Day #29 - Decision Trees

• Hierarchical, Divide and Conquer strategy, Supervised algorithm
• Works on numerical data
• Concepts discussed - Information gain, entropy computation (Shanon entropy)
• Pruning based on chi-square / Shannon entropy
• Convert all string / character into categorical / numerical mappings
• You can also bucketize continuous variables

Basic Python pointers

	#Data pre-process examples
	#Step 1 - Read Training data
	input_file = "Training.xls"
	df = pd.read_csv(input_file,header=0)
	#Step 2 - Replace with numerical values
	d = {'Male': 1, 'Female':2}
	df['Gender'] = df['Gender'].map(d)
	#Step 3 - Remove insignificant id column
	df.drop(['SerialNo'],1,inplace=True)
	#Step 4 - Handle Missing Data
	df = df.fillna(-99)
	#Step 5
	#Get list of all column names
	features = list(df.columns)
	#Step 6
	#Identify X (Features) and Y (Predictor) Values
	features = list(df.columns[:18])
	y = df['Predictor']
	x = df[features]

view raw pythondatabasics.py hosted with ❤ by GitHub

Good Reads
Link1 , Link2, Link3, Link4, Link5, Link6

Happy Learning!!!