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https://github.com/bhawesh-agrawal/randomsonggenerator

A fun project that generates random song
https://github.com/bhawesh-agrawal/randomsonggenerator

fun machine-learning model python

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A fun project that generates random song

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README 

        
If you’d like to try this project yourself, follow these steps to run the Jupyter Notebook (`.ipynb`) on your local machine.



Steps to Run:



1. **Install Required Dependencies:**

   - Make sure you have Python installed. If you don't, download and install it from [python.org](https://www.python.org/).

   - You’ll also need `pip` to install the necessary libraries. Most systems come with `pip` pre-installed.



2. **Clone the GitHub Repository:**

   - Clone the repository containing the `.ipynb` file to your local machine.

     ```bash

     git clone https://github.com/Bhawesh-Agrawal/randomsonggenerator

     cd your-repository

     ```



3. **Set Up a Virtual Environment (optional but recommended):**

   - It’s good practice to create a virtual environment for your project to keep dependencies isolated.

     ```bash

     python -m venv venv

     source venv/bin/activate  # On Windows use `venv\Scripts\activate`

     ```



4. **Install Required Python Libraries:**

   - Install `numpy`:

     ```bash

     pip install numpy 

     ```



5. **Launch Jupyter Notebook:**

   - Start Jupyter Notebook using the following command:

     ```bash

     jupyter notebook

     ```

   - This will open Jupyter in your web browser. Navigate to the project folder and open the `.ipynb` file.



6. **Run the Notebook:**

   - Once the notebook is open, you can execute the cells step by step. This will walk you through the entire process,

     from loading the data to generating random text based on the Markov Chain model.



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Markov Chain-Based Text Generation Project



As I embark on my journey into Machine Learning and Data Science, one of the first projects I’ve worked on is a simple yet fascinating model based on Markov Chains.

It’s a foundational concept often used in stochastic processes and is a great way to understand probabilistic models.



What is a Markov Chain?

A Markov Chain is a mathematical model that helps us predict the probability of a future event based on the current event. It follows a principle called the Markov Property,

which means that the next state depends only on the current state, not on the sequence of events that preceded it. 

In simple terms, it is a "memoryless" process.



For example, imagine you have a sequence of words in a sentence. By using a Markov Chain, we can predict the next word in the sequence based on the current word,

relying on how often certain words follow each other in the given data.



The Idea Behind My Project

In this project, I’ve applied the concept of Markov Chains to generate random text — in this case, a random song. Here’s how it works:



Input Data (Training Phase):



We start with a collection of sentences or lyrics as input data. The model goes through each sentence, word by word, and records which word follows which.

Building a Transition Matrix (Dictionary):



The model creates a dictionary where each word is a key, and the values associated with that key are the words that follow it in the input data. For example:

python

Copy code

{

  "I": ["am", "love", "will"],

  "am": ["happy", "excited"],

  "love": ["you", "this"],

  ...

}

Then, it calculates the probability distribution of the words that follow a given word, based on their frequency of occurrence.

Prediction (Generation Phase):



Starting with a random word, the model suggests the next word based on the learned probabilities. For instance,

if the word "love" has a 70% chance of being followed by "you" and a 30% chance of being followed by "this," the model will predict the next word accordingly.

Generating a Random Song:



The model continues predicting one word after another, chaining them together until a complete "song" is generated. 

The outcome may not always be meaningful, especially with small datasets, but the randomness can lead to fun and creative outputs.

Example:

Let’s say the input data consists of sentences like these:



"I love you"

"I am happy"

"You are my sunshine"

"I will always love this song"

After training on this small dataset, the model might generate something like:



"I love this song"

"You are my happy sunshine"

"I will always love you"

The beauty of this project lies in how it uses randomness and learned probabilities to produce unpredictable, yet coherent text based on the given dataset.



What’s Next?

While this project is a great start, there’s room for improvement:



Larger Datasets: With more data, the model will produce more realistic and meaningful results.

Tuning: Adjusting the order of the Markov Chain (e.g., considering two or more previous words instead of just one) can make the predictions more accurate.

Applications: Beyond songs, this technique can be extended to generate creative writing, mimic speech patterns, or even help with tasks like predictive text.

This project is a simple yet effective introduction to the world of machine learning, providing hands-on experience with probabilistic models and stochastic processes.