MachineLearning_Module1.py

# Importing libraries 
import numpy as np 
import pandas as pd 
from scipy.stats import mode 
import matplotlib.pyplot as plt 
import seaborn as sns 
from sklearn.preprocessing import LabelEncoder 
from sklearn.model_selection import train_test_split, cross_val_score 
from sklearn.svm import SVC 
from sklearn.naive_bayes import GaussianNB 
from sklearn.ensemble import RandomForestClassifier 
from sklearn.metrics import accuracy_score, confusion_matrix 
  
%matplotlib inline

# Reading the train.csv by removing the  
# last column since it's an empty column 
DATA_PATH = "/Users/amrithah/Desktop/Training.csv"
data = pd.read_csv(DATA_PATH).dropna(axis = 1) 
  
# Checking whether the dataset is balanced or not 
disease_counts = data["prognosis"].value_counts() 
temp_df = pd.DataFrame({ 
    "Disease": disease_counts.index, 
    "Counts": disease_counts.values 
}) 
  
plt.figure(figsize = (18,8)) 
sns.barplot(x = "Disease", y = "Counts", data = temp_df) 
plt.xticks(rotation=90) 
plt.show()

#results are balanced as 120 samples are present for each disease

# Encoding the target value into numerical 
# value using LabelEncoder 
encoder = LabelEncoder()  #convert to numerical datatype value which is what is needed to run in a machine learning model
data["prognosis"] = encoder.fit_transform(data["prognosis"]) 

#split data for testing and training
X = data.iloc[:,:-1] 
y = data.iloc[:, -1] 
X_train, X_test, y_train, y_test =train_test_split( 
X, y, test_size = 0.2, random_state = 24) 

print(f"Train: {X_train.shape}, {y_train.shape}") 
print(f"Test: {X_test.shape}, {y_test.shape}")

# Training and testing SVM Classifier 
svm_model = SVC() 
svm_model.fit(X_train, y_train) 
preds = svm_model.predict(X_test) 
  
print(f"Accuracy on train data by SVM Classifier\ 
: {accuracy_score(y_train, svm_model.predict(X_train))*100}") 
  
print(f"Accuracy on test data by SVM Classifier\ 
: {accuracy_score(y_test, preds)*100}") 
cf_matrix = confusion_matrix(y_test, preds) 
plt.figure(figsize=(12,8)) 
sns.heatmap(cf_matrix, annot=True) 
plt.title("Confusion Matrix for SVM Classifier on Test Data") 
plt.show() 
  
# Training and testing Naive Bayes Classifier 
nb_model = GaussianNB() 
nb_model.fit(X_train, y_train) 
preds = nb_model.predict(X_test) 
print(f"Accuracy on train data by Naive Bayes Classifier\ 
: {accuracy_score(y_train, nb_model.predict(X_train))*100}") 
  
print(f"Accuracy on test data by Naive Bayes Classifier\ 
: {accuracy_score(y_test, preds)*100}") 
cf_matrix = confusion_matrix(y_test, preds) 
plt.figure(figsize=(12,8)) 
sns.heatmap(cf_matrix, annot=True) 
plt.title("Confusion Matrix for Naive Bayes Classifier on Test Data") 
plt.show() 
  
# Training and testing Random Forest Classifier 
rf_model = RandomForestClassifier(random_state=18) 
rf_model.fit(X_train, y_train) 
preds = rf_model.predict(X_test) 
print(f"Accuracy on train data by Random Forest Classifier\ 
: {accuracy_score(y_train, rf_model.predict(X_train))*100}") 
  
print(f"Accuracy on test data by Random Forest Classifier\ 
: {accuracy_score(y_test, preds)*100}") 
  
cf_matrix = confusion_matrix(y_test, preds) 
plt.figure(figsize=(12,8)) 
sns.heatmap(cf_matrix, annot=True) 
plt.title("Confusion Matrix for Random Forest Classifier on Test Data") 
plt.show()