README.md

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.
Created by LuccasDev Follow me :)

• 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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