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https://github.com/girish119628/codsoft
Data Enthusiast | Predictive Modeler | Turning Insights into Strategies
https://github.com/girish119628/codsoft
cross-validation data-cleaning-and-preprocessing exploratory-data-analysis model-selection-and-evaluation
Last synced: about 1 month ago
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Data Enthusiast | Predictive Modeler | Turning Insights into Strategies
- Host: GitHub
- URL: https://github.com/girish119628/codsoft
- Owner: girish119628
- Created: 2024-10-28T18:01:42.000Z (2 months ago)
- Default Branch: main
- Last Pushed: 2024-11-15T16:06:09.000Z (about 2 months ago)
- Last Synced: 2024-11-15T17:19:51.443Z (about 2 months ago)
- Topics: cross-validation, data-cleaning-and-preprocessing, exploratory-data-analysis, model-selection-and-evaluation
- Language: Jupyter Notebook
- Homepage:
- Size: 1.16 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Codsoft - Machine Learning Model Repository
---
## Introduction
A collection of machine learning projects developed as part of the Codsoft Data Science initiative. This repository includes projects ranging from predictive analytics to classification models.
---
# Model 1: MOVIE RATING PREDICTION
- Dataset:[Click Here](https://www.kaggle.com/datasets/adrianmcmahon/imdb-india-movies)
- The Movie Rating Prediction project estimates movie ratings based on features like genre, director, and actors. By analyzing historical data, it reveals key factors that influence ratings, providing insights into audience and critic preferences.
---
# Model 2: IRIS FLOWER CLASSIFICATION
- Dataset: [Click Here](https://www.kaggle.com/datasets/arshid/iris-flower-dataset)
- The Iris Classification project aims to classify iris flowers into three species—setosa, versicolor, and virginica—based on sepal and petal measurements. Using the widely recognized Iris dataset, this model demonstrates fundamental classification techniques for distinguishing between species accurately.
---
# Model 3: SALES PREDICTION
- Dataset: [Click Here](https://www.kaggle.com/code/ashydv/sales-prediction-simple-linear-regression/input)
- The Sales Prediction project focuses on forecasting product demand by analyzing factors like advertising spend, target audience, and platform choice. By applying machine learning techniques in Python, this model helps businesses optimize advertising strategies and maximize sales potential.
---
# Model 4: TITANIC SURVIVAL PREDICTION
- Dataset: [Click Here](https://www.kaggle.com/datasets/yasserh/titanic-dataset)
- The Titanic Survival Prediction project uses passenger data—such as age, gender, and ticket class—to predict survival outcomes. This classic dataset allows for a beginner-friendly exploration of classification techniques to determine factors influencing passenger survival.
---
## General Information
## Tools and Skills
- **Languages**: Python
- **Libraries**: NumPy, Pandas, Scikit-Learn, etc
- **Other Tools**: Jupyter Notebook, Google Colab, VS Code, Git, GitHub.
---
## Features and Techniques
- **Feature Engineering**: Data cleaning, normalization/Standardization
- **Modeling Techniques**: Linear Regression, Random Forest, Decision tree, Classification, etc.
- **Evaluation Metrics**: Accuracy, F1-score, MSE, MAE, R2, etc
---
## Contact Information
For questions or collaboration, feel free to reach out:
Girish Kumar
Email: [email protected]
GitHub: https://github.com/girish119628