This repository contains a collection of Jupyter Notebook labs for learning data analysis with Python. Each lab focuses on a specific aspect of data analysis, including data acquisition, data wrangling, exploratory data analysis, model development and model evaluation and refinement.

1. Data Acquisition: Learn different methods to acquire data and load datasets into Jupyter Notebook.
2. Data Wrangling: Handle missing values, correct data format, standardize and normalise data.
3. Exploratory Data Analysis: Explore features to predict car prices using visualization and descriptive statistics.
4. Model Development: Develop prediction models to estimate car prices.
5. Model Evaluation and Refinement: Evaluate, refine, and select the best prediction models using techniques like Ridge Regression and Grid Search.

To run the lab notebooks locally, follow these steps:

1. Clone the repository: git clone https://github.com/your-username/data-analysis-with-python.git
2. Navigate to the repository: cd data-analysis-with-python
3. Create a virtual environment: python -m venv env
4. Activate the virtual environment:
   • For Windows: env\Scripts\activate
   • For macOS/Linux: source env/bin/activate
5. Install the required dependencies: pip install -r requirements.txt
6. Start Jupyter Notebook: jupyter notebook
7. Open the desired lab notebook and start learning!

The labs in this repository require the following dependencies:

• Python 3
• Jupyter Notebook
• Pandas
• NumPy
• Matplotlib
• Seaborn
• Scikit-learn

You can install the required dependencies by running pip install -r requirements.txt.

Contributions are welcome! If you find any issues or have suggestions for improvements, please open an issue or submit a pull request.

This repository is licensed under the MIT License. See the LICENSE file for more details.