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Ewa Devaney

Head of IT and Data Insights

Davy's of London

About Ewa

Fuel Economy Prediction Model

Fuel Economy Prediction ModelThis project predicts vehicle fuel efficiency using linear regression in Python (pandas, scikit-learn, statsmodels). The process includes EDA, feature selection, and model evaluation, addressing issues with inconsistent data formats and missing values.

Fuel Economy Prediction Model

Fuel Economy Prediction ModelThis project predicts vehicle fuel efficiency using linear regression in Python (pandas, scikit-learn, statsmodels). The process includes EDA, feature selection, and model evaluation, addressing issues with inconsistent data formats and missing values.

Credit Score Logistic Regression

Credit Score Logistic RegressionThe primary goal of this project is to predict low credit score customers based on various features from a dataset of credit transactions. The project aims to develop a classification model capable of identifying customers with low credit score.

Credit Score Logistic Regression

Credit Score Logistic RegressionThe primary goal of this project is to predict low credit score customers based on various features from a dataset of credit transactions. The project aims to develop a classification model capable of identifying customers with low credit score.

Bank Customer Churn Data Cleaning, EDA, and Preparation

Bank Customer Churn Data Cleaning, EDA, and PreparationIn this project, I aim to clean, explore, and prepare a dataset for machine learning modelling to predict customer churn for a bank. The dataset is provided in a flat Excel file. The goal is to conduct data preparation and exploratory data analysis (EDA).

Bank Customer Churn Data Cleaning, EDA, and Preparation

Bank Customer Churn Data Cleaning, EDA, and PreparationIn this project, I aim to clean, explore, and prepare a dataset for machine learning modelling to predict customer churn for a bank. The dataset is provided in a flat Excel file. The goal is to conduct data preparation and exploratory data analysis (EDA).

K-Means Clustering and PCA

K-Means Clustering and PCAThis project is the culmination of the Data Science in Python: Unsupervised Learning course, designed to demonstrate the application of key unsupervised learning techniques to a real-world business problem.

K-Means Clustering and PCA

K-Means Clustering and PCAThis project is the culmination of the Data Science in Python: Unsupervised Learning course, designed to demonstrate the application of key unsupervised learning techniques to a real-world business problem.

Retaining Customers: Predicting Bank Churn with Machine Learning

Retaining Customers: Predicting Bank Churn with Machine LearningIn this project, I aim to build and evaluate a bank customer churn classification model, with a primary focus on recall as the most critical performance metric. Correctly identifying customers who are at risk of churning is essential for this project.

Retaining Customers: Predicting Bank Churn with Machine Learning

Retaining Customers: Predicting Bank Churn with Machine LearningIn this project, I aim to build and evaluate a bank customer churn classification model, with a primary focus on recall as the most critical performance metric. Correctly identifying customers who are at risk of churning is essential for this project.

Penny-Wise Treats: The Halloween Candy Quest

Penny-Wise Treats: The Halloween Candy QuestThis project is focused on identifying the best Halloween candies in terms of both popularity and affordability.

Penny-Wise Treats: The Halloween Candy Quest

Penny-Wise Treats: The Halloween Candy QuestThis project is focused on identifying the best Halloween candies in terms of both popularity and affordability.

Bank Customers Clustering with Python

Bank Customers Clustering with PythonIn this project, I applied machine learning techniques to segment bank customers to understand their unique needs better. The goal was to identify distinct customer segments, which could be targeted with tailored offers and services.

Bank Customers Clustering with Python

Bank Customers Clustering with PythonIn this project, I applied machine learning techniques to segment bank customers to understand their unique needs better. The goal was to identify distinct customer segments, which could be targeted with tailored offers and services.

About Ewa

Fuel Economy Prediction Model

Fuel Economy Prediction Model

Credit Score Logistic Regression

Credit Score Logistic Regression

Bank Customer Churn Data Cleaning, EDA, and Preparation

Bank Customer Churn Data Cleaning, EDA, and Preparation

K-Means Clustering and PCA

K-Means Clustering and PCA

Retaining Customers: Predicting Bank Churn with Machine Learning

Retaining Customers: Predicting Bank Churn with Machine Learning

Penny-Wise Treats: The Halloween Candy Quest

Penny-Wise Treats: The Halloween Candy Quest

Bank Customers Clustering with Python

Bank Customers Clustering with Python