{"id":22752040,"url":"https://github.com/zeuscoderbe/artificial-neural-network-model-to-classify-iris-flowers","last_synced_at":"2025-10-18T10:57:50.242Z","repository":{"id":226699393,"uuid":"769426573","full_name":"ZeusCoderBE/Artificial-neural-network-model-to-classify-Iris-flowers","owner":"ZeusCoderBE","description":"Building an artificial neural network to classify Iris flowers","archived":false,"fork":false,"pushed_at":"2024-03-14T11:03:55.000Z","size":566,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-02-05T08:51:28.674Z","etag":null,"topics":["artificial-neural-networks","machine-learning"],"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/ZeusCoderBE.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":"2024-03-09T03:36:48.000Z","updated_at":"2024-04-26T01:59:49.000Z","dependencies_parsed_at":"2024-03-09T04:31:28.025Z","dependency_job_id":"15daf79b-9ccf-437e-aec0-8177a8c8a765","html_url":"https://github.com/ZeusCoderBE/Artificial-neural-network-model-to-classify-Iris-flowers","commit_stats":null,"previous_names":["zeuscoderbe/artificial-neural-network-model-to-classify-iris-flowers"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZeusCoderBE%2FArtificial-neural-network-model-to-classify-Iris-flowers","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZeusCoderBE%2FArtificial-neural-network-model-to-classify-Iris-flowers/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZeusCoderBE%2FArtificial-neural-network-model-to-classify-Iris-flowers/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/ZeusCoderBE%2FArtificial-neural-network-model-to-classify-Iris-flowers/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/ZeusCoderBE","download_url":"https://codeload.github.com/ZeusCoderBE/Artificial-neural-network-model-to-classify-Iris-flowers/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":246285668,"owners_count":20752953,"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":["artificial-neural-networks","machine-learning"],"created_at":"2024-12-11T05:09:25.007Z","updated_at":"2025-10-18T10:57:45.209Z","avatar_url":"https://github.com/ZeusCoderBE.png","language":"Jupyter Notebook","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Iris Species Classification Project\n\n## Overview\nThis project aims to develop a machine learning model to classify iris flowers into different species based on their sepal and petal characteristics. The dataset used for this project is the famous Iris dataset.\n\n## Dataset\nThe dataset (`iris.data`) is loaded using Pandas, containing information about sepal length, sepal width, petal length, petal width, and the species of the iris flowers.\n\n### Exploratory Data Analysis (EDA)\n- Displaying basic information about the dataset using `df_iris.info()` and `df_iris.describe()`.\n- Visualizing the distribution of features and their relationship with the target variable using Seaborn and Matplotlib.\n\n## Data Preprocessing\n- Splitting the dataset into training and testing sets using `train_test_split`.\n- Encoding the target variable using Label Encoder and converting it to one-hot encoding.\n\n## Model Architecture\nA neural network model is implemented using TensorFlow and Keras with the following layers:\n1. Input layer with 128 neurons and ReLU activation.\n2. Hidden layer with 64 neurons and ReLU activation.\n3. Hidden layer with 32 neurons and ReLU activation.\n4. Output layer with 3 neurons and softmax activation.\n\n## Model Training\nThe model is compiled using the Adam optimizer, categorical crossentropy loss, and accuracy as a metric. It is then trained on the training data for 100 epochs with a batch size of 105.\n\n```python\nmodel.compile(optimizer=Adam(learning_rate=0.001), loss='categorical_crossentropy', metrics=['accuracy'])\nmodel.fit(train_X, train_y_one_hot, epochs=100, batch_size=105)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzeuscoderbe%2Fartificial-neural-network-model-to-classify-iris-flowers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fzeuscoderbe%2Fartificial-neural-network-model-to-classify-iris-flowers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fzeuscoderbe%2Fartificial-neural-network-model-to-classify-iris-flowers/lists"}