Use business cases:

The goal of this repository is to create jupyter notebooks with toy examples of some of the use cases of the business problems I have worked in my data science career. It contains:

(Currently building the repository)

Consumption prediction:

• Time series Statistical Learning approach to predict future water consumption (ARIMA).
• Time series Machine Learning approach to predict future water consumption (Prophet).
• Time series Deep Learning approach to predict future water consumption (LSTM).

Customers Churn Prediction:

• -- DONE -- Supervised Statistical Learning approach to predict the probability of churn of each client (Logistic regression).
• -- DONE -- Supervised Machine Learning approach to predict the probability of churn of each client (Boosting Trees).

Customers Lifetime Value Prediction:

• Statistical Learning approach to measure the averaged profit of a given customer (BG/NBD Gamma-Gamma).

Customers Segmentation:

• Unsupervised Machine Learning approach to perform clients segmentation (Hierarquical clustering).
• -- DONE -- Unsupervised Machine Learning approach to perform (RFM) clients segmentation (k-means clustering).

Fraud Detection:

• -- DONE -- Supervised Machine Learning approach to detect fraud on clients consumption (Boosting Trees).
• -- DONE -- Unsupervised Machine Learning approach to detect fraud on clients consumption (DBScan Clustering).
• -- DONE -- Unsupervised Deep Learning approach to detect fraud on clients card transactions (Autoencoders).

Pricing:

• In process...

Sales prediction:

• Time series Statistical Learning approach to predict future sales (ARIMA).
• Time series Machine Learning approach to predict future sales (Prophet).

Sentiment Analysis:

• NLP Supervised Statistical Learning approach to classify tweets sentiment (Naïve Bayes).

Topic detection:

• NLP Unsupervised Statistical Learning approach to detect topics in a set of documents (Latent Dirichlet Allocation).