Predicting and Analyzing income using Census Dataset

We will use the census data set, to predict annual income greater or less than 50k using classification method.
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• Let's go analyze the dataset

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

from collections import Counter

from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier, ExtraTreesClassifier, VotingClassifier
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV, cross_val_score, StratifiedKFold, learning_curve, train_test_split, KFold
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import accuracy_score


df = pd.read_csv('dataset\\census.csv')
df.describe()

	age	final-weight	education-num	capital-gain	capital-loos	hour-per-week
count	32561.000000	3.256100e+04	32561.000000	32561.000000	32561.000000	32561.000000
mean	38.581647	1.897784e+05	10.080679	1077.648844	87.303830	40.437456
std	13.640433	1.055500e+05	2.572720	7385.292085	402.960219	12.347429
min	17.000000	1.228500e+04	1.000000	0.000000	0.000000	1.000000
25%	28.000000	1.178270e+05	9.000000	0.000000	0.000000	40.000000
50%	37.000000	1.783560e+05	10.000000	0.000000	0.000000	40.000000
75%	48.000000	2.370510e+05	12.000000	0.000000	0.000000	45.000000
max	90.000000	1.484705e+06	16.000000	99999.000000	4356.000000	99.000000

On describe we can see the minimal age as 17 years ago and maximum age as 90 years ago.

data = df.iloc[:, 0:14].values
target = df.iloc[:, 14].values

Show the income count, more than 50k vs more less 50k.

sns.countplot(df['income'],label="Count")
plt.show()

Income / Age

g = sns.FacetGrid(df, col='income')
g = g.map(sns.distplot, "age")
plt.show()

Transform target to 0 or 1 (0 for <=50k, 1 for > 50k).

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelEncoder_classe = LabelEncoder()
df['income'] = labelEncoder_classe.fit_transform(target)

Income > 50k : Male vs Female

g = sns.catplot(x="sex",y="income",data=df,kind="bar" ,height=4,
palette = "muted")
g.despine(left=True)
g = g.set_ylabels("Income > 50k")
plt.show()

Education Num vs Income

g = sns.catplot(x="education-num",y="income",data=df,kind="bar",height=6,palette="muted")
g.despine(left=True)
g = g.set_ylabels(">50K probability")

Relationship vs Income

g = sns.catplot(x="relationship",y="income",data=df,kind="bar",height=6,palette="muted")
g.despine(left=True)
g = g.set_ylabels("Income >50K Probability")
plt.show()

Now transform features

from sklearn.preprocessing import LabelEncoder, OneHotEncoder

labelEncoder= LabelEncoder()
data[:, 1] = labelEncoder.fit_transform(data[:, 1])
data[:, 3] = labelEncoder.fit_transform(data[:, 3])
data[:, 5] = labelEncoder.fit_transform(data[:, 5])
data[:, 6] = labelEncoder.fit_transform(data[:, 6])
data[:, 7] = labelEncoder.fit_transform(data[:, 7])
data[:, 8] = labelEncoder.fit_transform(data[:, 8])
data[:, 9] = labelEncoder_previsores.fit_transform(data[:, 9])
data[:, 13] = labelEncoder.fit_transform(data[:, 13])

Scheduling

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
data = scaler.fit_transform(data)

Create Model

validation_size = 0.20
seed = 7
num_folds = 10
scoring = 'accuracy'
X_train, X_validation, Y_train, Y_validation = train_test_split(data,target,
    test_size=validation_size,random_state=seed)

# Params for Random Forest
num_trees = 100
max_features = 3

#Spot Check 5 Algorithms (LR, LDA, KNN, CART, GNB, SVM)
models = []
models.append(('LR', LogisticRegression()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('CART', DecisionTreeClassifier()))
models.append(('NB', GaussianNB()))
models.append(('RF', RandomForestClassifier(n_estimators=num_trees, max_features=max_features)))
#models.append(('SVM', SVC()))

# evalutate each model in turn
results = []
names = []
for name, model in models:
    kfold = KFold(n_splits=10, random_state=seed)
    cv_results = cross_val_score(model, X_train, Y_train, cv=kfold, scoring='accuracy')
    results.append(cv_results)
    names.append(name)
    msg = "%s: %f (%f)" % (name, cv_results.mean(), cv_results.std())
    print(msg)

LR: 0.825591 (0.004918)
LDA: 0.815840 (0.005894)
KNN: 0.826781 (0.005494)
CART: 0.808469 (0.006892)
NB: 0.802020 (0.009341)
RF: 0.855344 (0.004833)

figure = plt.figure()
figure.suptitle('Algorith Comparison')
ax= figure.add_subplot(111)
plt.boxplot(results)
ax.set_xticklabels(names)
plt.show()

Best algorithm score is RF, accuracy: 0.85

random_forest = RandomForestClassifier(n_estimators=250,max_features=5)
random_forest.fit(X_train, Y_train)
predictions = random_forest.predict(X_validation)
print("Accuracy: %s%%" % (100*accuracy_score(Y_validation, predictions)))
print(confusion_matrix(Y_validation, predictions))
print(classification_report(Y_validation, predictions))

Accuracy: 85.90511285122064%
[[4605  346]
 [ 572  990]]
             precision    recall  f1-score   support

          0       0.89      0.93      0.91      4951
          1       0.74      0.63      0.68      1562

avg / total       0.85      0.86      0.86      6513

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