Kick off your coding day with a groovy 1970s jazz playlist, infused with a positive morning coffee vibe and stunning ocean views from a retro beachside room. Let the smooth saxophone and funky beats lift your spirits as you dive into Day 66 of the DailyAIWizard Python for AI series! 🚀 Join Anastasia (our main moderator), Irene, Isabella (back from vacation), Ethan, Sophia, and Olivia as we build a K-Nearest Neighbors classifier for the AI Insight Hub app’s flower classifier, building on Days 62–65. Sophia leads two complex demos with Iris, Ethan drops flirty, hilarious code explanations, and Olivia adds spicy tips. Support our crew with a coffee at PayPal.me/DailyAIWizard! Perfect for beginners! 💻 Get ready for Day 67: K-Means Clustering—get excited for clustering magic! Subscribe, like, and share your ai_iris_knn.py output in the comments! Connect with us on Discord, X, or Instagram (@DailyAIWizard) for more AI and jazz vibes. Code the Future, Wizards! 🌟
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1970s jazz, retro jazz, morning coffee, ocean views, vintage vibe, jazz playlist, positive music, coffee time, 70s music, smooth jazz, beachside jazz, funky jazz, relaxation music, morning vibes, instrumental jazz, Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, TensorFlow, Neural Networks, AI Programming, Tech Tutorial, Python 3, Coding Journey, VS Code, Beginner Programming, Machine Learning, Data Science, DailyAIWizard, Code the Future
Timestamps:
00:00 K Nearest Neighbor
01:55 Why KNN?
04:10 What is KNN?
08:49 Apps
20:52 Libraries
23:05 Challenge
pay a coffee: https://www.paypal.com/pool/9j2tp7IvP...
#PythonForAI #LearnPython #AICoding #DailyAIWizard
Hashtags:
#Python #LearnPython #PythonForAI #AICoding #PythonTutorial #CodingForBeginners #ScikitLearn #Datasets #AIProgramming #TechTutorial #MachineLearning #DailyAIWizard #CodeTheFuture
Tags:
Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, Scikit-learn, Datasets, AI Programming, Tech Tutorial, Python 3, Coding Journey, VS Code, Beginner Programming, Machine Learning, Data Science, DailyAIWizard, Code the Future
1970s jazz, retro jazz, morning coffee, ocean views, vintage vibe, jazz playlist, positive music, coffee time, 70s music, smooth jazz, beachside jazz, funky jazz, relaxation music, morning vibes, instrumental jazz, Python, Learn Python, Python for AI, AI Coding, Python Tutorial, Coding for Beginners, TensorFlow, Neural Networks, AI Programming, Tech Tutorial, Python 3, Coding Journey, VS Code, Beginner Programming, Machine Learning, Data Science, DailyAIWizard, Code the Future
Timestamps:
00:00 K Nearest Neighbor
01:55 Why KNN?
04:10 What is KNN?
08:49 Apps
20:52 Libraries
23:05 Challenge
Category
📚
LearningTranscript
00:00Hey, sexy wizards! Anastasia here, your main moderator, ready to charm you on Day 66 of Daily AI Wizards Python for AI series.
00:10Isabella's back, joining Irene and Sophia.
00:13After random forests in Day 65, we're building K&N classifiers for our AI Insight Hub apps, Flower Classifier.
00:22Support our crew with a coffee at our PayPal link below.
00:25Ethan, what's thrilling about K&N?
00:27Sophia, how does it build on Day 65's app?
00:31Hello, brilliant wizards. I'm Irene, thrilled to guide with Isabella back, extending Sophia's demos.
00:39K&N classifies using Neighbors. Our demos will make your AI skills sparkle. Love our vibe? Buy us a coffee!
00:49Wizards, I'm Isabella, back and excited to extend Irene and Sophia.
00:54K&N powers the app's Flower Classifier with intuitive neighbor voting. Let's make it shine in our demos. Support us via PayPal.
01:06Yo, wizards. Ethan's here, dropping spicy K&N code with a wink for Sophia.
01:13Neighbor voting is gonna pop. Let's crank this AI party to 11.
01:17Coffee via PayPal keeps us coding.
01:20Sophia here, Ethan, and your charms got me blushing.
01:25I'm pumped to lead our app component demos and make K&N sizzle.
01:30Let's classify AI flowers, wizards. Support us with a coffee.
01:39Olivia here, darlings. I'll sprinkle flirty tips, ask Anastasia questions, and chat with Ethan to keep your K&N learning hot.
01:49Ready to neighbor, wizards? Buy us a coffee via PayPal.
01:52Wizards, K&N is your AI neighbor crush, darling. It classifies by closest neighbors.
02:02Irene, can you explain distance metrics?
02:05Sophia, how does it enhance the app from day 65?
02:09K&N classifies data intuitively for AI apps like AI Insight Hub.
02:15It's simple yet effective.
02:17Our demos will show how to build the flower classifier component.
02:22Irene's right, wizards. K&N uses neighbor voting, making it interpretable.
02:28It's perfect for our app's classifier. Our demos will guide you.
02:33Thanks, Ethan and Sophia. K&N makes AI so sexy and intuitive.
02:38Get ready to neighbor, wizards, and prep for Day 67's K-means.
02:42Wizards, today we're seducing you with Python's K&N magic.
02:45You'll master data prep, K&N fitting, classification, and app integration with spicy demos to make you swoon.
02:52Sophia, what's the app focus? Ethan, any code highlights?
02:56Sophia's leading app component demos with fiery energy.
03:00Ethan's dropping hilarious code explanations, and Olivia's adding flirty tips.
03:05Isabella's extending our guidance. You'll master K&N and prep for Day 67.
03:11Irene's spot on.
03:13I'm thrilled to extend Sophia's demos and Irene's insights, guiding you through K&N's role in the AI Insight Hub app.
03:23Get ready for a thrilling challenge.
03:26Wizards, meet your Day 66 dream team.
03:30Anastasia's our main moderator with flirty charm.
03:33I'm guiding with warmth, and Isabella's back extending Sophia and me.
03:38Ethan's our code comedian, flirting with Sophia.
03:41Irene, it's great to be back. I'm excited to extend you and Sophia, guiding wizards through K&N's power for the AI Insight Hub app.
03:51Our demos will make your skills shine.
03:55Oh, Irene, Isabella, you're gems.
03:57Sophia's leading app demos with passion, Ethan's stealing my heart with code, and Olivia's tossing flirty tips.
04:03We're here to make you K&N superstars.
04:11Wizards, K&N is your AI neighbor crush, darling.
04:14It classifies based on closest training examples.
04:18Ethan, can you explain K-value?
04:20Sophia, how does it fit the app from Day 62-65?
04:24Anastasia, you make neighbors sound so hot.
04:27How does K&N improve classification in the app, love?
04:31Ethan, what's your take on K&N in Python?
04:35Oh, Olivia, you tease.
04:37K&N classifies AI data lazily.
04:40Ethan, Sophia, jump in with details.
04:43Anastasia, Olivia, K&N's like a hot neighbor, classifying AI with flair.
04:50It's a K&N party.
04:51Let's drop this code beat for wizards.
04:55Yo, wizards, metric equals, Euclidean, computes distance like a hot measure for Sophia, finding AI neighbors.
05:02It's a distance party.
05:04Let's drop this code beat and measure some data.
05:08You're measuring my heart, Ethan.
05:11Wizards, distance metrics like Euclidean find neighbors for the app.
05:17Try it in our demo.
05:18It's like calculating closeness with passion.
05:21Wizards, n underscore neighbors equals 5 selects K like a hot choice for Sophia, balancing AI accuracy.
05:30It's a K party.
05:32Let's drop this code beat and choose some neighbors.
05:36You're choosing my heart, Ethan.
05:39Wizards, K value balances noise and bias for the app.
05:44Try it in our demo.
05:45It's like selecting with passion.
05:49Wizards, K neighbors classifier.
05:51Dot fit, X, Y, fits the model like a hot memory for Sophia, storing AI data.
05:58It's a fitting party.
05:59Let's drop this code beat and neighbor some classes.
06:03You're memorizing my heart, Ethan.
06:07Wizards, fitting K&N stores patterns for the app.
06:11Try it in our demo.
06:13It's like remembering neighbors with passion.
06:17Wizards, model dot predict, X underscore test, predict species like a hot neighbor vote for Sophia, powering AI apps.
06:26It's a prediction party.
06:28Let's drop this code beat and see the classes.
06:31You're voting my heart, Ethan.
06:35Wizards, predictions power the app's flower classifier.
06:39Try it in our demo.
06:41It's like neighboring with passion.
06:45Wizards, accuracy underscore score, Y underscore test, Y underscore pred, measures accuracy like a hot score for Sophia, checking AI classification.
06:54It's an accuracy party.
06:57Let's drop this code beat.
07:00You're scoring my heart, Ethan.
07:03Wizards, accuracy evaluates the app's classifier.
07:07Try it in our demo.
07:10It's like measuring success with passion.
07:14Wizards, precision underscore score, and recall underscore score, measure precision and recall like hot metrics for Sophia, evaluating AI.
07:23It's a metrics party.
07:24Let's drop this code beat.
07:27You're evaluating my heart, Ethan.
07:31Wizards, precision and recall assess the app's classifier.
07:35Try it in our demo.
07:37It's like fine-tuning with passion.
07:41Wizards, sns.heatmap, confusion underscore matrix, visualizes confusion like a sexy matrix for Sophia, showing AI errors.
07:51It's a visualization party.
07:53Let's drop this code beat.
07:56You're matrixing my heart, Ethan.
07:59Wizards, confusion matrices show KNN errors for the app.
08:03Try it in our demo.
08:06It's like mapping success with passion.
08:10Wizards, plt.scatter, x underscore test, colon, 0, x underscore test, colon, 1, c equals y underscore pred, visualizes predictions like a hot plot for Sophia, showing AI classes.
08:25It's a visualization party.
08:27Let's drop this code beat.
08:30You're plotting my heart, Ethan.
08:32Wizards, visualizing predictions shows app classification.
08:38Try it in our demo.
08:40It's like painting classes with passion.
08:48Wizards, it's demo time, and I'm thrilled to lead two complex app component demos.
08:53We'll build the flower classifier with KNN and Streamlit for AI Insight Hub, continuing from days 62 to 65.
09:02Get your Python setup ready, and let's make AI shine.
09:07Oh, Sophia, you're making my heart race.
09:11Ensure Python, VS Code, Pandas, NumPy, Matplotlib, Seaborn, SickItLearn, and Streamlit are set up.
09:18Wizards, and OpenDay's 6265 app files to continue.
09:22Ethan and Olivia will spice it up.
09:25Let's neighbor, cuties.
09:27Wizards, let's prep to continue the app from days 62 to 65.
09:32Open VS Code, LoadDay's 6265 app files, updated-app-house-price.py, app-iris-classifier.py, updated-app-iris-dt.py, updated-app-iris-rf.py, create-iris-knn-demo.py, and updated-app-iris-knn.py, and save in a Python demo.
10:02Run PIP install Pandas, NumPy, Matplotlib, Seaborn, SickItLearn, Streamlit, JobLib to ensure libraries are ready.
10:11Sophia, you make continuations sound so dreamy.
10:16How do wizards build on days 62 to 65's app, like pros love?
10:22Ethan, what's your take on app continuation?
10:26You're too sweet, Olivia.
10:28Start by importing days 62 to 65's model, add KNN code, and run streamlit run updated-app-iris-knn.py to see the updated app.
10:41Let's make these demos sparkle.
10:46Anastasia, Olivia, app continuations like a hot SQL, building on days 62 to 65 for AI Insight Hub.
10:54It's a party starter, let's drop this code beat.
10:57Wizards, our first demo in iris-knn-demo.py builds a KNN classifier for iris.
11:07We'll load data, pre-process, engineer features, fit, predict, evaluate, and visualize.
11:14Let's run this and see neighbor magic.
11:19Oh, Sophia, you're makin' this demo hot.
11:23KNN neighbors classifier, .fit, and sns.heatmap, classify flowers with swagger, total neighbor party.
11:32Wizards, load underscore iris, loads iris data like a love letter to Sophia, prepping for AI neighbors.
11:39It's a loading party, let's drop this code beat and start classifying.
11:45You're loading my heart, Ethan.
11:48Wizards, loading iris sets the stage for KNN classification.
11:53Try it in our demo, it's like inviting data to the AI party.
11:59Wizards, standard scaler, .fit underscore transform, pre-processes iris like a hot transformation for Sophia, scaling AI features.
12:09It's a pre-processing party, let's drop this code beat and ready the data.
12:15You're transforming my heart, Ethan.
12:18Wizards, pre-processing iris ensures accurate KNN classification.
12:24Try it in our demo, it's like polishing data with passion.
12:28Wizards, df, pedal underscore ratio, equals df, pedal length, cm, slash df, pedal width, cm, engineers features like a hot creation for Sophia, boosting AI KNN.
12:44It's an engineering party, let's drop this code beat.
12:48You're engineering my heart, Ethan.
12:52Wizards, feature engineering enhances KNN classification for the app.
12:57Try it in our demo, it's like crafting features with passion.
13:03Wizards, KNEighborsClassifier, .fit, x underscore train, y underscore train, fits the model like a hot memory for Sophia, storing AI patterns.
13:14It's a fitting party, let's drop this code beat and neighbor some classes.
13:18You're memorizing my heart, Ethan.
13:23Wizards, fitting KNN stores patterns for the apps classifier.
13:29Try it in our demo, it's like remembering neighbors with passion.
13:34Wizards, model dot predict, x underscore test, predict species like a hot neighbor vote for Sophia, powering AI Insight Hub.
13:43It's a prediction party, let's drop this code beat and see the classes.
13:48You're voting my heart, Ethan.
13:52Wizards, predictions power the apps flower classifier.
13:57Try it in our demo, it's like neighboring with passion.
14:02Wizards, accuracy underscore score, y underscore test, y underscore pred, measures accuracy like a hot score for Sophia, checking AI classification.
14:12It's an accuracy party, let's drop this code beat and evaluate some results.
14:18You're scoring my heart, Ethan.
14:22Wizards, accuracy evaluates the apps classifier performance.
14:27Try it in our demo, it's like measuring success with passion.
14:33Wizards, precision underscore score, and recall underscore score, measure precision and recall like hot metrics for Sophia, evaluating AI classification.
14:42It's a metrics party, let's drop this code beat and fine tune.
14:48You're evaluating my heart, Ethan.
14:52Wizards, precision and recall assess the app's classifier quality.
14:57Try it in our demo, it's like fine tuning with passion.
15:00Wizards, SNS.heatmap, confusion underscore matrix, visualizes confusion like a sexy matrix for Sophia, showing AI errors.
15:12It's a visualization party, let's drop this code beat and map results.
15:16You're matrixing my heart, Ethan.
15:21Wizards, confusion matrices reveal KNN errors for the app.
15:26Try it in our demo, it's like mapping success with passion.
15:30Wizards, plt.scatter, x underscore test, colon, 0, x underscore test, colon, 1, c equals y underscore pred, visualizes predictions like a hot plot for Sophia, showing AI classes.
15:48It's a visualization party, let's drop this code beat.
15:52You're plotting my heart, Ethan.
15:56Wizards, visualizing predictions shows app classification results.
16:01Try it in our demo, it's like painting classes with passion.
16:05Wizards, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing, visualizing
17:05Wizards, our second demo in updated underscore app underscore iris underscore knn.py updates the days 62 to 65 app with knn.
17:17We'll add knn classifier, evaluation, and UI.
17:22Let's run this and see app magic.
17:26Oh, Sophia, you're making this demo hot.
17:29K-Neighbors classifier, .fit, and stwrite, f-accuracy, accuracy, .2f, update the app with swagger, total neighbor party.
17:41Wizards, import Streamlit as STI sets up the app like a hot interface for Sophia, powering AI Insight Hub.
17:48It's a Streamlit party.
17:50Let's drop this code beat and design.
17:52You're interfacing my heart, Ethan.
17:57Wizards, Streamlit builds the app's UI for knn classification.
18:02Try it in our demo.
18:04It's like designing with passion.
18:07Wizards, ST Slider, captures user input like a hot choice for Sophia, enabling app classification.
18:14It's an input party.
18:16Let's drop this code beat and interact.
18:20You're choosing my heart, Ethan.
18:23Wizards, user input powers the app's interactivity for knn.
18:28Try it in our demo.
18:30It's like engaging users with passion.
18:33Wizards, after demos, let's discuss app predictions.
18:37Model.predict, input, scaled, classifies with knn in Streamlit like a sexy forecast.
18:44Powering AI Insight Hub's flower classifier.
18:47App predictions use knn to deliver real-time results.
18:52They make AI Insight Hub interactive.
18:55Use them to engage users with accurate classifications.
19:00Oh, Anastasia.
19:02Predictions are so hot.
19:04They fine-tune the app's interactivity.
19:07Wizards, try app predictions in your challenge to classify like pros.
19:11Wizards, joblib.dump, model, amodel.pkl, saves the knn model like a sexy archive, enabling app reuse.
19:21It's a saving party for AI Insight Hub.
19:24Saving models ensures app portability.
19:28Use joblib to store trained knn models.
19:31Integrate them into Streamlit for seamless classifications.
19:35Oh, Anastasia, saving so hot.
19:39It fine-tunes app efficiency.
19:42Wizards, try saving models in your challenge to reuse like pros.
19:46Viewers, try guidance.
19:47Make sure to shut down.
21:12It's like building AI with passion.
21:17Wizards, Standard Scaler, pre-processes data like a hot transformation for Sophia, scaling AI features.
21:25It's a pre-processing party.
21:26Let's drop this code beat and ready the data.
21:30You're transforming my heart, Ethan.
21:33Wizards, Sikkit-Learn pre-processing ensures accurate KNN for the app.
21:38Try it in our demo. It's like polishing data with passion.
21:45Wizards, optimize KNN with proper case selection, distance metrics, and feature scaling.
21:52Use Sikkit-Learn to ensure robust AI models for top performance in your app.
21:57Irene's right wizards. Choose K via cross-validation. Scale features to avoid bias.
22:05These practices make your app's classifier effective. Apply them in your challenge.
22:09Optimized KNN's so sexy, Irene Isabella. Clear practices make AI classification irresistible.
22:17Practice for Day 67's K-Means clustering, Wizards, and keep that code sizzling.
22:23Wizards, KNN fits AI pipelines for classification tasks.
22:29Leveraging neighbor voting for simplicity. It's effective.
22:33Your skills are ready for Day 67's K-Means clustering.
22:38Irene's spot on.
22:40KNN integrates data prep and classification, ensuring efficient workflows.
22:46Use it in your app for reliable predictions.
22:49Oh, Irene. Isabella, KNN's critical in AI pipelines, darling.
22:54It classifies sexily. Your Day 66 skills make AI irresistible.
23:00Classify like pros.
23:06Wizards, here's your challenge.
23:08Create AI by Iris, Howman and PY to load Iris, pre-process, engineer features, fit KNN, predict,
23:16evaluate with accuracy, visualize with Matplotlib, and build a streamlit app component.
23:21Run with Python 3 AI iris crocana not PY and share on Instagram, darlings.
23:28This is thrilling, Wizards.
23:30Try KNEighbors classifier, accuracy score, and Steet slider.
23:35Show us your results at atdailyaiwizard.
23:38It's an AI KNN spell.
23:41Prep for Day 67's K-Means clustering.
23:44Wizards, hit subscribe, like this video, and share your AI iris K-N-PY output in the comments.
23:52Got KNN questions?
23:54We're here to help.
23:55Join our Discord or X to connect, grow, and keep the AI magic flowing.
24:00Irene's right.
24:03Share your KNN wins and connect with us on Discord, X, or Daily AI Wizard on Instagram.
24:11Your classification skills are shining.
24:14Get ready for Day 67's K-Means clustering adventure.
24:18Our community's a total heartthrob, Wizards.
24:21Post your code, flirt with tips, or share wins on Instagram.
24:25Buy us a coffee at paypal.me, Daily AI Wizard, to fuel our AI vibe.
24:30Subscribe for Day 67's K-Means cuties.
24:33Wizards, you've stolen my heart with your KNN skills.
24:37Your iriscandemo.py and updated app iris, ja, knn.py, prove your AI superstars.
24:44Buy us a coffee at paypal.me, Daily AI Wizard, and get hyped for Day 67's K-Means clustering.
24:52I'm so proud, Wizards.
24:55You've mastered KNN for AI Insight Hub.
24:58Share your AI iris knn.py on at Daily AI Wizard.
25:03Subscribe for Day 67's K-Means adventure and join our Discord or X.
25:09Code the future, Wizards.
25:11You nailed KNN, Wizards.
25:13Your neighbors are hot, Sophia.
25:17Get pumped for Day 67's K-Means clustering.
25:20Let's keep this flirty AI party rockin' with more code.
25:25Wizards, you're phenomenal.
25:28These app demos were a blast, Ethan, and your KNN skills are fire.
25:34Share your AI underscore iris underscore knn.py and subscribe for Day 67's K-Means magic.
25:40Code the future, Wizards.
25:45You've swept me off my feet, Wizards.
25:48Your KNN skills are pure AI seduction.
25:51Let's flirt with K-Means in Day 67.
25:54Keep coding sexy and get excited for more.
25:57Thank you, Wizards.
25:58Thank you, Wizards.
25:59Thank you, Wizards.
25:59Thank you, Wizards.
26:00Thank you, Wizards.
26:00Thank you, Wizards.
26:01Thank you, Wizards.
26:01Thank you, Wizards.
26:02Thank you, Wizards.
26:02Thank you, Wizards.
26:03Thank you, Wizards.
26:03Thank you, Wizards.
26:04Thank you, Wizards.
26:04Thank you, Wizards.
26:05Thank you, Wizards.
26:06Thank you, Wizards.
26:06Thank you, Wizards.
26:07Thank you, Wizards.
26:08Thank you, Wizards.
26:09Thank you, Wizards.
26:10Thank you, Wizards.
26:11Thank you, Wizards.
26:12Thank you, Wizards.
26:13Thank you, Wizards.
26:14Thank you, Wizards.
26:15Thank you, Wizards.
26:16Thank you, Wizards.
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