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
• Repository Structure
• Datasets
• Technologies Used
• Usage
• Contributing
• 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:

git clone https://github.com/burhanahmed1/machine-learning-analysis.git
cd machine-learning-analysis

2. Run Jupyter Notebook:

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.