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!!!

Day #28 - R - Forecast Library Examples

Following Examples discussed. Library used - R - Forecast Library

• Moving Average
• Single Exponential Smoothing - Uses single smoothing factor
• Double Exponential Smoothing - Uses two constants and is better at handling trends
• Triple Exponential Smoothing - Smoothing factor, trend, seasonal factors considered
• ARIMA

	#Create Dataset
	#Generate sample data of 100 records with mean = 850 and standard deviation = 900
	myvector <- rnorm(1000, 850, 900)
	myvector
	#Split it into timeseries weekly data for 104 weeks
	myts = ts(myvector, start=c(2014,1),end=c(2016,1),frequency=104)
	myts
	#Plot Data
	plot(myts)
	#Apply moving average
	sm = ma(myts, order=4)#4 week average
	plot(sm)
	#Plotting with Single Exponential
	fit = HoltWinters(myts,beta = FALSE, gamma = FALSE)
	plot(fit)
	#Plotting with Double Exponential
	fit = HoltWinters(myts, gamma = FALSE)
	plot(fit)
	#triple exponential
	fit = HoltWinters(myts)
	#Additive triple exponential
	fit1 = HoltWinters(myts, seasonal = "multiplicative")
	#Multiplicative triple exponential
	fit2 = HoltWinters(myts, seasonal = "additive")
	#ARIMA
	arimafit = arima(myts, order = c(0,0,0))
	summary(arimafit)
	plot(myts, xlim=c(2014,2016),lw=2,col="blue")
	lines(predict(arimafit,n.ahead=50)$pred,lw=2,col="red")
	#Auto Regression
	arfit = ar(myts)
	summary(arfit)
	pred = predict(arfit,n.ahead=30)
	plot(myts,type="l",xlim=c(2014,2016),ylim=c(100,2200),xlab="weeks",ylab="forecast")
	lines(pred$pred,col="red")

view raw RForecastExample.R hosted with ❤ by GitHub

Happy Learning!!!

Applied Machine Learning Notes

Supervised Learning

• Classification (Discrete Labels)
• Regression (Output is continuous, Example - Age, Stock prices)
• Past data + Past Outputs used

Unsupervised Learning

• Dimensionality reduction (Data in higher dimensions, Remove dimension without losing lot of information)
• Reducing dimensionality makes it easy for computation (Continuous values)
• Clustering (Discrete labels)
• No Past outputs, Only current data

Reinforcement Learning

• All Game Playing is unsupervised
• Learning Policy
• Negative / Positive reward for each step

Type of Models

• Inductive (Learn model, Learn from a function) vs Transductive (Lazy learning ex- Opinion from like minded people)
• Online (Learn from every new incoming tweet) vs Offline (Look past 1 Yeat tweet)
• Generative (Apply Gaussian on Data, Use ML and compute Mean / Variance) vs Discriminative (Two sides of Line)
• Parametric vs Non-Parametric Models

Happy Learning!!!