Deciphering Machine Learning: A Comprehensive Guide to Algorithms and Their Real-World Applications

Across many industries, machine learning has transformed problem-solving techniques by providing strong instruments for identifying trends and arriving at well-informed judgments. This article guides you on a journey of discovery by demonstrating the effective application of different algorithms to solve practical business problems. We'll look at real-world applications that turn complicated data into useful insights, including anticipating consumer satisfaction or identifying fraudulent transactions.

PREDICTION OF CUSTOMER SATISFACTION USING DECISION TREES

My journey into machine learning started with using Decision Tree to predict customer satisfaction at Santander Bank. The approach followed was to build 3 models with varying decision tree parameters like “max_depth”,”max_leaf_nodes” ,”max_features” and “min_impurity_decrease”.

The learning from exploring multiple models was

• Feature engineering and exploration is essential to improve model accuracy.

• The feature_importance aided in finding top 10 features that are impactful.

DETECTION OF FRAUDULENT TRANSACTIONS USING DECISION TREES AND RANDOM FOREST CLASSIFIERS

The experience of using both classifiers helped in understand the advantage of Random Forest, which is an ensemble learning method. Random Forest(RF) combines the impact of multiple decision trees while ensuring that each tree receives a random sample of data. Random Forest also ensures that a random subset of features is used while building a tree so that the robustness of the model increases. The major advantages of RF are the reduction of overfitting and the improvement of model accuracy.

The learnings from the experiment were that:

• One hot encoding is essential if there are categorical variables.

• Hyperparameter tuning improves model accuracy.

• A Random Forest Classifier is more apt when the dataset is huge and has multiple features and categorical variables. RF is better at handling overfitting problems.

STACKING MODEL FOR PURCHASE DECISION PREDICTION

Stacking different algorithms enables one to handle non-linear relationships, reduce overfitting, and solve complex problems effectively. I have used SMOTE and stacking to predict the probability of a customer purchasing a given quote.

PROCESS FOLLOWED: 

• Using the concept of SMOTE to synthetically generate more data points of minority class points.

• Building a stacked model using multiple algorithms KNN, Support Vector Machine, Random Forest, Decision Tree

• Used random methods for hyperparameter tuning for the stacked model.

Observations:

• I observed that experimenting with "n_inter" and cv is an efficient way to reduce overfitting and improve model accuracy. The “n_iter” denotes the number of different permutations and combinations of hyperparameters that would be tried from “param_distributions”.

• Implemented the base models. KNeighborsClassifier() took comparatively ten times the time taken by Decision Tree or RF. As it is a lazy learning algorithm, the learning is spontaneous, and it is memory intensive. Obtaining accuracy for one-fold of SVC took about 2 hours.SVC is also an eager learning algorithm.
  

Post my experience with trying multiple algorithms and completion of the course”Machine Learning in Business,I have summarized the criteria of algorithm selection VS Business problem in the below table.

Business Problem	Algorithm Name	Criteria	Example Projects
Regression
	Linear Regression	Predicting a continuous value; simpler relationships	Predicting house prices based on features
	Decision Trees	Non-linear relationships; interpretability	Predicting energy usage of buildings
	Random Forests	Improved accuracy; can handle complex relationships	Estimating medical expenses for patients
	Gradient Boosted Machines	High accuracy; computationally intensive	Predicting customer lifetime value
Classification
	Logistic Regression	Binary categorization; simpler relationships	Email spam detection
	Decision Trees	Non-linear; interpretability	Diagnosing diseases from symptoms
	Random Forests	Improved accuracy; complex relationships	Customer churn prediction
	Support Vector Machines	High-dimensional spaces; margin maximization	Image classification for fashion items
	Neural Networks	High complexity; best performance	Handwriting recognition
Clustering
	K-means	Simple centroid-based clustering	Market segmentation based on customer behavior
	DBSCAN	Density-based; no need to specify number of clusters	Identifying regions of high traffic congestion
	Hierarchical Clustering	Nested clusters; interpretability	Organizing articles by topics for news aggregation.

As we've explored machine learning algorithms, we've seen how selecting the appropriate approach may have a big impact on the results of many business scenarios. It's still vital to match the advantages of each algorithm with our unique goals as we work to fully utilize their potential. Understanding and successfully utilizing these algorithms will be key to company decision-making in the future, opening the door to data-driven innovation and strategic success