Springboard ML Engineering Bootcamp

Welcome to my GitHub repository containing all materials and projects from the Springboard ML Engineering Bootcamp. This bootcamp covered a wide array of machine learning (ML) engineering concepts, including hands-on mini-projects and larger ML-focused projects that demonstrate practical applications.

Table of Contents

• Overview
• Mini Projects
• ML Engineering Projects
• Getting Started
• Requirements
• License

Overview

This repository showcases my work and progress in the Springboard ML Engineering Bootcamp. Each folder includes code, documentation, and relevant datasets. Key topics covered in the bootcamp include:

• Data preprocessing
• Feature engineering
• Model selection and tuning
• Deployment of ML models
• Model evaluation
• A/B testing and validation
• Data pipelines

Mini Projects

The mini-projects provided practical applications of machine learning concepts, allowing for an iterative understanding of core ML workflows. Below are some highlights:

1. Data Preprocessing and Cleaning
   Techniques and scripts to clean raw datasets, handle missing values, standardize data, and perform exploratory data analysis (EDA).

2. Feature Engineering
   Methods for transforming features, encoding categorical variables, and engineering new features to enhance model performance.

3. Model Selection and Evaluation
   Experimenting with different algorithms like Decision Trees, K-Nearest Neighbors, and Support Vector Machines, along with evaluation metrics such as accuracy, precision, recall, and F1-score.

4. Cross-Validation and Hyperparameter Tuning
   Using k-fold cross-validation and Grid Search/Randomized Search for optimizing model parameters.

ML Engineering Projects

Here are the primary machine learning projects created during the bootcamp, each designed to simulate real-world scenarios:

1. ML Chef: Recipe Recommendation Platform

• Description: A deep learning-based recipe recommendation system that suggests recipes based on user-uploaded food images.
• Tech Stack: Keras, TensorFlow, custom ConvNet, TF-IDF vectorization, k-means clustering, PCA.
• Highlights:
  • Utilized a pre-trained image classifier, retrained on a custom dataset of 10,000+ recipes.
  • Ingredient classification through clustering techniques.
  • Built a convolutional neural network (ConvNet) for accurate image recognition.

2. Customer Churn Prediction

• Description: Predicts customer churn in a subscription-based business using classification algorithms.
• Tech Stack: Scikit-learn, Pandas, Matplotlib.
• Highlights:
  • Feature engineering to improve predictive power.
  • Comparison of multiple models and metrics (ROC-AUC, precision-recall).
  • Deployment-ready with a simple API to handle predictions.

3. Sentiment Analysis on Product Reviews

• Description: Performs sentiment analysis on e-commerce product reviews.
• Tech Stack: NLP with spaCy, NLTK, and Scikit-learn.
• Highlights:
  • Cleaned and processed large textual data.
  • Built and fine-tuned models (Naive Bayes, Logistic Regression).
  • Implemented a basic pipeline for sentiment categorization.

4. House Price Prediction

• Description: Regression model predicting real estate prices based on features like location, size, and amenities.
• Tech Stack: Scikit-learn, Pandas, Seaborn.
• Highlights:
  • EDA and visualization to understand correlations.
  • Applied regularization techniques to enhance model performance.
  • Web-based interface for inputting property details.

Getting Started

To explore each project:

1. Clone this repository:

   git clone https://github.com/mona-arami/springboard-ml-engineering-bootcamp.git
   cd springboard-ml-engineering-bootcamp