Date created January 2024

Project Title - Used Cars Pricing Project

Description - This Project is part of the IBM Data Analyst specializatiion. This dataset was hosted on IBM Cloud object. The Automobile Data set is an online source:

Data source: https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data Data type: csv

The project is divided into 5 sections:

Importing the Dataset - Imported the dataset from a CSV file to a Pandas dataframe to uncover some basic insights about the dataset

Data Wrangling - Handled missing data in different ways. Corrected the data type of different data values as per requirement. Standardized and normalized the appropriate data attributes. Visualized the data as grouped bar graph using Binning. Converted a categorical data into numerical indicator variables.

Exploratory Data Analysis - I explored features or characteristics to predict price of car. Analyzed patterns and ran descriptive statistical analysis. Grouped the data based on identified parameters and created pivot tables. Identified the effect of independent attributes on price of cars.

Model Development - In this section, I developed several models that will predict the price of the car using the variables or features. The value i got was just an estimate but gave me an objective idea of how much the car should cost.

Some questions i had to ask were:

"Do I know if the dealer is offering fair value for my trade-in"? "Do I know if I put a fair value on my car"?

Model Evaluation and Refinement - Evaluated and refined the prediction models. Identified overfitting and underfitting of the models. Performed Ridge regression for polynomial feature models. Used Grid search to identify best choice of hyperparameters.

SETUP/INSTALATION - Python 3.5 and above Machine Learning Libraries: NumPy, scipy, Pandas, Scikit-Learn, ipywidgets, Matplotplib, Seaborn.

Author: Alameen Waziri

Acknowledgements: IBM Skills Network that provided the platform for the project