Raquel Jones - Data Science Portfolio

My Data Voyage

Data Scientist | Machine Learning Expert | Insight Generator

Data Science Projects

Anomaly Detected!

Detecting Anomalous Activity of a Ship's Engine

Explored a dataset to identify patterns, preprocessed data, and performed feature engineering. Applied statistical techniques and machine learning algorithms to detect anomalies, followed by a detailed report summarising findings and recommendations.

Technologies: Python, Pandas, Scikit-learn, Statistical Methods

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PCA t-SNE Clustering

Customer Segmentation with Clustering

Analysed a dataset through exploration and preprocessing, conducted feature engineering, determined the optimal number of clusters (k), and applied machine learning models to segment customers effectively.

Technologies: Python, Scikit-learn, Pandas, Clustering Algorithms

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XGBoost Tree 1 x>0.5 Tree 2 x>0.3 Tree N x>0.7 ... Sum of all tree predictions Key Features: • Gradient Boosting • Regularization • Tree Pruning • Parallel Processing

Predicting Student Dropout with Supervised Learning

Conducted phased data exploration, preprocessing, and feature engineering. Built and compared predictive models using XGBoost and a neural network to forecast student dropout rates with high accuracy.

Technologies: Python, XGBoost, TensorFlow, Pandas

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More Projects

Null Hypothesis Reject Reject Hypothesis Testing

Applying Hypothesis Testing to Organisational Scenarios

Applied statistical hypothesis testing to evaluate organizational data scenarios.

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Correlation A B C Causation Correlation vs Causation

Interpreting Correlation and Causation

Explored the differences between correlation and causation in data analysis.

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MODEL Building & Interpreting Models

Building Models, Interpreting Results

Developed and interpreted machine learning models for data-driven insights.

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1 3 5 7 9 Non-Parametric Tests Data doesn't follow normal distribution

Applying Non-Parametric Tests to a Data Set

Utilized non-parametric statistical tests on a dataset.

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F1 F2 F3 F4 FEATURE SELECTION F2 F3 Feature Selection

Selecting Features

Performed feature selection to optimize model performance.

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PCA t-SNE PCA and t-SNE

Implementing PCA and t-SNE

Applied dimensionality reduction techniques like PCA and t-SNE.

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Regression Classification Regression vs Classification

Regression Versus Classification Problems

Compared regression and classification approaches in machine learning.

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Manual Propagation

Performing Manual Propagation

Implemented manual forward and backward propagation in neural networks.

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Basic Neural Network

Building a Basic Neural Network

Constructed a foundational neural network for predictive modeling.

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LR BS L Hyperparameter Tuning Performance Model 1

Experimenting with Hyperparameter Tuning

Explored techniques for optimizing model hyperparameters.

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Exploring Evaluation Metrics Accuracy Precision Recall F1 Score AUC-ROC 0% 25% 50% 75% 100% Metric Performance Comparison 70% 85% 60%

Technical Skills

Python TensorFlow Scikit-learn Pandas NumPy Matplotlib Seaborn XGBoost NLP Neural Networks Statistical Analysis Data Visualisation

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