This repository consists of the tasks which I have completed given during Data Science internship at CODSOFT
CODSOFT (https://www.codsoft.in/) are IT services and IT consultancy that specializes in creating innovative solutions for businesses. They are passionate about technology and believe in the power of software to transform the world. Their internship programs are just one of the ways in which they invest in the future of the industry.
Their Aim : To help students lacking basic skills by offering hands-on learning through live projects. They believe practical knowledge is the key to success in the tech industry
In this task, I have used the Titanic dataset to build a model that predicts whether a passenger on the Titanic survived or not. The dataset typically used for this project contains information about individual passengers, such as their age, gender, ticket class, fare, cabin, and whether or not they survived.
To see the implementation check this link - https://github.com/divyabharathynadar/CODSOFT/blob/main/Task%201/Titanic_task%201.ipynb
Here, I have builded a model that predicts the rating of a movie based on features like genre, director, and actors. Also, analyzed historical movie data and developed a model that accurately estimated the rating given to a movie by users or critics. Movie Rating Prediction project enabled me to explore data analysis, preprocessing, feature engineering, and machine learning modeling techniques. It provides insights into the factors that influenced movie ratings and allowed me to build a model that can estimate the ratings of movies accurately.
To see the implementation check this link - https://github.com/divyabharathynadar/CODSOFT/blob/main/Task%202/Movie%20Rating%20Prediction%20-%20Task%203%20.ipynb
The Iris flower dataset consists of three species: setosa, versicolor, and virginica which are distinguished based on their sepal and petal measurements. Here, I've trained a machine learning model that learn from these measurements and accurately classify the Iris flowers into their respective species.
To see the implementation click on this link - https://github.com/divyabharathynadar/CODSOFT/blob/main/TASK%203/IRIS%20flower%20Classification%20-%20Task%203.ipynb
Sales prediction involves forecasting the amount of a product that customers will purchase, taking into account various factors such as advertising expenditure, target audience segmentation, and advertising platform selection. Machine learning techniques are utilized in Python to analyze and interpret data, allowing to make informed decisions regarding advertising costs.
To see the implementation click on this link - https://github.com/divyabharathynadar/CODSOFT/blob/main/Task%204/Sales%20prediction%20Task%204.ipynb
Machine learning model that is builded identifies fraudulent credit card transactions. Here, the transaction data was preprocessed and normalizeed, class imbalance issues were handled, and dataset was splitted into training and testing sets. The model's performance was evaluated using metrics like precision, recall, F1-score, and techniques like oversampling or undersampling for improving results were considered.
To see the implementation click on this link - https://github.com/divyabharathynadar/CODSOFT/blob/main/Task%205/Credit%20card%20fraud%20detection%20Task%205.ipynb
- Special Thanks to CODSOFT for this Wonderful Internship Experience and Inspiration to do more.