Create PCA_dataset analysis.py

Author: Adp-797

PCA_dataset analysis.py

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+#Reading the dataset student.mat
+#Make sure that the attributes are numerical
+import matplotlib.pyplot as plt
+import pandas as pd
+import numpy as np
+
+data=pd.read_csv('/content/student-mat.csv',sep=';')
+print(data)
+
+#Taking only numerical attributes
+x=data[['age','Medu','Fedu','traveltime','studytime','failures','famrel','freetime','goout','Dalc','Walc','health','absences']]
+print(x)
+print(x.shape)
+
+#Choosing G3 as target
+y=data['G3']
+print(y)
+print(y.shape)
+
+#Applying linear regression(multiple features) without PCA
+from sklearn.model_selection import train_test_split
+x_train, x_test, y_train, y_test = train_test_split(x, y, test_size = 1/3, random_state = 0)
+
+from sklearn.linear_model import LinearRegression
+regressor_2 = LinearRegression()
+regressor_2.fit(x_train, y_train)
+y_pred=regressor_2.predict(x_test)
+
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error
+
+print('MSE',mean_squared_error(y,regressor_2.predict(x)))
+print('MSA',mean_absolute_error(y,regressor_2.predict(x)))
+print('Accuracy',regressor_2.score(x_test,y_test)*100)
+
+#Finding optimum of PCA components to achieve 95% varation
+from sklearn.decomposition import PCA
+pca=PCA(0.95)
+pca.fit(x)
+reduced=pca.transform(x)
+print(reduced.shape)
+#which means, 6 components are required!
+
+#Applying PCA
+from sklearn.decomposition import PCA
+
+# Choosing 6 PCA components
+
+pca = PCA(n_components=6)
+principalComponents = pca.fit_transform(x)
+principalDf = pd.DataFrame(data = principalComponents
+             , columns = ['PC1','PC2','PC3','PC4','PC5','PC6'])
+
+print(principalDf)
+
+#Variance
+var = pca.explained_variance_ratio_
+print(var)
+
+plt.bar(['PC1', 'PC2','PC3','PC4','PC5','PC6'], var)
+plt.title('Variance vs PC1, PC2, PC3, PC4, PC5, PC6')
+plt.xlabel('Principal Components')
+plt.ylabel('Variance')
+
+#Applying Linear Regression using PCA components
+from sklearn.model_selection import train_test_split
+x2=principalDf
+x2_train, x2_test, y2_train, y2_test = train_test_split(x2, y, test_size = 1/3, random_state = 0)
+
+from sklearn.linear_model import LinearRegression
+regressor_3 = LinearRegression()
+regressor_3.fit(x2_train, y2_train)
+y_pred2=regressor_3.predict(x2_test)
+
+from sklearn.metrics import mean_squared_error
+from sklearn.metrics import mean_absolute_error
+
+print('MSE',mean_squared_error(y,regressor_3.predict(x2)))
+print('MSA',mean_absolute_error(y,regressor_3.predict(x2)))
+print('Accuracy',regressor_3.score(x2_test,y2_test)*100)