In [28]: # 1.
Import all the required Python Libraries
import pandas as pd
import numpy as np
In [28]: # 1. Import all the required Python Libraries
import pandas as pd
import numpy as np
In [29]: # 2. Dataset Description
description = """
Dataset Name: Automobile Dataset
Source: https://www.kaggle.com/datasets/toramky/automobile-dataset
This dataset contains various characteristics of cars such as fuel type, number of doors,
engine size, horsepower, and more. It is commonly used for machine learning projects
like regression, classification, and price prediction.
You uploaded the dataset locally as 'autodata.csv'.
"""
print(description)
Dataset Name: Automobile Dataset
Source: https://www.kaggle.com/datasets/toramky/automobile-dataset
This dataset contains various characteristics of cars such as fuel type, number of doors,
engine size, horsepower, and more. It is commonly used for machine learning projects
like regression, classification, and price prediction.
You uploaded the dataset locally as 'autodata.csv'.
In [31]: # 3. Load the Dataset into pandas dataframe
df = pd.read_csv("C:/Users/prajw/Desktop/Indexs/DSBDA print/GROUP A/Assignment 1 (Data Wranglin I)/autodata.csv")
df.head()
Out[31]: num-
Unnamed: normalized- body- drive- engine- wheel- compression- pe
symboling make aspiration of- ... horsepower
0 losses style wheels location base ratio r
doors
alfa-
0 0 3 122 std two convertible rwd front 88.6 ... 9.0 111.0 500
romero
alfa-
1 1 3 122 std two convertible rwd front 88.6 ... 9.0 111.0 500
romero
alfa-
2 2 1 122 std two hatchback rwd front 94.5 ... 9.0 154.0 500
romero
3 3 2 164 audi std four sedan fwd front 99.8 ... 10.0 102.0 550
4 4 2 164 audi std four sedan 4wd front 99.4 ... 8.0 115.0 550
5 rows × 30 columns
In [ ]: # 4. Data Preprocessing
In [33]: # Check for missing values
print("Missing values per column:\n", df.isnull().sum())
Missing values per column:
Unnamed: 0 0
symboling 0
normalized-losses 0
make 0
aspiration 0
num-of-doors 0
body-style 0
drive-wheels 0
engine-location 0
wheel-base 0
length 0
width 0
height 0
curb-weight 0
engine-type 0
num-of-cylinders 0
engine-size 0
fuel-system 0
bore 0
stroke 4
compression-ratio 0
horsepower 2
peak-rpm 2
city-mpg 0
highway-mpg 0
price 0
city-L/100km 0
horsepower-binned 2
diesel 0
gas 0
dtype: int64
In [34]: # Get initial statistics
print("\nDescriptive statistics:\n", df.describe(include="all"))
Descriptive statistics:
Unnamed: 0 symboling normalized-losses make aspiration \
count 201.000000 201.000000 201.00000 201 201
unique NaN NaN NaN 22 2
top NaN NaN NaN toyota std
freq NaN NaN NaN 32 165
mean 100.000000 0.840796 122.00000 NaN NaN
std 58.167861 1.254802 31.99625 NaN NaN
min 0.000000 -2.000000 65.00000 NaN NaN
25% 50.000000 0.000000 101.00000 NaN NaN
50% 100.000000 1.000000 122.00000 NaN NaN
75% 150.000000 2.000000 137.00000 NaN NaN
max 200.000000 3.000000 256.00000 NaN NaN
num-of-doors body-style drive-wheels engine-location wheel-base ... \
count 201 201 201 201 201.000000 ...
unique 2 5 3 2 NaN ...
top four sedan fwd front NaN ...
freq 115 94 118 198 NaN ...
mean NaN NaN NaN NaN 98.797015 ...
std NaN NaN NaN NaN 6.066366 ...
min NaN NaN NaN NaN 86.600000 ...
25% NaN NaN NaN NaN 94.500000 ...
50% NaN NaN NaN NaN 97.000000 ...
75% NaN NaN NaN NaN 102.400000 ...
max NaN NaN NaN NaN 120.900000 ...
compression-ratio horsepower peak-rpm city-mpg highway-mpg \
count 201.000000 199.000000 199.000000 201.000000 201.000000
unique NaN NaN NaN NaN NaN
top NaN NaN NaN NaN NaN
freq NaN NaN NaN NaN NaN
mean 10.164279 103.396985 5117.587940 25.179104 30.686567
std 4.004965 37.553843 480.521824 6.423220 6.815150
min 7.000000 48.000000 4150.000000 13.000000 16.000000
25% 8.600000 70.000000 4800.000000 19.000000 25.000000
50% 9.000000 95.000000 5200.000000 24.000000 30.000000
75% 9.400000 116.000000 5500.000000 30.000000 34.000000
max 23.000000 262.000000 6600.000000 49.000000 54.000000
price city-L/100km horsepower-binned diesel gas
count 201.000000 201.000000 199 201.000000 201.000000
unique NaN NaN 3 NaN NaN
top NaN NaN Low NaN NaN
freq NaN NaN 151 NaN NaN
mean 13207.129353 9.944145 NaN 0.099502 0.900498
std 7947.066342 2.534599 NaN 0.300083 0.300083
min 5118.000000 4.795918 NaN 0.000000 0.000000
25% 7775.000000 7.833333 NaN 0.000000 1.000000
50% 10295.000000 9.791667 NaN 0.000000 1.000000
75% 16500.000000 12.368421 NaN 0.000000 1.000000
max 45400.000000 18.076923 NaN 1.000000 1.000000
[11 rows x 30 columns]
In [35]: # Check dimensions
print("\nShape of the dataset:", df.shape)
Shape of the dataset: (201, 30)
In [36]: # Column names
print("\nColumns in dataset:\n", df.columns)
Columns in dataset:
Index(['Unnamed: 0', 'symboling', 'normalized-losses', 'make', 'aspiration',
'num-of-doors', 'body-style', 'drive-wheels', 'engine-location',
'wheel-base', 'length', 'width', 'height', 'curb-weight', 'engine-type',
'num-of-cylinders', 'engine-size', 'fuel-system', 'bore', 'stroke',
'compression-ratio', 'horsepower', 'peak-rpm', 'city-mpg',
'highway-mpg', 'price', 'city-L/100km', 'horsepower-binned', 'diesel',
'gas'],
dtype='object')
In [39]: # 5. Data Formatting and Normalization
# Check data types of each column
print("\nData types before conversion:\n", df.dtypes)
# Example conversions: ensure numeric columns are numeric
# (force conversion if needed using errors='coerce')
df["price"] = pd.to_numeric(df["price"], errors='coerce')
df["horsepower"] = pd.to_numeric(df["horsepower"], errors='coerce')
df["peak-rpm"] = pd.to_numeric(df["peak-rpm"], errors='coerce')
# Fill missing values with mean
df["price"] = df["price"].fillna(df["price"].mean())
df["horsepower"] = df["horsepower"].fillna(df["horsepower"].mean())
df["peak-rpm"] = df["peak-rpm"].fillna(df["peak-rpm"].mean())
# Fill 'num-of-doors' missing values with mode
df["num-of-doors"] = df["num-of-doors"].fillna(df["num-of-doors"].mode()[0])
# Drop rows where target or crucial column is still missing (if any)
df.dropna(subset=["price"], axis=0, inplace=True)
print("\nData types after conversion:\n", df.dtypes)
Data types before conversion:
Unnamed: 0 int64
symboling int64
normalized-losses int64
make object
aspiration object
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
city-L/100km float64
horsepower-binned object
diesel int64
gas int64
dtype: object
Data types after conversion:
Unnamed: 0 int64
symboling int64
normalized-losses int64
make object
aspiration object
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
city-L/100km float64
horsepower-binned object
diesel int64
gas int64
dtype: object
In [40]: # 6. Turn categorical variables into quantitative variables
# Select object (categorical) columns
categorical_columns = df.select_dtypes(include=['object']).columns
print("Categorical columns:\n", categorical_columns)
# One-hot encode categorical columns
df_encoded = pd.get_dummies(df, columns=categorical_columns)
df_encoded.head()
Categorical columns:
Index(['make', 'aspiration', 'num-of-doors', 'body-style', 'drive-wheels',
'engine-location', 'engine-type', 'num-of-cylinders', 'fuel-system',
'horsepower-binned'],
dtype='object')
Out[40]: Unnamed: normalized- wheel- curb- engine- fuel- fuel- fuel
symboling length width height bore ...
0 losses base weight size system_2bbl system_4bbl system_id
0 0 3 122 88.6 0.811148 0.890278 48.8 2548 130 3.47 ... False False Fals
1 1 3 122 88.6 0.811148 0.890278 48.8 2548 130 3.47 ... False False Fals
2 2 1 122 94.5 0.822681 0.909722 52.4 2823 152 2.68 ... False False Fals
3 3 2 164 99.8 0.848630 0.919444 54.3 2337 109 3.19 ... False False Fals
4 4 2 164 99.4 0.848630 0.922222 54.3 2824 136 3.19 ... False False Fals
5 rows × 80 columns
In [41]: # Save final cleaned and encoded dataset (optional)
df_encoded.to_csv("cleaned_autodata.csv", index=False)
print("✅ Cleaned dataset saved as 'cleaned_autodata.csv'")
✅ Cleaned dataset saved as 'cleaned_autodata.csv'
In [ ]:
