💡 Python: Simple K-Means Clustering Project

K-Means is a popular unsupervised machine learning algorithm used to partition n observations into k clusters, where each observation belongs to the cluster with the nearest mean (centroid). This simple project demonstrates K-Means on the classic Iris dataset using scikit-learn to group similar flower species based on their measurements.

import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import numpy as np

# 1. Load the Iris dataset
iris = load_iris()
X = iris.data # Features (sepal length, sepal width, petal length, petal width)
y = iris.target # True labels (0, 1, 2 for different species) - not used by KMeans

# 2. (Optional but recommended) Scale the features
# K-Means is sensitive to the scale of features
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

# 3. Define and train the K-Means model
# We know there are 3 species in Iris, so we set n_clusters=3
kmeans = KMeans(n_clusters=3, random_state=42, n_init=10) # n_init is important for robust results
kmeans.fit(X_scaled)

# 4. Get the cluster assignments for each data point
labels = kmeans.labels_

# 5. Get the coordinates of the cluster centroids
centroids = kmeans.cluster_centers_

# 6. Visualize the clusters (using first two features for simplicity)
plt.figure(figsize=(8, 6))

# Plot each cluster
colors = ['red', 'green', 'blue']
for i in range(3):
    plt.scatter(X_scaled[labels == i, 0], X_scaled[labels == i, 1],
                s=50, c=colors[i], label=f'Cluster {i+1}', alpha=0.7)

# Plot the centroids
plt.scatter(centroids[:, 0], centroids[:, 1],
            s=200, marker='X', c='black', label='Centroids', edgecolor='white')

plt.title('K-Means Clustering on Iris Dataset (Scaled Features)')
plt.xlabel('Scaled Sepal Length')
plt.ylabel('Scaled Sepal Width')
plt.legend()
plt.grid(True)
plt.show()

# You can also compare with true labels (for evaluation, not part of clustering process itself)
# print("True labels:", y)
# print("K-Means labels:", labels)

Code explanation: This script loads the Iris dataset, scales its features using StandardScaler, and then applies KMeans to group the data into 3 clusters. It visualizes the resulting clusters and their centroids using a scatter plot with the first two scaled features.

#Python #MachineLearning #KMeans #Clustering #DataScience

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By: @DataScienceM ✨

📌 Using Claude Skills with Neo4j

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A hands-on exploration of Claude Skills and their potential applications in Neo4j

📌 Water Cooler Small Talk, Ep. 9: What “Thinking” and “Reasoning” Really Mean in AI and LLMs

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Understanding how AI models “reason” and why it’s not what humans do when we think

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🕒 Date: 2024-05-31 | ⏱️ Read time: 7 min read

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🧠 Quiz: What is the primary objective of data mining?

A) To physically store large volumes of data
B) To discover patterns, trends, and useful insights from large datasets
C) To design and implement database management systems
D) To encrypt and secure sensitive data

✅ Correct answer: B

Explanation: Data mining is a process used to extract valuable, previously unknown patterns, trends, and knowledge from large datasets. Its goal is to find actionable insights that can inform decision-making.

#DataMining #BigData #Analytics

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By: @DataScienceM ✨

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📌 What 10 Years at Uber, Meta and Startups Taught Me About Data Analytics

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Advice for Data Scientists and Managers

📌 Automating Data Pipelines with Python & GitHub Actions

🗂 Category: DATA ENGINEERING

🕒 Date: 2024-05-30 | ⏱️ Read time: 10 min read

A simple (and free) way to run data workflows

🤖🧠 Reflex: Build Full-Stack Web Apps in Pure Python — Fast, Flexible and Powerful

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📚 AI News & Trends

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#Reflex #FullStack #WebDevelopment #Python #OpenSource #WebApps

📌 Building a Rules Engine from First Principles

🗂 Category: ALGORITHMS

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How recasting propositional logic as sparse algebra leads to an elegant and efficient design

🤖🧠 MLOps Basics: A Complete Guide to Building, Deploying and Monitoring Machine Learning Models

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#MLOps #MachineLearning #DevOps #ModelDeployment #DataScience #ProductionAI

🤖🧠 MiniMax-M2: The Open-Source Revolution Powering Coding and Agentic Intelligence

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📚 AI News & Trends

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#MiniMaxM2 #OpenSource #MachineLearning #CodingAI #AgenticIntelligence #MixtureOfExperts

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Intro Organizations are increasingly investing in AI as these new tools are adopted in everyday…

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💡 Pandas Cheatsheet

A quick guide to essential Pandas operations for data manipulation, focusing on creating, selecting, filtering, and grouping data in a DataFrame.

1. Creating a DataFrame
The primary data structure in Pandas is the DataFrame. It's often created from a dictionary.

import pandas as pd

data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 32, 28],
        'City': ['New York', 'Paris', 'New York']}
df = pd.DataFrame(data)

print(df)
#       Name  Age       City
# 0    Alice   25   New York
# 1      Bob   32      Paris
# 2  Charlie   28   New York

• A dictionary is defined where keys become column names and values become the data in those columns. pd.DataFrame() converts it into a tabular structure.

2. Selecting Data with .loc and .iloc
Use .loc for label-based selection and .iloc for integer-position based selection.

# Select the first row by its integer position (0)
print(df.iloc[0])

# Select the row with index label 1 and only the 'Name' column
print(df.loc[1, 'Name'])

# Output for df.iloc[0]:
# Name       Alice
# Age           25
# City    New York
# Name: 0, dtype: object
#
# Output for df.loc[1, 'Name']:
# Bob

• .iloc[0] gets all data from the row at index position 0.
• .loc[1, 'Name'] gets the data at the intersection of index label 1 and column label 'Name'.

3. Filtering Data
Select subsets of data based on conditions.

# Select rows where Age is greater than 27
filtered_df = df[df['Age'] > 27]
print(filtered_df)
#       Name  Age       City
# 1      Bob   32      Paris
# 2  Charlie   28   New York

• The expression df['Age'] > 27 creates a boolean Series (True/False).
• Using this Series as an index df[...] returns only the rows where the value was True.

4. Grouping and Aggregating
The "group by" operation involves splitting data into groups, applying a function, and combining the results.

# Group by 'City' and calculate the mean age for each city
city_ages = df.groupby('City')['Age'].mean()
print(city_ages)
# City
# New York    26.5
# Paris       32.0
# Name: Age, dtype: float64

• .groupby('City') splits the DataFrame into groups based on unique city values.
• ['Age'].mean() then calculates the mean of the 'Age' column for each of these groups.

#Python #Pandas #DataAnalysis #DataScience #Programming

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By: @DataScienceM ✨