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:
- Contribute and Discuss: Feel free to open issues 🐛, submit pull requests 🛠️, or start discussions 💬 to help improve this repository!
