🧠Python ML Projects

Machine Learning algorithms implemented from scratch & with Scikit-learn

A hands-on collection of ML implementations — from linear regression to time series forecasting — built to understand the math behind every algorithm.

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📚 What's Inside

Topic	Algorithms / Techniques	Notebook
Regression	Linear Regression, Polynomial Regression, Ridge, Lasso	regression/
Classification	Logistic Regression, KNN, Decision Tree, SVM	classification/
Clustering	K-Means, Hierarchical, DBSCAN	clustering/
Time Series	Moving Average, ARIMA, Trend Analysis	time_series/
Data Processing	EDA, Feature Engineering, Normalization	preprocessing/
Visualization	Matplotlib, Seaborn plots for every model	throughout

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🛠� Tech Stack

• Language: Python 3.10+
• Core Libraries: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn
• Environment: Jupyter Notebook

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🚀 Getting Started

1. Clone the repo

git clone https://github.com/Bhavan790/Python_ML.git
cd Python_ML

2. Install dependencies

pip install numpy pandas scikit-learn matplotlib seaborn jupyter

3. Launch Jupyter

jupyter notebook

Open any .ipynb file and run cells top to bottom.

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💡 Key Learnings

• How gradient descent works under the hood in linear models
• Why feature scaling matters — and when it doesn't
• The bias-variance tradeoff through practical examples
• Time series decomposition: trend, seasonality, and residuals
• Choosing the right clustering algorithm for different data shapes

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📊 Sample Output

Each notebook includes:

• Dataset loading + EDA
• Model training with explanations
• Evaluation metrics (MSE, accuracy, silhouette score)
• Visualizations of results

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🗺� Roadmap

• Regression algorithms
• Classification algorithms
• Clustering (K-Means, Hierarchical)
• Time series analysis
• Neural networks from scratch (NumPy only)
• Deep learning with TensorFlow/Keras
• End-to-end ML pipeline with deployment

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👨�💻 Author

Bhavan Kumar RT — B.E. Electrical & Electronics, Rajalakshmi Engineering College

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_{â­� Star this repo if it helped you understand ML better!}