Data Science and Machine Learning Basics

This repository serves as a beginner-friendly introduction to data science and machine learning. The content is organized into Jupyter Notebooks covering various topics.

Notebooks

1. Python Basics 1.ipynb

   • Perform basic Python operations.

2. Python Basics 2.ipynb

   • Perform basic Python operations using Python data structures.

3. Numpy.ipynb

   • Perform basic array operations using the NumPy library.

4. Pandas.ipynb

   • Perform operations on series and dataframes using pandas.

5. Data Visualization.ipynb

   • Read raw data and visualize them using various visualization techniques.

6. PreProcessing.ipynb

   • Load data, analyze, rescale, and transform the data.

7. Classification.ipynb

   • Perform classification using Decision Tree, Naive Bayes, SVM, and KNN algorithms.

8. Clustering.ipynb

   • Perform clustering on raw data using k-means algorithm and DBSCAN.

9. Spark_Wordcount.ipynb

   • Use Spark and Hadoop with map reduce techniques to obtain word count from big data.

Extra - Simple Neural Network Implementation

A Python demonstration of a simple neural network as an educational introduction to the fundamental functions of a neural network

Prerequisites

Make sure you have Jupyter Notebook installed to run the notebooks. Additionally, install required Python libraries using:

pip install numpy pandas matplotlib seaborn scikit-learn findspark