https://github.com/lordmitrii/ml-and-ds-projects

Machine learning and Data science projects
https://github.com/lordmitrii/ml-and-ds-projects

catboost computer-vision data-science jupyter-notebook lightgbm machine-learning matplotlib numpy pandas python sc scikit-learn seaborn

Last synced: 3 months ago
JSON representation

Machine learning and Data science projects

• Host: GitHub
• URL: https://github.com/lordmitrii/ml-and-ds-projects
• Owner: lordmitrii
• Created: 2023-10-01T15:04:00.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-04-24T06:55:57.000Z (over 1 year ago)
• Last Synced: 2025-06-05T17:46:55.201Z (4 months ago)
• Topics: catboost, computer-vision, data-science, jupyter-notebook, lightgbm, machine-learning, matplotlib, numpy, pandas, python, sc, scikit-learn, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 18.2 MB
• Stars: 2
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Description of projects

| **Project**  | **Description**  | **Libraries** | **Skills** |

| :----------: | :-----------: | :-----: | :---------: |

| [Automation of cow quality assessment](https://github.com/inskyeee/Data_science/blob/main/01%20Automation%20of%20cow%20quality%20assessment.ipynb)    | Automating the calculation of a cow's approximate milk yield and predicting the taste of its milk | `Pandas` `Numpy` `Matplotlib` `Seaborn` `Scikit-learn` `Scipy` | `Machine Learning` `Data Analysis` `Regression` `Classification`|

| [Automation of personnel management](https://github.com/inskyeee/Data_science/blob/main/02%20Automation%20of%20personnel%20management.ipynb)     | Creating a model that can predict employee satisfaction levels and a model that can predict potential employee terminations | `Pandas` `Numpy` `Matplotlib` `Seaborn` `Scikit-learn` `Shap` `Phik` | `Machine Learning` `Data Analysis` `Regression` `Classification`|

| [Selecting a drilling location using a linear model](https://github.com/inskyeee/Data_science/blob/main/03%20Selecting%20a%20drilling%20location%20using%20a%20linear%20model.ipynb)| Creating a model to identify the most profitable and analysing possible profit and risks | `Pandas` `Numpy` `Matplotlib` `Seaborn` `Scikit-learn` | `Machine Learning` `Data Analysis` `Regression` `Bootstrap` |

| [Determining the cost of cars](https://github.com/inskyeee/Data_science/blob/main/04%20Determining%20the%20cost%20of%20cars.ipynb) | Creation of a model to determine the market value of the machine   | `Pandas` `Numpy` `Matplotlib` `Seaborn` `Scikit-learn` `Phik` `LightGBM` | `Machine Learning`  `Data Analysis` `Regression` `Gradient Boosting` |

| [Forecasting taxi orders](https://github.com/inskyeee/Data_science/blob/main/05%20Forecasting%20taxi%20orders.ipynb) | Creating a model to predict values based on time series | `Pandas` `Numpy` `Matplotlib` `Scikit-learn` `Statsmodel` `LightGBM` | `Machine Learning`  `Data Analysis` `Regression` `Gradient Boosting` `TimeSeries` |

| [Classification of text sentiment](https://github.com/inskyeee/Data_science/blob/main/06%20Classification%20of%20text%20sentiment.ipynb) | Creating a model to classify the toxicity of comments | `Pandas` `Numpy` `Matplotlib` `Scikit-learn` `LightGBM` `nltk` `tdqm`  `WordCloud` `SpaCy`| `Machine Learning`  `Data Analysis` `Classification` `Gradient Boosting` `Text` `NLP` `TF-IDF`|

| [Determining the age of buyers from photographs](https://github.com/inskyeee/Data_science/blob/main/07%20Determining%20the%20age%20of%20buyers%20from%20photographs.ipynb) | Creating a model to predict a person's age from images | `Pandas` `Numpy` `Matplotlib` `keras` `TensorFlow` | `Machine Learning`  `Data Analysis` `Regression` `Computer Vision` `TensorFlow`|

| [Determining the churn of telecom operator customers](https://github.com/inskyeee/Data_science/blob/main/08%20Determining%20the%20churn%20of%20telecom%20operator%20customers.ipynb) | Creating a model for predicting the termination of a contract with a telecom operator by a client | `Pandas` `Numpy` `Matplotlib` `Seaborn` `Scikit-learn` `Phik` `LightGBM` `Catboost`| `Machine Learning`  `Data Analysis` `Classification` `Gradient Boosting`|