{"id":25722423,"url":"https://github.com/julscadenas/ds-ml-projects","last_synced_at":"2026-05-18T14:08:12.587Z","repository":{"id":276810040,"uuid":"930381059","full_name":"julsCadenas/DS-ML-Projects","owner":"julsCadenas","description":"Collection of my Data Science and Machine Learning Projects.","archived":false,"fork":false,"pushed_at":"2025-02-24T14:11:06.000Z","size":9038,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-24T15:27:30.684Z","etag":null,"topics":["data-science","machine-learning","python"],"latest_commit_sha":null,"homepage":"","language":"Jupyter Notebook","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/julsCadenas.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2025-02-10T14:45:58.000Z","updated_at":"2025-02-24T14:11:09.000Z","dependencies_parsed_at":"2025-02-24T15:27:34.855Z","dependency_job_id":"301d61df-2ed2-49c7-b789-ec6d120a4719","html_url":"https://github.com/julsCadenas/DS-ML-Projects","commit_stats":null,"previous_names":["julscadenas/house-price-regression","julscadenas/ds-ml-projects"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/julsCadenas%2FDS-ML-Projects","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/julsCadenas%2FDS-ML-Projects/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/julsCadenas%2FDS-ML-Projects/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/julsCadenas%2FDS-ML-Projects/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/julsCadenas","download_url":"https://codeload.github.com/julsCadenas/DS-ML-Projects/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":240733804,"owners_count":19848929,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-science","machine-learning","python"],"created_at":"2025-02-25T19:34:35.828Z","updated_at":"2026-05-18T14:08:12.582Z","avatar_url":"https://github.com/julsCadenas.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# **Data Science \u0026 Machine Learning Project-Based Learning Plan**\nThis repository contains my hands-on projects as part of my Machine Learning Project-Based Learning Plan. Each project follows a structured learning approach, covering various ML concepts and techniques.\n\n## 📖 Table of Contents  \n1. [🏡 House Price Prediction (Regression)](https://github.com/julsCadenas/DS-ML-Projects/tree/main/HousePricePrediction)  \n2. [📧 Spam Email Classifier](https://github.com/julsCadenas/DS-ML-Projects/tree/main/SpamEmailClassifier)  \n3. [📺 Investigating Netflix Movies (DataCamp)](https://github.com/julsCadenas/DS-ML-Projects/tree/main/InvestigateNetflixMovies)\n4. [🙍‍♂️ Customer Churn Prediction](https://github.com/julsCadenas/DS-ML-Projects/tree/main/CustomerChurnPrediction)\n5. [👮 Investigating Crime in Los Angeles (DataCamp)](https://github.com/julsCadenas/DS-ML-Projects/tree/main/CrimesInLA)\n6. [🌾 Predictive Modeling for Agriculture (DataCamp)](https://github.com/julsCadenas/DS-ML-Projects/tree/main/PredictiveModelAgriculture)\n7. [🌡️ Predicting Temperature in London (DataCamp)](https://github.com/julsCadenas/DS-ML-Projects/tree/main/LondonTemps)\n8. [🐤 Sentiment Analysis on Tweets](https://github.com/julsCadenas/DS-ML-Projects/tree/main/TwtSentimentAnalysis)\n9. [1️⃣ MNIST Handwritten Digit Classifier](https://github.com/julsCadenas/DS-ML-Projects/tree/main/TFMnist)\n---\n\n### **🏡 House Price Prediction (Regression)**  \n- **Dataset:** [Kaggle: House Prices - Advanced Regression Techniques](https://www.kaggle.com/competitions/house-prices-advanced-regression-techniques)  \n- **Key Concepts:** Regression, Feature Engineering, Hyperparameter Tuning  \n- **Tools Used:** Scikit-Learn, Pandas, Matplotlib, NumPy  \n- **Summary:**  \n  - Used **Linear Regression \u0026 Lasso Regression** to predict house prices.  \n  - Performed **EDA**, **feature engineering**, and **hyperparameter tuning**.  \n  - Evaluated model using **RMSE, MAE, and R² score**.  \n- **Status:** ✅ Completed  \n- **Code \u0026 Notebook:** [House Price Prediction](https://github.com/julsCadenas/DS-ML-Projects/tree/main/HousePricePrediction)  \n\n---\n\n### **📧 Spam Email Classifier (Classification)**  \n- **Dataset:** [Kaggle: Email Spam Detection Dataset](https://www.kaggle.com/datasets/shantanudhakadd/email-spam-detection-dataset-classification)  \n- **Key Concepts:** Naïve Bayes, NLP, Text Preprocessing  \n- **Tools Used:** Scikit-Learn, NLTK, Pandas, Matplotlib, Seaborn, NumPy  \n- **Summary:**  \n  - Trained and compared **Multinomial Naïve Bayes**, **Bernoulli Naïve Bayes**, and **MLP Classifier**.  \n  - Evaluated performance using **Accuracy, Precision, Recall, and F1-score**.  \n- **Status:** ✅ Completed  \n- **Code \u0026 Notebook:** [Spam Email Classifier](https://github.com/julsCadenas/DS-ML-Projects/tree/main/SpamEmailClassifier)  \n\n---\n\n### **📺 Investigating Netflix Movies (DataCamp)**\n- **Dataset:** [Investigating Netflix Movies](https://app.datacamp.com/learn/projects/investigating_netflix/guided/Python)  \n- **Key Concepts:** Exploratory Data Analysis  \n- **Tools Used:** Pandas, Matplotlib  \n- **Summary:**  \n  - Perform Exploratory Data Analysis on the dataset\n- **Status:** ✅ Completed \n- **Code \u0026 Notebook:** [Netflix Data EDA](https://github.com/julsCadenas/DS-ML-Projects/tree/main/InvestigateNetflixMovies)  \n\n---\n\n### **🙍‍♂️ Customer Churn Prediction**\n- **Datasets:**\n  - [Customer Churn Dataset](https://www.kaggle.com/datasets/muhammadshahidazeem/customer-churn-dataset)  \n  - [Telco Churn Dataset](https://www.kaggle.com/datasets/mnassrib/telecom-churn-datasets)  \n  - [Bank Customer Churn Dataset](https://www.kaggle.com/datasets/gauravtopre/bank-customer-churn-dataset)  \n- **Key Concepts:** Classification, Model Evaluation, EDA  \n- **Tools Used:** XGBoost, Scikit-Learn, SHAP, Pandas, Matplotlib, Seaborn  \n- **Summary:**  \n  - Trained XGBoost model for churn prediction.\n  - Evaluated using Accuracy, Precision, Recall, F1-score, and AUC-ROC.\n  - Used SHAP analysis to interpret feature importance (e.g., payment delays, support calls).\n  - Achieved 91% recall with an AUC-ROC of 0.9006.\n- **Status:** ✅ Completed\n- **Code \u0026 Notebook:** [Customer Churn Pred](https://github.com/julsCadenas/DS-ML-Projects/tree/main/CustomerChurnPrediction)  \n\n---\n\n### **👮 Analyzing Crime in Los Angeles (DataCamp)**\n- **Dataset:** [Analyzing Crime in LA](https://app.datacamp.com/learn/projects/investigating_netflix/guided/Python)\n- **Key Concepts:** Exploratory Data Analysis\n- **Tools used:** Pandas, Matplotlib, Seaborn\n- **Summary:**\n  - Perform EDA on the dataset\n- **Status:** ✅ Completed\n- **Code \u0026 Notebook:** [Crime in LA](https://github.com/julsCadenas/DS-ML-Projects/tree/main/CrimesInLA)\n\n---\n\n### **🌾 Predictive Modeling for Agriculture (DataCamp)**\n- **Dataset:** [Predictive Model Agriculture](https://app.datacamp.com/learn/projects/1772)\n- **Key Concepts:** Exploratory Data Analysis, Logistic Regression, Model Evaluation\n- **Tools used:** Pandas, Scikit-Learn\n- **Summary:**\n  - Perform EDA on the dataset\n  - Use Logistic Regression on each feature.\n  - Find the most performant feature.\n  - Evaluate the model using F1 score.\n- **Status:** ✅ Completed\n- **Code \u0026 Notebook:** [Agriculture Model](https://github.com/julsCadenas/DS-ML-Projects/tree/main/PredictiveModelAgriculture)\n\n---\n\n### **🌡️ Predicting Temperature in London (DataCamp)**\n- **Dataset:** [Predicting Temperature in London](https://app.datacamp.com/learn/projects/predicting_temperature_in_london/guided/Python)\n- **Key Concepts:** Exploratory Data Analysis, Linear Regression, Decision Trees, Random Forest, Model Evaluation, MLflow\n- **Tools used:** Pandas, Numpy, Scikit-Learn, MLflow\n- **Summary:**\n  - Load and clean historical weather data from London.\n  - Perform EDA to understand seasonal trends and feature correlations.\n  - Select features relevant for predicting mean temperature.\n  - Preprocess the data using imputation and scaling.\n  - Train and evaluate Linear Regression, Decision Tree, and Random Forest models.\n  - Log models, parameters, and performance metrics using MLflow.\n- **Status:** ✅ Completed\n- **Code \u0026 Notebook:** [London Temps](https://github.com/julsCadenas/DS-ML-Projects/tree/main/LondonTemps)\n\n---\n\n### **🐤 Sentiment Analysis on Tweets**\n- **Dataset:** [Sentiment140 Dataset](https://www.kaggle.com/datasets/kazanova/sentiment140/code)\n- **Key Concepts:** Natural Language Processing (NLP), Text Processing, Binary Classification, Model Evaluation, Long Short-Term Memory (LSTM) \n- **Tools used:**  Pandas, Numpy, Scikit-Learn, NLTK, Matplotlib, Seaborn, Tensorflow, Keras\n- **Summary:**\n  - Loaded and cleaned 1.6 million tweets labeled as positive or negative.\n  - Applied text preprocessing: lowercasing, punctuation and stopword removal, tokenization.\n  - Converted text to sequences using Keras `Tokenizer`, and padded them to equal length.\n  - Built a neural network using TensorFlow/Keras with embedding and dense layers.\n  - Trained the model and evaluated it on a 320,000-sample test set.\n  - Achieved the following evaluation metrics:\n    - **Accuracy:** 81.12%\n    - **Precision:** 81.26%\n    - **Recall:** 80.96%\n    - **F1-Score:** 81.11%\n- **Status:** ✅ Completed\n- **Code \u0026 Notebook:** [Tweet Sentiment Analysis](https://github.com/julsCadenas/DS-ML-Projects/tree/main/TwtSentimentAnalysis)\n\n---\n\n### **1️⃣ MNIST Handwritten Digit Recognition**\n- **Dataset:** MNIST (from `tf.keras.datasets`)\n- **Key concepts:** Neural networks, image classification, data normalization, softmax \u0026 reLU activation, model evaluation\n- **Tools used:** TensorFlow, Keras, matplotlib, numpy\n- **Summary:**\n  - Built a fully connected neural network (FCNN) to classify 28x28 grayscale images of handwritten digits.\n  - Preprocessed the dataset by normalizing pixel values; trained over 5 epochs and visualized predictions.\n  - Evaluated the model using accuracy and loss metrics on 10,000 test images.\n- **Status:** ✅ Completed\n- **Code \u0026 Notebook:** [TFMnist](https://github.com/julsCadenas/DS-ML-Projects/tree/main/TFMnist)","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjulscadenas%2Fds-ml-projects","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjulscadenas%2Fds-ml-projects","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjulscadenas%2Fds-ml-projects/lists"}