Welcome to my repository containing the projects I completed during my Data Science and Business Analytics internship at The Sparks Foundation. These projects demonstrate my skills and expertise in solving real-world problems using various data science and machine learning techniques.
In this project, I developed a supervised machine learning model to predict scores based on the number of study hours. The goal was to analyze the relationship between study hours and scores and create a model that could accurately predict scores for future study hours. I utilized linear regression as the predictive model and evaluated its performance using appropriate metrics.
In this project, I focused on the classification of the Iris dataset using unsupervised machine learning techniques. The objective was to cluster the Iris flower samples based on their features, such as sepal length, sepal width, petal length, and petal width. I employed the K-means clustering algorithm to group the samples and visualized the results to gain insights into the underlying patterns.
Project 1/: Contains the code, dataset, and Jupyter Notebook for the prediction of scores using study hours project.Project 2/: Contains the code, dataset, and Jupyter Notebook for the classification of Iris dataset project.
Feel free to explore each project folder for more details, including the code implementations, data, and the Jupyter Notebooks with explanations and results.
To run the code and reproduce the results, you will need Python 3.x and the following libraries:
- NumPy
- Pandas
- Matplotlib
- Scikit-learn
Install the required libraries using pip or conda by running the following command: pip install numpy pandas matplotlib scikit-learn
I would like to express my gratitude to The Sparks Foundation for providing me with the opportunity to work on these projects. The internship has been instrumental in enhancing my skills and knowledge in the field of data science and business analytics.
Happy exploring! 🚀