The Ultimate Beginner‘s Guide to Winning Classification Hackathons: Insights from a Telecom Churn Prediction Use Case

Machine learning (ML) hackathons have exploded in popularity in recent years. These online competitions provide a fantastic opportunity for data science enthusiasts to put their skills to the test, learn from the community, and even make some prize money.

For beginners though, diving into a hackathon can seem daunting. Where do you even start? What techniques should you use? How do you climb the leaderboard? Fear not – we‘ve got you covered! In this ultimate guide, we‘ll break down a beginner-friendly framework to approach classification hackathons, one of the most common types of ML problems.

What is Classification in Machine Learning?

In supervised learning, we have a dataset with input features (X) and a target variable (y) to predict. If the target is categorical (e.g. "Yes" vs "No", or "Dog", "Cat", "Horse"), we call it a classification problem. The goal is to learn a mapping function from the input features to the correct target class.

This is in contrast to regression problems, where the target is a continuous numeric value, or unsupervised problems like clustering, where there is no pre-specified target. Some examples of classification problems:

• Predicting customer churn (will a customer cancel their subscription?)
• Email spam detection (is an email spam or not?)
• Cancer diagnosis (does a patient have cancer based on test results?)
• Sentiment analysis (is a movie review positive or negative?)

According to data from Kaggle, the leading platform for data science competitions, classification problems make up over 50% of all active and completed competitions. And a 2021 survey by Kaggle found that logistic regression and tree-based models, commonly used for classification, are among the top 5 most popular ML algorithms in the industry.

ML Problem Type	% of Kaggle Competitions
Classification	52%
Regression	31%
Clustering	8%
Other	9%

Source: Kaggle Competitions Database, 2022

A Typical Hackathon Workflow

While every dataset is unique, most classification hackathons follow a typical ML project flow. Here‘s a high-level overview, which we‘ll dive into with more details and an example use case:

1. Understand the problem and evaluation metric
2. Explore the data (EDA)
3. Preprocess the data
4. Feature engineering
5. Train a baseline model
6. Evaluate and iterate
7. Experiment with advanced techniques
8. Ensemble models
9. Make final predictions

1. Problem Understanding

First things first – deeply understand the problem statement, dataset and evaluation metric. What are you trying to predict? What input data do you have? How will your predictions be evaluated?

Most classification hackathons use one of the following metrics:

• Accuracy: % of predictions that exactly match the true class
• F1 score: Harmonic mean of precision and recall
• Log loss: Negative log likelihood of true class given predicted probabilities
• AUC: Area under the ROC curve plotting true positive rate vs false positive rate

Understanding the metric is key to guiding your modeling choices. For example, log loss and AUC are better for imbalanced target classes than plain accuracy.

2. Exploratory Data Analysis (EDA)

Next, dive deep into the data. The goal of EDA is to gain intimate statistical and visual understanding of the dataset to guide your feature engineering and modeling later on. Some key things to analyze:

• Basic stats: # of data points, features, missing values
• Target distribution: % of samples in each class, imbalance
• Feature distributions: Histograms, box plots, outliers
• Feature-target relationships: Correlations, scatter plots, grouped aggregations

Python libraries like Pandas, Matplotlib and Seaborn are popular for EDA. For example, here‘s a quick snapshot of some EDA functions:

import pandas as pd

data = pd.read_csv("train.csv")

# Basic stats
print(data.shape)
print(data.info())

# Target distribution 
print(data[‘target‘].value_counts(normalize=True))

# Numeric feature distribution
print(data.describe())

# Correlation heatmap
corr_mat = data.corr()
sns.heatmap(corr_mat, annot=True, cmap="coolwarm")

Many Kagglers recommend spending 70% of your time on EDA and data preprocessing. It‘s worth thoroughly exploring the data to shape your intuition for the next steps.

3. Data Preprocessing

Raw hackathon datasets often need extensive cleaning and preprocessing. Key steps:

• Handling missing values: Simple imputation (e.g. mean) or removing rows
• Encoding categorical variables: One-hot, label or target encoding
• Feature scaling: Standardization (zero mean, unit variance) or normalization (to 0-1 range)
• Handling outliers and high cardinality features

Scikit-learn provides handy transformers like SimpleImputer, OneHotEncoder, StandardScaler etc. that make this process smooth. Proper preprocessing is essential for models to perform well.

4. Feature Engineering

Feature engineering is the art of creating new input features from raw data to help ML models learn better. This is where domain expertise can really shine. Some common feature engineering techniques:

• Combining features through math operations (e.g. ratio of two columns)
• Grouping features to generate aggregated stats
• Extracting date/time features (e.g. day of week, hour of day)
• Binning continuous features
• Calculating rolling window statistics
• Encoding domain-specific patterns (e.g. is the transaction amount a round number?)

The best engineered features often come from deep domain understanding, so it pays to research the subject area. That said, some automated feature engineering tools like Featuretools are emerging to help generate feature ideas.

5. Baseline Model

With clean data and an expanded feature set, it‘s time to train a first baseline model. This serves as a reference point to compare more advanced models against.

For classification, some common baseline models:

• Logistic regression
• Decision trees
• Naive Bayes
• K-nearest neighbors

Pick one, train it with default hyperparameters, and evaluate it via 3 to 5-fold cross-validation on the training set. Note down the CV scores, and move on to iterating.

6. Evaluation and Iteration

With a baseline to beat, it‘s time to iteratively improve your model. Some key levers:

• Experiment with more advanced algorithms like random forests, XGBoost, LightGBM, CatBoost, or even neural networks
• Hyperparameter tuning: Grid search or random search over key parameters
• Feature selection: Removing low-importance or highly correlated features
• Synthetic feature creation: Interaction terms, polynomial features, domain-inspired features
• Dealing with imbalanced targets through class weights, undersampling/oversampling

Keep track of your experiments and scores. Each iteration should yield small gains on your CV score. A disciplined approach of experimenting, analyzing, and learning is key.

7. Advanced Techniques

In later stages of the hackathon, it‘s worth exploring some advanced techniques for extra accuracy points:

• Stacking: Using base model predictions as meta-features
• Pseudo-labeling: Using high-confidence predictions on test data for re-training
• Neural network embeddings: Training neural nets to learn dense latent representations
• Automated ML: Tools like auto-sklearn, TPOT to find optimal pipelines

These are powerful techniques, but can be tricky to implement correctly. Experiment on a separate branch, and only merge into main pipeline when you have clear gains.

8. Ensembling

Ensembling is a tried-and-tested strategy for boosting model accuracy. The idea is to combine predictions from a diverse set of well-performing models. Common ensemble techniques:

• Voting: Taking majority vote of predicted classes from base models
• Averaging: Taking mean of predicted probabilities from base models
• Stacking: Using base model predictions as features for a meta-model
• Blending: Taking weighted average of base models

Libraries like vecstack and mlxtend make ensembling straightforward in Python. In practice, even a simple average or vote ensemble can add a significant boost to your solo best model.

9. Final Predictions

In the last few hours of the hackathon, it‘s important to reserve time to prepare your final submission. Key things to do:

• Retrain models on full train data (no CV splits)
• Generate test set predictions using your best model(s)
• Assemble ensemble predictions
• Verify file format matches competition requirements
• Make multiple submission attempts in case of errors

Once you make your final submission, sit back and watch the leaderboard! Regardless of the outcome, you will have gained valuable experience.

Example Use Case: Telecom Customer Churn Prediction

Let‘s cement these concepts with a start-to-finish example from a recent hackathon on Analytics Vidhya. The problem is to predict whether a telecom customer will churn (i.e. cancel their subscription) based on their demographics, services used, and transaction history. The evaluation metric is AUC.

Here‘s an abbreviated version of a top 1% solution:

EDA

• Dataset has 5000 customers, 21 features, 16% churn rate
• Key correlations: Churn vs monthly charges (0.20), internet service (-0.15)
• Missing values in 3 columns, imbalanced target class

Preprocessing

• Imputed missing tenure with mean, others with mode
• One-hot encoded categorical features
• Standardized continuous features
• Clipped outliers to 5th/95th percentile

Feature Engineering

• Grouped city into urban/rural based on population data
• Interaction: tenure (months) × monthly charges = lifetime value
• Binned tenure into 1 year, 2 year, 3+ year cohorts
• Date features: Join month, join day of week
• Domain feature: Has customer made support calls in first 90 days?

Baseline Model

• Logistic regression with default params
• 5-fold CV AUC: 0.73

Iteration and Tuning

• Compared decision tree, random forest, gradient boosting models
• Hyperparameter tuned with random search: max_depth, learning_rate, n_estimators etc.
• Used RFECV for feature selection, dropped 4 low-importance features
• Synthetic features: total services used, payment method × monthly charges
• Best single model: Tuned XGBoost, 5-fold CV AUC 0.86

Ensembling

• Trained "diverse" models: LogReg, RandomForest, XGBoost, LightGBM, CatBoost
• Simple average ensemble of top 3 models by validation AUC
• Stacked ensemble: Using above model predictions as features to a meta LogReg model
• Best ensemble: Stacked ensemble of XGBoost + CatBoost + LightGBM, AUC 0.89

Leaderboard Movement

• EDA + Preproc + Baseline: 120 / 200 (60th percentile)
• Feature Engineering + Tuning : 45 / 200 (78th percentile)
• Ensembling: 12 / 200 (94th percentile)
• 🥇 Final Rank: 2 / 200

(Actual leaderboard scores have been normalized for simplicity)

This shows a realistic trajectory of how a disciplined approach can lead to steady rank improvements from start to finish.

Advice From Kaggle Grandmasters

To conclude, let‘s see what top Kagglers have to say about succeeding in classification competitions. In a 2020 Kaggle survey of 50+ Grandmasters (top 1% ranked data scientists), here were some key pieces of advice:

• "Start simple, score, then iterate and add complexity" – Gilberto Titericz, 6x Kaggle Gold Medalist
• "Your model is only as good as your cross-validation" – Bojan Tunguz, 18x Kaggle Gold Medalist
• "Understand the data deeply before modeling. Good features will get you 80% there" – Leustagos, Kaggle Grandmaster
• "Ensemble, but more importantly, ensemble different modeling approaches" – Jean-Francois Puget, 5x Kaggle Gold Medalist

Conclusion

Classification hackathons can seem overwhelming at first, but by following a systematic approach of EDA, preprocessing, feature engineering, modeling, and ensembling, you can methodically work your way up the leaderboard. The telecom churn case study showed how this might play out in practice.

Beyond any specific techniques though, the key to success is staying curious and continuously learning. Some helpful resources:

• Kaggle Learn Courses – Free hands-on tutorials
• Machine Learning Mastery – In-depth guides and walkthroughs
• KaggleX – Video breakdowns of top solutions

Most importantly, enjoy the process and learn from every hackathon. With practice and perseverance, you‘ll be taking home gold medals soon enough. Happy hacking!