ML Learning Journey 🚀

This repository contains my machine learning learning journey and will be continuously updated as I learn and implement new concepts.

📚 Learning Topics Covered

📥 Data Gathering

• From API - Fetching data from REST APIs
• Web Scraping - Extracting data from websites
• From CSV - Working with CSV files

🔧 Data Preprocessing

• Numerical Data Preprocessing - Preprocessing numerical datasets
• Handle Missing Values - Techniques for dealing with missing data
• Handle Imbalanced Data - Methods for handling imbalanced datasets
• Text Data Preprocessing - Text cleaning and feature extraction
• Text Data Preprocessing 2 - Advanced text processing techniques

⚙️ Feature Engineering

Feature Scaling

• Standardization - Z-score normalization (mean=0, std=1)
• Normalization - Min-Max scaling

Encode Categorical Data

• One Hot Encoding - Converting categorical to binary columns
• Ordinal & Label Encoding - Converting categories to integers

Encoding Numerical Data

• Binarization - Converting numerical data to binary
• Discretization - Binning continuous variables

Transformers

• Column Transformer - Apply different transformations to different columns
• Function Transformer - Custom transformations using functions
• Power Transformer - Box-Cox and Yeo-Johnson transformations

Pipelines

• Titanic Without Pipeline - Manual preprocessing steps
• Titanic With Pipeline - Using sklearn Pipeline
• Predict Without Pipeline - Manual prediction workflow
• Predict With Pipeline - Prediction using Pipeline

🤖 Machine Learning Projects

Classification

• Diabetes Prediction - Binary classification for diabetes prediction
• Sleep Disorder Prediction - Multi-class classification for sleep disorders
• Fake News Detection - NLP classification for fake news
• Wine Quality Prediction - Multi-class classification for wine quality
• Sonar Rocks vs Mine - Binary classification using sonar data
• Loan Status Prediction - Predicting loan approval status

Regression

• House Price Prediction - Regression model for house prices
• House Price Prediction 2 - Advanced regression techniques
• Car Price Prediction - Regression model for car prices
• Gold Price Prediction - Time series prediction for gold prices

Exploratory Data Analysis

• Pokemon Data Analysis - Comprehensive EDA on Pokemon dataset

️ Technologies Used

• Python - Primary programming language
• pandas - Data manipulation and analysis
• scikit-learn - Machine learning algorithms and preprocessing
• numpy - Numerical computations
• matplotlib/seaborn - Data visualization
• requests/BeautifulSoup - Web scraping and API calls

📁 Structure

├── README.md
├── requirements.txt
├── data_gathering/
│   ├── from_api.ipynb
│   ├── web_scrapping.ipynb
│   └── with_csv.ipynb
├── data_preprocessing/
│   ├── numerical_ds_preprocessing.ipynb
│   ├── handle_missing_values.ipynb
│   ├── handle_imbalanced_dp.ipynb
│   ├── text_ds_preprocessing.ipynb
│   └── text_ds_preprocessing2.ipynb
├── feature engineering/
│   ├── feature scaling/
│   │   ├── standaization.ipynb
│   │   └── normalization.ipynb
│   ├── encode categorical data/
│   │   ├── one_hot_encoding.ipynb
│   │   └── ordinal_and_label_encoding.ipynb
│   ├── encoding numerical data/
│   │   ├── binarization.ipynb
│   │   └── discritization.ipynb
│   ├── transformer/
│   │   ├── column_transformer.ipynb
│   │   ├── function_transformer.ipynb
│   │   └── power_transformer.ipynb
│   └── pipelines/
│       ├── titanic_without_pipeline.ipynb
│       ├── titanic_with_pipeline.ipynb
│       ├── predict_without_pipeline.ipynb
│       └── predict_with_pipeline.ipynb
└── projects/
    ├── diabeties_prediction.ipynb
    ├── Sleep Disorder Prediction.ipynb
    ├── fake_news_prediction.ipynb
    ├── wine_quality_prediction.ipynb
    ├── sonar_rocks_vs_mine_predition.ipynb
    ├── loan_status_prediction.ipynb
    ├── house_price_prediction.ipynb
    ├── house_price_prediction2.ipynb
    ├── price_card_prediction.ipynb
    ├── gold_price_prediction.ipynb
    └── pokemon.ipynb

🚀 Getting Started

1. Clone the repository
2. Install dependencies: pip install -r requirements.txt
3. Open any notebook to explore the learning materials

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This repository represents my ongoing journey in machine learning.