Update Automation.md

Automation.md

@@ -1,8 +1,61 @@
-Task
+Task And Solutions 
 -------
 Week 1 
-When i began my reasearch on 
-- [x] using datasets of molecular-like structures statistical task using sum of squared errors
+-----
+When i began my reasearch on Artificial Intelligence and it's application in the antimicrobial i had cardinal questions which became a task, i also delve deep to provide a solution check it out below.
+
+Task
+-------
+- [x] how to identify potential antimicronbial compounds using AI, not using the traditional way which is time consuming 
+--------
+
+Solution 
+----------
+
+-  by using datasets of molecular-like structures statistical task using sum of squared errors
+
+step 1 -  Loading Datasets :
+
+import pandas as pd
+
+Load the dataset
+url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/0024/data_features.csv'
+dataset = pd.read_csv(url, header=0)
+
+step 2 - Feature Selection
+
+For our solution , we'll only use the features x1, x2, x3, and x4.
+
+#Select the features
+
+#features = ['x1', 'x2', 'x3', 'x4']
+
+#dataset = dataset[features]
+
+step 3 - Data Preprocessing
+#Convert categorical data to numerical data
+from sklearn.preprocessing import LabelEncoder
+
+
+#for feature in features:
+    labelencoder = LabelEncoder()
+    dataset[feature] = labelencoder.fit_transform(dataset[feature])
+
+
+#Scale our features
+from sklearn.preprocessing import StandardScaler
+
+
+#scaler = StandardScaler()
+dataset = pd.DataFrame(scaler.fit_transform(dataset), columns=features)
+
+step 4 -  Statistical Task
+
+#Load the target variable
+url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/0024/data_target.csv'
+target = pd.read_csv(url, header=0)
 
 
-
+#Compute the SSE
+sse = sum((dataset['x1'] - target['y']) ** 2)
+print('SSE:', sse)