This repository contains my mentorness internship codes and project resources.

--> Python is a high-level, general-purpose, and very popular programming language.

--> Python programming language (latest Python 3) is being used in web development, Machine Learning applications, along with all cutting-edge technology in Software Industry.

--> Python is available across widely used platforms like Windows, Linux, and macOS.

--> The biggest strength of Python is huge collection of standard library.

--> Colaboratory, or “Colab” for short, is a product from Google Research which allows anybody to write and execute python code in Jupyter notebook through the browser.

--> Visit colab at: 

--> Create account using google account.

--> Once account creation is done, we can directly start coding in colab.

--> It supports Python and R.

--> Files are directly saved in Google Drive.

--> To install python library this command is used-

pip install library_name 

Complete Description about the project and resources used.

• My article delves into the world of Hyperparameter Tuning.
  
  
• It offers a clear explanation of this crucial process in machine learning, detailing how fine-tuning these parameters can significantly boost model performance.
  
  
• I've covered various techniques, providing practical insights and examples to help readers understand and implement them effectively.

• In this project I made a streamlit website in which you can apply multiple supervised learning algorithm on Customer churn dataset.
  
  
• A multipage streamlit application is made which shows all stages of ml pipeline.
  
  
• I also did Data Visualization to show the working of this algorithms on the dataset.
  
  
• I have deployed this website using streamlit.
  
  
• Visit Website from : Customer Churn Prediction

• Data Visualization is the presentation of data in pictorial format.
  
  
• Target was to see the performance analysis and variations using data visualization.
  
  
• In this project visualization of CSV file containing data of players is done in python.
  
  
• Data visualization is done to analyze performance of team and players.
  
  
• Patterns found in the analysis are listed.

Short Description about all libraries used in Project.

• Pandas (Panel Data/ Python Data Analysis) - This library is mostly used for analyzing, cleaning, exploring, and manipulating data.
• Matplotlib - It is a data visualization and graphical plotting library.
• Seaborn - It is an extension of Matplotlib library used to create more attractive and informative statistical graphics.
• Streamlit - It is a Python library that makes it easy to create and share web apps for machine learning and data science projects.

Drop a 🌟 if you find this repository useful.

If you have any doubts or suggestions, feel free to reach me.

📫 How to reach me: