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Machine Learning Algorithms

Overview

This repository contains implementations of various Machine Learning algorithms in Python. It provides well-structured, easy-to-understand code for common ML techniques, including supervised and unsupervised learning.

Features

  • Supervised Learning: Implements algorithms like Linear Regression, Logistic Regression, Decision Trees, and Support Vector Machines (SVM).
  • Unsupervised Learning: Includes clustering techniques like K-Means and Hierarchical Clustering.
  • Performance Evaluation: Utilizes metrics such as accuracy, precision, recall, and confusion matrices.
  • Data Preprocessing: Covers feature scaling, encoding, and handling missing values.
  • Visualization: Uses Matplotlib and Seaborn for data insights and model evaluation.

Dependencies

To install the required libraries, run:

pip install numpy pandas scikit-learn matplotlib seaborn

Code Breakdown

1. Importing Libraries

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import accuracy_score, classification_report

These libraries are used for data handling, model training, and performance evaluation.

2. Loading & Preprocessing Data

def load_and_preprocess_data(filepath):
    df = pd.read_csv(filepath)
    df.dropna(inplace=True)  # Handle missing values
    return df

Reads the dataset and removes missing values for cleaner data.

3. Implementing a Simple Model (Logistic Regression)

from sklearn.linear_model import LogisticRegression

def train_logistic_regression(X, y):
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
    model = LogisticRegression()
    model.fit(X_train, y_train)
    y_pred = model.predict(X_test)
    print(classification_report(y_test, y_pred))
    return model

Splits the data, trains a Logistic Regression model, and evaluates its performance.

4. Clustering Example (K-Means)

from sklearn.cluster import KMeans

def apply_kmeans(X, n_clusters=3):
    kmeans = KMeans(n_clusters=n_clusters, random_state=42)
    kmeans.fit(X)
    return kmeans.labels_

Applies K-Means clustering to group data into clusters.

Usage

  1. Clone the repository:
git clone https://github.com/Al-Scripting/Mechine-Learning-Alogrithms-
  1. Install dependencies:
pip install -r requirements.txt
  1. Run ML models:
python main.py

Contributing

Contributions are welcome! Feel free to open issues or pull requests.

License

This project is open-source and available under the MIT License.