{"id":14959021,"url":"https://github.com/chhayac/machine-learning-notebooks","last_synced_at":"2025-05-02T12:31:19.314Z","repository":{"id":38368693,"uuid":"114438844","full_name":"chhayac/Machine-Learning-Notebooks","owner":"chhayac","description":"15+  Machine/Deep Learning Projects in Ipython Notebooks","archived":false,"fork":false,"pushed_at":"2020-04-03T05:31:41.000Z","size":4028,"stargazers_count":127,"open_issues_count":0,"forks_count":103,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-04-07T01:52:28.654Z","etag":null,"topics":["cifar-10","ipython-notebook","iris","iris-dataset","keras","keras-neural-networks","keras-tensorflow","linear-regression","machine-learning","multivariate-regression","notebook","tensorflow","tensorflow-models"],"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/chhayac.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}},"created_at":"2017-12-16T06:18:15.000Z","updated_at":"2025-03-30T05:53:25.000Z","dependencies_parsed_at":"2022-07-12T01:47:11.499Z","dependency_job_id":null,"html_url":"https://github.com/chhayac/Machine-Learning-Notebooks","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chhayac%2FMachine-Learning-Notebooks","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chhayac%2FMachine-Learning-Notebooks/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chhayac%2FMachine-Learning-Notebooks/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/chhayac%2FMachine-Learning-Notebooks/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/chhayac","download_url":"https://codeload.github.com/chhayac/Machine-Learning-Notebooks/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":252038135,"owners_count":21684630,"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":["cifar-10","ipython-notebook","iris","iris-dataset","keras","keras-neural-networks","keras-tensorflow","linear-regression","machine-learning","multivariate-regression","notebook","tensorflow","tensorflow-models"],"created_at":"2024-09-24T13:18:42.478Z","updated_at":"2025-05-02T12:31:18.540Z","avatar_url":"https://github.com/chhayac.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Machine Learning and Deep learning Notebooks\n\n| Notebook Description| Link | Notes |\n| -------------------| -----|--------|\n| Iris Flower Classification | [Iris_flower_classification.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Iris_flower_classification.ipynb) | Build a neural network model using Keras \u0026 Tensorflow. Evaluated the model using scikit learn's k-fold cross validation. | \n| Recognizing CIFAR-10 images (Part I   - Simple model) | [Recognizing-CIFAR-10-images-Simple-Model.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Simple-Model.ipynb) | Build a simple Convolutional Neural Network(CNN) model to classify CIFAR-10 image dataset with Keras deep learning library achieving classification accuracy of 67.1%. |\n| Recognizing CIFAR-10 images (Part II  - Improved model) | [Recognizing-CIFAR-10-images-Simple-Model.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Improved-Model.ipynb) | Build an improved CNN model by adding more layers with Keras deep learning library achieving classification accuracy of 78.65%. |\n| Recognizing CIFAR-10 images (Part III - Data Augmentation) | [Recognizing-CIFAR-10-images-Improved-Model-Data-Augmentation.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Recognizing-CIFAR-10-images-Improved-Model-Data-Augmentation.ipynb) | Build an improved CNN model by data augmentation with Keras deep learning library achieving classification accuracy of 80.73%. |\n| Traffic Sign Recognition using Deep Learning | [Traffic-Sign-Recognition.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Traffic-Sign-Recognition.ipynb) | Build a deep learning model to detect traffic signs using the German Traffic Sign Recognition Benchmark(GTSRB) dataset achieving an accuracy of 98.4%. |\n| Movie Recommendation Engine | [Movie_Recommendation_Engine.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Movie_Recommendation_Engine.ipynb) | Build a movie recommendation engine using k-nearest neighbour algorithm implemented from scratch. |\n| Linear Regression | [Linear_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Linear_Regression.ipynb) | Build a simple linear regression model to predict profit of food truck based on population and profit of different cities. |\n| Multivariate Linear Regression | [Multivariate_Linear_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Multivariate_Linear_Regression.ipynb) | Build a simple multivariate linear regression model to predict the price of a house based on the size of the house in square feet and number of bedrooms in the house. |\n| Sentiment Analysis of Movie Reviews| [Sentiment_Analysis.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Sentiment_Analysis.ipynb)| Experiment to analyze sentiment according to their movie reviews. |\n| Wine quality prediction | [Predicting_wine_quality.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Predicting_wine_quality.ipynb)| Experiment to predict wine quality with feature selection (In progress). |\n| Unsupervised Learning | [unsupervised_learning-Part_1.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/unsupervised_learning-Part_1.ipynb)| Hands-on with Unsupervised learning. |\n| Autoencoders using Fashion MNIST| [Autoencoder_Fashion_MNIST.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Autoencoder_Fashion_MNIST.ipynb)| Building an autoencoder as a classifier using Fashion MNIST dataset. |\n| Logistic Regression| [Logistic_Regression.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/Logistic_Regression.ipynb)| Build a logistic regression model from scratch - Redoing it |\n| Fuzzy string matching| [fuzzywuzzy.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/fuzzy_string_matching.ipynb)| To study how to compare strings and determine how similar they are in Python. |\n| Spam email classification| [spam_email_classification.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/spam_email_classification.ipynb)| Build a spam detection classification model using an email dataset.\n| Customer churn prediction| [customer_churn_prediction.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/customer_churn_prediction.ipynb)| To predict if customers churn i.e. unsubscribed or cancelled their service.- In Progress|\n| Predicting Credit Card Approvals| [predicting_credit_card_approvals.ipynb](https://github.com/chhayac/Machine-Learning-Notebooks/blob/master/predicting_credit_card_approvals.ipynb)| To predict the approval or rejection of a credit card application|\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchhayac%2Fmachine-learning-notebooks","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fchhayac%2Fmachine-learning-notebooks","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fchhayac%2Fmachine-learning-notebooks/lists"}