicted Overall	Difference (%)
1	Cristiano Ronaldo	Portugal	Juventus	94	91.973701	-2.155638
10	R. Lewandowski	Poland	FC Bayern München	90	88.135513	-2.071652
23	S. Agüero	Argentina	Manchester City	89	87.807637	-1.339733
48	C. Immobile	Italy	Lazio	87	85.933234	-1.226168
159	Louri Beretta	Brazil	Atlético Mineiro	83	81.583941	-1.706096
193	Rodrigo	Spain	Valencia CF	83	81.784946	-1.463921
179	S. Gnabry	Germany	FC Bayern München	83	79.978980	-3.639783
315	David Villa	Spain	New York City FC	82	81.259066	-0.903578
362	Paco Alcácer	Spain	Borussia Dortmund	81	81.836532	1.032756
518	Alexandre Pato	Brazil	Tianjin Quanjian FC	80	78.322831	-2.096461
499	L. de Jong	Netherlands	PSV	80	79.993062	-0.008672
523	K. Gameiro	France	Valencia CF	80	79.130702	-1.086622
721	B. Yılmaz	Turkey	Trabzonspor	79	78.092396	-1.148866
693	S. Jovetić	Montenegro	AS Monaco	79	79.353044	0.446891
591	L. Alario	Argentina	Bayer 04 Leverkusen	79	79.066446	0.084109
569	André Silva	Portugal	Sevilla FC	79	79.925229	1.171175
588	M. Philipp	Germany	Borussia Dortmund	79	78.962674	-0.047248
561	L. Martínez	Argentina	Inter	79	79.411940	0.521443
874	A. Dzyuba	Russia	NaN	78	76.855093	-1.467829
825	S. García	Uruguay	Godoy Cruz	78	77.375588	-0.800528
909	V. Germain	France	Olympique de Marseille	77	77.509005	0.661045
1095	N. Jørgensen	Denmark	Feyenoord	77	76.745918	-0.329976
992	J. Sand	Argentina	Deportivo Cali	77	78.886169	2.449570
1137	Rubén Castro	Spain	UD Las Palmas	77	77.797984	1.036343
895	M. Harnik	Austria	SV Werder Bremen	77	76.926679	-0.095222
1413	Alan Carvalho	Brazil	Guangzhou Evergrande Taobao FC	76	75.922866	-0.101492
1327	K. Dolberg	Denmark	Ajax	76	76.060831	0.080041
1496	F. Montero	Colombia	Sporting CP	76	77.017187	1.338404
1240	I. Popov	Bulgaria	Spartak Moscow	76	75.734350	-0.349540
1357	I. Slimani	Algeria	Fenerbahçe SK	76	76.494507	0.650667
...	...	...	...	...	...	...
17484	J. Lankester	England	Ipswich Town	54	56.121884	3.929415
17469	J. Gallagher	Republic of Ireland	Atlanta United	54	54.692444	1.282304
17501	M. Saavedra	Chile	Audax Italiano	54	54.137463	0.254561
17361	E. McKeown	England	Colchester United	54	52.796085	-2.229473
17399	Mao Haoyu	China PR	Tianjin TEDA FC	54	53.964477	-0.065783
17313	M. Howard	England	Preston North End	54	53.339370	-1.223389
17355	V. Barbero	Argentina	Belgrano de Córdoba	54	54.011344	0.021008
17422	Y. Ogaki	Japan	Nagoya Grampus	54	54.041024	0.075970
17447	Xie Weijun	China PR	Tianjin TEDA FC	54	53.452376	-1.014118
17367	T. Lauritsen	Norway	Odds BK	54	54.944641	1.749336
17482	F. Al Birekan	Saudi Arabia	Al Nassr	54	52.727175	-2.357084
17609	S. Jamieson	Scotland	St. Mirren	53	53.509650	0.961604
17716	M. Knox	Scotland	Livingston FC	53	52.826053	-0.328201
17578	Lei Wenjie	China PR	Shanghai SIPG FC	53	52.770581	-0.432867
17665	J. Smylie	Australia	Central Coast Mariners	53	52.469974	-1.000049
17611	Felipe Ferreyra	Brazil	Curicó Unido	53	52.861431	-0.261451
17765	A. Georgiou	Cyprus	Stevenage	52	52.167786	0.322665
17757	L. Smyth	Northern Ireland	Stevenage	52	51.999942	-0.000111
17923	A. Reghba	Republic of Ireland	Bohemian FC	51	51.075501	0.148041
17956	C. Murphy	Republic of Ireland	Cork City	51	51.731985	1.435265
17971	M. Najjar	Australia	Melbourne City FC	51	51.035541	0.069688
18013	W. Møller	Denmark	Esbjerg fB	51	50.796960	-0.398118
18062	Gao Dalun	China PR	Jiangsu Suning FC	50	49.677371	-0.645259
18094	M. Al Dhafeeri	Saudi Arabia	Al Batin	50	51.553964	3.107928
18063	R. Hackett-Fairchild	England	Charlton Athletic	50	50.140762	0.281524
18028	D. Asonganyi	England	Milton Keynes Dons	50	50.349896	0.699792
18140	K. Hawley	England	Morecambe	49	49.787332	1.606799
18166	N. Ayéva	Sweden	Örebro SK	48	48.802935	1.672781
18177	R. Roache	Republic of Ireland	Blackpool	48	49.226015	2.554197
18200	J. Young	Scotland	Swindon Town	47	48.019387	2.168908

538 rows × 6 columns

Is that amazing? With the result, you’re confident to use this model to estimate the overall ratings of any soccer player in the world!

Now let’s do some plotting to visualize it.

# Plot outputs
plt.scatter(range(0,y_test.shape[0]), y_test,  color='blue', label="Actual")
plt.plot(range(0,y_test.shape[0]), y_pred, color='red', label="Predicted")

# add ticks, labels, legend
plt.xticks(())
plt.xlabel("Players (Sorted by Actual Overall ratings)")
plt.ylabel("Overall ratings")
plt.legend(loc='upper right')
plt.show()

Final graph