Before we jump into various ML concepts and algorithms, let’s have a quick look into basic workflow when we apply Machine Learning to a problem.
A short brief about Machine Learning, it’s association with AI or Data Science world is here.
How does Machine Learning help?
Machine Learning is about having a training algorithm that helps predict an output based on the past data. This input data can keep on changing and accordingly the algorithm can fine tune to provide better output.
It has vast applications across. For example, Google is using it to predict natural disasters like floods. A very common use we hear these days in news are usage in Politics and how to attack the demography of voters.
How does Machine Learning work?
Data is the key here. More the data is, better the algorithm can learn and fine tune. For any problem output, there would be multiple factors at play. Some of them would have more affect then others. Analyzing and applying all such findings are part of a machine learning problem. Mathematically, ML converts a problem output as a function of multiple input factors.
Y = f(x)
Y = predicted output
x = multiple factors as an input
How does a typical ML workflow look?
There is a structured way to apply ML on a problem. I tried to put the workflow in a pictorial view to easily visualize and understand it:
It’s goes in a cycle and once we have some insights from the published model, it goes back into the funnel as learning to make output better.
Roughly, Data scientists spend
60%of their time on cleaning and organizing data
Walk through an example?
Load data from file
Data could be in various format. Easiest is to have it in a csv and then load it using pandas. More details around how to play with data is discussed here.
# lets load and see the info of the dataset titanicdf = pd.read_csv("data-files/titanic.csv") print(titanicdf.info())
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1309 entries, 0 to 1308 Data columns (total 14 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 pclass 1309 non-null int64 1 survived 1309 non-null int64 2 name 1309 non-null object 3 sex 1309 non-null object 4 age 1046 non-null float64 5 sibsp 1309 non-null int64 6 parch 1309 non-null int64 7 ticket 1309 non-null object 8 fare 1308 non-null float64 9 cabin 295 non-null object 10 embarked 1307 non-null object 11 boat 486 non-null object 12 body 121 non-null float64 13 home.dest 745 non-null object dtypes: float64(3), int64(4), object(7) memory usage: 143.3+ KB None
# A quick sample view titanicdf.head(3)
|0||1||1||Allen, Miss. Elisabeth Walton||female||29||0||0||24160||211.3375||B5||S||2||NaN||St Louis, MO|
|1||1||1||Allison, Master. Hudson Trevor||male||0.9167||1||2||113781||151.55||C22 C26||S||11||NaN||Montreal, PQ / Chesterville, ON|
|2||1||0||Allison, Miss. Helen Loraine||female||2||1||2||113781||151.55||C22 C26||S||NaN||NaN||Montreal, PQ / Chesterville, ON|
Data cleanup – Drop the irrelevant columns
It’s not always that the data captured is the only data you need. You will have a superset of data that has additional information which are not relevant for your problem statement. This data will work as a noise and thus it’s better to clean the dataset before starting to work on them for any ML algorithm.
# there seems to be handful of columns which # we might not need for our analysis # lets drop all those column that we know # are irrelevant titanicdf.drop(['embarked','body','boat','name', 'cabin','home.dest','ticket', 'sibsp', 'parch'], axis='columns', inplace=True) titanicdf.head(2)
There could be various ways to analyze data. Based on the problem statement, we would need to know the general trend of the data in discussion. Statistics Probability Distribution knowledge help here. For gaining insights to understand more around correlations and patterns, data visualization based insights help.
# let's see if there are any highly corelated data # if we observe something, we will remove that # one of the feature to avoid bias import seaborn as sns sns.pairplot(titanicdf) # looking at graphs, seems we don't have # anything right away to remove.
Data transform – Ordinal/Nominal/Datatype, etc
In order to work through data, it’s easy to interpret once they are converted into numbers (from strings) if possible. This helps them input them into various statistics formulas to get more insights. More details on how to apply numerical modifications to data is discussed here.
# There seems to be 3 class of people, # lets represent class as numbers # We don't have info of relative relation of # class so we will one-hot-encode it titanicdf = pd.get_dummies(titanicdf, columns=['pclass']) # Lets Convert sex to a number from sklearn import preprocessing le = preprocessing.LabelEncoder() titanicdf["sex"] = le.fit_transform(titanicdf.sex) titanicdf.head(2)
Data imputation: Fill the missing values
There are always some missing data or an outlier. Running algorithms with missing data could lead to inconsistent results or algorithm failure. Based on the context, we can choose to remove them or fill/replace them with an appropriate value.
# When we saw the info, lots of age were missing # Missing ages values filled with mean age. titanicdf.loc[ titanicdf["age"].isnull(), "age" ] = titanicdf["age"].mean() # titanicdf.loc[ titanicdf["fare"].isnull(), "fare"] # = titanicdf["fare"].mean() # => can do but we will use another way titanicdf.info()
<class 'pandas.core.frame.DataFrame'> RangeIndex: 1309 entries, 0 to 1308 Data columns (total 7 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 survived 1309 non-null int64 1 sex 1309 non-null int64 2 age 1309 non-null float64 3 fare 1308 non-null float64 4 pclass_1 1309 non-null uint8 5 pclass_2 1309 non-null uint8 6 pclass_3 1309 non-null uint8 dtypes: float64(2), int64(2), uint8(3) memory usage: 44.9 KB
# When we saw the info, # 1 fare was missing # Lets drop that one record titanicdf.dropna(inplace=True) titanicdf.info() #<class 'pandas.core.frame.DataFrame'> #Int64Index: 1308 entries, 0 to 1308
Normalize training data
At times, various data in context are of different scales. In such cases, if the data is not normalized, algorithm can induce bias towards the data that has higher magnitude. Eg, feature A value range is 0-10 and feature B range is 0-10000. In such case, even though a small change in magnitude of A can make a difference but if data is not normalized, feature B will influence results more (which could be not the actual case).
X = titanicdf y = X['survived'] X = X.drop(['survived'], axis=1) # Scales each column to have 0 mean and 1 std.dev from sklearn import preprocessing X_scaled = preprocessing.scale(X)
Split data – Train/Test dataset
It’s always best to split the dataset into two unequal parts. Bigger one to train the algorithm and then then smaller one to test the trained algorithm. This way, algorithm is not biased to just the input data and results for test data can provide better picture.
from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(X_scaled,y) X_train.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 981 entries, 545 to 864 Data columns (total 6 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 sex 981 non-null int64 1 age 981 non-null float64 2 fare 981 non-null float64 3 pclass_1 981 non-null uint8 4 pclass_2 981 non-null uint8 5 pclass_3 981 non-null uint8 dtypes: float64(2), int64(1), uint8(3) memory usage: 33.5 KB
Run ML algorithm data
Once we have our training dataset ready as per our need, we can apply machine learning algorithms and find which model fits in best.
# for now, picking any one of the classifier - KN # Ignore details or syntax for now from sklearn.neighbors import KNeighborsClassifier dtc = KNeighborsClassifier(n_neighbors=5) dtc.fit(X_train,y_train)
Check the accuracy of model
In order to validate the model, we use test dataset where comparing the predicted value by model to actual data helps us know about ML model accuracy.
import sklearn.metrics as met pred_knc = dtc.predict(X_test) print( "Nearest neighbors: %.3f" % (met.accuracy_score(y_test, pred_knc)))
Nearest neighbors: 0.817
Voila! With basic workflow, we have a model that can predict the survivor with more than 80% probability.
Jupyter notebook with more samples can be downloaded or forked from my GitHub to look or play around: https://github.com/sandeep-mewara/machine-learning
Currently, it covers examples on following datasets:
- Titanic Survivors
- Sci-kit Iris
- Sci-kit Digits
- Bread Basket Bakery
Over time, I would continue building on the same repository with more samples with different algorithms.
Believe, now it’s pretty clear on how we can attack any problem for an insight using Machine learning. Try out and see for yourself.
We can apply Machine Learning to multiple problems in multiple fields. I have shared a pictorial view of sectors in the AI section that are already leveraging it’s benefit.
samples GitHub Profile Readme
Learn Python – Beginners step by step – Basics and Examples
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Matplotlib plot samples
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Machine Learning workflow