https://github.com/priyankapriyadarshini50/autos_data_analysis

Data Analysis of auto dataset
https://github.com/priyankapriyadarshini50/autos_data_analysis

matplotlib numpy pandas psycopg2 scikit-learn scipy seaborn

Last synced: about 1 month ago
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Data Analysis of auto dataset

• Host: GitHub
• URL: https://github.com/priyankapriyadarshini50/autos_data_analysis
• Owner: priyankapriyadarshini50
• License: mit
• Created: 2025-04-16T05:10:46.000Z (10 months ago)
• Default Branch: master
• Last Pushed: 2025-07-01T01:47:25.000Z (7 months ago)
• Last Synced: 2025-07-01T02:37:31.652Z (7 months ago)
• Topics: matplotlib, numpy, pandas, psycopg2, scikit-learn, scipy, seaborn
• Language: Python
• Homepage:
• Size: 172 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

          
### Auto Data Analysis Project

This project provides the correlation between the different parameters of auto dataset. It reads the data from a csv file

and prepare data for analysis by cleaning and replacing missing values, standardize and normalize the data, binning the data

for visualization, converting categorial to columns and finding the correlation between the variable such as horsepower and price,

engine-size and price etc.

### raw data:

CAR_DATASET_URL="https://archive.ics.uci.edu/ml/machine-learning-databases/autos/imports-85.data"  

   |symboling| normalized_losses|make      |fuel_type| aspiration| num_of_doors|  ... |compression-ratio| horsepower| peak_rpm | city_mpg|highway_mpg|price

   |---------|------------------|----------|---------|-----------|-------------|------|-----------------|-----------|----------|---------|----------|---------|

0  |       3 |               ?  |alfa-romero|     gas|       std |        two  |...   |           9.0   |    111    | 5000     |  21     |     27   |13495

1  |       3 |               ?  |alfa-romero|     gas|       std |        two  |...   |           9.0   |    111    | 5000     |  21     |     27   |16500

2  |       1 |               ?  |alfa-romero|     gas|       std |        two  |...   |           9.0   |    154    | 5000     |  19     |     26   |16500

3  |       2 |             164  |      audi |     gas|       std |       four  |...   |          10.0   |    102    | 5500     |  24     |     30   |13950

4  |       2 |             164  |      audi |     gas|       std |       four  |...   |           8.0   |    115    | 5500     |  18     |     22   |17450

*getting total number of null values based on columns:*  

number of NaN values for the column normalized_losses : 41  

number of NaN values for the column price : 4  

Updated the datatype to correct format:  

Data types assigned to pandas df=  

symboling               int64  

normalized_losses       int64  

make                   object  

fuel_type              object  

aspiration           category  

num_of_doors           object  

body-style             object  

drive_wheels           object  

engine_location        object  

wheel_base            float64  

length                float64  

width                 float64  

height                float64  

curb_weight             int64  

engine_type            object  

num_of_cylinders       object  

engine_size             int64  

fuel_system            object  

bore                  float64  

stroke                float64  

compression-ratio     float64  

horsepower            float64  

peak_rpm              float64  

city_mpg                int64  

highway_mpg             int64  

price                 float64  

dtype: object  

*The horsepower column data has been binned that provides better data visualization.*  



  

  

This histogram shows that most of the cars in the data set has Low horsepower.

 


After data cleaning, the correlation data for some columns:

|                  |   bore|   stroke|compression-ratio|horsepower| engine_size|   price|

|------------------|-------|---------|-----------------|----------|------------|--------|

|bore              |1.000000|-0.055390|        0.001263|  0.566936|    0.572609|0.543155|

|stroke            |-0.055390|1.000000|        0.187871|  0.098267|    0.205928|0.082269|

|compression-ratio |0.001263 |0.187871|        1.000000| -0.214514|    0.028889|0.071107|

|horsepower        |0.566936 |0.098267|       -0.214514|  1.000000|    0.822676|0.809575|

|engine_size       |0.572609 |0.205928|        0.028889|  0.822676|    1.000000|0.872335|

|price             |0.543155 |0.082269|        0.071107|  0.809575|    0.872335|1.000000|

From the above table, we can find out there is a positive correlation between engine size and price,

horsepower and size.  

db_connection is a script which gets data from db table using sql query.

### Data Visualization  



  

  

The above scattered regression plot shows that "Engine size" as potential predictor variable of "price"




  

  

The above box plot shows the relationship between categorical variables "body-style" and "price"  




  

  

The above box plot shows the relationship between categorical variables "body-style", "wheel-base" and "price"

We can see cars with convertible body style and rwd wheel-base has maximum price.