Update Day 3_Data_PreProcessing.md

Data_Preprocessing/Day 3_Data_PreProcessing.md

@@ -6,50 +6,6 @@
 :point_up: This infograph Design by my buddy [Sheikh Anikul Islam Hani](https://github.com/SheikhAnikulIslam):octocat:.But Information get from me. :v:
 
 As shown in the infograph we will break down data preprocessing in 6 essential steps.
-Get the dataset from [Here](https://github.com/harunshimanto/100-Days-Of-ML-Code/blob/master/Datasets/ShopSellData.csv) that is used in this example.
+Get the dataset from [Here](https://github.com/harunshimanto/100-Days-Of-ML-Code/blob/master/Datasets/ShopSellData.csv) that is used in this [example](http://bit.ly/2KDkTfT).
 
-## Step 1: Importing the libraries
-```Python
-import numpy as np
-import pandas as pd
-```
-## Step 2: Importing dataset
-```python
-dataset = pd.read_csv('ShopSellData.csv')
-X = dataset.iloc[ : , :-1].values
-Y = dataset.iloc[ : , 3].values
-```
-## Step 3: Handling the missing data
-```python
-from sklearn.preprocessing import Imputer
-imputer = Imputer(missing_values = "NaN", strategy = "mean", axis = 0)
-imputer = imputer.fit(X[ : , 1:3])
-X[ : , 1:3] = imputer.transform(X[ : , 1:3])
-```
-## Step 4: Encoding categorical data
-```python
-from sklearn.preprocessing import LabelEncoder, OneHotEncoder
-labelencoder_X = LabelEncoder()
-X[ : , 0] = labelencoder_X.fit_transform(X[ : , 0])
-```
-### Creating a dummy variable
-```python
-onehotencoder = OneHotEncoder(categorical_features = [0])
-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 
-```python
-from sklearn.cross_validation 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 6: Feature Scaling
-```python
-from sklearn.preprocessing import StandardScaler
-sc_X = StandardScaler()
-X_train = sc_X.fit_transform(X_train)
-X_test = sc_X.fit_transform(X_test)
-```
 ### Done :v: