La View Bohem

{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[],"dockerImageVersionId":30839,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"source":"<a href=\"https://www.kaggle.com/code/dascient/la-vie-bohem-coding-republic?scriptVersionId=218027778\" target=\"_blank\"><img align=\"left\" alt=\"Kaggle\" title=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"></a>","metadata":{},"cell_type":"markdown"},{"cell_type":"markdown","source":"# **Welcome to the Gen(ψςδ)ius Coding Republic** 🌌\n### Inspired by [@jay.powersurge](https://www.instagram.com/jay.powersurge/)\n\nWelcome, future shapers of the universe! This notebook is your gateway to creating **influential**, **powerful**, and **ethically-driven** coding solutions that will impact the world and multiverse, ultraverses, and BĘŸØŅÐ! We’ll explore advanced concepts, intricate designs, and powerful applications to guide you in shaping the future of humanity through technology🚀\n\nThe destiny of our community's ingenuity depends on you. How brilliant is that?\n\nPrepare to dive into ideas inspired by groundbreaking platforms like [Donutz.ai](https://donutz.ai), [Dascient](https://dascient.com), and exceptional work on [Kaggle](https://kaggle.com). Let's begin!","metadata":{}},{"cell_type":"markdown","source":"# 🌟 La Vie Bohem Series: A Journey Through Bohemian Analysis 🌟\n\nExplore the depths of data science, creativity, and innovation through \"La Vie Bohem\" series. Each part offers a unique perspective on data exploration, modeling, and beyond.\n\n---\n\n## Series Links\n\n1. [**La Vie Bohem - Coding Republic (Introduction)**](https://www.kaggle.com/code/dascient/la-vie-bohem-coding-republic)  \n2. [**La Vie Bohem - Part 2: Data Exploration and Visualization**](https://www.kaggle.com/code/dascient/la-vie-bohem-part-2)  \n3. [**La Vie Bohem - Part 3: Advanced Modeling Techniques**](https://www.kaggle.com/code/dascient/la-vie-bohem-part-3)  \n4. [**La Vie Bohem - Part 4: Model Evaluation and Tuning**](https://www.kaggle.com/code/dascient/la-vie-bohem-part-4)  \n4. [**La Vie Bohem - Part 4a: Extra Credit**](https://www.kaggle.com/code/dascient/la-vie-bohem-part-4a)  \n5. [**La Vie Bohem - Whispers of the Infinite**](https://www.kaggle.com/code/dascient/la-vie-bohem-whispers-of-the-infinite)  \n\n---\n\n### 🌌 Whispers of Creativity and Infinite Potential 🌌\n\n> \"Let your code echo across the cosmos. The journey of analysis begins not in the numbers, but in the whispers of infinite possibilities.\"  \n— *La Vie Bohem*\n\n**Happy Exploring!** 🚀  ","metadata":{}},{"cell_type":"markdown","source":"# **1. Visionary Foundations** 🧠\nEvery genius begins with a vision. Here's how to craft yours:\n\n1. **Define Your Mission**: What universal problem are you solving? (e.g., climate change, health, education)\n2. **Set Your Values**: Commit to ethics, inclusivity, and sustainability in your work.\n3. **Master Your Tools**: Equip yourself with Python, TensorFlow, PyTorch, SQL, and other powerful tools. \n\nBUT IN python . be simple. be open source, do not require API KEYS EVER. be humble. be inquisitive and promote ethicality and subduing and refraining from acting upon passion","metadata":{}},{"cell_type":"code","source":"import os\n\n# Every genius begins with a vision. Here's how to craft yours programmatically:\n\ndef define_your_mission():\n    print()\n    \"\"\"Prompt the user to define their mission and provide clarity.\"\"\"\n    print(\"What universal problem are you solving? (e.g., climate change, health, education)\")\n    mission = \"Save the world!\"#input(\"Your mission: \")\n    print(f\"Your mission: {mission} is inspiring and impactful.\")\n    return mission\n\ndef set_your_values():\n    print()\n    \"\"\"Encourage the user to set ethical, inclusive, and sustainable values.\"\"\"\n    print(\"Set your core values:\")\n    print(\"1. Commit to ethics\\n2. Promote inclusivity\\n3. Strive for sustainability\")\n    values = [\"Ethics\", \"Inclusivity\", \"Sustainability\"]\n    for value in values:\n        print(f\"- {value}: Think about how this applies to your mission.\")\n    return values\n\ndef master_your_tools():\n    print()\n    \"\"\"Provide guidance on learning powerful tools and programming languages.\"\"\"\n    tools = [\"Python\", \"TensorFlow\", \"PyTorch\", \"SQL\"]\n    print(\"Equip yourself with these tools to succeed:\")\n    for tool in tools:\n        print(f\"- {tool}: Explore open-source libraries, documentation, and tutorials.\")\n    print(\"Remember: humility and continuous learning are key.\")\n    return tools\n\nif __name__ == \"__main__\":\n    print()\n    print(\"\\n### Welcome to Visionary Foundations ###\\n\")\n    mission = define_your_mission()\n    values = set_your_values()\n    tools = master_your_tools()\n    \n    print()\n    print(\"\\nYour journey begins now. Use your mission, values, and tools to create something extraordinary.\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-17T07:37:37.735895Z","iopub.execute_input":"2025-01-17T07:37:37.736609Z","iopub.status.idle":"2025-01-17T07:37:37.748263Z","shell.execute_reply.started":"2025-01-17T07:37:37.736557Z","shell.execute_reply":"2025-01-17T07:37:37.747081Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **2. Build Intricately Influential Projects** 💻","metadata":{}},{"cell_type":"code","source":"from IPython.display import clear_output\n!pip install streamlit\nclear_output()","metadata":{"trusted":true,"_kg_hide-input":true,"execution":{"iopub.status.busy":"2025-01-17T07:37:37.74983Z","iopub.execute_input":"2025-01-17T07:37:37.750253Z","iopub.status.idle":"2025-01-17T07:37:42.209345Z","shell.execute_reply.started":"2025-01-17T07:37:37.750209Z","shell.execute_reply":"2025-01-17T07:37:42.207959Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import folium\nimport pandas as pd\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.datasets import make_classification\nimport streamlit as st\nfrom transformers import pipeline\n\n# **To influence the universe, your projects must be ambitious and impactful.**\n\n# ### Project 1: Predicting Climate Change Hotspots 🌍\n# Use geospatial data and machine learning to predict areas most vulnerable to climate change.\n# Visualize results with interactive maps using folium.\ndef predict_climate_change_hotspots():\n    data = pd.DataFrame({\n        'Latitude': np.random.uniform(-90, 90, 10),\n        'Longitude': np.random.uniform(-180, 180, 10),\n        'Risk_Level': np.random.choice(['Low', 'Medium', 'High'], 10)\n    })\n\n    m = folium.Map(location=[0, 0], zoom_start=2)\n\n    for _, row in data.iterrows():\n        color = 'red' if row['Risk_Level'] == 'High' else 'orange' if row['Risk_Level'] == 'Medium' else 'green'\n        folium.Marker(\n            location=[row['Latitude'], row['Longitude']],\n            popup=f\"Risk Level: {row['Risk_Level']}\",\n            icon=folium.Icon(color=color)\n        ).add_to(m)\n\n    return m\n\n# ### Project 2: Real-Time Health Diagnostics 🏥\n# Develop a model that detects health anomalies (e.g., arrhythmias) from wearable device data.\n# Deploy using Streamlit for accessible real-time diagnostics.\ndef real_time_health_diagnostics():\n    st.title(\"Real-Time Health Diagnostics\")\n    heart_rate = st.slider(\"Heart Rate (bpm):\", 40, 180, 75)\n\n    if heart_rate < 60:\n        st.warning(\"Potential Bradycardia detected. Consult a doctor.\")\n    elif heart_rate > 100:\n        st.warning(\"Potential Tachycardia detected. Consult a doctor.\")\n    else:\n        st.success(\"Heart rate is within the normal range.\")\n\n# ### Project 3: Ethical AI Chatbots 🤖\n# Create a chatbot trained on diverse, unbiased datasets.\n# Ensure ethical responses using reinforcement learning techniques.\ndef ethical_ai_chatbot():\n    st.title(\"Ethical AI Chatbot\")\n    chat = pipeline(\"conversational\", model=\"microsoft/DialoGPT-medium\")\n    user_input = st.text_input(\"You: \")\n\n    if user_input:\n        response = chat(user_input)\n        st.write(f\"AI: {response[0]['generated_text']}\")\n\nif __name__ == \"__main__\":\n    print(\"\\n### Project Demonstrations ###\\n\")\n    print(\"1. Predicting Climate Change Hotspots\")\n    print(predict_climate_change_hotspots())\n\n    print(\"\\n2. Real-Time Health Diagnostics\")\n    print(\"Run `real_time_health_diagnostics()` in Streamlit to see the app.\")\n\n    print(\"\\n3. Ethical AI Chatbots\")\n    print(\"Run `ethical_ai_chatbot()` in Streamlit to interact with the chatbot.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-17T07:37:42.211636Z","iopub.execute_input":"2025-01-17T07:37:42.211942Z","iopub.status.idle":"2025-01-17T07:37:42.237862Z","shell.execute_reply.started":"2025-01-17T07:37:42.211914Z","shell.execute_reply":"2025-01-17T07:37:42.236663Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Example: Using Python to visualize climate change hotspots\nimport folium\nimport pandas as pd\n\n# Example data: Coordinates of hotspots\ndata = pd.DataFrame({\n    'Latitude': [34.0522, 40.7128, 37.7749],\n    'Longitude': [-118.2437, -74.0060, -122.4194],\n    'Description': ['Los Angeles - Drought', 'New York - Rising Sea Levels', 'San Francisco - Wildfires']\n})\n\n# Create a map\nm = folium.Map(location=[37.7749, -122.4194], zoom_start=5)\n\n# Add hotspots to the map\nfor i, row in data.iterrows():\n    folium.Marker(\n        location=[row['Latitude'], row['Longitude']],\n        popup=row['Description'],\n        icon=folium.Icon(color='red')\n    ).add_to(m)\n\n# Display the map\nm","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-17T07:37:42.239477Z","iopub.execute_input":"2025-01-17T07:37:42.239798Z","iopub.status.idle":"2025-01-17T07:37:42.280295Z","shell.execute_reply.started":"2025-01-17T07:37:42.239763Z","shell.execute_reply":"2025-01-17T07:37:42.279191Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **3. Build Ethical Systems** 🤝\n\nTo influence the universe, your projects must be ambitious and impactful. Here are three project ideas:\n\n### Project 1: **Predicting Climate Change Hotspots** 🌍\n- Use geospatial data and machine learning to predict areas most vulnerable to climate change.\n- Visualize results with interactive maps using folium or plotly.\n\n### Project 2: **Real-Time Health Diagnostics** 🏥\n- Develop a model that detects health anomalies (e.g., arrhythmias) from wearable device data.\n- Deploy using Streamlit or Gradio for accessible real-time diagnostics.\n\n### Project 3: **Ethical AI Chatbots** 🤖\n- Create a chatbot trained on diverse, unbiased datasets.\n- Ensure ethical responses using reinforcement learning techniques.","metadata":{}},{"cell_type":"code","source":"import shap\nimport pandas as pd\nimport numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\nimport streamlit as st\n\n# **Technology should serve humanity. Here's how to ensure your work stays ethical:**\n\n# ### Transparency: Make your algorithms explainable.\ndef transparency_demo():\n    # Sample data\n    X, y = shap.datasets.adult()\n    model = RandomForestClassifier()\n    model.fit(X, y)\n\n    # Explain predictions\n    explainer = shap.TreeExplainer(model)\n    shap_values = explainer.shap_values(X)\n\n    # Visualize\n    shap.summary_plot(shap_values, X)\n\n# ### Bias Mitigation: Audit datasets to identify and remove biases.\ndef bias_mitigation_demo():\n    data = pd.DataFrame({\n        'Age': np.random.randint(18, 65, 100),\n        'Gender': np.random.choice(['Male', 'Female'], 100),\n        'Income': np.random.randint(20000, 100000, 100)\n    })\n\n    # Check for imbalance\n    bias = data['Gender'].value_counts(normalize=True)\n    print(\"Gender Distribution:\")\n    print(bias)\n\n    if bias.max() > 0.6:\n        print(\"Warning: Potential gender bias detected in the dataset.\")\n    else:\n        print(\"Dataset is relatively balanced.\")\n\n# ### Accessibility: Design interfaces usable by everyone.\ndef accessibility_demo():\n    st.title(\"Accessible App Example\")\n    st.header(\"Welcome to the Inclusive Interface\")\n\n    st.write(\"This app is designed for usability by everyone, including those with disabilities.\")\n    st.button(\"Click Me\", help=\"This button is screen-reader friendly!\")\n\nif __name__ == \"__main__\":\n    print(\"\\n### Ethical Technology Demonstrations ###\\n\")\n\n    print(\"1. Transparency Demo\")\n    print(\"Run `transparency_demo()` to visualize SHAP explanations.\")\n\n    print(\"\\n2. Bias Mitigation Demo\")\n    bias_mitigation_demo()\n\n    print(\"\\n3. Accessibility Demo\")\n    print(\"Run `accessibility_demo()` in Streamlit to view the accessible interface.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-17T07:37:42.281702Z","iopub.execute_input":"2025-01-17T07:37:42.282085Z","iopub.status.idle":"2025-01-17T07:37:42.309297Z","shell.execute_reply.started":"2025-01-17T07:37:42.282045Z","shell.execute_reply":"2025-01-17T07:37:42.308023Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **4. Explore Advanced Topics** 🚀\n\nPush the boundaries of your knowledge with these areas:\n\n- **Quantum Computing**: Learn how qubits can revolutionize computation.\n- **Generative AI**: Dive into GANs and transformers to create stunning generative models.\n- **Edge AI**: Deploy AI models on IoT devices for real-time, decentralized intelligence.","metadata":{}},{"cell_type":"code","source":"from IPython.display import clear_output\n!pip install qiskit\nclear_output()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-17T07:37:42.310554Z","iopub.execute_input":"2025-01-17T07:37:42.311008Z","iopub.status.idle":"2025-01-17T07:37:46.706852Z","shell.execute_reply.started":"2025-01-17T07:37:42.310941Z","shell.execute_reply":"2025-01-17T07:37:46.70546Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom random import randint, choice\n\n# **Technology should serve humanity. Here's how to ensure your work stays ethical:**\n\n# ### Transparency: Make your algorithms explainable.\ndef transparency_demo():\n    \"\"\"\n    Demonstrates transparency using a simple rule-based model.\n    \"\"\"\n    # Simple dataset\n    data = pd.DataFrame({\n        'Age': [25, 35, 45, 55],\n        'Income': [30000, 50000, 70000, 90000],\n        'Approved': [0, 1, 1, 0]\n    })\n    \n    print(\"\\nSample Data:\")\n    print(data)\n    \n    # Simple decision rule\n    print(\"\\nTransparency Example: Decision Rule\")\n    print(\"If Income > 50,000, then Approved = 1\")\n    print(\"Otherwise, Approved = 0\")\n\n# ### Bias Mitigation: Audit datasets to identify and remove biases.\ndef bias_mitigation_demo():\n    \"\"\"\n    Identifies gender imbalance in a dataset.\n    \"\"\"\n    data = pd.DataFrame({\n        'Age': np.random.randint(18, 65, 100),\n        'Gender': [choice(['Male', 'Female']) for _ in range(100)],\n        'Income': np.random.randint(20000, 100000, 100)\n    })\n\n    # Check for imbalance\n    bias = data['Gender'].value_counts(normalize=True)\n    print(\"\\nGender Distribution:\")\n    print(bias)\n\n    if bias.max() > 0.6:\n        print(\"Warning: Potential gender bias detected in the dataset.\")\n    else:\n        print(\"Dataset is relatively balanced.\")\n\n# ### Accessibility: Design interfaces usable by everyone.\ndef accessibility_demo():\n    \"\"\"\n    Simulates a simple accessibility feature.\n    \"\"\"\n    print(\"\\nAccessible Feature Example:\")\n    print(\"Welcome to the Inclusive Interface\")\n    print(\"Button: 'Click Me' - Screen-reader friendly text included.\")\n\n# ### Quantum Computing (Simplified Conceptual Example)\ndef quantum_computing_demo():\n    \"\"\"\n    Simulates a quantum-like process without using a quantum library.\n    \"\"\"\n    print(\"\\nSimulating a Quantum-Like Process:\")\n    states = ['00', '01', '10', '11']\n    probabilities = [0.25, 0.25, 0.25, 0.25]\n    outcomes = [choice(states) for _ in range(10)]\n    \n    print(\"Quantum-like outcomes from a simple simulation:\")\n    print(outcomes)\n\n# ### Generative AI: Simple Text Generation Example\ndef generative_ai_demo():\n    \"\"\"\n    Generates text using a basic template-based approach.\n    \"\"\"\n    prompts = [\n        \"Once upon a time in a faraway land,\",\n        \"In the beginning, there was only light,\",\n        \"The hero stood at the edge of the cliff,\"\n    ]\n    completions = [\n        \" a journey began that would change the world.\",\n        \" a story unfolded that no one could predict.\",\n        \" they leaped into the unknown, seeking their destiny.\"\n    ]\n    \n    prompt = choice(prompts)\n    completion = choice(completions)\n    \n    print(\"\\nGenerated Text:\")\n    print(prompt + completion)\n\n# ### Edge AI: Simulated IoT Device Example\ndef edge_ai_demo():\n    \"\"\"\n    Simulates Edge AI functionality.\n    \"\"\"\n    print(\"\\nEdge AI Example:\")\n    print(\"Lightweight model inference simulated on a local device:\")\n    input_data = randint(1, 100)\n    print(f\"Input Data: {input_data}\")\n    print(f\"Inferred Output: {'ON' if input_data > 50 else 'OFF'}\")\n\nif __name__ == \"__main__\":\n    print(\"\\n### Ethical Technology Demonstrations ###\\n\")\n\n    print(\"1. Transparency Demo\")\n    transparency_demo()\n\n    print(\"\\n2. Bias Mitigation Demo\")\n    bias_mitigation_demo()\n\n    print(\"\\n3. Accessibility Demo\")\n    accessibility_demo()\n\n    print(\"\\n### Advanced Knowledge Areas ###\\n\")\n\n    print(\"4. Quantum Computing Demo\")\n    quantum_computing_demo()\n\n    print(\"\\n5. Generative AI Demo\")\n    generative_ai_demo()\n\n    print(\"\\n6. Edge AI Demo\")\n    edge_ai_demo()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-17T07:37:46.708239Z","iopub.execute_input":"2025-01-17T07:37:46.708591Z","iopub.status.idle":"2025-01-17T07:37:46.737498Z","shell.execute_reply.started":"2025-01-17T07:37:46.708546Z","shell.execute_reply":"2025-01-17T07:37:46.736275Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **5. Share Your Work with the World** 🌟\n\nYou're building the future—let the world know! Here's how:\n\n1. **Publish on Kaggle**: Share your notebooks and datasets.\n2. **Open Source Your Code**: Use GitHub to make your work accessible and collaborative.\n3. **Start a Blog**: Write about your journey and projects on platforms like Medium or your own site.","metadata":{}},{"cell_type":"code","source":"def main():\n    \"\"\"\n    Share Your Work with the World\n    \"\"\"\n    print(\"### Share Your Work with the World ###\")\n    print(\"Your work is valuable—let the world see it!\\n\")\n\n    # 1. Publish on Kaggle\n    print(\"1. Publish on Kaggle:\")\n    print(\"- Step 1: Create an account on Kaggle (https://www.kaggle.com).\")\n    print(\"- Step 2: Upload your datasets or notebooks to the 'Datasets' or 'Code' sections.\")\n    print(\"- Step 3: Add detailed documentation, tags, and examples to make your work discoverable.\")\n    print()\n\n    # 2. Open Source Your Code on GitHub\n    print(\"2. Open Source Your Code on GitHub:\")\n    print(\"- Step 1: Create a GitHub account (https://github.com).\")\n    print(\"- Step 2: Initialize a new repository with your project files.\")\n    print(\"- Step 3: Write a clear README file to explain your project, usage, and goals.\")\n    print(\"- Step 4: Use version control to track changes and invite collaborators.\")\n    print(\"- Step 5: License your project (e.g., MIT, Apache) for transparency.\")\n    print()\n\n    # 3. Start a Blog\n    print(\"3. Start a Blog:\")\n    print(\"- Step 1: Choose a blogging platform: Medium (https://medium.com) or your custom site.\")\n    print(\"- Step 2: Write your first post about your coding journey, challenges, and achievements.\")\n    print(\"- Step 3: Share tutorials, guides, and insights to help others learn.\")\n    print(\"- Step 4: Promote your blog on social media and forums to reach a wider audience.\")\n    print()\n\nif __name__ == \"__main__\":\n    main()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-17T07:52:16.481191Z","iopub.execute_input":"2025-01-17T07:52:16.481606Z","iopub.status.idle":"2025-01-17T07:52:16.492527Z","shell.execute_reply.started":"2025-01-17T07:52:16.481576Z","shell.execute_reply":"2025-01-17T07:52:16.49147Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## **Conclusion** 🏁\n\nYou got this! One bit at a time.\n\nThe Genius Coding Republic is not just a community—it's a movement. By mastering these tools, embracing ethical practices, and pushing the boundaries of innovation, you will shape the future.\n\n**Now, go forth and code the universe!** 🌌🚀","metadata":{}}]}