Remove preprocessing warnings

Code/Day 11 K-NN.md

@@ -4,11 +4,11 @@
   <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%207.jpg">
 </p>
 
-## The DataSet | Social Network 
+## The DataSet | Social Network
 
 <p align="center">
   <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Other%20Docs/data.PNG">
-</p> 
+</p>
 
 
 ## Importing the libraries
@@ -27,7 +27,7 @@ y = dataset.iloc[:, 4].values
 
 ## Splitting the dataset into the Training set and Test set
 ```python
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
 ```
 ## Feature Scaling

Code/Day 13 SVM.md

@@ -16,7 +16,7 @@ y = dataset.iloc[:, 4].values
 
 ## Splitting the dataset into the Training set and Test set
 ```python
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
 ```
 

Code/Day 1_Data PreProcessing.md

@@ -37,9 +37,9 @@ X = onehotencoder.fit_transform(X).toarray()
 labelencoder_Y = LabelEncoder()
 Y =  labelencoder_Y.fit_transform(Y)
 ```
-## Step 5: Splitting the datasets into training sets and Test sets 
+## Step 5: Splitting the datasets into training sets and Test sets
 ```python
-from sklearn.cross_validation import train_test_split
+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)
 ```
 

Code/Day 25 Decision Tree.md

@@ -18,7 +18,7 @@ y = dataset.iloc[:, 4].values
 ```
 ### Splitting the dataset into the Training set and Test set
 ```python
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
 ```
 

Code/Day 34 Random_Forest.md

@@ -19,7 +19,7 @@ y = dataset.iloc[:, 4].values
 ```
 ### Splitting the dataset into the Training set and Test set
 ```python
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
 ```
 

Code/Day 6 Logistic Regression.md

@@ -5,13 +5,13 @@
   <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Info-graphs/Day%204.jpg">
 </p>
 
-## The DataSet | Social Network 
+## The DataSet | Social Network
 
 <p align="center">
   <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Other%20Docs/data.PNG">
-</p> 
+</p>
 
-This dataset contains information of users in a social network. Those informations are the user id the gender the age and the estimated salary. A car company has just launched their brand new luxury SUV. And we're trying to see which of these users of the social network are going to buy this brand new SUV And the last column here tells If yes or no the user bought this SUV we are going to build a model that is going to predict if a user is going to buy or not the SUV based on two variables which are going to be the age and the estimated salary. So our matrix of feature is only going to be these two columns.
+This dataset contains information of users in a social network. Those information are the user id the gender the age and the estimated salary. A car company has just launched their brand new luxury SUV. And we're trying to see which of these users of the social network are going to buy this brand new SUV And the last column here tells If yes or no the user bought this SUV we are going to build a model that is going to predict if a user is going to buy or not the SUV based on two variables which are going to be the age and the estimated salary. So our matrix of feature is only going to be these two columns.
 We want to find some correlations between the age and the estimated salary of a user and his decision to purchase yes or no the SUV.
 
 ## Step 1 | Data Pre-Processing
@@ -35,7 +35,7 @@ y = dataset.iloc[:, 4].values
 ### Splitting the dataset into the Training set and Test set
 
 ```python
-from sklearn.cross_validation import train_test_split
+from sklearn.model_selection import train_test_split
 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
 ```
 
@@ -83,8 +83,8 @@ cm = confusion_matrix(y_test, y_pred)
 
 <p align="center">
   <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Other%20Docs/training.png">
-</p> 
+</p>
 
 <p align="center">
   <img src="https://github.com/Avik-Jain/100-Days-Of-ML-Code/blob/master/Other%20Docs/testing.png">
-</p> 
+</p>

Code/Day2_Simple_Linear_Regression.md

@@ -16,8 +16,8 @@ dataset = pd.read_csv('studentscores.csv')
 X = dataset.iloc[ : ,   : 1 ].values
 Y = dataset.iloc[ : , 1 ].values
 
-from sklearn.cross_validation import train_test_split
-X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0) 
+from sklearn.model_selection import train_test_split
+X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, random_state = 0)
 ```
 
 # Step 2: Fitting Simple Linear Regression Model to the training set
@@ -30,8 +30,8 @@ X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, rand
  ```python
  Y_pred = regressor.predict(X_test)
  ```
- 
- # Step 4: Visualization 
+
+ # Step 4: Visualization
  ## Visualising the Training results
  ```python
  plt.scatter(X_train , Y_train, color = 'red')
@@ -41,4 +41,4 @@ X_train, X_test, Y_train, Y_test = train_test_split( X, Y, test_size = 1/4, rand
  ```python
  plt.scatter(X_test , Y_test, color = 'red')
  plt.plot(X_test , regressor.predict(X_test), color ='blue')
- ``` 
+ ```

Code/Day3_Multiple_Linear_Regression.md

@@ -36,7 +36,7 @@ X = X[: , 1:]
 
 ### Splitting the dataset into the Training set and Test set
 ```python
-from sklearn.cross_validation import train_test_split
+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)
 ```
 ## Step 2: Fitting Multiple Linear Regression to the Training set