CodSoft Data Science Internship Projects

Welcome to my collection of data science projects completed during my internship at CodSoft. Each project focuses on building machine learning models to solve real-world problems using various datasets.

Table of Contents

1. Iris Flower Classification
2. Sales Prediction
3. Fraudulent Credit Card Transactions
4. Movie Rating Prediction
5. Titanic Survival Prediction

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Iris Flower Classification

Overview

This project utilizes the Iris dataset to classify iris flowers into three species based on sepal and petal measurements. The dataset includes features such as sepal_length, sepal_width, petal_length, petal_width, and species.

Technologies Used

• Python
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn

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Sales Prediction

Overview

In this project, a machine learning model predicts future sales based on factors like advertising expenditures across different platforms (TV, Radio, and Newspaper). The goal is to optimize advertising strategies.

Technologies Used

• Python
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn

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Fraudulent Credit Card Transactions

Overview

This project involves building a model to identify fraudulent credit card transactions. The dataset includes various features such as transaction time and amounts, while handling class imbalance is critical in this task.

Technologies Used

• Python
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn

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Movie Rating Prediction

Overview

This project aims to predict movie ratings based on features like genre, director, and actors. The goal is to analyze historical movie data to develop an accurate rating estimation model.

Technologies Used

• Python
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn

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Titanic Survival Prediction

Overview

This classic beginner project utilizes the Titanic dataset to build a model that predicts whether a passenger survived the disaster. The dataset contains information about passengers, such as their age, gender, ticket class, fare, cabin, and survival status.

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Technologies Used

• Python
• Pandas
• Scikit-learn
• Matplotlib
• Seaborn

Model Approach

1. Data Exploration: Analyze the dataset to understand the features.
2. Data Preprocessing: Handle missing values and convert categorical variables.
3. Feature Engineering: Select relevant features for model training.
4. Model Training: Implement machine learning algorithms to predict survival.
5. Model Evaluation: Assess the model's performance using accuracy, precision, and recall.

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Conclusion

These projects demonstrate the application of data science techniques and machine learning algorithms to solve various problems. Each project is a step towards mastering data analysis and predictive modeling.

Feel free to explore each project in detail and reach out if you have any questions!

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License

This project is licensed under the MIT License - see the LICENSE file for details.