https://github.com/surayasumona/ford_used_car_analysis

Explanatory Data Analysis with Python
https://github.com/surayasumona/ford_used_car_analysis

data-science data-visualization matplotlib numpy pandas python seaborn

Last synced: 27 days ago
JSON representation

Explanatory Data Analysis with Python

• Host: GitHub
• URL: https://github.com/surayasumona/ford_used_car_analysis
• Owner: SurayaSumona
• Created: 2021-07-08T20:57:08.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-10-09T11:02:16.000Z (over 3 years ago)
• Last Synced: 2024-11-24T16:12:11.581Z (3 months ago)
• Topics: data-science, data-visualization, matplotlib, numpy, pandas, python, seaborn
• Language: Jupyter Notebook
• Homepage:
• Size: 4.79 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

        
# Data Analysis and Data Visualization of the Ford used cars

In this dataset, there has some ford used car's information. Here are the descriptions of the columns for the dataset:

**Target variable:**

* **Price:** selling price of the cars

**Features:**

* **model:** list of the Ford cars

* **year:** when the car was made

* **transmission:** transmission adapts the output of the internal combustion engine to the drive wheels

* **mileage:** The mileage of a vehicle is the number of miles that it can travel using one gallon or litre of fuel

* **fuelType:** different fuels a vehicle may use

* **mpg**: miles per gallon the vehicle can travel

* **engineSize:** engineSize is the volume of fuel and air that can be pushed through a car's cylinders

### Goal of this project:

#### Learn Data visualization and predict the resale price of the used cars using Machine learning algorithm

**Exploratory Data Analysis**:

* Read the data as Pandas Dataframe

* Check the data types and missing values

* Check the basic statistics of numerical features

* Find the percentage of unique values and reset the index,rename and round the catergorical variables 

**Exploring the data using different data visualization plots**:

* Barplot

* Scatterplot

* Trendline or Regression plot

* Histogram

* Distribution plot

* ECDF ( Emperical Cumulative Distribution Function)

* Boxplot

* Violinplot 

**EDA using GroupBy/Pivot_Table and Barplot  based on some features such as model, transmission, and fuelType**

* What are the top 5 selling car models in the dataset?

* What's the average selling price of the top 5 selling car models?

* What's the total sale of the top 5 selling car models?

# Machine Learning Algorithms

**Supervised Learning: Linear Regression and Regression accuracy metrics**:

* Understanding the equation of a straight line

* feature coefficient (slope, gradient, m)

* bias coeffcient (y-intercept, c)

* loss function, cost function, objective function, error function

* Mean Absolute Error (MAE)

* Mean Absolute Percentage Error (MAPE)

* Mean Squared Error (MSE)

* Root Mean Squared Error (RMSE)

* R-squared or coefficient of determination

* Prediction result evaluation

### Reference of this Dataset: https://www.kaggle.com/aishwaryamuthukumar/cars-dataset-audi-bmw-ford-hyundai-skoda-vw