Used Car Sale Price Prediction Model (Feedforward Artificial Neural Networks)

December 2025

By: Bryce Gelarden

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Import

import pandas as pd
import numpy as np

import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
%matplotlib inline

import seaborn as sns
sns.set(style="darkgrid")

import tensorflow as tf

Load & Preview

data_path = r"C:\Users\Bryce Gelarden\OneDrive - Indiana University\Documents\Github - brycemgelarden\Used_Car_Price_Forecasting_NN\train-data.csv"

car_dataset = pd.read_csv(data_path, engine='python')

car_dataset.head()

	Unnamed: 0	Name	Location	Year	Kilometers_Driven	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power	Seats	New_Price	Price
0	0	Maruti Wagon R LXI CNG	Mumbai	2010	72000	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp	5.0	NaN	1.75
1	1	Hyundai Creta 1.6 CRDi SX Option	Pune	2015	41000	Diesel	Manual	First	19.67 kmpl	1582 CC	126.2 bhp	5.0	NaN	12.50
2	2	Honda Jazz V	Chennai	2011	46000	Petrol	Manual	First	18.2 kmpl	1199 CC	88.7 bhp	5.0	8.61 Lakh	4.50
3	3	Maruti Ertiga VDI	Chennai	2012	87000	Diesel	Manual	First	20.77 kmpl	1248 CC	88.76 bhp	7.0	NaN	6.00
4	4	Audi A4 New 2.0 TDI Multitronic	Coimbatore	2013	40670	Diesel	Automatic	Second	15.2 kmpl	1968 CC	140.8 bhp	5.0	NaN	17.74

Audit & Rid Missing Data

car_dataset.isnull().mean()

Unnamed: 0           0.000000
Name                 0.000000
Location             0.000000
Year                 0.000000
Kilometers_Driven    0.000000
Fuel_Type            0.000000
Transmission         0.000000
Owner_Type           0.000000
Mileage              0.000332
Engine               0.005981
Power                0.005981
Seats                0.006978
New_Price            0.863100
Price                0.000000
dtype: float64

car_dataset = car_dataset.drop(['Unnamed: 0', 'New_Price'], axis = 1)

plt.rcParams["figure.figsize"] = [8, 6]

numeric_df = car_dataset.select_dtypes(include=['number'])

sns.heatmap(numeric_df.corr(), annot=True, cmap="coolwarm")
plt.show()

import warnings
warnings.filterwarnings('ignore')

sns.distplot(car_dataset['Price'])

<Axes: xlabel='Price', ylabel='Density'>

car_dataset.nunique()

Name                 1876
Location               11
Year                   22
Kilometers_Driven    3093
Fuel_Type               5
Transmission            2
Owner_Type              4
Mileage               442
Engine                146
Power                 372
Seats                   9
Price                1373
dtype: int64

print(car_dataset.dtypes)

Name                     str
Location                 str
Year                   int64
Kilometers_Driven      int64
Fuel_Type                str
Transmission             str
Owner_Type               str
Mileage                  str
Engine                   str
Power                    str
Seats                float64
Price                float64
dtype: object

Feature Engineering & Preprocessing

numerical_data = car_dataset.drop(['Name', 'Location','Fuel_Type', 'Transmission', 'Owner_Type','Mileage','Engine', 'Power'], axis=1) 

numerical_data.head()

	Year	Kilometers_Driven	Seats	Price
0	2010	72000	5.0	1.75
1	2015	41000	5.0	12.50
2	2011	46000	5.0	4.50
3	2012	87000	7.0	6.00
4	2013	40670	5.0	17.74

categorical_data = car_dataset.filter(['Location', 'Fuel_Type', 'Transmission', 'Owner_Type', 'Mileage','Engine','Power'], axis=1)

categorical_data.head()

	Location	Fuel_Type	Transmission	Owner_Type	Mileage	Engine	Power
0	Mumbai	CNG	Manual	First	26.6 km/kg	998 CC	58.16 bhp
1	Pune	Diesel	Manual	First	19.67 kmpl	1582 CC	126.2 bhp
2	Chennai	Petrol	Manual	First	18.2 kmpl	1199 CC	88.7 bhp
3	Chennai	Diesel	Manual	First	20.77 kmpl	1248 CC	88.76 bhp
4	Coimbatore	Diesel	Automatic	Second	15.2 kmpl	1968 CC	140.8 bhp

categorical_data__one_hot = pd.get_dummies(categorical_data, drop_first= True) 

categorical_data__one_hot.head()

	Location_Bangalore	Location_Chennai	Location_Coimbatore	Location_Delhi	Location_Hyderabad	Location_Jaipur	Location_Kochi	Location_Kolkata	Location_Mumbai	Location_Pune	...	Power_97.6 bhp	Power_97.7 bhp	Power_98.59 bhp	Power_98.6 bhp	Power_98.79 bhp	Power_98.82 bhp	Power_98.96 bhp	Power_99 bhp	Power_99.6 bhp	Power_null bhp
0	False	False	False	False	False	False	False	False	True	False	...	False	False	False	False	False	False	False	False	False	False
1	False	False	False	False	False	False	False	False	False	True	...	False	False	False	False	False	False	False	False	False	False
2	False	True	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
3	False	True	False	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
4	False	False	True	False	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False

5 rows × 975 columns

complete_dataset = pd.concat([numerical_data, categorical_data__one_hot ], axis=1)

complete_dataset.head()

	Year	Kilometers_Driven	Seats	Price	Location_Bangalore	Location_Chennai	Location_Coimbatore	Location_Delhi	Location_Hyderabad	Location_Jaipur	...	Power_97.6 bhp	Power_97.7 bhp	Power_98.59 bhp	Power_98.6 bhp	Power_98.79 bhp	Power_98.82 bhp	Power_98.96 bhp	Power_99 bhp	Power_99.6 bhp	Power_null bhp
0	2010	72000	5.0	1.75	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
1	2015	41000	5.0	12.50	False	False	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
2	2011	46000	5.0	4.50	False	True	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
3	2012	87000	7.0	6.00	False	True	False	False	False	False	...	False	False	False	False	False	False	False	False	False	False
4	2013	40670	5.0	17.74	False	False	True	False	False	False	...	False	False	False	False	False	False	False	False	False	False

5 rows × 979 columns

complete_dataset.isnull().mean()

Year                  0.000000
Kilometers_Driven     0.000000
Seats                 0.006978
Price                 0.000000
Location_Bangalore    0.000000
                        ...   
Power_98.82 bhp       0.000000
Power_98.96 bhp       0.000000
Power_99 bhp          0.000000
Power_99.6 bhp        0.000000
Power_null bhp        0.000000
Length: 979, dtype: float64

complete_dataset.dropna(inplace = True)

X = complete_dataset.drop(['Price'], axis=1)
y = complete_dataset['Price']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=20)

from sklearn.preprocessing import StandardScaler

scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)
X_test = scaler.transform(X_test) 

Model Architecture & Compilation

from tensorflow.keras.layers import Input, Dense, Activation, Dropout

from tensorflow.keras.models import Model

input_layer = Input(shape=(X.shape[1],))
dense_layer0 = Dense(100, activation='relu')(input_layer)
dense_layer1 = Dense(50, activation='relu')(dense_layer0)
dense_layer2 = Dense(25, activation='relu')(dense_layer1)
dense_layer3 = Dense(10, activation='relu')(dense_layer2)
dense_layer4 = Dense(5, activation='relu')(dense_layer3)
dense_layer5 = Dense(2, activation='relu')(dense_layer4)
output = Dense(1)(dense_layer5) 

model = Model(inputs = input_layer, outputs=output)
model.compile(loss="mean_absolute_error", optimizer="adam", metrics=["mean_absolute_error"]) 

from keras.utils import plot_model

plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True) 

history = model.fit(X_train, y_train, batch_size=5, epochs=5, verbose=1, validation_split=0.2) 

Epoch 1/5
765/765 ━━━━━━━━━━━━━━━━━━━━ 3s 2ms/step - loss: 3.7058 - mean_absolute_error: 3.7058 - val_loss: 2.3034 - val_mean_absolute_error: 2.3034
Epoch 2/5
765/765 ━━━━━━━━━━━━━━━━━━━━ 2s 2ms/step - loss: 2.1566 - mean_absolute_error: 2.1566 - val_loss: 2.0548 - val_mean_absolute_error: 2.0548
Epoch 3/5
765/765 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 1.7729 - mean_absolute_error: 1.7729 - val_loss: 1.9634 - val_mean_absolute_error: 1.9634
Epoch 4/5
765/765 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 1.6036 - mean_absolute_error: 1.6036 - val_loss: 2.0077 - val_mean_absolute_error: 2.0077
Epoch 5/5
765/765 ━━━━━━━━━━━━━━━━━━━━ 1s 2ms/step - loss: 1.5216 - mean_absolute_error: 1.5216 - val_loss: 1.8519 - val_mean_absolute_error: 1.8519

Training

plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])

plt.title('loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train','test'], loc='upper left')
plt.show() 

y_pred = model.predict(X_test)

38/38 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step

Evaluation & Results

from sklearn import metrics

print('Mean Absolute Error:', metrics.mean_absolute_error(y_test, y_pred))

print('Mean Squared Error:', metrics.mean_squared_error(y_test, y_pred))

print('Root Mean Squared Error:', np.sqrt(metrics.mean_squared_error(y_test, y_pred)))

Mean Absolute Error: 1.8337928853744647
Mean Squared Error: 21.361399005350542
Root Mean Squared Error: 4.6218393530444715

car_dataset['Price'].mean()

np.float64(9.47946835022429)

comparison_df = pd.DataFrame({'Actual': y_test.values.tolist(), 'Predicted': y_pred.tolist()})

comparison_df 

	Actual	Predicted
0	8.25	[7.690873622894287]
1	5.08	[4.437170028686523]
2	4.50	[6.3706374168396]
3	28.50	[17.33601188659668]
4	7.25	[10.439645767211914]
...	...	...
1191	7.50	[5.23049783706665]
1192	21.67	[20.28693199157715]
1193	4.60	[5.025176525115967]
1194	8.00	[14.569637298583984]
1195	2.65	[4.9420552253723145]

1196 rows × 2 columns

X_test[1].shape

(978,)

single_point = X_test[1].reshape(1,-1)

single_point.shape

(1, 978)

model.predict(X_test[1].reshape(1,-1))

1/1 ━━━━━━━━━━━━━━━━━━━━ 0s 26ms/step

array([[4.4371696]], dtype=float32)

y_test.values[1]

np.float64(5.08)

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