ML-Algorithm-Implementations

This repository contains a collection of machine learning algorithm implementations designed to help beginners and intermediate users to understand and apply essential concepts. Each practical focuses on a specific topic, providing hands-on experience with real-world datasets and commonly used Python libraries such as NumPy, pandas, scikit-learn, Matplotlib, and TensorFlow/Keras. 🎯📘💻

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🎨📈🛠️Practicals Overview

Data Visualization and Exploration

1. 📂 Load Titanic dataset.
2. 📊 Create visualizations: histograms, scatter plots, box plots, and heatmaps.
3. 📈 Interpret data distributions and relationships. 

Linear Regression

1. 📊 Load Boston Housing dataset.
2. 📉 Implement simple linear regression and multiple linear regression using scikit-learn.
3. 📈 Evaluate the models using mean squared error and visualize the regression line.

Logistic Regression

1. 📊 Load a binary classification dataset i.e. Breast Cancer dataset.
2. 🧮 Implement logistic regression using scikit-learn.
3. 📊 Evaluate the model using accuracy, precision, recall, and plot the ROC curve.

Decision Trees and Random Forests

1. 📊 Load a classification dataset Iris dataset.
2. 🌳 Implement decision tree and random forest classifiers using scikit-learn.
3. 📋 Evaluate the models, visualize the decision trees, and analyze feature importance.

Support Vector Machines (SVM)

1. 📊 Load a binary classification dataset i.e. Iris dataset.
2. 📏 Implement SVM with different kernels (linear, polynomial, RBF) using scikit-learn.
3. 📉 Evaluate the models and visualize the decision boundaries.

Principal Component Analysis (PCA)

1. 📊 Load a high-dimensional dataset Wine dataset.
2. 📉 Implement PCA using scikit-learn.

K-means Clustering

1. 📊 Load a dataset suitable for clustering Customer Segmentation dataset. 🔍
2. 📊 Implement k-means clustering using scikit-learn. ⚙️
3. 📈 Determine the optimal number of clusters using the elbow method and visualize the clusters. 📊

Ensemble Methods

1. 📊 Load a dataset for classification Titanic dataset.
2. 🎯 Implement ensemble methods (Random Forest, AdaBoost, Gradient Boosting) using scikit-learn.
3. 📈 Compare their performance and visualize the results.

Artificial Neural Networks (ANN)

1. 📊 Load a classification dataset MNIST dataset.
2. 🧠 Build and train a simple feed-forward neural network using TensorFlow or Keras.
3. 📈 Evaluate the model and visualize the training process (loss and accuracy curves).

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🛠️🔧📦Installation and Setup

Clone the Repository

git clone https://github.com/Rishi52/ML-Algorithm-Implementations.git

Navigate to the Project Directory

cd ML-Algorithm-Implementations

Install Required Libraries

pip install -r requirements.txt

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📔💻📌Usage

Each practical is implemented as a Jupyter notebook. Follow these steps to explore the notebooks:

1. 📖 Start Jupyter Notebook

   jupyter notebook

2. 📘 Open a Notebook
   • Navigate to the notebook corresponding to the practical you want to explore. 

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📂📊📁Datasets

Most of the datasets are provided by scikit-learn which are not present you can find follow the links. The repository includes references to popular datasets from Kaggle like:

• 📂 Titanic Dataset
• 📂 Boston Housing Dataset
• 📂 Breast Cancer Dataset
• 📂 Iris Dataset
• 📂 Wine Dataset
• 📂 Customer Segmentation dataset
• 📂 MNIST dataset

Ensure to download these datasets from reliable sources like kaggle or scikit-learn if not provided.

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📚🔧🧰Key Libraries Used

• 📚 NumPy: Efficient numerical computations.
• 📊 Pandas: Data manipulation and analysis.
• 📈 Matplotlib & Seaborn: Data visualization tools.
• 🤖 Scikit-learn: Machine learning algorithms and evaluation metrics.
• 🧠 TensorFlow: Deep learning models and frameworks. 

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🤝✨📜Contribution

Contributions are welcome! If you’d like to enhance the repository or fix issues, feel free to:

1. 🌟 Fork this repository.
2. 🛠️ Make your changes.
3. 📬 Submit a pull request.

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📜⚖️📄License

This project is licensed under the MIT License. See the LICENSE file for more details.