The Heart Disease Prediction Project is a machine learning project aimed at developing a model to predict the presence of heart disease in patients based on various health-related features. The dataset used in this project contains information about patients' age, gender, chest pain type, resting blood pressure, cholesterol levels, fasting blood sugar, electrocardiographic results, maximum heart rate achieved, exercise-induced angina, and other relevant attributes.
The primary goal of this project is to build a reliable machine learning model capable of accurately classifying patients as either having heart disease or not. By utilizing historical data and applying various machine learning algorithms, the model will be trained to recognize patterns and relationships between the input features and the target variable (presence of heart disease).
The project begins with an exploratory data analysis (EDA) to gain insights into the dataset and identify any data quality issues. During this phase, data cleaning steps will be performed to handle missing values and correct any anomalies in certain columns such as 'caa' and 'thall', where incorrect entries were observed.
To achieve the best predictive performance, several machine learning algorithms will be applied, including K-Nearest Neighbors (KNN), Decision Trees, Logistic Regression, AdaBoost, Random Forest, Neural Networks, and Naive Bayes. Each algorithm will be trained, validated, and fine-tuned using cross-validation techniques to optimize their hyperparameters.
The performance of each model will be evaluated using various metrics such as accuracy, precision, recall, and F1-score. The model with the highest accuracy and robust generalization capability will be selected as the final prediction model for heart disease.
The Heart Disease Prediction Project aims to provide a valuable tool for early detection and risk assessment of heart disease in patients. By leveraging machine learning techniques and thorough data analysis, this project seeks to contribute to better healthcare outcomes by assisting medical professionals in making more informed decisions and improving patient care.
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
import seaborn as sns
from scipy import stats
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix
from sklearn.neighbors import KNeighborsClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import AdaBoostClassifier, RandomForestClassifier
from sklearn.neural_network import MLPClassifier
from sklearn.naive_bayes import BernoulliNB, GaussianNB
from sklearn.preprocessing import StandardScalerds = pd.read_csv('/datasets/heart.csv')
ds_copy = pd.read_csv('/datasets/heart.csv')This repository contains a dataset about patients related to heart diseases.
-
age: Patient's age.
-
sex: Patient's gender.
- 0: Female (F).
- 1: Male (M).
-
cp: Chest pain type.
- 1: Typical angina.
- 2: Atypical angina.
- 3: Non-anginal pain.
- 4: Asymptomatic.
-
trtbps: Resting blood pressure (in mm Hg).
-
chol: Cholesterol in mg/dl obtained via IMC sensor (Serum cholesterol in mg/dl).
-
fbs: Fasting blood sugar level.
- 1: Fasting blood sugar > 120 mg/dl.
- 0: Fasting blood sugar <= 120 mg/dl.
-
rest_ecg: Resting electrocardiographic results.
- 0: Normal.
- 1: ST-T wave abnormality (inverted T wave and/or ST segment elevation or depression > 0.05 mV).
- 2: Probable or definite left ventricular hypertrophy by Estes' criteria.
-
thalach: Maximum heart rate achieved.
- 0: Lower chance of heart attack.
- 1: Higher chance of heart attack.
-
exng: Exercise induced angina.
- 1: Yes.
- 0: No.
-
Oldpeak: ST depression induced by exercise relative to rest.
ds.head()| age | sex | cp | trtbps | chol | fbs | restecg | thalachh | exng | oldpeak | slp | caa | thall | output | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 63 | 1 | 3 | 145 | 233 | 1 | 0 | 150 | 0 | 2.3 | 0 | 0 | 1 | 1 |
| 1 | 37 | 1 | 2 | 130 | 250 | 0 | 1 | 187 | 0 | 3.5 | 0 | 0 | 2 | 1 |
| 2 | 41 | 0 | 1 | 130 | 204 | 0 | 0 | 172 | 0 | 1.4 | 2 | 0 | 2 | 1 |
| 3 | 56 | 1 | 1 | 120 | 236 | 0 | 1 | 178 | 0 | 0.8 | 2 | 0 | 2 | 1 |
| 4 | 57 | 0 | 0 | 120 | 354 | 0 | 1 | 163 | 1 | 0.6 | 2 | 0 | 2 | 1 |
Below we can check if there are null records in our database, and luckily none were found.
ds.isnull().sum()age 0
sex 0
cp 0
trtbps 0
chol 0
fbs 0
restecg 0
thalachh 0
exng 0
oldpeak 0
slp 0
caa 0
thall 0
output 0
dtype: int64
We can see that we also have no negative values in the data.
ds.min()age 29.0
sex 0.0
cp 0.0
trtbps 94.0
chol 126.0
fbs 0.0
restecg 0.0
thalachh 71.0
exng 0.0
oldpeak 0.0
slp 0.0
caa 0.0
thall 0.0
output 0.0
dtype: float64
We can see that there is 'a' duplicate line and we will remove it so as not to overfit
ds_copy.duplicated().sum()1
Removing the duplication
ds = ds.drop_duplicates()
ds_copy = ds_copy.drop_duplicates()ds.describe().transpose()| count | mean | std | min | 25% | 50% | 75% | max | |
|---|---|---|---|---|---|---|---|---|
| age | 302.0 | 54.420530 | 9.047970 | 29.0 | 48.00 | 55.5 | 61.00 | 77.0 |
| sex | 302.0 | 0.682119 | 0.466426 | 0.0 | 0.00 | 1.0 | 1.00 | 1.0 |
| cp | 302.0 | 0.963576 | 1.032044 | 0.0 | 0.00 | 1.0 | 2.00 | 3.0 |
| trtbps | 302.0 | 131.602649 | 17.563394 | 94.0 | 120.00 | 130.0 | 140.00 | 200.0 |
| chol | 302.0 | 246.500000 | 51.753489 | 126.0 | 211.00 | 240.5 | 274.75 | 564.0 |
| fbs | 302.0 | 0.149007 | 0.356686 | 0.0 | 0.00 | 0.0 | 0.00 | 1.0 |
| restecg | 302.0 | 0.526490 | 0.526027 | 0.0 | 0.00 | 1.0 | 1.00 | 2.0 |
| thalachh | 302.0 | 149.569536 | 22.903527 | 71.0 | 133.25 | 152.5 | 166.00 | 202.0 |
| exng | 302.0 | 0.327815 | 0.470196 | 0.0 | 0.00 | 0.0 | 1.00 | 1.0 |
| oldpeak | 302.0 | 1.043046 | 1.161452 | 0.0 | 0.00 | 0.8 | 1.60 | 6.2 |
| slp | 302.0 | 1.397351 | 0.616274 | 0.0 | 1.00 | 1.0 | 2.00 | 2.0 |
| caa | 302.0 | 0.718543 | 1.006748 | 0.0 | 0.00 | 0.0 | 1.00 | 4.0 |
| thall | 302.0 | 2.314570 | 0.613026 | 0.0 | 2.00 | 2.0 | 3.00 | 3.0 |
| output | 302.0 | 0.543046 | 0.498970 | 0.0 | 0.00 | 1.0 | 1.00 | 1.0 |
Observing the correlation of features; "Correlation does not imply Causality.".
plt.figure(figsize=(16,8))
sns.heatmap(ds.corr(), annot = True, cmap='Blues')
categoricas = ['sex', 'cp', 'fbs','restecg','exng', 'slp','caa', 'thall']
numericas = ['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']for c in ds_copy.columns:
plt.figure(figsize=(12,4))
plt.title(f'Coluna avaliada: {c}')
if c in categoricas:
sns.countplot(x = ds_copy[c], hue= ds.output)
plt.show()
n = ds_copy[c].loc[ds_copy.output == 0].value_counts()
y = ds_copy[c].loc[ds_copy.output == 1].value_counts()
print(' output 0: ')
for i in range(len(n)):
print(f' {c} '+str(i)+ ' = '+str(n[i])+' -> {:.4}'.format((n[i]/len(ds_copy[c].loc[ds_copy.output==0]))*100)+'%')
print('')
print(' output 1: ')
for i in range(len(y)):
print(f' {c} '+str(i)+' = '+str(y[i])+' -> {:.4}'.format((y[i]/len(ds_copy[c].loc[ds_copy.output==1]))*100)+'%')
print('')
print(' GERAL: ')
for i in range(len(n)):
print(f' {c} '+str(i)+' e output 0 = '+str(n[i])+' -> {:.4}'.format((n[i]/len(ds_copy[c]))*100)+'%')
print(f' {c} '+str(i)+' e output 1 = '+str(y[i])+' -> {:.4}'.format((y[i]/len(ds_copy[c]))*100)+'%')
print('')
if c in numericas:
sns.histplot(ds_copy[c], kde=True)
output 0:
sex 0 = 24 -> 17.39%
sex 1 = 114 -> 82.61%
output 1:
sex 0 = 72 -> 43.9%
sex 1 = 92 -> 56.1%
GERAL:
sex 0 e output 0 = 24 -> 7.947%
sex 0 e output 1 = 72 -> 23.84%
sex 1 e output 0 = 114 -> 37.75%
sex 1 e output 1 = 92 -> 30.46%
output 0:
cp 0 = 104 -> 75.36%
cp 1 = 9 -> 6.522%
cp 2 = 18 -> 13.04%
cp 3 = 7 -> 5.072%
output 1:
cp 0 = 39 -> 23.78%
cp 1 = 41 -> 25.0%
cp 2 = 68 -> 41.46%
cp 3 = 16 -> 9.756%
GERAL:
cp 0 e output 0 = 104 -> 34.44%
cp 0 e output 1 = 39 -> 12.91%
cp 1 e output 0 = 9 -> 2.98%
cp 1 e output 1 = 41 -> 13.58%
cp 2 e output 0 = 18 -> 5.96%
cp 2 e output 1 = 68 -> 22.52%
cp 3 e output 0 = 7 -> 2.318%
cp 3 e output 1 = 16 -> 5.298%
output 0:
fbs 0 = 116 -> 84.06%
fbs 1 = 22 -> 15.94%
output 1:
fbs 0 = 141 -> 85.98%
fbs 1 = 23 -> 14.02%
GERAL:
fbs 0 e output 0 = 116 -> 38.41%
fbs 0 e output 1 = 141 -> 46.69%
fbs 1 e output 0 = 22 -> 7.285%
fbs 1 e output 1 = 23 -> 7.616%
output 0:
restecg 0 = 79 -> 57.25%
restecg 1 = 56 -> 40.58%
restecg 2 = 3 -> 2.174%
output 1:
restecg 0 = 68 -> 41.46%
restecg 1 = 95 -> 57.93%
restecg 2 = 1 -> 0.6098%
GERAL:
restecg 0 e output 0 = 79 -> 26.16%
restecg 0 e output 1 = 68 -> 22.52%
restecg 1 e output 0 = 56 -> 18.54%
restecg 1 e output 1 = 95 -> 31.46%
restecg 2 e output 0 = 3 -> 0.9934%
restecg 2 e output 1 = 1 -> 0.3311%
output 0:
exng 0 = 62 -> 44.93%
exng 1 = 76 -> 55.07%
output 1:
exng 0 = 141 -> 85.98%
exng 1 = 23 -> 14.02%
GERAL:
exng 0 e output 0 = 62 -> 20.53%
exng 0 e output 1 = 141 -> 46.69%
exng 1 e output 0 = 76 -> 25.17%
exng 1 e output 1 = 23 -> 7.616%
output 0:
slp 0 = 12 -> 8.696%
slp 1 = 91 -> 65.94%
slp 2 = 35 -> 25.36%
output 1:
slp 0 = 9 -> 5.488%
slp 1 = 49 -> 29.88%
slp 2 = 106 -> 64.63%
GERAL:
slp 0 e output 0 = 12 -> 3.974%
slp 0 e output 1 = 9 -> 2.98%
slp 1 e output 0 = 91 -> 30.13%
slp 1 e output 1 = 49 -> 16.23%
slp 2 e output 0 = 35 -> 11.59%
slp 2 e output 1 = 106 -> 35.1%
output 0:
caa 0 = 45 -> 32.61%
caa 1 = 44 -> 31.88%
caa 2 = 31 -> 22.46%
caa 3 = 17 -> 12.32%
caa 4 = 1 -> 0.7246%
output 1:
caa 0 = 130 -> 79.27%
caa 1 = 21 -> 12.8%
caa 2 = 7 -> 4.268%
caa 3 = 3 -> 1.829%
caa 4 = 3 -> 1.829%
GERAL:
caa 0 e output 0 = 45 -> 14.9%
caa 0 e output 1 = 130 -> 43.05%
caa 1 e output 0 = 44 -> 14.57%
caa 1 e output 1 = 21 -> 6.954%
caa 2 e output 0 = 31 -> 10.26%
caa 2 e output 1 = 7 -> 2.318%
caa 3 e output 0 = 17 -> 5.629%
caa 3 e output 1 = 3 -> 0.9934%
caa 4 e output 0 = 1 -> 0.3311%
caa 4 e output 1 = 3 -> 0.9934%
output 0:
thall 0 = 1 -> 0.7246%
thall 1 = 12 -> 8.696%
thall 2 = 36 -> 26.09%
thall 3 = 89 -> 64.49%
output 1:
thall 0 = 1 -> 0.6098%
thall 1 = 6 -> 3.659%
thall 2 = 129 -> 78.66%
thall 3 = 28 -> 17.07%
GERAL:
thall 0 e output 0 = 1 -> 0.3311%
thall 0 e output 1 = 1 -> 0.3311%
thall 1 e output 0 = 12 -> 3.974%
thall 1 e output 1 = 6 -> 1.987%
thall 2 e output 0 = 36 -> 11.92%
thall 2 e output 1 = 129 -> 42.72%
thall 3 e output 0 = 89 -> 29.47%
thall 3 e output 1 = 28 -> 9.272%
caa: There are incorrect entries, according to the Kaggle link for this dataset, caa should range from 0 to 3, but we observed a type of incorrect entry. We will handle this with the following Python code:
ds.caa.loc[ds.caa == 4] = 0tha: There are incorrect entries, according to the Kaggle link for this dataset, thall should range from 1 to 3, but we observed a type of incorrect entry. We will handle this with the following Python code:
ds.thall.loc[ds.thall == 0] = 2We can observe outliers in the numerical variables by using histograms. To highlight them, let's check the boxplot with the following Python code:
plt.figure(figsize=(18,8))
sns.boxplot(data = ds_copy)
plt.figure(figsize=(12,4))
sns.boxplot(ds_copy.trtbps)
plt.show
ds.trtbps.loc[ds.trtbps > 170] = 170
plt.figure(figsize=(12,4))
sns.boxplot(ds.trtbps)
plt.show()
ds.trtbps.describe().transpose()
count 302.000000
mean 131.258278
std 16.605232
min 94.000000
25% 120.000000
50% 130.000000
75% 140.000000
max 170.000000
Name: trtbps, dtype: float64
plt.figure(figsize=(12,4))
sns.boxplot(ds_copy.chol)
plt.show()
ds.chol.loc[ds.chol > 360] = 360
plt.figure(figsize=(12,4))
sns.boxplot(ds.chol)
plt.show()
ds.chol.describe().transpose()
count 302.000000
mean 245.205298
std 47.049535
min 126.000000
25% 211.000000
50% 240.500000
75% 274.750000
max 360.000000
Name: chol, dtype: float64
plt.figure(figsize=(12, 4))
sns.boxplot(ds_copy.thalachh)
limite_inferior = ( ds.thalachh.quantile(0.25)) -1.5 * (ds.thalachh.quantile(0.75)-ds.thalachh.quantile(0.25))
ds.thalachh.loc[ds.thalachh < limite_inferior] = limite_inferior
plt.figure(figsize=(12, 4))
sns.boxplot(ds.thalachh)
plt.show()
ds.thalachh.describe().transpose()
count 302.000000
mean 149.612997
std 22.765983
min 84.125000
25% 133.250000
50% 152.500000
75% 166.000000
max 202.000000
Name: thalachh, dtype: float64
plt.figure(figsize=(18,8))
sns.boxplot(data = ds)
plt.show()
['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']
['sex', 'cp', 'fbs', 'restecg', 'exng', 'slp', 'caa', 'thall']
ds1 = pd.get_dummies(ds, columns = ['sex', 'cp', 'fbs', 'restecg', 'exng', 'slp', 'caa', 'thall'])
ds1| age | trtbps | chol | thalachh | oldpeak | output | sex_0 | sex_1 | cp_0 | cp_1 | ... | slp_0 | slp_1 | slp_2 | caa_0 | caa_1 | caa_2 | caa_3 | thall_1 | thall_2 | thall_3 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 63 | 145 | 233 | 150.0 | 2.3 | 1 | 0 | 1 | 0 | 0 | ... | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 |
| 1 | 37 | 130 | 250 | 187.0 | 3.5 | 1 | 0 | 1 | 0 | 0 | ... | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 2 | 41 | 130 | 204 | 172.0 | 1.4 | 1 | 1 | 0 | 0 | 1 | ... | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 3 | 56 | 120 | 236 | 178.0 | 0.8 | 1 | 0 | 1 | 0 | 1 | ... | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| 4 | 57 | 120 | 354 | 163.0 | 0.6 | 1 | 1 | 0 | 1 | 0 | ... | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 298 | 57 | 140 | 241 | 123.0 | 0.2 | 0 | 1 | 0 | 1 | 0 | ... | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| 299 | 45 | 110 | 264 | 132.0 | 1.2 | 0 | 0 | 1 | 0 | 0 | ... | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 |
| 300 | 68 | 144 | 193 | 141.0 | 3.4 | 0 | 0 | 1 | 1 | 0 | ... | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 |
| 301 | 57 | 130 | 131 | 115.0 | 1.2 | 0 | 0 | 1 | 1 | 0 | ... | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 1 |
| 302 | 57 | 130 | 236 | 174.0 | 0.0 | 0 | 1 | 0 | 0 | 1 | ... | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 |
302 rows × 29 columns
Function learn:
Split the dataset into training and testing subsets;
test size = 33% and training size = 67%;
Normalize numerical features with StandardScaler;
Invoke Machine Learning Algorithms;
Generate Report showing key ranking metrics;
Calculate the confusion matrix to assess the accuracy of a classification.
def learn(dataset, algoritmo, opt = 2):
X = dataset.drop('output', axis=1)
y = dataset.output
# train_test_split
X_train, X_test, y_train, y_test = train_test_split( X, y, test_size=0.33, random_state=42)
# Standardization of numerical features with StandardScaler;
# Models Not Based on Decision Trees benefit most from this type of standardization.
scaler = StandardScaler()
columns_scaler = ['age', 'trtbps', 'chol', 'thalachh', 'oldpeak']
X_train[columns_scaler] = scaler.fit_transform(X_train[columns_scaler])
X_test[columns_scaler] = scaler.transform(X_test[columns_scaler])
if opt == 0:
ml = algoritmo(max_iter = 1000)
elif opt == 1:
ml = algoritmo(n_estimators = 1000)
elif opt == 2:
ml = algoritmo()
# training
ml.fit(X_train, y_train)
print('Acurácia:')
score_train = ml.score(X_train, y_train)
print(' Treino = {:.4}'.format(score_train*100)+'%')
score_test = ml.score(X_test, y_test)
print(' Teste = {:.4}'.format(score_test*100)+'%')
# predict
y_predict = ml.predict(X_test)
print('\nClassification:\n',classification_report(y_test, y_predict))
print('Confusion Matrix:')
confusion = confusion_matrix(y_test, y_predict)
sns.heatmap(confusion, annot=True, cmap='Blues')
return score_train, score_test KNeighborsClassifier:
kn_train, kn_test = learn(ds1, KNeighborsClassifier)Accuracy:
Train = 89.6%
Test = 84.0%
Classification:
precision recall f1-score support
0 0.80 0.84 0.82 43
1 0.87 0.84 0.86 57
accuracy 0.84 100
macro avg 0.84 0.84 0.84 100
weighted avg 0.84 0.84 0.84 100
Confusion Matrix:
LogisticRegression:
log_train, log_test = learn(ds1, LogisticRegression,0)Accuracy:
Train = 86.63%
Test = 87.0%
Classification:
precision recall f1-score support
0 0.84 0.86 0.85 43
1 0.89 0.88 0.88 57
accuracy 0.87 100
macro avg 0.87 0.87 0.87 100
weighted avg 0.87 0.87 0.87 100
Confusion Matrix:
DecisionTreeClassifier:
tree_train, tree_test = learn(ds1, DecisionTreeClassifier)Accuracy:
Train = 100.0%
Test = 75.0%
Classification:
precision recall f1-score support
0 0.69 0.77 0.73 43
1 0.81 0.74 0.77 57
accuracy 0.75 100
macro avg 0.75 0.75 0.75 100
weighted avg 0.76 0.75 0.75 100
Confusion Matrix:
AdaBoostClassifier:
ada_train, ada_test = learn(ds1, AdaBoostClassifier)Accuracy:
Train = 95.54%
Test = 83.0%
Classification:
precision recall f1-score support
0 0.78 0.84 0.81 43
1 0.87 0.82 0.85 57
accuracy 0.83 100
macro avg 0.83 0.83 0.83 100
weighted avg 0.83 0.83 0.83 100
Confusion Matrix:
RandomForestClassifier:
rand_train, rand_test = learn(ds1, RandomForestClassifier)Accuracy:
Train = 100.0%
Test = 85.0%
Classification:
precision recall f1-score support
0 0.80 0.86 0.83 43
1 0.89 0.84 0.86 57
accuracy 0.85 100
macro avg 0.85 0.85 0.85 100
weighted avg 0.85 0.85 0.85 100
Confusion Matrix:
MLPClassifier:
mlp_train, mlp_test = learn(ds1, MLPClassifier)Accuracy:
Train = 88.12%
Test = 89.0%
Classification:
precision recall f1-score support
0 0.88 0.86 0.87 43
1 0.90 0.91 0.90 57
accuracy 0.89 100
macro avg 0.89 0.89 0.89 100
weighted avg 0.89 0.89 0.89 100
Confusion Matrix:
BernoulliNB:
bnb_train, bnb_test = learn(ds1, BernoulliNB)Accuracy:
Train = 86.14%
Test = 83.0%
Classification:
precision recall f1-score support
0 0.80 0.81 0.80 43
1 0.86 0.84 0.85 57
accuracy 0.83 100
macro avg 0.83 0.83 0.83 100
weighted avg 0.83 0.83 0.83 100
Confusion Matrix:
GaussianNB:
gnb_train, gnb_test = learn(ds1, GaussianNB)Accuracy:
Train = 79.7%
Test = 81.0%
Classification:
precision recall f1-score support
0 0.85 0.67 0.75 43
1 0.79 0.91 0.85 57
accuracy 0.81 100
macro avg 0.82 0.79 0.80 100
weighted avg 0.82 0.81 0.81 100
Confusion Matrix:
dados = {'Models' :['LogisticRegression',
'DecisionTreeClassifier',
'KNeighborsClassifier',
'RandomForestClassifier',
'AdaBoostClassifier',
'MLPClassifier',
'GaussianNB',
'BernoulliNB'],
'Accuracy Train' : [round(log_train*100,2),
round(tree_train*100,2),
round(kn_train*100,2),
round(rand_train*100,2),
round(ada_train*100,2),
round(mlp_train*100,2),
round(gnb_train*100,2),
round(bnb_train*100,2)],
'Accuracy Test' : [round(log_test*100,2),
round(tree_test*100,2),
round(kn_test*100,2),
round(rand_test*100,2),
round(ada_test*100,2),
round(mlp_test*100,2),
round(gnb_test*100,2),
round(bnb_test*100,2)],}
rank = pd.DataFrame(dados)
rank.sort_values(by='Accuracy Test', ascending=False, inplace = True)rank| Models | Accuracy Train | Accuracy Test | |
|---|---|---|---|
| 5 | MLPClassifier | 88.12 | 89.0 |
| 0 | LogisticRegression | 86.63 | 87.0 |
| 3 | RandomForestClassifier | 100.00 | 85.0 |
| 2 | KNeighborsClassifier | 89.60 | 84.0 |
| 4 | AdaBoostClassifier | 95.54 | 83.0 |
| 7 | BernoulliNB | 86.14 | 83.0 |
| 6 | GaussianNB | 79.70 | 81.0 |
| 1 | DecisionTreeClassifier | 100.00 | 75.0 |
MIT License
Copyright (c) 2022 João Bruno
Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.