Added code for linear regression

Author: bhanu_mittal

ML_grad_desc.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 13 15:29:14 2018
+
+@author: bh387886
+"""
+
+import pandas as pd
+import numpy as np
+import random
+import matplotlib.pyplot as plt
+
+
+
+def Grad_desc(X, y, m=0
+              ,iters=10000, alpha=0.001):
+     N = len(y)
+     print('Iterations: %s, Learning Rate: %s' % (iters,alpha))
+     c = (float(random.randint(0,30))/100)
+     for i in range(iters):
+         m_grad = 0
+         c_grad = 0
+         for j in range(N):
+             m_grad += -(y[j]-(m*X[j]+c))*X[j]/N
+             c_grad += -(y[j]-(m*X[j]+c))/N
+         m = m - alpha*m_grad
+         c = c - alpha*c_grad
+     cost = 0
+     for j in range(N):
+         cost += (y[j]-(m*X[j]+c))**2
+     return [m, c, cost/(2*N)]
+ 
+
+
+
+headers = ['cylinders','displacement','horsepower','weight'
+           ,'acceleration','model year','origin']
+
+
+
+raw = open('auto-mpg.data.txt')
+
+mpg = [] #Dependent variable : mpg
+features = [] #Independent variables: rest of them except car name
+
+
+#Preparing the data
+for line in raw:
+    i=0
+    var = []
+    for y in line.split():
+        if (i==0):
+            mpg.append([float(y)])
+        if (i<8 and i>0):
+            if(y=='?'): # Handaling missing values for horsepower 
+                y = 0   # (Setting them to 0)
+            if(i==4):
+                var.append(float(y.replace('.','')))
+            else:
+                var.append(float(y))
+        i+=1
+    features.append(var)
+    
+ 
+df_x = pd.DataFrame.from_records(features, columns = headers)
+df_y = pd.DataFrame.from_records(mpg, columns = ['mpg'])
+
+#replacing 0(missing) horsepower with average horsepower
+avg_bhp = (np.average(df_x['horsepower'])*len(df_x))/(len(df_x)-6)
+df_x['horsepower'] = df_x['horsepower'].replace(0,avg_bhp)
+
+x_org = df_x['horsepower'] #change this if you want another dependent var
+y_org = df_y['mpg']
+
+#Feature Scaling (Reducing the )
+df_new1 = (x_org-x_org.min())/(x_org.max()-x_org.min())
+df_y1 = (y_org - y_org.min())/(y_org.max() - y_org.min())
+
+
+ans = Grad_desc(df_new1.tolist(),df_y1.tolist())
+
+y_ans = ans[0]*df_new1 + ans[1]
+
+#Restoring the scaled features
+y_ans = y_ans*(y_org.max() - y_org.min()) + y_org.min()
+ans[1] = ans[1]*(y_org.max() - y_org.min()) + y_org.min()
+ans[0] = (y_ans[1]-y_ans[0])/(x_org[1]-x_org[0])
+   
+   
+print('ERROR = %s' % ans[2])
+print('Equation of best-fit-line: y=%sx + %s' % (ans[0],ans[1]))
+
+
+ 
+
+
+plt.scatter(x_org, df_y,  color='black')
+plt.plot(x_org, y_ans, color='blue', linewidth=3)

ML_linear_reg.py

@@ -0,0 +1,115 @@
+#!/usr/bin/env python2
+# -*- coding: utf-8 -*-
+"""
+Created on Tue Mar 13 10:27:11 2018
+
+@author: bh387886
+"""
+
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import sklearn
+from sklearn.linear_model import LinearRegression
+
+headers = ['cylinders','displacement','horsepower','weight'
+           ,'acceleration','model year','origin']
+
+
+
+raw = open('auto-mpg.data.txt')
+
+mpg = [] #Dependent variable : mpg
+features = [] #Independent variables: rest of them except car name
+
+
+#Preparing the data
+for line in raw:
+    i=0
+    var = []
+    for y in line.split():
+        if (i==0):
+            mpg.append([float(y)])
+        if (i<8 and i>0):
+            if(y=='?'): # Handaling missing values for horsepower 
+                y = 0   # (Setting them to 0)
+            if(i==4):
+                var.append(float(y.replace('.','')))
+            else:
+                var.append(float(y))
+        i+=1
+    features.append(var)
+    
+ 
+df_x = pd.DataFrame.from_records(features, columns = headers)
+df_y = pd.DataFrame.from_records(mpg, columns = ['mpg'])
+
+#replacing 0(missing) horsepower with average horsepower
+avg_bhp = (np.average(df_x['horsepower'])*len(df_x))/(len(df_x)-6)
+df_x['horsepower'] = df_x['horsepower'].replace(0,avg_bhp)
+
+lm = LinearRegression()
+lm1 = LinearRegression()
+
+X_train, X_test, Y_train, Y_test = sklearn.model_selection.train_test_split(
+        df_x,df_y,test_size=0.2,random_state = 5)
+
+
+
+######################## USING ALL INDEPENDENT VARS ###########################
+
+lm.fit(X_train, Y_train)
+
+pred_test = lm.predict(X_test)
+
+print('coefficients :(all vars)')
+print(lm.coef_)
+
+print('Intercept :(all vars)')
+print(lm.intercept_)
+
+print('Accuracy Score(all vars): %s' % lm.score(X_test,Y_test))
+
+residues = (Y_test - pred_test)
+
+#print('Residues: ')
+#print((residues))
+
+###############################################################################
+
+print('####################################################')
+
+##################### USING ALL ONE OF THE VAR(horse power) ###################
+
+df_new1 = df_x['horsepower']
+
+df_new = df_new1 [:, np.newaxis]
+
+X_train1, X_test1, Y_train1, Y_test1 = sklearn.model_selection.train_test_split(
+        df_new,df_y,test_size=0.2,random_state = 5)
+
+
+lm1.fit(X_train1, Y_train1)
+
+pred_test1 = lm1.predict(X_test1)
+
+print('coefficients :(single var)')
+print(lm1.coef_)
+
+print('Intercept :(single var)')
+print(lm1.intercept_)
+
+print('Accuracy Score(single vars): %s' % lm1.score(X_test1,Y_test1))
+
+residues = (Y_test1 - pred_test1)
+
+#print('Residues: ')
+#print((residues))
+
+
+plt.scatter(X_test1, Y_test1,  color='black')
+plt.plot(X_test1, pred_test1, color='blue', linewidth=3)
+
+###############################################################################
+