A comprehensive repository of machine learning algorithms, data preprocessing techniques, and predictive models built using Python.
This repository contains my coursework, scripts, and hands-on projects completed during the Internshala Machine Learning training program. It demonstrates my practical understanding of Data Science concepts, ranging from data wrangling to complex algorithm deployment.
- Data Manipulation: NumPy, Pandas
- Data Visualization: Matplotlib, Seaborn
- Machine Learning: Scikit-Learn (Classification, Regression, Clustering)
- Environment: Jupyter Notebooks / Python Scripts
Here is a breakdown of the core concepts implemented in this repository:
- Data Preprocessing: Handling missing values, categorical encoding, and feature scaling.
- Supervised Learning:
- Linear Regression & Multiple Regression
- Logistic Regression
- Decision Trees & Random Forests
- K-Nearest Neighbors (KNN)
- Unsupervised Learning:
- K-Means Clustering
- Performance Metrics: Confusion matrices, accuracy, precision, recall, and F1-score evaluation.
To run these scripts/notebooks locally on your machine:
1. Clone the repository: ```bash git clone https://github.com/Mr-Raza-Alam/Machine_Learning.git cd Machine_Learning ```
2. Set up a virtual environment (Optional but Recommended): ```bash python -m venv venv source venv/bin/activate # On Windows use: venv\Scripts\activate ```
3. Install the required libraries: ```bash pip install numpy pandas scikit-learn matplotlib seaborn jupyter ```
4. Run the code:
Execute individual python scripts (python script_name.py) or start a Jupyter Notebook session:
```bash
jupyter notebook
```