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Author: DevDerpi

Vgregression.py

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+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+import matplotlib.pyplot as plt
+
+#importing the dataset
+Dataset = pd.read_csv('vgsales.csv')
+#Label Encoding
+from sklearn.preprocessing import LabelEncoder
+number = LabelEncoder()
+
+Dataset['Platform'] = number.fit_transform(Dataset['Platform'].astype('str'))
+Dataset['Genre'] = number.fit_transform(Dataset['Genre'].astype('str'))
+Dataset['Publisher'] = number.fit_transform(Dataset['Publisher'].astype('str'))
+
+#extracting the feature vector and the dependant variable vector
+
+columns = ["Platform", "Genre", "Publisher", "NA_Sales", "EU_Sales"]
+
+
+y = Dataset["Global_Sales"].values
+X = Dataset[list(columns)].values
+
+#importing the linear model library
+from sklearn import linear_model
+
+regr = linear_model.LinearRegression()
+#importing the train test split library and splitting data into 80% for training 20% for testing
+from sklearn.model_selection import train_test_split
+X_train , X_test , y_train , y_test = train_test_split(X,y,test_size=0.2,random_state=0)
+#scaling the data 
+from sklearn.preprocessing import StandardScaler
+scaler = StandardScaler()
+scaler.fit(X_train)
+X_train = scaler.transform(X_train)
+# apply same transformation to test data
+X_test = scaler.transform(X_test)
+#fit the train data to the linear model
+regr.fit(X_train, y_train)
+
+#Printing Accuracy in our Linear model
+Accuracy = regr.score(X_train, y_train)
+print ("Accuracy in the training data with Linear Regression Model: ", Accuracy*100, "%")
+
+accuracy = regr.score(X_test, y_test)
+print ("Accuracy in the test data with Linear Regression model", accuracy*100, "%")
+#Comparing the model predicted results vs the Test set
+y_pred_Model1 = regr.predict(X_test)
+y_pred_Model1
+
+
+compare_Model1 = np.concatenate((y_pred_Model1.reshape(len(y_pred_Model1),1), y_test.reshape(len(y_test),1)),1)
+compare_Model1
+####*************************
+#Using DecisionTreeRegressor : 
+from sklearn.tree import DecisionTreeRegressor
+DTR  = DecisionTreeRegressor()
+DTR2 =DecisionTreeRegressor(min_samples_leaf=0.2)
+DTR3 =DecisionTreeRegressor(min_samples_leaf=15)
+DTR4 =DecisionTreeRegressor(min_samples_leaf=30)
+DTR5 =DecisionTreeRegressor(min_samples_leaf=35)
+
+
+DTR.fit(X_train, y_train)
+DTR2.fit(X_train,y_train)
+DTR3.fit(X_train,y_train)
+DTR4.fit(X_train,y_train)
+DTR5.fit(X_train,y_train)
+
+#printing Accuracy in our DTR Model
+
+Accuracy = DTR.score(X_train, y_train)
+print ("Accuracy in the training data with Decision Tree Regression model before tuning  : ", Accuracy*100, "%")
+
+accuracy = DTR.score(X_test, y_test)
+print ("Accuracy in the test data with Decision Tree Regression model before tuining : ", accuracy*100, "%")
+##########################
+Accuracy2 = DTR2.score(X_train, y_train)
+print ("\nAccuracy in the training data with Decision Tree Regression model After tuning with min_samples_leaf=3 : ", Accuracy2*100, "%")
+
+accuracy2 = DTR2.score(X_test, y_test)
+print ("Accuracy in the test data with Decision Tree Regression model After tuning with min_samples_leaf=3 : ", accuracy2*100, "%")
+##########################
+Accuracy3 = DTR3.score(X_train, y_train)
+print ("\nAccuracy in the training data with Decision Tree Regression model After tuning with min_samples_leaf=15 : ", Accuracy3*100, "%")
+
+accuracy3 = DTR3.score(X_test, y_test)
+print ("Accuracy in the test data with Decision Tree Regression model After tuning with min_samples_leaf=15 : ", accuracy3*100, "%")
+##########################
+Accuracy4 = DTR4.score(X_train, y_train)
+print ("\nAccuracy in the training data with Decision Tree Regression model After tuning with min_samples_leaf=30 : ", Accuracy4*100, "%")
+
+accuracy4 = DTR4.score(X_test, y_test)
+print ("Accuracy in the test data with Decision Tree Regression model After tuning with min_samples_leaf=30 : ", accuracy4*100, "%")
+##########################
+Accuracy5 = DTR5.score(X_train, y_train)
+print ("\nAccuracy in the training data with Decision Tree Regression model After tuning with min_samples_leaf=35 : ", Accuracy5*100, "%")
+
+accuracy5 = DTR5.score(X_test, y_test)
+print ("Accuracy in the test data with Decision Tree Regression model After tuning with min_samples_leaf=35 : ", accuracy5*100, "%")
+##########################
+
+#Comparing the model predicted results vs the Test set
+y_pred_Model2 = DTR.predict(X_test)
+y_pred_Model2
+
+y_pred_Model2_Tuned = DTR5.predict(X_test)
+y_pred_Model2_Tuned
+
+compare_Model2 = np.concatenate((y_pred_Model2.reshape(len(y_pred_Model2),1), y_test.reshape(len(y_test),1)),1)
+compare_Model2
+
+compare_Model2_Tuned = np.concatenate((y_pred_Model2_Tuned.reshape(len(y_pred_Model2_Tuned),1), y_test.reshape(len(y_test),1)),1)
+compare_Model2_Tuned