Update Score Prediction by Multi Linear Regression...!

models/Score_Prediction_by_GS_MLR.py

@@ -7,18 +7,15 @@
 from scipy import stats
 import matplotlib.pyplot as plt
 from dataset.cleanDs import cleanDs
-
 from sklearn.model_selection import train_test_split
 
 cleanData = cleanDs()
 df = cleanData.clean_db()
 
+# Creating plot with Global_Sales & User_Score
 x = df['Global_Sales']
 y = df['User_Score']
 
-# plt.xlim(0,20)
-# plt.ylim(0,12)
-
 slope, intercept, r, p, std_err = stats.linregress(x, y)
 
 
@@ -38,12 +35,10 @@ def myfunc(x):
 
 print(numpy.corrcoef(x, y))
 
+# Creating plot with Critic_Score & User_Score
 x = df['Critic_Score']
 y = df['User_Score']
 
-# plt.xlim(0,20)
-# plt.ylim(0,12)
-
 slope, intercept, r, p, std_err = stats.linregress(x, y)
 
 
@@ -63,46 +58,50 @@ def myfunc(x):
 
 print(numpy.corrcoef(x, y))
 
-
+# Creating plot with Critic_Count & User_Score
 x = df['Critic_Count']
 y = df['User_Score']
 
-#plt.xlim(0,5)
-#plt.ylim(0,1000)
-
 slope, intercept, r, p, std_err = stats.linregress(x, y)
 
+
 def myfunc(x):
     return slope * x + intercept
 
+
 mymodel = list(map(myfunc, x))
-plt.scatter(x,y , color='green')
+plt.scatter(x, y, color='green')
 plt.title('Critic_Count Vs User_Score', fontsize=14)
 plt.xlabel('Critic_Count', fontsize=14)
 plt.ylabel('User_Score', fontsize=14)
 plt.grid(True)
 plt.plot(x, mymodel)
 plt.show()
 import numpy
-print(numpy.corrcoef(x,y))
+
+print(numpy.corrcoef(x, y))
+
+
 
 import pandas as pd
 from sklearn import linear_model
 import statsmodels.api as sm
 
-# df = pd.df(Stock_Market,columns=['Year','Month','Interest_Rate','Unemployment_Rate','Stock_Index_Price'])
-
-X = df[['Global_Sales', 'Critic_Count',
-        'Critic_Score']]  # here we have 2 variables for multiple regression. If you just want to use one variable for simple linear regression, then use X = df['Interest_Rate'] for example.Alternatively, you may add additional variables within the brackets
+X = df[['Critic_Score','Global_Sales','Critic_Count']]
 Y = df['User_Score']
 
+X_train, X_test,Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=0)
+
 # with sklearn
 regr = linear_model.LinearRegression()
 regr.fit(X, Y)
+y_pred = regr.predict(X_test)
 
 print('Intercept: \n', regr.intercept_)
 print('Coefficients: \n', regr.coef_)
 
+print(pd.DataFrame({'Actual':Y_test,'Predicted':y_pred}))
+
 # prediction with sklearn
 Global_Sales = 23.21
 Critic_Score = 91
@@ -111,10 +110,20 @@ def myfunc(x):
 print('Predicted User Score: \n', regr.predict([[Global_Sales, Critic_Score, Critic_Count]]))
 
 # with statsmodels
-X = sm.add_constant(X)  # adding a constant
+X = sm.add_constant(X)
 
 model = sm.OLS(Y, X).fit()
 predictions = model.predict(X)
 
 print_model = model.summary()
-print(print_model)
+print(print_model)
+
+# To find Accuracy
+import sklearn.metrics as sm
+print("Mean absolute error =", round(sm.mean_absolute_error(Y_test,y_pred), 2))
+print("Mean squared error =", round(sm.mean_squared_error(Y_test,y_pred), 2))
+print("Median absolute error =", round(sm.median_absolute_error(Y_test,y_pred), 2))
+print("Explain variance score =", round(sm.explained_variance_score(Y_test,y_pred), 2))
+print("R2 score =", round(sm.r2_score(Y_test,y_pred), 2))
+
+print(regr.score(X_test,Y_test))