diff --git a/ex1/ex1.py b/ex1/ex1.py
index 000dbbd..032fd36 100644
--- a/ex1/ex1.py
+++ b/ex1/ex1.py
@@ -5,33 +5,12 @@
 import matplotlib.pyplot as plt
 
 def warmUpExercise():
-    """
-    Example Function in Python
-    Instructions: Return the 5x5 identity matrix. In Python,
-                  define the object to be returned within the
-		  function, and then return it at the bottom
-		  with the "return" statement.
-    """
-    return np.eye(5)
+	return np.eye(5)
 
 
 def plotData(x, y):
-    """
-    plotData -- Plots the data points x and y into a new figure and gives
-                the figure axes labels of population and profit. It returns
-		at matplotlib figure.
-     Instructions: Plot the training data into a figure by manipulating the
-		   axes object created for you below. Set the axes labels using
-                   the "xlabel" and "ylabel" commands. Assume the 
-                   population and revenue data have been passed in
-                   as the x and y arguments of this function.
     
-     Hint: You can use the 'rx' option with plot to have the markers
-           appear as red crosses. Furthermore, you can make the
-           markers larger by using plot(..., 'rx', markersize=10)
-    """
-    
-    fig, ax = plt.subplots() # create empty figure and set of axes
+    fig, ax = plt.subplots() # create empty figure
     ax.plot(x,y,'rx',markersize=10)
     ax.set_xlabel("Population of City in 10,000s")
     ax.set_ylabel("Profit in $10,000s")
@@ -40,139 +19,67 @@ def plotData(x, y):
 
 
 def normalEqn(X,y):
-    """
-    Computes the closed form least squares solution using normal
-    equations
-    theta = (X^T*X)^{-1}X^T*y
-    Returns: Array of least-squares parameters
-    """
     
     return np.dot((np.linalg.inv(np.dot(X.T,X))),np.dot(X.T,y))
 
 
 def gradientDescentMulti(X, y, theta, alpha, num_iters):
-    """
     
-    Performs gradient descent to learn theta by taking n_iters steps and
-    updating theta with each step at a learning rate alpha
-    """
-    # Initialize some useful values
     m = len(y) # number of training examples
     J_history = np.zeros(num_iters)
 
     for i in range(num_iters):
         theta = theta - (alpha/m)*np.sum((np.dot(X,theta)-y)[:,None]*X,axis=0)
-
-	# Save the cost J in every iteration
         J_history[i] = computeCost(X, y, theta)
-        print('Cost function has a value of: ', J_history[i])
+        print('Cost function: ', J_history[i])
     
     return (theta,J_History)
 
 
 def gradientDescent(X, y, theta, alpha, num_iters):
-    """
     
-    Performs gradient descent to learn theta by taking n_iters steps and
-    updating theta with each step at a learning rate alpha
-    """
-    # Initialize some useful values
     m = len(y) # number of training examples
     J_history = np.zeros(num_iters)
 
     for i in range(num_iters):
         theta = theta - (alpha/m)*np.sum((np.dot(X,theta)-y)[:,None]*X,axis=0)
-
-	# Save the cost J in every iteration
         J_history[i] = computeCost(X, y, theta)
-        print('Cost function as a value of: ',J_history[i])
+        print('Cost function: ',J_history[i])
     
     return (theta, J_history)
 
 
 def featureNormalize(X):
-    """
-    Normalizes (mean=0, std=1) the features in design matrix X
-    returns -- Normalized version of X where the mean of each
-               value of each feature is 0 and the standard deviation
-	       is 1. This will often help gradient descent learning
-	       algorithms to converge more quickly.
-    Instructions: First, for each feature dimension, compute the mean
-                  of the feature and subtract it from the dataset,
-		  storing the mean value in mu. Next, compute the 
-		  standard deviation of each feature and divide
-		  each feature by it's standard deviation, storing
-		  the standard deviation in sigma. 
-		  
-		  Note that X is a matrix where each column is a 
-		  feature and each row is an example. You need 
-		  to perform the normalization separately for 
-		  each feature. 
-		  
-		  Hint: You might find the 'mean' and 'std' functions useful.
-    """
-    
     return np.divide((X - np.mean(X,axis=0)),np.std(X,axis=0))
 
 
 def computeCost(X, y, theta):
-    """
-    
-    Compute cost using sum of square errors for linear 
-    regression using theta as the parameter vector for 
-    linear regression to fit the data points in X and y.
-    Note: Requires numpy in order to run, but is not imported as part of this
-	  script since it is imported in ex1.py and therefore numpy is part of
-	  the namespace when the function is actually run.
-    """
-    # Initialize some useful values
-    m = len(y) # number of training examples
-    
-    # Cost function J(theta)
+    m = len(y)
     J = (np.sum((np.dot(X,theta) - y)**2))/(2*m)
-
     return J
-	
-	
-# ==================== Part 1: Basic Function ====================
+
 print('Running warmUpExercise ... \n')
 print('5x5 Identity Matrix: \n')
 
-print(warmUpExercise()) # Prints object returned by WarmUpExercise 
+print(warmUpExercise()) 
 input('Program paused. Press enter to continue.\n')
 
-
-# ======================= Part 2: Plotting =======================
 print('Plotting Data ...\n')
 data = pd.read_csv("ex1data1.txt",names=["X","y"])
 x = np.array(data.X)[:,None] # population in 10,0000
 y = np.array(data.y) # profit for a food truck
-m = len(y) # number of training examples
-
-# Plot Data
+m = len(y) 
 fig = plotData(x,y)
 fig.show()
-
 input('Program paused. Press enter to continue.\n')
-
-## =================== Part 3: Gradient descent ===================
 print('Running Gradient Descent ...\n')
-
-ones = np.ones_like(x)
-X = np.hstack((ones,x)) # Add a column of ones to x
-theta = np.zeros(2) # initialize fitting parameters
-
-# Some gradient descent settings
+ones = np.ones_like(x) #an array of ones of same dimension as x
+X = np.hstack((ones,x)) # Add a column of ones to x. hstack means stacking horizontally i.e. columnwise
+theta = np.zeros(2) # initialize
 iterations = 1500
 alpha = 0.01
-
-# compute and display initial cost
 computeCost(X, y, theta)
-
-# run gradient descent
 theta, hist = gradientDescent(X, y, theta, alpha, iterations)
-
-# print theta to screen
 print('Theta found by gradient descent: ')
 print(theta[0],"\n", theta[1])
 
@@ -187,94 +94,55 @@ def computeCost(X, y, theta):
 
 predict2 = np.dot([1, 7],theta)
 print('For population = 70,000, we predict a profit of ', predict2*10000)
-
 input('Program paused. Press enter to continue.\n');
-
-
-## ============= Part 4: Visualizing J(theta_0, theta_1) =============
 print('Visualizing J(theta_0, theta_1) ...\n')
 
 # Grid over which we will calculate J 
 theta0_vals = np.linspace(-10, 10, 100)
 theta1_vals = np.linspace(-1, 4, 100)
-
-# initialize J_vals to a matrix of 0's
 J_vals = np.zeros((len(theta0_vals),len(theta1_vals)))
 
-# Fill out J_Vals 
-# Note: There is probably a more efficient way to do this that uses
-#	broadcasting instead of the nested for loops
 for i in range(len(theta0_vals)):
     for j in range(len(theta1_vals)):
         t = np.array([theta0_vals[i],theta1_vals[j]])
         J_vals[i][j] = computeCost(X,y,t)
-
-
+"""
 # Surface plot using J_Vals
 fig = plt.figure()
 ax = plt.subplot(111,projection='3d')
 Axes3D.plot_surface(ax,theta0_vals,theta1_vals,J_vals,cmap=cm.coolwarm)
 plt.show()
 
-# Contour plot
-# TO DO: Currently does not work as expected. Need to find a way to mimic
-#	 the logspace option in matlab
 fig = plt.figure()
 ax = plt.subplot(111)
 plt.contour(theta0_vals,theta1_vals,J_vals) 
-
-
-
-#----------------------------------------------------------
-#Linear regression with multiple variables
-
-## ================ Part 1: Feature Normalization ================
-
+"""
 print('Loading data ...','\n')
-
-## Load Data
 print('Plotting Data ...','\n')
-
 data = pd.read_csv("ex1data2.txt",names=["size","bedrooms","price"])
 s = np.array(data.size)
 r = np.array(data.bedrooms)
 p = np.array(data.price)
-m = len(r) # number of training examples
-
-# Design Matrix
+m = len(r) 
 s = np.vstack(s)
 r = np.vstack(r)
 X = np.hstack((s,r))
-
-# Print out some data points
 print('First 10 examples from the dataset: \n')
 print(" size = ", s[:10],"\n"," bedrooms = ", r[:10], "\n")
-
 input('Program paused. Press enter to continue.\n')
-
-# Scale features to zero mean and standard deviation of 1
 print('Normalizing Features ...\n')
-
 X = featureNormalize(X)
-
-# Add intercept term to X
 X = np.hstack((np.ones_like(s),X))
 
-## ================ Part 2: Gradient Descent ================
-
 print('Running gradient descent ...\n')
-
-# Choose some alpha value
 alpha = 0.05
 num_iters = 400
-
-# Init Theta and Run Gradient Descent 
 theta = np.zeros(3)
 
 # Multiple Dimension Gradient Descent
 theta, hist = gradientDescent(X, p, theta, alpha, num_iters)
 
-# Plot the convergence graph
+# Plot convergence graph
 fig = plt.figure()
 ax = plt.subplot(111)
 plt.plot(np.arange(len(hist)),hist ,'-b')
@@ -282,51 +150,39 @@ def computeCost(X, y, theta):
 plt.ylabel('Cost J')
 plt.show()
 
-# Display gradient descent's result
+
 print('Theta computed from gradient descent: \n')
 print(theta,'\n')
 
 # Estimate the price of a 1650 sq-ft, 3 br house
-
-# Recall that the first column of X is all-ones. Thus, it does
-# not need to be normalized.
+#the first column of X is all-ones.it doesnot need to be normalized.
 normalized_specs = np.array([1,((1650-s.mean())/s.std()),((3-r.mean())/r.std())])
 price = np.dot(normalized_specs,theta) 
-
-
 print('Predicted price of a 1650 sq-ft, 3 br house (using gradient descent):\n ',
       price)
-
 input('Program paused. Press enter to continue.\n')
 
-## ================ Part 3: Normal Equations ================
 print('Solving with normal equations...\n')
 
 data = pd.read_csv("ex1data2.txt",names=["sz","bed","price"])
 s = np.array(data.sz)
 r = np.array(data.bed)
 p = np.array(data.price)
-m = len(r) # number of training examples
-
-# Design Matrix
+m = len(r) 
 s = np.vstack(s)
 r = np.vstack(r)
 X = np.hstack((s,r))
-
-# Add intercept term to X
 X = np.hstack((np.ones_like(s),X))
 
-# Calculate the parameters from the normal equation
 theta = normalEqn(X, p)
 
-# Display normal equation's result
 print('Theta computed from the normal equations: \n')
 print(theta)
 print('\n')
 
 # Estimate the price of a 1650 sq-ft, 3 br house
-price = np.dot([1,1650,3],theta) # You should change this
+price = np.dot([1,1650,3],theta) 
 
 
 print('Predicted price of a 1650 sq-ft, 3 br house (using normal equations): \n',
-       price)
\ No newline at end of file
+       price)