Mis-Carriage (1).txt

# write a machine learning pprogram to find whether there is mis-carriage occured based on Age;BMI;Nmisc;Activity;Location;temp;bpm;stress;bp;Miscarriage/ No Miscarriage
# these attributes
# import the libraries
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# import the dataset
dataset = pd.read_csv('miscarriage.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 10].values

# Encoding categorical data
# Encoding the Independent Variable
from sklearn.preprocessing import LabelEncoder
labelencoder_X = LabelEncoder()
X[:, 4] = labelencoder_X.fit_transform(X[:, 4])
X[:, 8] = labelencoder_X.fit_transform(X[:, 8])
X[:, 9] = labelencoder_X.fit_transform(X[:, 9])
# Encoding the Dependent Variable
labelencoder_y = LabelEncoder()
y = labelencoder_y.fit_transform(y)

# Splitting the dataset into the Training set and Test set

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.25,random_state = 0)

# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)

X_test = sc.transform(X_test)

# Fitting K-NN to the Training set
from sklearn.neighbors import KNeighborsClassifier
classifier = KNeighborsClassifier(n_neighbors = 10, metric = 'minkowski', p=2)
classifier.fit(X_train, y_train)

# Predicting the Test set results
y_pred = classifier.predict(X_test)

# Making the Confusion Matrix

from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
print(cm)
# Accuracy
from sklearn.metrics import accuracy_score
accuracy = accuracy_score(y_test, y_pred)
print(accuracy)