Welcome to the Data Science Internship Projects repository! This repository contains a collection of data science projects I completed during my internship at Oasis Infobyte. Each project focuses on different aspects of data analysis, visualization, and machine learning.
In this project, I performed an analysis and classification of iris flower species using various machine learning algorithms. The famous Iris dataset was used to train the models, and their performance was evaluated on test data. The main goals of this project were to explore different classifiers and identify the best-performing model for iris flower classification.
Key Steps:
- Data preprocessing and exploration
- Model training using different classifiers
- Model evaluation using accuracy and other metrics
For this project, I analyzed and visualized unemployment rates using Python's data visualization libraries. The dataset provided valuable insights into unemployment trends, and various graphical representations were used to depict the patterns and fluctuations in unemployment rates across different states and time periods.
Key Steps:
- Data cleaning and handling missing values
- Data visualization using Seaborn and Matplotlib
In this project, I built a machine learning model to predict the prices of used cars. The dataset consisted of features related to various car attributes, and regression algorithms were used for training the model. The project's main objective was to create an accurate price prediction model for used cars.
Key Steps:
- Data preprocessing and feature engineering
- Model training using regression algorithms
- Model evaluation using Mean Squared Error and R-squared
For this project, I developed a machine learning model to classify emails as spam or not spam using the well-known SpamBase dataset. Different classifiers were tested, and the model's performance was assessed using multiple metrics to ensure reliable email classification.
Key Steps:
- Data preprocessing and feature extraction
- Model training using various classifiers
- Model evaluation using precision, recall, and F1-score
In this project, I created a machine learning model to predict product sales based on factors such as advertising cost, target audience, and platform. The dataset used was the Advertising dataset, and a linear regression model was implemented to achieve sales prediction.
Key Steps:
- Data preprocessing and feature selection
- Model training using linear regression
- Model evaluation using Mean Absolute Error and R-squared
Feel free to explore each project's directory for more detailed information, including code, data, and visualizations.
Please note that all projects were completed during my data science internship at Oasis Infobyte. The code and analysis presented here are solely for educational and demonstrational purposes.
If you have any questions or feedback, feel free to reach out to me. Happy coding!