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https://github.com/burhanahmed1/data-analysis-with-python
Data-Acquisition and Basic Insights, Data Wrangling, Exploratory Data Analysis (EDA), and Training Prediction Models(Machine Learning) on two datasets.
https://github.com/burhanahmed1/data-analysis-with-python
data-analysis data-aquisition data-insights data-science data-wrangling dataanalytics datascience-machinelearning eda exploratory-data-analysis machine-learning-models matlpotlib numpy pandas practice-programming prediction-model python scikit-learn scikitlearn-machine-learning seaborn
Last synced: 6 days ago
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Data-Acquisition and Basic Insights, Data Wrangling, Exploratory Data Analysis (EDA), and Training Prediction Models(Machine Learning) on two datasets.
- Host: GitHub
- URL: https://github.com/burhanahmed1/data-analysis-with-python
- Owner: burhanahmed1
- License: mit
- Created: 2024-06-30T15:18:04.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2024-07-06T06:10:25.000Z (4 months ago)
- Last Synced: 2024-07-06T07:26:41.315Z (4 months ago)
- Topics: data-analysis, data-aquisition, data-insights, data-science, data-wrangling, dataanalytics, datascience-machinelearning, eda, exploratory-data-analysis, machine-learning-models, matlpotlib, numpy, pandas, practice-programming, prediction-model, python, scikit-learn, scikitlearn-machine-learning, seaborn
- Language: Jupyter Notebook
- Homepage:
- Size: 912 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Data-Analysis-with-Python
This repository contains comprehensive notebooks for various stages of data analysis and machine learning model building, using two datasets: AutoMobiles and Laptop Pricing. The repository is organized into four main folders, each containing notebooks for both datasets.
## Table of Contents
- [Introduction](#introduction)
- [Repository Structure](#repository-structure)
- [Datasets](#datasets)
- [Technologies Used](#technologies-used)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
## Introduction
This repository provides a structured approach to data acquisition, data wrangling, exploratory data analysis (EDA), and prediction model building. The analysis is performed on two datasets: AutoMobiles and Laptop Pricing. Each stage of the process is documented in Jupyter notebooks, offering a clear and reproducible workflow.
## Repository Structure
The repository is organized into the following folders:
1. **Data Acquisition and Basic Insights**:
- `AutoMobiles_data_acquisition.ipynb`
- `Laptop_data_acquisition.ipynb`
2. **Data Wrangling**:
- `AutoMobiles_data_wrangling.ipynb`
- `Laptop_data_wrangling.ipynb`
3. **Exploratory Data Analysis (EDA)**:
- `AutoMobiles_EDA.ipynb`
- `Laptop_EDA.ipynb`
4. **Prediction Models**:
- `AutoMobiles_prediction_models.ipynb`
- `Laptop_prediction_models.ipynb`
Each notebook in the folders is designed to handle the respective dataset, providing a step-by-step guide through the different phases of data science.
## Datasets
The datasets used in this repository are included in the respective folders:
- **AutoMobiles Dataset**: Contains data related to various car attributes and prices.
- **Laptop Pricing Dataset**: Contains data related to laptop features and their corresponding prices.
## Technologies Used
- Scikit-learn
- Scipy
- Pandas
- Numpy
- Matplotlib
- Seaborn
- Jupyter Notebook
## Usage
1. Clone the repository:
```bash
git clone https://github.com/burhanahmed1/machine-learning-analysis.git
cd machine-learning-analysis
```
2. Run Jupyter Notebook:
```bash
jupyter notebook
```
3. Navigate to the respective folder and open the notebook of your choice. Follow the instructions and run the cells to execute the analysis.
## Contributing
Contributions are welcome! If you would like to contribute to this project, you can fork the repository and create a pull request with your improvements. Here's how you can do it:
1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Make your changes and commit them.
4. Push your changes to your forked repository.
5. Create a pull request from your branch to the main repository.
## License
This project is licensed under the MIT License.