Python Learning Repository

This repository documents my comprehensive journey through learning Python programming, data science, and machine learning concepts. It includes code, notebooks, and projects covering fundamental Python skills, data manipulation, visualization, machine learning algorithms, and practical applications.

What I've Learned

This repository covers a wide range of topics organized into the following sections:

Python Fundamentals

• Part 1-5: Basic syntax, data types, control structures, functions, and advanced concepts like sets, dictionaries, tuples, lists, and exercises.

Data Handling

• NUMPY & PANDAS: Array operations, data manipulation, and analysis with NumPy and Pandas libraries.

Data Collection & Web Scraping

• Techniques for collecting data, web scraping with Python, and data cleaning processes.

Data Visualization

• Part 1 & 2: Creating plots, bar charts, scatter plots, pie charts, histograms, box plots, stack plots, and using Seaborn for advanced visualizations.

Mathematics for AI

• Understanding distributions and mathematical foundations relevant to AI.

Supervised Machine Learning

• Parts 1-6: Linear regression, logistic regression, Lasso regression, Naive Bayes, k-Nearest Neighbors (kNN), Decision Trees, Support Vector Machines (SVM), Random Forest, Gradient Boosting, AdaBoost, XGBoost, and ensemble methods like Voting and Stacking.
• Projects include house price prediction, employee turnover analysis, loan approval systems, and more.

Unsupervised Learning

• Parts 1-2: K-Means clustering, hierarchical clustering, DBSCAN, PCA (Principal Component Analysis), anomaly detection, and isolation forests.

Time Series Forecasting

• Forecasting techniques using Python, with examples on sales data.

Projects

• SmartCart Clustering Project: Customer segmentation using clustering algorithms.
• CreditWise Loan System: Loan approval prediction model.
• NovaGen Project: Advanced ML application.

How to Download

To download and explore this repository:

1. Clone the Repository:

   git clone https://github.com/your-username/your-repo-name.git
   

   Replace your-username and your-repo-name with your actual GitHub username and repository name.

2. Navigate to the Directory:

   cd your-repo-name
   

3. Prerequisites:

   • Python 3.x installed
   • Jupyter Notebook or JupyterLab for running .ipynb files
   • Required libraries: Install via pip:

     pip install numpy pandas matplotlib seaborn scikit-learn jupyter
     

     Additional libraries may be needed for specific notebooks (e.g., for web scraping: requests, beautifulsoup4).

4. Running the Notebooks:

   • Launch Jupyter:

     jupyter notebook
     

   • Open any .ipynb file and run the cells to see the code in action.

Repository Structure

• 1_PythonFundamentals(Part 1)/ to 5_PythonFundamentals(Part 5)/: Python basics
• 6_DATA/, 7_NUMPY/, 8_PANDAS(Part 1)/, 9_PANDAS(Part 2)/: Data handling
• 10_DataCollection/, 12_WebScraping/: Data collection techniques
• 13_DataVisualization(Part 1)/, 14_DataVisualization(Part 2)/: Visualization tutorials
• 15_MathForAI(Part 1)/: Math concepts for AI
• 16_SupervisedML(Part 1 , 2 , 3)/ to 21_SupervisedML(Part6)/: ML algorithms
• 22_UnsupervisedLearning(Part 1 ,2)/: Unsupervised learning
• 23_TimeSeriesForecastingInPython/: Time series analysis
• 17_Scratch_Implementations/: From-scratch algorithm implementations
• 18_CreditWiseLoanSystem/, 24_SmartCartClusteringProject/: Practical projects

Feel free to explore, learn, and contribute! If you have questions or suggestions, open an issue or pull request.