📚 Python Data Structures: Introduction | DarchumsTech

Machine Learning Basics with Python

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🚀 Introduction

Machine Learning teaches computers to learn from data without explicit programming. Python is the king of ML because it's simple, powerful, and has tons of ready-to-use libraries.

🛠️ Requirements

Python 3.x installed
Libraries: numpy, pandas, matplotlib, scikit-learn
Basic Python skills

pip install numpy pandas matplotlib scikit-learn

📥 Step 1: Load the Dataset

import numpy as np
import pandas as pd
from sklearn.datasets import load_iris

iris = load_iris()
X = iris.data
y = iris.target

🔍 Step 2: Explore the Data

df = pd.DataFrame(X, columns=iris.feature_names)
df['species'] = iris.target_names[y]
print(df.head())

📊 Step 3: Visualize with Matplotlib

import matplotlib.pyplot as plt

plt.scatter(df.iloc[:, 0], df.iloc[:, 1], c=y, cmap='plasma')
plt.xlabel(iris.feature_names[0])
plt.ylabel(iris.feature_names[1])
plt.title("Iris Flower Dataset")
plt.show()

✂️ Step 4: Split & Scale Data

from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test)

🤖 Step 5: Train the Model

from sklearn.neighbors import KNeighborsClassifier

model = KNeighborsClassifier(n_neighbors=5)
model.fit(X_train, y_train)

🧪 Step 6: Evaluate the Model

from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

y_pred = model.predict(X_test)

print("Accuracy:", accuracy_score(y_test, y_pred))
print("Confusion Matrix:\n", confusion_matrix(y_test, y_pred))
print("Report:\n", classification_report(y_test, y_pred))

📢 Note: Try adjusting n_neighbors to improve your model’s performance!

💡 Key ML Concepts

Supervised Learning: Learn with labeled data
Unsupervised Learning: Discover hidden patterns
Overfitting: Model too good on training, bad on real data
Underfitting: Model too simple, bad on both

🎯 Challenge:

Replace k-NN with a Decision Tree and compare accuracy!

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