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https://github.com/ourahma/python-data-viz

This project focuses on data analysis and visualization to estimate used car prices. It includes preprocessing, statistical analysis, regression modeling, and visualizations to provide insights into the car pricing market.
https://github.com/ourahma/python-data-viz

jupyter-notebook matplotlib numpy pandas python scikit-learn seaborn

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This project focuses on data analysis and visualization to estimate used car prices. It includes preprocessing, statistical analysis, regression modeling, and visualizations to provide insights into the car pricing market.

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README 

        
# Data Visualization with Python - Project



## Overview



This project is the culmination of my learning in the **"Data Visualization Using Python"** course by IBM issued by Cognitive Class, where I have applied various data analysis and visualization techniques to gain insights and develop predictive models. The course provided a comprehensive understanding of data preprocessing, statistical analysis, and model development, which I used to estimate used car prices, visualize the data, and build prediction models.



## Key Learnings and Techniques Applied



### 1. **Data Analysis and Visualization**

   - **Exploratory Data Analysis (EDA):** Conducted initial analysis to understand the structure and relationships within the dataset.

   - **Data Preprocessing:** Handled missing values, formatted data, and normalized variables to prepare the dataset for analysis.

   - **Correlation and Descriptive Statistics:** Used correlation matrices and statistical methods to explore relationships and trends in the data.



### 2. **Data Preparation**

   - **Dealing with Missing Values:** Implemented techniques to handle missing data by either dropping or imputing values.

   - **Data Normalization and Binning:** Applied normalization techniques to scale the data and used binning for categorizing continuous variables.

   - **Categorical to Quantitative Transformation:** Transformed categorical variables into quantitative data for analysis.



### 3. **Model Development**

   - **Linear Regression:** Built linear and multiple linear regression models to estimate used car prices based on various features.

   - **Polynomial Regression:** Explored polynomial regression and pipelines for better accuracy and more flexible modeling.

   - **Model Evaluation:** Employed visualization techniques to evaluate model performance and diagnose potential issues like overfitting and underfitting.



### 4. **Visualization Techniques**

   - Created various types of visualizations, including histograms, scatter plots, and regression plots to visualize relationships between variables and the performance of different models.

   - Used libraries like **Matplotlib**, **Seaborn**, and **Pandas** to plot the data and refine the model evaluation process.



## Course Summary



Throughout this project, I followed the instructions and methodologies taught in the **"Data Visualization Using Python"** course by IBM issued by Cognitive class. Key topics covered in the course included:



- **Scientific Computing and Algorithm Libraries:** An introduction to important Python libraries such as Pandas, NumPy, and SciPy.

- **Data Import/Export:** Techniques for importing and exporting data to various formats like CSV and Excel.

- **Exploratory Data Analysis (EDA):** Applying statistical methods and visual tools to explore the data.

- **Data Preprocessing:** Methods for cleaning and preparing data for analysis, including normalization, dealing with missing values, and binning.

- **Model Development and Evaluation:** Building predictive models using regression techniques and evaluating them with visualizations.



## Project Purpose



The goal of this project was to apply the knowledge gained during the course to estimate the prices of used cars using real-world data. Through this, I have learned how to prepare data, build predictive models, and visualize the results, providing valuable insights into the car pricing market.



## Tools and Technologies Used



- **Python:** Programming language used for data analysis and visualization.

- **Pandas:** Data manipulation and analysis.

- **Matplotlib & Seaborn:** Data visualization libraries.

- **Scikit-learn:** Machine learning library for building and evaluating regression models.



## Conclusion



This project has enabled me to gain practical experience in data visualization, preprocessing, and model development, while also helping me understand the significance of data analysis in making informed decisions. The course content provided a solid foundation for performing robust data analysis and visualization using Python.