Welcome to my GitHub repository! Here, you will find my solutions and code for various Kaggle competitions that I have participated in. As a current expert in Kaggle, I have worked extensively on multiple competitions, utilizing advanced techniques in data analysis, machine learning, and deep learning to achieve competitive results. Below are some of the competitions I have actively participated in, along with links to my solutions:
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Clone the Repository: Clone the Kaggle Elite Coder repository to your local machine using the following command:
git clone https://github.com/shivam6862/Kaggle-Elite-Coder.git -
Install Dependencies: Ensure you have Python installed on your system. Navigate to the project directory and run the following command to install the required packages:
pip install -r requirements.txt
In this competition, I applied advanced regression techniques to predict house prices based on various features. My solution achieved a competitive score of 0.12061.
The Spaceship Titanic competition challenged participants to predict survival rates on a galactic spaceship. I successfully tackled this problem and achieved a score of 0.7933.
In this natural language processing competition, I focused on analyzing disaster-related tweets. By employing advanced NLP techniques, I achieved a score of 0.79190, showcasing my expertise in text analysis and classification.
The Titanic competition involved predicting survival outcomes based on passenger information. Leveraging machine learning techniques, my solution achieved a score of 0.80622, demonstrating my ability to handle classic machine learning problems effectively.
Digit Recognizer is a high-performance deep learning model for handwritten digit classification. Achieving an impressive accuracy score of 0.9876, it showcases the effectiveness of convolutional neural networks and artificial neural networks. This project, available on Kaggle, offers a powerful solution for accurately identifying and classifying digits, demonstrating state-of-the-art performance in image recognition tasks.
Pet Classify Hub is an application designed for classifying images of pets, specifically cats and dogs. This project utilizes a user-friendly web interface built with Next.js for the frontend and a backend server powered by Flask. The classification models are implemented using TensorFlow, Convolutional Neural Network (CNN) models.
To learn more about my Kaggle expertise, you can visit my Kaggle profile and follow my activity:
- Kaggle Profile: Shivam's Kaggle Profile
- Follow My Kaggle Activity: Click on the "Follow" button on my Kaggle profile page to receive updates about my competitions, kernels, and datasets.
Once you have cloned the repository and installed the dependencies, you can explore the Kaggle competition solutions and related code files. Each competition solution is organized into its respective folder, allowing you to delve into specific projects easily.
Feel free to analyze the code, run the notebooks, and explore the solutions provided. If you encounter any issues or have questions, don't hesitate to reach out.
Happy coding! 🚀🚀